US20230033607A1

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Publication number: US20230033607A1
Application number: US17/874,619
Authority: US
Inventors: Kevin Carr
Original assignee: Individual
Current assignee: Edera L3C
Priority date: 2021-07-28
Filing date: 2022-07-27
Publication date: 2023-02-02
Also published as: WO2023009665A1; WO2023009660A1; US20230036791A1; WO2023009659A1; US20230034949A1; US20230032138A1; WO2023009661A1; WO2023009640A1; US20230031651A1; WO2023009644A1; WO2023009647A1; US20230032833A1; US20230031353A1; US12354744B2; US20230031071A1; WO2023009650A1

Abstract

A method, computer program product, and computing system for monitoring the diction of a medical specialist using a virtual assistant; processing at least a portion of the diction to identify at least one task to be performed within a medical management system; and if at least one task is detected, effectuating the at least one task on the medical management system.

Description

RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application No. 63/226,708, filed on 28 Jul. 2021, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to communication systems and, more particularly, to communication systems that utilize virtual assistants within the business space.

BACKGROUND

Handsfree communication is becoming very popular. Our cars allow us to verbally communicate with them and virtual assistants (e.g., Apple's Ski and Amazon's Alexa) allow us to obtain information in response to spoken requests, as well as adjust thermostats, dim room lighting, change our television channels, etc.

Unfortunately, the manner in which voice control and virtual assistants have been integrated into business application platforms is often superficial at best. For example, such business application platforms often only allow for voice control of simple cursory tasks . . . as opposed to more complex multi-step processes.

SUMMARY OF DISCLOSUREHands Free, Voice-Based Automation of Tasks

In one implementation, a computer-implemented method is executed on a computing device and includes: monitoring the diction of a medical specialist using a virtual assistant; processing at least a portion of the diction to identify at least one task to be performed within a medical management system; and if at least one task is detected, effectuating the at least one task on the medical management system.

In another implementation, a computer program product resides on a computer readable medium and has a plurality of instructions stored on it. When executed by a processor, the instructions cause the processor to perform operations including monitoring the diction of a medical specialist using a virtual assistant; processing at least a portion of the diction to identify at least one task to be performed within a medical management system; and if at least one task is detected, effectuating the at least one task on the medical management system.

In another implementation, a computing system includes a processor and a memory system configured to perform operations including monitoring the diction of a medical specialist using a virtual assistant; processing at least a portion of the diction to identify at least one task to be performed within a medical management system; and if at least one task is detected, effectuating the at least one task on the medical management system.

The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG.1 is a diagrammatic view of a distributed computing network including a computing device that executes a communication process according to an embodiment of the present disclosure;

FIG.2 is a flowchart of the communication process ofFIG.1 according to an embodiment of the present disclosure;

FIG.3 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure;

FIG.4 is a diagrammatic view of a hospital room (including a hospital bed and a television) according to an embodiment of the present disclosure;

FIG.5 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure;

FIG.6 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure;

FIG.7 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure;

FIG.8 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure;

FIG.9 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure; and

FIG.10 is a flowchart of the communication process ofFIG.1 according to another embodiment of the present disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

System Overview

Communication process

10smay be a server application and may reside on and may be executed by computingdevice12, which may be connected to network14 (e.g., the Internet or a local area network). Examples ofcomputing device12 may include, but are not limited to: a personal computer, a server computer, a series of server computers, a mini computer, a mainframe computer, or a cloud-based computing platform.

The instruction sets and subroutines ofcommunication process10s,which may be stored onstorage device16 coupled tocomputing device12, may be executed by one or more processors (not shown) and one or more memory architectures (not shown) included withincomputing device12. Examples ofstorage device16 may include but are not limited to: a hard disk drive; a RAID device; a random-access memory (RAM); a read-only memory (ROM); and all forms of flash memory storage devices.

Network

14 may be connected to one or more secondary networks (e.g., network18), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example.

Examples of communication processes10c1,10c2,10c3,10c4 may include but are not limited to a web browser, a game console user interface, a mobile device user interface, or a specialized application (e.g., an application running on e.g., the Android™ platform, the iOS™ platform, the Windows™ platform, the Linux™ platform or the UNIX platform). The instruction sets and subroutines of communication processes10c1,10c2,10c3,10c4, which may be stored on

storage devices

20,22,24,26 (respectively) coupled to client

electronic devices

28,30,32,34 (respectively), may be executed by one or more processors (not shown) and one or more memory architectures (not shown) incorporated into client

electronic devices

28,30,32,34 (respectively). Examples of

storage devices

20,22,24,26 may include but are not limited to: hard disk drives; RAID devices; random access memories (RAM); read-only memories (ROM), and all forms of flash memory storage devices.

Examples of client

electronic devices

28,30,32,34 may include, but are not limited to, a smartphone (not shown), a personal digital assistant (not shown), a tablet computer (not shown),

laptop computers

28,30,virtual assistant32,personal computer34, a notebook computer (not shown), a server computer (not shown), a gaming console (not shown), and a dedicated network device (not shown). Client

electronic devices

28,30,32,34 may each execute an operating system, examples of which may include but are not limited to Microsoft Windows™, Android™, iOS™, Linux™, or a custom operating system.

Users

36,38,40,42 may accesscommunication process10 directly throughnetwork14 or throughsecondary network18. Further,communication process10 may be connected to network14 throughsecondary network18, as illustrated withlink line44.

The various client electronic devices (e.g., client

electronic devices

28,30,32,34) may be directly or indirectly coupled to network14 (or network18). For example,laptop computer28 andlaptop computer30 are shown wirelessly coupled tonetwork14 viawireless communication channels44,46 (respectively) established betweenlaptop computers28,30 (respectively) and cellular network/bridge48, which is shown directly coupled tonetwork14. Further,virtual assistant32 is shown wirelessly coupled tonetwork14 viawireless communication channel50 established betweenvirtual assistant32 and wireless access point (i.e., WAP52), which is shown directly coupled tonetwork14. Additionally,personal computer34 is shown directly coupled tonetwork18 via a hardwired network connection.

WAP

52 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n, Wi-Fi, and/or Bluetooth device that is capable of establishingwireless communication channel50 betweenlaptop computer32 andWAP52. As is known in the art, IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. As is known in the art, Bluetooth is a telecommunications industry specification that allows e.g., mobile phones, computers, and personal digital assistants to be interconnected using a short-range wireless connection.

Communication Process Overview

As will be discussed below in greater detail,communication process10 may be configured to monitor the conversations between various entities (e.g., users, professionals, callers, patients) so that e.g., professionals may be monitored and assistance may be rendered.

Automated Monitoring for Bias Detection

Referring also toFIG.2,communication process10 may monitor100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56).

The conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56) may include one or more of: an in-person conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56); a telephone conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56); and an AV conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56).

- In-Person Conversation: Examples of such an in-person conversation (e.g., conversation54) may include but are not limited to an in-person conversation (e.g., conversation54) between a medical professional (e.g., user40) and a patient (e.g., third party56); an in-person conversation (e.g., conversation54) between a supervisor (e.g., user40) and an employee (e.g., third party56); and an in-person conversation (e.g., conversation54) between a help center employee (e.g., user40) and a caller (e.g., third party56).
- Telephone Conversation: Examples of such a telephone conversation (e.g., conversation54) may include but are not limited to a telephone conversation (e.g., conversation54) between a medical professional (e.g., user40) and a patient (e.g., third party56); a telephone conversation (e.g., conversation54) between a supervisor (e.g., user40) and an employee (e.g., third party56); and a telephone conversation (e.g., conversation54) between a help center employee (e.g., user40) and a caller (e.g., third party56).
- AV Conversation: Examples of such an AV conversation (e.g., conversation54) may include but are not limited to an AV conversation (e.g., conversation54) between a medical professional (e.g., user40) and a patient (e.g., third party56); an AV conversation (e.g., conversation54) between a supervisor (e.g., user40) and an employee (e.g., third party56); and an AV conversation (e.g., conversation54) between a help center employee (e.g., user40) and a caller (e.g., third party56).

When monitoring100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56),communication process10 may monitor102 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56) using a virtual assistant (e.g., virtual assistant32). Additionally/alternatively and when monitoring100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56),communication process10 may directly monitor the telephone conversation (e.g., conversation54) between the professional (e.g., user40) and the third party (e.g., third party56) and/or directly monitor the AV conversation (e.g., conversation54) between the professional (e.g., user40) and a third party (e.g., third party56).

As is known in the art, a virtual assistant is a software agent that can perform tasks or services for an individual based on commands or questions. The term “chatbot” is sometimes used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with speech-based commands. A similar concept, however with differences, lays under the dialogue systems. As of 2017, the capabilities and usage of virtual assistants are expanding rapidly, with new products entering the market and a strong emphasis on both email and voice user interfaces. Apple and Google have large installed bases of users on smartphones. Microsoft has a large installed base of Windows-based personal computers, smartphones and smart speakers. Amazon has a large install base for smart speakers. Conversica has over 100 million engagements via its email and SMS interface intelligent virtual assistants for business.

Communication process

10 may process104 at least a portion of the conversation (e.g., conversation54) to identify at least one instance of bias, wherein the at least one instance of bias may include but is not limited to one or more of: at least one instance of racial bias (i.e., treating people differently based upon their race); at least one instance of gender bias (i.e., treating people differently based upon their gender); at least one instance of military status bias (i.e., treating people differently based upon their military status); at least one instance of disability bias (i.e., treating people differently based upon their disabilities); and at least one instance of age bias (i.e., treating people differently based upon their age). This above-described instances of bias are intended to be illustrative and not all inclusive. Accordingly, other instances of bias are possible and are considered to be within the scope of this disclosure.

When processing104 at least a portion of the conversation (e.g., conversation54) to identify at least one instance of bias,communication process10 may process106 at least a portion of the conversation (e.g., conversation54) using natural language processing. As is known in the art, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When processing104 at least a portion of the conversation (e.g., conversation54) to identify at least one instance of bias,communication process10 may also:

- process108 at least a portion of the conversation (e.g., conversation54) to identify one or more bias-indicative trigger words (e.g., “honey”, “darling”, “sweetie”, “old”, “aged”);
- process110 at least a portion of the conversation (e.g., conversation54) to identify one or more bias-indicative conversational structures (e.g., “people like you”, “where are you from?”, “emotional types”); and
- process112 at least a portion of the conversation (e.g., conversation54) to identify one or more bias-indicative vocal tones/inflections (e.g., condescending tones/inflections, sarcastic tones/inflections, derogatory tones/inflections).

The above-described bias-indicative trigger words, bias-indicative conversational structures, and bias-indicative vocal tones/inflections may be manually defined or may be automatically defined. For example, an administrator ofcommunication process10 may manually define one or more lists (e.g., lists58) that identify such bias-indicative trigger words, bias-indicative conversational structures, and bias-indicative vocal tones/inflections. Additionally/alternatively, an administrator ofcommunication process10 may define seed data (e.g., seed data60) that may be processed via artificial intelligence (AI)process62 that may be configured to expandseed data60 to define the above-referenced lists (e.g., lists58).

As is known in the art, a machine learning system or model may generally include an algorithm or combination of algorithms that has been trained to recognize certain types of patterns. For example, machine learning approaches may be generally divided into three categories, depending on the nature of the signal available: supervised learning, unsupervised learning, and reinforcement learning. As is known in the art, supervised learning may include presenting a computing device with example inputs and their desired outputs, given by a “teacher”, where the goal is to learn a general rule that maps inputs to outputs. With unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). As is known in the art, reinforcement learning may generally include a computing device interacting in a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize. While three examples of machine learning approaches have been provided, it will be appreciated that other machine learning approaches are possible within the scope of the present disclosure.

In order to harness greater processing power, when processing104 at least a portion of the conversation (e.g., conversation54) to identify at least one instance of bias,communication process10 may process114 at least a portion of the conversation (e.g., conversation54) on a cloud-based computing resource (e.g., cloud resource64) to identify at least one instance of bias. As is known in the art, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

If at least one instance of bias is detected,communication process10 may implement116 at least one remedial task (e.g., task66), wherein these remedial tasks (e.g., task66) may include one or more of: notifying management; encouraging supplemental training; requiring supplemental training; and intervening in the conversation (e.g., conversation54).

- Notifying Management: A supervisor (e.g., user42) of user40 (as well asuser40 themselves) may be notified bycommunication process10. Accordingly, the at least one instance of bias identified bycommunication process10 may be addressed more discretely (by notifyinguser40 only) or less discretely (by notifying user42).
- Encouraging Supplemental Training: In less problematic situations,user40 may be asked/encouraged to attend some form of supplemental training to address the at least one instance of bias identified bycommunication process10.
- Requiring Supplemental Training: In more problematic situations,user40 may be required to attend some form of supplemental training to address the at least one instance of bias identified bycommunication process10.
- Intervening in the Conversation: In highly problematic situations,communication process10 may intervene in the conversation (e.g., conversation54) by e.g., looping in management or terminating the conversation (e.g., conversation54) to effectuate some form of damage control.

As would be expected, when implementing116 at least one remedial task (e.g., task66),communication process10 may parse118 the at least one at least one remedial task (e.g., task66) into a plurality of subtasks (e.g., subtasks68), whereincommunication process10 may then effectuate120 the plurality of subtasks (e.g., subtasks68). For example, in order to accomplishtask66,communication process10 may effectuate a plurality of discrete subtasks (e.g., subtasks68), examples of which may include but are not limited to contacting the supervisor ofuser40 and providing a private guidance message touser40.

Automated Monitoring for Suicide Prevention/Depression Detection

Referring also toFIG.3 and as discussed above,communication process10 may monitor100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56).

As also discussed above, the conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56) may include one or more of: an in-person conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56); a telephone conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56); and an AV conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56).

Further and as discussed above, when monitoring100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56),communication process10 may monitor102 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56) using a virtual assistant (e.g., virtual assistant32). Additionally/alternatively and when monitoring100 a conversation (e.g., conversation54) between a professional (e.g., user40) and a third party (e.g., third party56),communication process10 may directly monitor the telephone conversation (e.g., conversation54) between the professional (e.g., user40) and the third party (e.g., third party56) and/or directly monitor the AV conversation (e.g., conversation54) between the professional (e.g., user40) and a third party (e.g., third party56).

Communication process

10 may process200 at least a portion of the conversation (e.g., conversation54) to identify at least one indicator of depression, wherein the at least one indicator of depression may include one or more of: an indicator of negative self-talk (e.g., an indicator that thethird party56 has a low opinion of themself and/or talks about themself in a derogatory fashion); an indicator of a possibility of self-harm (e.g., an indicator thatthird party56 may hurt/harm themself); an indicator of a possibility of alcohol abuse (e.g., an indicator thatthird party56 may abuse alcohol to cope with their situation); an indicator of a possibility of drug abuse (e.g., an indicator thatthird party56 abuse drugs to cope with their situation); and an indicator of a possibility of suicide (e.g., an indicator thatthird party56 may attempt to take their own life to cope with their situation). This above-described indicators of depression are intended to be illustrative and not all inclusive. Accordingly, other indicator of depression are possible and are considered to be within the scope of this disclosure

When processing200 at least a portion of the conversation (e.g., conversation54) to identify at least one indicator of depression,communication process10 may process202 at least a portion of the conversation (e.g., conversation54) using natural language processing. As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When processing200 at least a portion of the conversation (e.g., conversation54) to identify at least one indicator of depression,communication process10 may also:

- process204 at least a portion of the conversation (e.g., conversation54) to identify one or more depression-indicative trigger words (e.g., awful, hopeless, suicide, worthless);
- process206 at least a portion of the conversation (e.g., conversation54) to identify one or more depression-indicative conversational structures (e.g., “can't take it anymore”, “there is nothing left”, “why bother”, “not worth it”; and/or
- process208 at least a portion of the conversation (e.g., conversation54) to identify one or more depression-indicative vocal tones/inflections (e.g., sad tones/inflections, hopeless tones/inflections, derogatory tones/inflections).

The above-described depression-indicative trigger words, depression-indicative conversational structures, and depression-indicative vocal tones/inflections may be manually defined or may be automatically defined. For example, an administrator ofcommunication process10 may manually define one or more lists (e.g., lists58) that identify such depression-indicative trigger words, depression-indicative conversational structures, and depression-indicative vocal tones/inflections. Additionally/alternatively, an administrator ofcommunication process10 may define seed data (e.g., seed data60) that may be processed via artificial intelligence (AI)process62 that may be configured to expandseed data60 to define the above-referenced lists (e.g., lists58).

In order to harness greater processing power, when processing200 at least a portion of the conversation (e.g., conversation54) to identify at least one indicator of depression,communication process10 may process210 at least a portion of the conversation (e.g., conversation54) on a cloud-based computing resource (e.g., cloud resource64) to identify at least one indicator of depression. As discussed above, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

If at least one indicator of depression is detected,communication process10 may implement212 at least one remedial task (e.g., task66), wherein these remedial tasks (e.g., task66) may include one or more of: notifying management; notifying emergency services; notifying law enforcement; modifying the conversation (e.g., conversation54); and intervening in the conversation (e.g., conversation54).

- Notifying Management: In less problematic situations, a supervisor (e.g., user42) of user40 (as well asuser40 themselves) may be notified bycommunication process10 concerning the indicator of depression detected with respect tothird party56.
- Notifying Emergency Services: In more problematic situations,communication process10 may notify emergency services to address the indicator of depression detected with respect tothird party56.
- Notifying Law Enforcement: In more problematic situations,communication process10 may notify law enforcement to address the indicator of depression detected with respect tothird party56.
- Modifying the Conversation: In more problematic situations,communication process10 may modify the conversation (e.g., by providing guidance to user40) to steer the conversation into a desired area.
- Intervening in the Conversation: In highly problematic situations,communication process10 may intervene in the conversation (e.g., conversation54) by e.g., looping in management to triage and/or gain control of the situation (as well as notifying emergency services/law enforcement).

As discussed above, when implementing212 at least one remedial task (e.g., task66),communication process10 may parse214 the at least one at least one remedial task (e.g., task66) into a plurality of subtasks (e.g., subtasks68), whereincommunication process10 may then effectuate216 the plurality of subtasks (e.g., subtasks68). For example, in order to accomplishtask66,communication process10 may effectuate a plurality of discrete subtasks (e.g., subtasks68), examples of which may include but are not limited to contacting the supervisor ofuser40 and providing a private guidance message touser40.

Referring also toFIG.4 and as will be discussed below in greater detail,communication process10 may be configured to provide a new level of convenience and connectivity for patients when admitted to a hospital. Specifically, hospital rooms (e.g., hospital room300) typically include a corded controller (not shown) that enables a patient (e.g., patient302) within the hospital to e.g., contact the nurse's station, and control the hospital bed (e.g., hospital bed304) and the television (e.g., television306). Unfortunately, this corded controller (not shown) is often difficult to locate and unsanitary to touch.

Hands Free, Voice-Based Interaction with Medical Staff in a Hospital

Referring also toFIG.5,communication process10 may monitor400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300). When monitoring400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300),communication process10 may monitor402 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300) using a virtual assistant (e.g., virtual assistant32).

Additionally and when monitoring400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300),communication process10 may monitor404 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300) to listen for the utterance of a wake-up word. Examples of such wake-up words may include but are not limited to “Siri”, “Alexa”, “Google” and “Edera”.

Communication process

10 may process406 at least a portion of the diction to identify at least one communication request within the hospital room (e.g., hospital room300), wherein examples of such communication requests within the hospital room (e.g., hospital room300) may include but are not limited to one or more of: placing a food/beverage order; requesting medication; contacting the nurse's station; and calling for emergency assistance.

- Placing a Food/Beverage Order; For example, patient (e.g., patient302) may order lunch by saying “Hey Edera, I would like to order lunch”.
- Requesting Medication; For example, patient (e.g., patient302) may request medication by saying “Hey Edera, can I please have my pain medication”.
- Contacting the Nurse's Station; For example, patient (e.g., patient302) may request non-emergency assistance by saying “Hey Edera, can I please have some assistance getting to the bathroom”.
- Calling for Emergency Assistance: For example, patient (e.g., patient302) may request emergency assistance by saying “Hey Edera, Help! I am having chest pains”.

When processing406 at least a portion of the diction to identify at least one communication request (e.g., placing a food/beverage order; requesting medication; contacting the nurse's station; and calling for emergency assistance) within the hospital room (e.g., hospital room300),communication process10 may process at least a portion of the diction using natural language processing. As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

In order to harness greater processing power, when processing406 at least a portion of the diction to identify at least one communication request (e.g., placing a food/beverage order; requesting medication; contacting the nurse's station; and calling for emergency assistance) within the hospital room (e.g., hospital room300),communication process10 may process408 at least a portion of the diction on a cloud-based computing resource (e.g., cloud resource64) to identify at least one communication request within the hospital room (e.g., hospital room300). As discussed above, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

If at least one communication request (e.g., placing a food/beverage order; requesting medication; contacting the nurse's station; and calling for emergency assistance) is detected,communication process10 may establish410 communication (e.g., via connection308) between the hospital room (e.g., hospital room300) and a remote location (e.g., nurse's station310) within the hospital (e.g., hospital312).

In order to effectuate such communication with the remote location (e.g., nurse's station310),communication process10 may interface412 the virtual assistant (e.g., virtual assistant32) with the remote location (e.g., nurse's station310) within the hospital (e.g., hospital312).

When interfacing412 the virtual assistant (e.g., virtual assistant32) with the remote location (e.g., nurse's station310) within the hospital (e.g., hospital312),communication process10 may enable414 functionality on the virtual assistant (e.g., virtual assistant32) to effectuate cloud-based communication (e.g., via connection308) between the virtual assistant (e.g., virtual assistant32) and the remote location (e.g., nurse's station310) within the hospital (e.g., hospital312). For example, one or more applications (e.g., application314) may be installed/executed on the virtual assistant (e.g., virtual assistant32) to enable communication with the remote location (e.g., nurse's station310) viacommunication process10.

Communication process

10 may process416 at least a portion of the diction to identify at least one supplemental command to be performed within the hospital room (e.g., hospital room300), wherein the at least one supplemental command to be performed within the hospital room (e.g., hospital room300) may include but are not limited to one or more of: controlling a hospital bed (e.g., hospital bed302); controlling a room lighting system (e.g., lighting system316); and controlling a television (e.g., television306).

When processing416 at least a portion of the diction to identify at least one supplemental command (e.g., controlling a hospital bed; controlling a room lighting system; and controlling a television) to be performed within the hospital room (e.g., hospital room300),communication process10 may process418 at least a portion of the diction on a cloud-based computing resource (e.g., cloud resource64) to identify at least one supplemental command (e.g., controlling a hospital bed; controlling a room lighting system; and controlling a television) to be performed within the hospital room (e.g., hospital room300).

Enabling Hands Free, Voice-Based Control of a Hospital Bed

Referring also toFIG.6,communication process10 may monitor400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300). When monitoring400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300),communication process10 may monitor402 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300) using a virtual assistant (e.g., virtual assistant32).

Additionally and when monitoring400 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300),communication process10 may monitor500 the diction of a patient (e.g., patient302) within a hospital room (e.g., hospital room300) to listen for the utterance of a wake-up word. Examples of such wake-up words may include but are not limited to “Siri”, “Alexa”, “Google” and “Edera”.

Communication process

10 may process502 at least a portion of the diction to identify at least one bed-control-command to be performed on a hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300), wherein examples of such bed-control-commands to be performed on a hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300) may include but are not limited to one or more of: a head raise/lower bed-control-command; a knee raise/lower bed-control-command; a feet raise/lower bed-control-command; a bed raise/lower bed-control-command; a heater on/off bed-control-command; and a massager on/off bed-control-command.

- Head Raise/Lower Bed-Control-Command For example, patient (e.g., patient302) may say “Hey Edera, Please raise my head”.
- Knee Raise/Lower Bed-Control-Command For example, patient (e.g., patient302) may say “Hey Edera, Please lower my knees”.
- Feet Raise/Lower Bed-Control-Command For example, patient (e.g., patient302) may say “Hey Edera, Please raise my feet”.
- Bed Raise/Lower Bed-Control-Command For example, patient (e.g., patient302) may say “Hey Edera, Please lift the bed”.
- Heater On/Off Bed-Control-Command: For example, patient (e.g., patient302) may say “Hey Edera, Please turn on my bed heater”.
- Massager On/Off Bed-Control-Command For example, patient (e.g., patient302) may say “Hey Edera, Please turn on my bed messager”.

If at least one bed-control-command (e.g., a head raise/lower bed-control-command; a knee raise/lower bed-control-command; a feet raise/lower bed-control-command; a bed raise/lower bed-control-command; a heater on/off bed-control-command; and a massager on/off bed-control-command) is detected,communication process10 may effectuate506 the at least one bed-control-command on the hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300).

In order to effectuate such communication with the hospital bed (e.g., hospital bed304),communication process10 may interface508 the virtual assistant (e.g., virtual assistant32) with the hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300).

When interfacing508 the virtual assistant (e.g., virtual assistant32) with the hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300),communication process10 may enable510 functionality on the virtual assistant (e.g., virtual assistant32) to effectuate cloud-based communication between the virtual assistant (e.g., virtual assistant32) and the hospital bed (e.g., hospital bed304) within the hospital room (e.g., hospital room300). For example, one or more applications (e.g., application314) may be installed/executed on the virtual assistant (e.g., virtual assistant32) to enable communication with the hospital bed (e.g., hospital bed304) viacommunication process10.

Communication process

10 may process512 at least a portion of the diction to identify at least one supplemental command to be performed within the hospital room (e.g., hospital room300), wherein examples of the at least one supplemental command to be performed within the hospital room (e.g., hospital room300) may include but are not limited to one or more of: placing a food/beverage order; requesting medication; contacting the nurse's station (e.g., nurse's station310); calling for emergency assistance; controlling a room lighting system (e.g., lighting system316); and controlling a television (e.g., television306).

When processing512 at least a portion of the diction to identify at least one supplemental command (e.g., placing a food/beverage order; requesting medication; contacting the nurse's station; calling for emergency assistance; controlling a room lighting system; and controlling a television) to be performed within the hospital room (e.g., hospital room300),communication process10 may process514 at least a portion of the diction on a cloud-based computing resource (e.g., cloud resource64) to identify at least one supplemental command (e.g., placing a food/beverage order; requesting medication; contacting the nurse's station; calling for emergency assistance; controlling a room lighting system; and controlling a television) to be performed within the hospital room (e.g., hospital room300).

Whilecommunication process10 was discussed above as being utilized in a work environment and in a hospital environment, other configurations are possible. For example and as will be discussed below in greater detail,communication process10 may be configured to provide assistance to users while they are in their homes.

Enabling Hands Free, Voice-Based Refills of Medications

Referring also toFIG.7,communication process10 may monitor600 the diction of a prescription recipient (e.g., user40) using a virtual assistant (e.g., virtual assistant32). For this example,user40 may be a recipient of a prescription (e.g., blood pressure medication) that is utilized on a long-term basis and is therefore repeatedly and frequently refilled.

Additionally and when monitoring600 the diction of a prescription recipient (e.g., user40) using a virtual assistant (e.g., virtual assistant32),communication process10 may monitor602 the diction of a prescription recipient (e.g., user40) using a virtual assistant (e.g., virtual assistant32) to listen for the utterance of a wake-up word. Examples of such wake-up words may include but are not limited to “Ski”, “Alexa”, “Google” and “Edera”.

Communication process

10 may process604 at least a portion of the diction to identify at least one prescription refill task (e.g., task66). One example of such a prescription refill task (e.g., task66) may be the prescription recipient (e.g., user40) using the virtual assistant (e.g., virtual assistant32) to ask to have their blood pressure medication refilled.

When processing604 at least a portion of the diction to identify at least one prescription refill task (e.g., task66),communication process10 may process606 at least a portion of the diction using natural language processing. As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When processing604 at least a portion of the diction to identify at least one prescription refill task (e.g., task66),communication process10 may also:

- process608 at least a portion of the diction to identify one or more refill-indicative trigger words (e.g., “medicine”, “prescription”, “refill”); and
- process610 at least a portion of the diction to identify one or more refill-indicative conversational structures (e.g., “can I have this filled”, “I need more medicine?”, “please refill my prescription”).

The above-described refill-indicative trigger words and refill-indicative conversational structures may be manually defined or may be automatically defined. For example, an administrator ofcommunication process10 may manually define one or more lists (e.g., lists58) that identify such refill-indicative trigger words and refill-indicative conversational structures. Additionally/alternatively, an administrator ofcommunication process10 may define seed data (e.g., seed data60) that may be processed via artificial intelligence (AI)process62 that may be configured to expandseed data60 to define the above-referenced lists (e.g., lists58).

In order to harness greater processing power, when processing604 at least a portion of the diction to identify at least one prescription refill task (e.g., task66),communication process10 may process612 at least a portion of the diction on a cloud-based computing resource to identify at least one prescription refill task (e.g., task66). As discussed above, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users

If at least one prescription refill task (e.g., task66) is detected (e.g.,user40 asking to have their blood pressure medication refilled),communication process10 may effectuate606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., medical management system70), wherein examples ofmedical management system70 may include but are not limited to one or more of: a medical office management system; a medical office billing system; and a pharmacy management system.

- Medical Office Management System: A medical office management system may be configured to enable medical professionals to manage a medical practice by e.g., scheduling appointments, scheduling staff, maintaining patient databases, maintaining patient electronic health records, issuing prescriptions, etc.
- Medical Office Billing System: A medical office billing system may be configured to enable medical professionals to manage accounts (e.g., account receivables and account payables) within a medical practice by e.g., enabling monetary inflows into the medical practice and enabling monetary outflows out of the medical practice.
- Pharmacy Management System: A pharmacy management system may be configured to enable pharmaceutical professionals to manage a pharmaceutical practice by e.g., processing prescriptions, ordering inventory, scheduling staff, maintaining client databases, maintaining client electronic pharmaceutical records, etc.

Accordingly, and as used in this disclosure, medical management system (e.g., medical management system70) may include a management system and/or a billing system that is used in any type of medical establishment, example of which may include but are not limited to: a doctor's office, a medical practice, an urgent care facility, a long-term care facility, a rehabilitation facility, a nursing facility, a hospice care facility, a hospital facility/organization, a life sciences facility/organization, and a pharmacy facility/organization.

As will be discussed below, when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system),communication process10 may parse614 the at least one prescription refill task (e.g., task66) into a plurality of subtasks; and effectuate616 the plurality of subtasks (e.g., subtasks68) on the medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system).

For example and when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system),communication process10 may identify618 which prescription medication(s) are currently refillable by the prescription recipient (e.g., user40). For example, assume that the prescription refill task (e.g., task66) issued by the prescription recipient (e.g., user40) was nonspecific (e.g., Please refill my prescription”) and the prescription recipient (e.g., user40) is currently receiving three prescription medications (e.g., a blood pressure medication, a cholesterol medication and an arthritis medication). Accordingly and when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system),communication process10 may identify618 these three prescription medication(s) that are currently refillable by the prescription recipient (e.g., user40).

Further and when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system),communication process10 may authenticate620 the identity of the prescription recipient (e.g., user40); ask622 the prescription recipient (e.g., user40) to define the specific medication to be refilled, thus defining a desired medication; and determine624 if the desired medication is currently refillable. For example,communication process10 may authenticate620 the identity of the prescription recipient (e.g., user40) using a voice print or via a PIN #/passcode; ask622 the prescription recipient (e.g., user40) to define the specific medication to be refilled, thus defining a desired medication (e.g., by selecting the blood pressure medication of the three medications that are refillable); and determine624 if the desired medication is currently refillable (e.g., which may be determined by accessing the medical office management system and/or the pharmacy management system).

As is known in the art, a voice print is a digital model of the unique vocal characteristics of an individual. Voiceprints are created by specialized computer programs which process speech samples. The creation of a voiceprint is often referred to as “enrollment” in a biometric system. There are two general approaches to the creation and use of voiceprints. In traditional voice biometric systems that use classical machine learning algorithms, a voiceprint is created by performing “feature extraction” on one or more speech samples. This feature extraction process essentially creates personalized calculations or vectors related to specific attributes that make the user's speech unique. In these systems, feature extraction is also used to create a Universal Background Model or “UBM”.

Additionally and when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system): if the desired medication (e.g., the blood pressure medication) is currently refillable,communication process10 may effectuate626 the refilling of the desired medication (e.g., the blood pressure medication) via the pharmacy management system; and notify628 the prescription recipient (e.g., user40) that the desired medication (e.g., the blood pressure medication) will be refilled.

Further and when effectuating606 the at least one prescription refill task (e.g., task66) on a medical management system (e.g., a medical office management system, a medical office billing system or a pharmacy management system): if the desired medication is not currently refillable,communication process10 may engage630 the medical management system to determine if the desired medication (e.g., the blood pressure medication) may be refilled for the prescription recipient (e.g., user40). If the desired medication (e.g., the blood pressure medication) may be refilled for the prescription recipient (e.g., user40),communication process10 may effectuate632 the refilling of the desired medication (e.g., the blood pressure medication) via a pharmacy management system and notify634 the prescription recipient (e.g., user40) that the desired medication (e.g., the blood pressure medication) will be refilled.

If the desired medication (e.g., the blood pressure medication) may not be refilled for the prescription recipient (e.g., user40),communication process10 may notify636 the prescription recipient (e.g., user40) that the desired medication (e.g., the blood pressure medication) will not be refilled.

As is known in the art, clinical research trials are utilized to gather clinical information concerning e.g., new drugs/processes/devices that are being tested in the marketplace. Unfortunately, one of the more difficult parts of such clinical research trials is gathering such clinical information from the trial participants. As will be discussed below in greater detail,communication process10 may be configured to provide assistance with respect to gathering such clinical information from the trial participants.

Enabling Hands Free, Voice-Based Survey Responses Via a Secure VA

Referring also toFIG.8,communication process10 may interface700 a clinical research system (e.g., clinical research system72) with a virtual assistant (e.g., virtual assistant32) accessible by a clinical trial participant (e.g., user40). Examples of the clinical research system (e.g., clinical research system72) may include a system that allows for the implementation of such clinical research trials and the gathering of such clinical information from the trial participants (e.g., user40). For this example, assume that the trial participant (e.g., user40) is involved in a clinical trial for the blood pressure medication that that they are taking.

When interfacing700 a clinical research system (e.g., clinical research system72) with a virtual assistant (e.g., virtual assistant32) accessible by a clinical trial participant (e.g., user40),communication process10 may enable702 functionality on the virtual assistant (e.g., virtual assistant32) to effectuate cloud-based communication between the virtual assistant (e.g., virtual assistant32) and the clinical research system (e.g., clinical research system72). For example, one or more applications (e.g., application74) may be installed/executed on the virtual assistant (e.g., virtual assistant32) to enable communication with the clinical research system (e.g., clinical research system72) viacommunication process10.

Communication process

10 may provide704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32), wherein the clinical research survey (e.g., clinical research survey76) may define one or more questions (e.g., questions78). Examples of such questions (e.g., questions78) may include questions concerning the efficacy of the blood pressure medication and the side effects of the blood pressure medication.

When providing704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may utilize706 text-to-speech technology to generate one or more speech-based questions based, at least in part, upon the one or more questions (e.g., questions78) defined within the clinical research survey (e.g., clinical research survey76).

As is known in the art, a text-to-speech (TTS) system converts normal language text into speech and is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

When providing704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may notify708 the clinical trial participant (e.g., user40) of the availability of the clinical research survey (e.g., clinical research survey76). For example,communication process10 may notify708 the clinical trial participant (e.g., user40) of the availability ofclinical research survey76 by e.g., havingvirtual assistant32 make a unique sound or flash a unique light. Additionally/alternatively,communication process10 may notify708 the clinical trial participant (e.g., user40) of the availability ofclinical research survey76 by sending user40 a text message or an email.

When providing704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may confirm710 the identity of the clinical trial participant (e.g., user40) before enabling the clinical trial participant (e.g., user40) to respond to the clinical research survey (e.g., clinical research survey76). For example,communication process10 may confirm710 the identity of the clinical trial participant (e.g., user40) using a voice print or via a PIN #/passcode.

As discussed above, a voice print is a digital model of the unique vocal characteristics of an individual. Voiceprints are created by specialized computer programs which process speech samples. The creation of a voiceprint is often referred to as “enrollment” in a biometric system. There are two general approaches to the creation and use of voiceprints. In traditional voice biometric systems that use classical machine learning algorithms, a voiceprint is created by performing “feature extraction” on one or more speech samples. This feature extraction process essentially creates personalized calculations or vectors related to specific attributes that make the user's speech unique. In these systems, feature extraction is also used to create a Universal Background Model or “UBM”.

When providing704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may provide712 one or more speech-based questions based, at least in part, upon the clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) so that the clinical trial participant (e.g., user40) may provide a speech-based answer as part of the survey response. For example,clinical research survey76 may include a plurality of text-based questions (e.g., questions78), whereincommunication process10 may utilize text-to-speech technology to generate speech-based questions from these text-based questions.Communication process10 may then provide712 these speech-based questions to the clinical trial participant (e.g., user40) so that the clinical trial participant (e.g., user40) may provide a speech-based answer as part of a survey response (e.g., survey response80).

Additionally/alternatively, the virtual assistant (e.g., virtual assistant32) may include a display screen (not shown) that allows for the displaying of text & images. An example of such a virtual assistant may include but is not limited to an Amazon Show™ device. Accordingly and in such a configuration, when providing704 a clinical research survey (e.g., clinical research survey76) to the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may render one or more text-based questions on the display screen (not shown) of the virtual assistant (e.g., virtual assistant32), wherein these one or more text-based questions may be based, at least in part, upon the one or more questions (e.g., questions78) defined within the clinical research survey (e.g., clinical research survey76).

Communication process

10 may receive714 the survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32), wherein the survey response (e.g., survey response80) defines one or more answers (e.g., answers82) to the one or more questions (e.g., questions78) defined within the clinical research survey (e.g., clinical research survey76).

When receiving714 a survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may process716 at least a portion of the survey response (e.g., survey response80) using natural language processing.

As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When receiving714 a survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may receive718 one or more speech-based answers from the clinical trial participant (e.g., user40) as part of the survey response (e.g., survey response80). For example,communication process10 may provide the clinical trial participant (e.g., user40) with speech-based questions that are based upon the text-based questions (e.g., questions78) included withinclinical research survey76. The clinical trial participant (e.g., user40) may then provide speech-based answers as part of the survey response (e.g., survey response80).

When receiving714 a survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may generate720 one or more text-based answers (e.g., answers82) from the one or more speech-based answers received from the clinical trial participant (e.g., user40) as part of the survey response (e.g., survey response80).

As is known in the art, speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the main benefit of searchability. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis. Some speech recognition systems require “training” (also called “enrollment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called “speaker-independent” systems. Systems that use training are called “speaker dependent”.

As discussed above, the virtual assistant (e.g., virtual assistant32) may include a display screen (not shown) that allows for the displaying of text & images. An example of such a virtual assistant may include but is not limited to an Amazon Show device. Accordingly and in such a configuration, when receiving714 a survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may enable the clinical trial participant (e.g., user40) to type out responses on the display screen (not shown) of the virtual assistant (e.g., virtual assistant32).

Additionally,communication process10 may require the clinical trial participant (e.g., user40) to provide/upload information from a personal medical device if required by the clinical research survey (e.g., clinical research survey76). For example, if the clinical research survey (e.g., clinical research survey76) concerns the efficacy of a drug to control blood sugar,communication process10 may require the clinical trial participant (e.g., user40) to upload information from their personal blood glucose monitor.

When receiving714 a survey response (e.g., survey response80) to the clinical research survey (e.g., clinical research survey76) from the clinical trial participant (e.g., user40) via the virtual assistant (e.g., virtual assistant32),communication process10 may associate722 the one or more text-based answers (e.g., one or more of answers82) with the one or more questions (e.g., questions78) defined within the clinical research survey (e.g., clinical research survey76). Accordingly,communication process10 may e.g.,associate722 Answer #1 (e.g., included within answers82) with Question #1 (e.g., included within questions78), and may associate722 Answer #2 (e.g., included within answers82) with Question #2 (e.g., included within questions78), and so on.

As will be discussed below in greater detail,communication process10 may be configure to allow for the utilization of a generic virtual assistant (e.g., virtual assistant32) by a medical management system (e.g., medical management system70) to automate the processing of redundant and/or time-consuming tasks.

Using a Generic VA to interface with Medical Office/Pharmacy Management Software

Referring also toFIG.9,communication process10 may interface800 a generic virtual assistant (e.g., virtual assistant32) with a medical management system (e.g., medical management system70), wherein examples ofmedical management system70 may include but are not limited to one or more of: a medical office management system; a medical office billing system; and a pharmacy management system

As discussed above, medical management system (e.g., medical management system70) may include a management system and/or a billing system that is used in any type of medical establishment, example of which may include but are not limited to: a doctor's office, a medical practice, an urgent care facility, a long-term care facility, a rehabilitation facility, a nursing facility, a hospice care facility, a hospital facility/organization, a life sciences facility/organization, and a pharmacy facility/organization.

When interfacing800 a generic virtual assistant (e.g., virtual assistant32) with a medical management system (e.g., medical management system70),communication process10 may enable802 functionality on the generic virtual assistant (e.g., virtual assistant32) to effectuate cloud-based communication between the generic virtual assistant (e.g., virtual assistant32) and the medical management system (e.g., medical management system70). For example, one or more applications (e.g., application74) may be installed/executed on the virtual assistant (e.g., virtual assistant32) to enable communication with the medical management system (e.g., medical management system70) viacommunication process10.

Communication process

10 may monitor804 the diction of a medical specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32). For example and when monitoring804 the diction of a medical specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32),communication process10 may monitor806 the diction of the medical specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32) to listen for the utterance of a wake-up word. Examples of such wake-up words may include but are not limited to “Ski”, “Alexa”, “Google” and “Edera”.

The medical specialist (e.g., user40) utilizing the generic virtual assistant (e.g., virtual assistant32) may be one of many different professionals that work in the medical field. Accordingly and when monitoring804 the diction of a medical specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32),communication process10 may include one or more of the following:

- monitor804 the diction of a claim processing specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32), wherein a claim processing specialist may e.g., process insurance claims within a medical office for submission to insurance companies. For example, a claim processing specialist may say “Hey Edera, please submit a claim to Insurance Company X for a CAT Scan for Patient Mary Jones”.
- monitor804 the diction of a billing specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32), wherein a billing specialist may e.g., process account payable invoices to effectuate billing and process account receivable invoices to effectuate payment. For example, a billing specialist may say “Hey Edera, please generate and submit an invoice to Patient Mary Jones for a $100 CAT Scan copay”.
- monitor804 the diction of a data processing specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32), wherein a data processing specialist may e.g., generate & update data records. For example, a data processing specialist may say “Hey Edera, please update the contact phone number for Patient Mary Jones to 123-456-7890”.
- monitor804 the diction of an ordering specialist (e.g., user40) using the generic virtual assistant (e.g., virtual assistant32), wherein an ordering specialist may e.g., effectuate the ordering of supplies and the ordering of medical procedures. For example, a data processing specialist may say “Hey Edera, please order a CAT Scan for Patient Mary Jones”.

Communication process

10 may process808 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70); and if at least one task (e.g., task66) is detected,communication process10 may effectuate810 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70).

When processing808 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may process812 at least a portion of the diction using natural language processing. As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When processing808 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may also:

- process814 at least a portion of the diction to identify one or more task-indicative trigger words (e.g., “submit”, “claim”, “generate”, “update”, “order”); and
- process816 at least a portion of the diction to identify one or more task-indicative conversational structures (e.g., “please bill”, “I need to update”, “submit this invoice”).

The above-described task-indicative trigger words, and task-indicative conversational structures may be manually defined or may be automatically defined. For example, an administrator ofcommunication process10 may manually define one or more lists (e.g., lists58) that identify such task-indicative trigger words, and task-indicative conversational structures. Additionally/alternatively, an administrator ofcommunication process10 may define seed data (e.g., seed data60) that may be processed via artificial intelligence (AI)process62 that may be configured to expandseed data60 to define the above-referenced lists (e.g., lists58).

When processing808 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may process818 at least a portion of the diction on a cloud-based computing resource (e.g., cloud resource64) to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70). As is known in the art, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

When effectuating810 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70),communication process10 may parse820 the at least one task (e.g., task66) into a plurality of subtasks (e.g., subtasks68); and effectuate822 the plurality of subtasks (e.g., subtasks68) on the medical management system (e.g., medical management system70). For example, in order to accomplishtask66,communication process10 may effectuate a plurality of discrete subtasks (e.g., subtasks68), examples of which may include but are not limited to identifying any outstanding balance owed by Patient Mary Jones, incrementing that amount by a $100 copay for the ordered CAT Scan, generating an invoice for that incremented amount, and submitting that invoice to Patient Mary Jones.

Further and when effectuating810 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70):communication process10 may access824 the medical management system (e.g., medical management system70) using an application program interface (e.g., API84) of the medical management system (e.g., medical management system70) to effectuate the at least one task (e.g., task66) on the medical management system (e.g., medical management system70).

As is known in the art, an application programming interface (API) is a way for two or more computer programs to communicate with each other. It is a type of software interface, offering a service to other pieces of software. A document or standard that describes how to build or use such a connection or interface is called an API specification. A computer system that meets this standard is said to implement or expose an API. The term API may refer either to the specification or to the implementation.

Enabling Hands Free, Voice-Based Automation of Tasks

Referring also toFIG.10,communication process10 may monitor900 the diction of a medical specialist (e.g., user40) using a virtual assistant (e.g., virtual assistant32). For example and when monitoring900 the diction of a medical specialist (e.g., user40) using a virtual assistant (e.g., virtual assistant32),communication process10 may monitor902 the diction of a medical specialist (e.g., user40) using a virtual assistant (e.g., virtual assistant32) to listen for the utterance of a wake-up word. Examples of such wake-up words may include but are not limited to “Siri”, “Alexa”, “Google” and “Edera”.

As discussed above, the medical specialist (e.g., user40) utilizingcommunication process10 may be one of many different professionals that work in the medical field. Accordingly and when monitoring900 the diction of a medical specialist (e.g., user40) using the virtual assistant (e.g., virtual assistant32),communication process10 may include one or more of the following:

- monitor900 the diction of a claim processing specialist (e.g., user40) using the virtual assistant (e.g., virtual assistant32), wherein a claim processing specialist may e.g., process insurance claims within a medical office for submission to insurance companies. For example, a claim processing specialist may say “Hey Edera, please submit a claim to Insurance Company X for a CAT Scan for Patient Mary Jones”.
- monitor900 the diction of a billing specialist (e.g., user40) using the virtual assistant (e.g., virtual assistant32), wherein a billing specialist may e.g., process account payable invoices to effectuate billing and process account receivable invoices to effectuate payment. For example, a billing specialist may say “Hey Edera, please generate and submit an invoice to Patient Mary Jones for a $100 CAT Scan copay”.
- monitor900 the diction of a data processing specialist (e.g., user40) using the virtual assistant (e.g., virtual assistant32), wherein a data processing specialist may e.g., generate & update data records. For example, a data processing specialist may say “Hey Edera, please update the contact phone number for Patient Mary Jones to 123-456-7890”.
- monitor900 the diction of an ordering specialist (e.g., user40) using the virtual assistant (e.g., virtual assistant32), wherein an ordering specialist may e.g., effectuate the ordering of supplies and the ordering of medical procedures. For example, a data processing specialist may say “Hey Edera, please order a CAT Scan for Patient Mary Jones”.

Communication process

10 may process908 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70), wherein examples ofmedical management system70 may include but are not limited to one or more of: a medical office management system; a medical office billing system; and a pharmacy management system.

When processing908 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may process910 at least a portion of the diction using natural language processing. As discussed above, natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When processing908 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may also:

- process912 at least a portion of the diction to identify one or more task-indicative trigger words (e.g., “submit”, “claim”, “generate”, “update”, “order”); and
- process914 at least a portion of the diction to identify one or more task-indicative conversational structures (e.g., “please bill”, “I need to update”, “submit this invoice”).

When processing908 at least a portion of the diction to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70),communication process10 may process916 at least a portion of the diction on a cloud-based computing resource to identify at least one task (e.g., task66) to be performed within a medical management system (e.g., medical management system70). As discussed above, cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and typically using a “pay-as-you-go” model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

If at least one task (e.g., task66) is detected,communication process10 may effectuate916 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70).

When effectuating916 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70),communication process10 may parse918 the at least one task (e.g., task66) into a plurality of subtasks (e.g., subtasks68); and effectuate920 the plurality of subtasks (e.g., subtasks68) on the medical management system (e.g., medical management system70). For example, in order to accomplishtask66,communication process10 may effectuate a plurality of discrete subtasks (e.g., subtasks68), examples of which may include but are not limited to identifying any outstanding balance owed by Patient Mary Jones, incrementing that amount by a $100 copay for the ordered CAT Scan, generating an invoice for that incremented amount, and submitting that invoice to Patient Mary Jones.

Further and when effectuating916 the at least one task (e.g., task66) on the medical management system (e.g., medical management system70):communication process10 may access922 the medical management system (e.g., medical management system70) using an application program interface (e.g., API84) of the medical management system (e.g., medical management system70) to effectuate the at least one task (e.g., task66) on the medical management system (e.g., medical management system70).

General

As will be appreciated by one skilled in the art, the present disclosure may be embodied as a method, a system, or a computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium.

Any suitable computer usable or computer readable medium may be utilized. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a transmission media such as those supporting the Internet or an intranet, or a magnetic storage device. The computer-usable or computer-readable medium may also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-usable medium may include a propagated data signal with the computer-usable program code embodied therewith, either in baseband or as part of a carrier wave. The computer usable program code may be transmitted using any appropriate medium, including but not limited to the Internet, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the present disclosure may be written in an object oriented programming language such as Java, Smalltalk, C++ or the like. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through a local area network/a wide area network/the Internet (e.g., network14).

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer/special purpose computer/other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures may illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.

A number of implementations have been described. Having thus described the disclosure of the present application in detail and by reference to embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the disclosure defined in the appended claims.