US20180143989A1 - System to assist users of a software application - Google Patents

Abstract

A system configured to assist users of a software application is provided. The system includes a virtual agent server100 configured to receive input that identifies a primary user and at least one secondary user, wherein the primary user and the secondary user are parties to an event. Further, the virtual agent server100 receives input identifying one or more preferences of the primary user and the secondary user corresponding to one or more categories of preferences. The virtual agent server100 then assigns relative weightage to preferences within a category among categories of preferences. Subsequently, the virtual agent server100 generates one or more suggestions corresponding to the event based on the relative weightage.

Description

CROSS-REFERENCE TO RELATED APPLICATION
• This application claims priority under 35 U.S.C. § 120 as a continuation-in-part of currently pending U.S. patent application Ser. No. 15/356,512, filed Nov. 18, 2016, which is a non-provisional application claiming the benefit under 35 U.S.C. § 119(e) of Provisional Patent Application Nos. 62/257,722, 62/275,043 and 62/318,762, filed Nov. 20, 2015, Jan. 5, 2016 and Apr. 5, 2016, respectively. The entire disclosures of all said prior applications are hereby incorporated by reference.
BACKGROUNDField of Invention
• The subject matter in general relates to the field of virtual assistant systems. More particularly, but not exclusively, the subject matter relates to a virtual assistant system that makes suggestions and executes actions corresponding to events in which preferences of multiple entities matter.
Discussion of Related Art
• In our day-to-day lives we face many situations that require coordination between multiple people. However, multiple opinions arise when there are multiple people, and it becomes tough to come up with recommendations that are suitable to everyone's taste, resulting in a lot of time, effort and coordination needed to come to a decision which is acceptable by everyone. Conventional systems are incapable of making suggestions and executing actions corresponding to events in which preferences of multiple entities matter. Also, there are no provisions to take tastes and opinions of multiple people into consideration. Further, there are no systems which may help the users in fulfilling the actions that were selected based on preferences of multiple people.
• Customers who wish to place an order, complete a transaction or clear doubts regarding a product or a service generally try to contact the customer service of the respective organization. Customers' communication with call centers mostly take place over phone calls or e-mails and frequently leads to confusions and errors in communication between the customer and the call center representative, especially with communication network issues, hearing issues and misinterpretation of instructions related to functioning/use of products. Further, there are instances where the call center representative may not understand the needs or requirements of the customer, which leaves them unable to help the customer. Such situations lead to customer dissatisfaction and often result in bad reviews and complaints made by the customer regarding the organization.
• Conventional systems do not help customers when they face confusions while trying to communicate with a call center representative. Further, there is no provision to support and help the call center representative in case they need further help while communicating with the customer.
• Further, in conventional systems, customers talk to the customer service of the business on the phone. Therefore, the current customer service experiences are restricted to a communication wherein the customer can only hear an agent's speech on a telephone line. Such voice based communication has limitations in terms of how well one can express and address issues, and how well one can interpret and understand information presented through such communication.
• An image and/or video are worth 1000 sentences. However, the current customer service experiences are restricted to a communication wherein the user can only hear an agent's speech on a telephone line.
• Thus, the conventional systems failed to solve the above problems, which result in inconveniences to planners and people who are coordinating between multiple persons. Further, with the current increase in the coordinated work between multiple people at residences, offices and other campuses, it is of prime importance to have improved systems.
SUMMARY
• Accordingly, an improved technique to overcome the above problems is needed. To fulfil this need, a system configured to assist users of a software application is provided. The system comprises a virtual agent configured to receive input that identifies a primary user and at least one secondary user, wherein the primary user and the secondary user are parties to an event. Further, the virtual agent receives input identifying one or more preferences of the primary user and the secondary user corresponding to one or more categories of preferences. The virtual agent then assigns relative weightage to preferences within a category among categories of preferences. Subsequently, the virtual agent generates one or more suggestions corresponding to the event based on the relative weightage.
• Further, a system configured to assist in customer service is provided. The system comprises a database comprising multimedia files, wherein one or more tags are associated with one or more of the multimedia files. The system further comprises a virtual agent configured to receive input from a first user and a second user; and process the received input to understand a context of the input. Further, the virtual agent identifies one or more multimedia files based on the context and the tags and shares at least one of the identified multimedia files with at least one of the first user and the second user to enable better understanding between them.
• Further, a system configured to complete a transaction is provided. The system comprises a first virtual agent assisting a customer and a second virtual agent assisting an organization. The first virtual agent is further configured to receive input from the customer and process the input to determine an action desired by the customer and context of the action. Further, the first virtual agent communicates the action desired by the customer and the context of the action to the second virtual agent. The second virtual agent is configured to receive communication of the action desired by the customer and the context of the action and execute the action based on the context of the action.
• Other objects, features, and advantages of the present invention will become apparent from the following detailed description. It should be understood that however, that the detailed description and specific examples, while indicating preferred embodiments of the present invention, are given by way of illustration only and various modifications may naturally be performed without deviating from the present invention.
BRIEF DESCRIPTION OF DIAGRAMS
• Embodiments are illustrated by way of example and not limitation in the Figures of the accompanying drawings, in which like references indicate similar elements and in which:
• FIG. 1 depicts an exemplary architecture of avirtual agent server100 for assisting a user.
• FIG. 2 depicts asystem200 including thevirtual agent server100 for assisting a user, in accordance with an embodiment;
• FIG. 3 depicts a flowchart of anexemplary method300 for assisting users using avirtual agent server100, in accordance with an embodiment.
• FIG. 4 depicts a system400 including thevirtual agent servers100aand100bfor assisting a user, in accordance with an embodiment;
• FIG. 5 depicts a flowchart of anexemplary method500 for optimizing a user's desired actions, in accordance with an embodiment.
• FIG. 6 depicts asystem600 including thevirtual agent server100 for assisting users, in accordance with an embodiment;
• FIG. 7 depicts a flowchart of anexemplary method700 for assisting users using a user's mobile device202, in accordance with an embodiment;
• FIGS. 8-11 depict exemplary user interfaces for assisting users using a user's mobile device202, in accordance with an embodiment; and
• FIG. 12 depicts a system including thevirtual agent server100 for assisting users in a conversation with acustomer service representative208, in accordance with an embodiment.
DETAILED DESCRIPTION OF THE INVENTION
• The following detailed description includes references to the accompanying drawings, which form part of the detailed description. The drawings show illustrations in accordance with example embodiments. These example embodiments are described in enough details to enable those skilled in the art to practice the present subject matter. However, it will be apparent to one of ordinary skill in the art that the present invention may be practised without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. The embodiments can be combined, other embodiments can be utilized or structural and logical changes can be made without departing from the scope of the invention. The following detailed description is, therefore, not to be taken as a limiting sense.
• In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one. In this document, the term “or” is used to refer to a non-exclusive “or,” such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.
• It should be understood that the capabilities of the invention described in the present disclosure and elements shown in the figures may be implemented in various forms of hardware, firmware, software, non-transitory recordable medium or combinations thereof.
Overview
• The embodiments disclose techniques used to solve problems in communication between multiple entities with the help of avirtual agent server100. Avirtual agent server100 can help a user and a group of people to decide on a task. Sometimes, when there are a number of people involved in an activity, deciding on a single course of action becomes difficult due to the differences in opinions of the people involved. In such a case, a user can use thevirtual agent server100 to help in deciding on an acceptable course of action. Thevirtual agent server100 can understand natural speech and will determine preferences of people who are parties to an event. It will then come up with overall best-suited suggestions by taking everyone's preferences into consideration. The user and other people involved can choose whichever suggestions they prefer, and thevirtual agent server100 will execute the selected suggestions based on the feedback of the user and others. Further, thevirtual agent server100 can search for multimedia files that are relevant to the suggestions and share them with the user. Thevirtual agent server100 can also communicate with external applications to optimize and execute the user's desired actions such that the optimizations are in favour of the user.
• The disclosed system may be used in any chat, voice or phone-based communication. Thevirtual agent server100 can identify multimedia files such as images, audio and video clips that are relevant to the topic of communication. Further, thevirtual agent server100 can stream or share the identified multimedia files with one or more users. Hence, thevirtual agent server100 can be used for event planning, task planning, customer support, and other related applications.
• Further, the embodiments disclose techniques used to solve problems in customer service with the help ofvirtual agent servers100. Avirtual agent server100 can help a user who has contacted a customer service representative.
Virtual Agent System
• FIG. 1 represents an exemplary architecture of avirtual agent server100, in accordance with an embodiment. Thevirtual agent server100 may include a Natural Language Understanding (NLU)module102 to understand the speech of the users, apreference module104 to determine preferences of the users, alocation module106, asuggestion generation module108, acontroller module110, asecurity module112, amultimedia module114, anexecution module116 and anoptimization processor module118.
• Thevirtual agent server100 may receive input identifying a primary user and one or more secondary users, wherein the primary user and the secondary users are parties in an event that needs coordination between the primary and secondary users. Further, thevirtual agent server100 may receive one or more inputs to identify one or more preferences of the primary user and the secondary user. The preferences may correspond to one or more categories of preferences. Subsequently, thevirtual agent server100 may assign one or more relative weightage to preferences within a category and generate one or more suggestions for the primary and secondary users based on the relative weightage. Thevirtual agent server100 may then communicate with the primary and secondary users to help them execute their selected suggestion.
• In an implementation, a primary user may communicate with thevirtual agent server100 using their user'smobile device202a. The primary user may request thevirtual agent server100 to initiate communication with one or more secondary users using theirmobile device202bin order to include them as a party in the event being coordinated by the primary user. Alternatively, the primary user may contact one or more secondary users by themselves as depicted inFIG. 8.
• In an implementation, the Natural Language Understanding Module102 (hereafter called NLU module102) may be used by thevirtual agent server100 to understand the natural speech of the primary or secondary users.
• In an implementation, theNLU module102 may receive the primary and/or secondary user's natural speech as an input. This natural speech may be in the form of audio or text. Further, theNLU module102 may parse information from the primary and/or secondary user's natural language speech in order to collect information about the primary and/or secondary user's desired action and context of the desired action.
• In an implementation, the input to theNLU module102 may be received through one or more of the following situations: the primary user may communicate with thevirtual agent server100, or with one or more secondary users. The input may further include inputs received from the primary or secondary users in previous conversations. TheNLU module102 may generate text from the primary or secondary user's speech. In an implementation, thevirtual agent server100 may label parts of the text of the primary or secondary user. These labels may be used by thevirtual agent server100 to understand the speech of the primary or secondary users.
• In an implementation, for example, in case the user comments “I'm looking for Indian restaurants which are close to my house”, thevirtual agent server100 may label “looking for” as ‘desired action’, “restaurants” as ‘desired event’, “Indian” as ‘desired cuisine’, and “close to my home” as ‘desired distance’.
• The labels for the text may be generated using Hidden Markov Models or Conditional Random Field models. Alternatively, theNLU module102 may use one or more slot-filling algorithms or manually configured rules or natural language analysis or a machine learning classifier to determine the primary or secondary user's desired action and context from the primary or secondary user's natural speech. Subsequently, theNLU module102 may communicate the primary or secondary user's desired action and context to other modules of thevirtual agent server100.
• Thepreference module104 be used to determine the preferences of the primary or secondary users. These preferences may be used by thevirtual agent server100 to create relevant suggestions for the primary or secondary users.
• In an implementation, thepreference module104 may receive inputs from other modules of thevirtual agent server100 and process the inputs to determine the preferences of the primary and secondary users.
• In an implementation, thepreference module104 may also determine one or more preferences of the primary user and/or the secondary user based on a conversation between two or more of, the primary user, the secondary user and thevirtual agent server100. The conversation may be a present conversation or one or more past conversations which may be received from an external database or server. Thevirtual agent server100 may identify one or more preferences based on the frequency of certain words used in the conversation. Further, thevirtual agent server100 may assign weightage to the preferences based on the frequency of the words used in the conversation.
• Further, thepreference module104 may communicate with one or more databases to receive a history of the primary or secondary users. Thepreference module104 may represent the preferences of the primary or secondary users by creating a matrix of their preferences along one or more parameters.
• In an implementation, a representative location of the entire group of primary and/or secondary users may be derived from a conversation history or from the primary and/or secondary user. In another implementation, a centroid may be computed by laying out the longitude and latitude of the primary and/or secondary users.
• In an implementation, thepreference module104 may convert the strength of the user's preferences into a number. The strength of the user's preferences may be measured by features such as frequency of usage of the preference, tone of voice, pitch of voice, frequency of repeated words, history or social profile of the primary or secondary users, among others. Weightage may be assigned to one or more preferences based on the above mentioned features. A weighted table may be constructed for the primary and secondary users by considering their user preference scores and frequency of the preferences of the primary and secondary users.
• In an implementation, thelocation module106 may be used to receive or determine one or more location information related to the primary or secondary users. Thevirtual agent server100 may use this location information as a preference to generate suggestions for the primary and secondary users.
• Further, thelocation module106 may communicate the determined locations of the primary and secondary users with other modules of thevirtual agent server100.
• In an implementation, the location of the primary and secondary users may be derived from a previous or present conversation between at least two of the primary user, secondary user, and thevirtual agent server100. Alternatively, the location of the primary or secondary users may be determined based on the GPS location information of the primary and/or secondary users'mobile devices202a,202b.
• In an implementation, the location information of the primary or secondary users may include one or more current locations where the primary or secondary users are currently present at. Further, the location information may include an address of a preferred location, wherein thevirtual agent server100 has determined, using thepreference module104, that the primary or secondary user shows preference for the preferred location over other locations.
• In an implementation, the location information shared by thelocation module106 with other modules may include an address of one or more locations of consensus. Thevirtual agent server100 may be configured to determine one or more locations of consensus as explained below. These locations of consensus may be determined based on the location information corresponding to the primary user or the secondary user. Further, the suggestions may also be based on the locations of consensus.
• In an implementation, thesuggestion generation module108 may take into consideration the different preferences of the primary and secondary users to generate suggestions that may be used by the primary and secondary users.
• In an implementation, thesuggestion generation module108 may receive information from the other modules of thevirtual agent server100. Thesuggestion generation module108 may receive location information from thelocation module106 and preference information from thepreference module104. Further, thesuggestion generation module108 may receive information regarding the desired action and context of the desired action of the primary user from theNLU module102.
• In an implementation, thesuggestion generation module108 may take the preferences of the primary and secondary users; and receive a user profile and/or a user history from one or more databases. Further, thesuggestion generation module108 may process the received information to generate suggestions applicable to the primary and secondary users; and rank the generated suggestions so that thevirtual agent server100 may suggest the higher ranked suggestions before the lower ranked suggestions. To generate suggestions, the preferences of the primary or secondary users may be converted into a single user vector for search and recommendations use cases. The user vector may comprise elements that hold information representing one or more of the location of the various users, set of preferences of the primary or secondary users, and history of the users, amongst others. A group user vector may then be used to rank the elements, which may be further used during various actions such as search and suggestion, among others.
• As an example, thesuggestion generation module108 may create a user vector for a user ‘Dan’ as shown below. The desired action and context of user Dan may be to locate a restaurant where he may have lunch. One or more information about user Dan received from databases and present and previous conversations comprising location information and food preferences may be included in the elements of the user vector as follows:
• User Dan:
• location: {37.4292 N, 122.1381}
• preferences: {Italian 4.1}, {Thai 3.5}, {Indian: 2.0}
• favourite_dishes: {Lasagne 10.1}, {Drunken Noodles 8.5}
• In an implementation, in case the group size comprising the primary and secondary users increases, there may not be many preferences that match the interest of all users. In this case, thesuggestion generation module108 may construct a query with less number of restrictions to get a higher number of suggestion results. This query may result in one or more suggestions that may be preferred by more primary or secondary users. Further, thesuggestion generation module108 may rank the results of this query. Subsequently, the ranked results may be suggested to the primary and secondary users. Thesuggestion generation module108 may use one or more of the following methods to rank the suggestions.
• In an implementation, the first iteration of ranking may be done by an algorithm such as a simple Euclidean distance between a combination of user and query vector and result vector. Further, the second iteration of ranking may use an expensive Gradient Boosted Decision Tree learn to rank algorithm with features from the user vector.
• In another implementation, the ranking may be generated by communicating with one or more contacts of the primary or secondary users and requesting them for their advice or opinion to identify one or more suggestions. The contacts of the primary user may be retrieved from one or more social networks or from a contact list on the primary user'smobile device202a. In case the primary user has not given any explicit input regarding which contact's opinion is to be requested, thevirtual agent server100 may rank the contacts of the primary and/or secondary users in social network/contacts to optimize the requesting process for a timely feedback from the contacts. Further, one or more contacts may be selected based on the ranking to receive their opinion or advice regarding the preferences of the primary or secondary users. In an implementation, ranking of social contacts may use a Gradient Boosted Decision Tree learn to rank algorithm.
• In an implementation, thevirtual agent server100 may decide to ask the primary user for feedback in case the primary user has explicitly asked thevirtual agent server100 to do so. Alternatively, thevirtual agent server100 may run a machine learning classifier to determine if it needs to contact other users to get feedback on the restaurant in the conversation.
• In an implementation, thesuggestion generation module108 may be configured to determine one or more locations of consensus. These locations of consensus may be locations which may be acceptable by a majority of the primary and secondary users. These locations of consensus may be determined based on location information corresponding to the primary user or the secondary user. Further, the suggestions generated by thesuggestion generation module108 may also be based on the locations of consensus.
• In an implementation, thesuggestion generation module108 may further communicate with otherexternal systems404, such as social network platforms210, related to the primary or secondary users to collect information that may help in generating suggestions. In an embodiment, thesuggestion generation module108 may communicate with a calendar application on the primary and/or secondary user'smobile device202a,202bto determine the time and date of the user's free time or appointments. Further, thesuggestion generation module108 may communicate with external applications such as Google maps to determine estimated time, locations and routes. Such information collected fromexternal systems404 may be used to generate suggestions for the primary or secondary users.
• As an example, the primary user may like an Indian restaurant which is 5 miles away. Thevirtual agent server100 may have contacted one or moreexternal systems404 and determined that there is an accident on route to the restaurant. Further, thevirtual agent server100 may have communicated with the calendar application of the primary and/or secondary user'smobile device202a,202band determined that the primary or secondary user has a meeting scheduled in 10 minutes. In this case, thevirtual agent server100 may alert the user that it may be tough for the primary or secondary user to attend the meeting if he chooses that Indian restaurant. Thevirtual agent server100 may generate a suggestion to the primary or secondary user such as “I can show you the driving directions to the restaurant you chose but it looks like you have a meeting in another 10 minutes and there is an accident on the way. You might be late to the meeting if we proceed”.
• In an implementation, the generated suggestion may be communicated to the primary or secondary users as one or more of a spoken dialog, a text message or an e-mail, among others. The generation of the suggestion may be alerted to the primary or secondary user by using a spoken dialog, a ring tone or a phone vibration.
• In an implementation, thecontroller module110 may be used to coordinate actions between the other modules of thevirtual agent server100. Thecontroller module110 may comprise the main instructions to be followed by thevirtual agent server100 while taking into consideration the preferences of the multiple users and suggesting actions to be chosen by the users which may be further implemented by thevirtual agent server100.
• In an implementation, thecontroller module110 may determine the best alerting mechanism to be used for alerting the primary or secondary user in case of a generated suggestion. Thecontroller module110 may use a combination of rules configured against events and a Machine Learning/Predictive classifier to determine the type of alert that may be used in a given scenario. Some variables that may be used as input into the algorithm/rules are:
• a) Is the user moving as measured by the phone location?
  b) What is the type of the message? Is it a date meet or lunch meet with co-workers?
  c) Is the user in an important meeting?
  d) How many people are currently engaged with the user?
  e) How far is the phone to the user? Is the phone in a pocket or on a table or in hand?
• In an implementation, the answers to one or more of these questions may be measured by tracking the time lag between changes in the position of a user's mobile device202. For instance, in an example it may take an approximately constant time for the primary or secondary user to move user's mobile device202 from his pocket to his hand and complete an unlock action or click action on the screen of the user's mobile device202. Thevirtual agent server100 may keep a track of time periods when the user may have put the user's mobile device202 down. Alternatively, thevirtual agent server100 may instruct a user's mobile device202 sensors to log metadata information about the surroundings of the user's mobile device202.
• In an implementation, thecontroller module110 may determine that thevirtual agent server100 may have stopped communication with the user'smobile device100. This may have occurred either due to network failure or server malfunctioning. In such scenarios, in an implementation there may be avirtual agent client212 on the primary and/or secondary user'smobile device202a,202bwherein thevirtual agent client212 may have a preconfigured time period to receive a response from thevirtual agent server100. In case thevirtual agent server100 does not respond within the preconfigured time period, thevirtual agent client212 may make decisions with the available information using one or more software codes and data lookup tables stored locally on the user's mobile device.
• In an implementation, thevirtual agent client212 on the primary and/or secondary user'smobile device202a,202bmay push data and logical steps needed for the user to make decisions about the generated suggestions in case thevirtual agent server100 has stopped communication with the user'smobile device100 due to low or no network. Thevirtual agent client212 on the primary and/or secondary user'smobile device202a,202bmay push the data and the logical steps of the virtual agent process either as a periodic process or after running a machine learning classifier using features such as user location and network availability in the location.
• In an implementation, the code may be a JavaScript code for thevirtual agent client212 to execute, wherein data may be stored in a nested hashmap structure. The hashmap data structure may assign unique keys to values to be stored. The values may be stored in a map by forming a key-value pair. The values may be retrieved at any point by passing the assigned key through an appropriate command.
• As an example, consider a user trying to find a restaurant when thevirtual agent server100 may stop communicating with the primary and/or secondary user'smobile device202a,202b:
• • User: “I am looking for an Indian Restaurant”
  • Virtual agent server100: “It looks like you are around Fremont. Based on your food taste, you might like food from the restaurant Shalimar” (Virtual agent server100 may show pictures of dishes at Shalimar in the conversation using implementations discussed above)
  • User: “Call Shalimar”
• Further, thevirtual agent server100 may stop communication with the user. Thevirtual agent client212 may determine that there is no connection to thevirtual agent server100. Thevirtual agent client212, based on the context of user location and network type might run a Machine Learning classifier algorithm and execute the action instructed by the user. In this case, when the user says “Call Shalimar”, thevirtual agent client212 may forward the phone number of the selected restaurant and the code to call the phone number to the user'smobile device100. The user'smobile device100 may then initiate a call to the restaurant “Shalimar” when the user says “Call Shalimar”, even though it can't communicate with thevirtual agent server100.
• The discussed implementations may be applicable to all customer-to-business interactions including one or more of buying products on retail websites and buying airline tickets, among others. The disclosure discusses techniques in the context of natural speech interaction between users andvirtual agent servers100. These techniques may be easily extended to natural text interactions as seen in conventional chat applications.
• In an implementation, thesecurity module112 may be used to maintain the security of the user. Thevirtual agent server100 may ensure that the security of the primary or secondary users is not breached. This may be accomplished by not sharing any unnecessary or extra information while completing the primary or secondary users' request while matching the questions against slots. Further, thesecurity module112 may restrict revealing any extra information unless explicitly approved by the primary or secondary user.
• In an implementation, themultimedia module114 may be used by thevirtual agent server100 to search and share multimedia files and suggestions with the primary or secondary users to give them a better understanding of the generated suggestions. Thevirtual agent server100 may stream one or more multimedia such as images, files, audio or video, among other multimedia related to context of the conversation. In an implementation, this may be accomplished by training a Convolutional Neural Network with tagged multimedia files.
• In an implementation, themultimedia module114 may be used by thevirtual agent server100 to identify one or more multimedia files that the user may be interested in based on the user's intent from one or more text-based chat and/or phone communication. Further, themultimedia module114 may share the identified multimedia files with one user and receive that user's feedback regarding the identified multimedia files. Subsequently, themultimedia module114 may share one or more of the identified multimedia files with another user based on the feedback received. The multimedia files and textual tags may be stored in a database as explained below inFIG. 6.
• In an implementation, theexecution module116 may be used by thevirtual agent server100 to receive and process feedback from the primary and/or secondary users corresponding to the generated suggestions. Further, theexecution module116 may execute one or more of the suggestions that have been selected by the primary or secondary users using the feedback and the context derived from the natural speech of the primary or secondary users. Thevirtual agent server100 may receive feedback from the primary or secondary users regarding the generated suggestion that they may have selected. In case the selected suggestion may be executed without contacting anyexternal system404, theexecution module116 may directly initiate the execution of the selected suggestion.
• In an implementation, there may be situations wherein thevirtual agent server100 may need to communicate with one or moreexternal systems404 in order to execute the selected suggestion. In this case, theexecution module116 may first determine the type ofexternal system404 it needs to communicate with.
• In an implementation, theexternal system404 may be a software application comprising aweb application206 or it may be a natural person such as acustomer service representative208. In case theexecution module116 has determined that it needs to contact anexternal web application206, thevirtual agent server100 may call an external Application Programming Interface and execute the selected suggestion with the parsed parameters from the natural language speech of the primary or secondary users. In case theexternal system404 doesn't comprise of an Application Programming Interface, theexecution module116 may need to search for a phone number or customer service application related to theexternal system404 in order to execute the suggestion selected by the primary or secondary users.
• In both cases, theexecution module116 may need to collect one or more parameters related to the context of the execution. These parameters may include one or more of conversation summary, user's first name and last name, time, date, location and operating system of the primary and/or secondary user'smobile device202a,202b, among others. The possible ways in which this information may be collected are follows:
• • a) Question and Answering problem: In this implementation, the context, conversation history and inference from the conversation are used for answering a query regarding the value of the parameter for the Application Programming Interface. Attributes such as meta-data of the Application Programming Interface and the collected parameters are used to construct the question. In an implementation, this may be done by using manually developed pattern matching rules. Alternatively, a combination of Memory Networks, Recurrent Neural Network models with pattern matching rules may be used to answer questions for the Application Programming Interface. Crowdsourcing platforms may be used to create training data for training neural networks.
  • b) Slot filling Algorithms: In this implementation, slot filling algorithms may be used to figure out the parameters for the Application Programming Interface after passing the conversation context through one or more entity recognition algorithms.
  • c) Manual rules: Manual rules may be configured against the textual patterns using regular expressions and grammars to parse out the parameters.
• In another implementation, thevirtual agent100 may determine the parameters for the Application Programming Interface call from the natural language of the primary and/or secondary user, by storing, searching, and inferring information from the user dialog history and runtime context. In an implementation, the past ‘n” dialogs are retrieved and one or more words represented by tokens may be annotated with labels using entity name recognition techniques. Further, inference rules may be run on the annotated dialogs to derive any extra information, following which, parameters for the Application Programming Interface call are gathered.
• In an implementation, theoptimization processor module118 may be used by avirtual agent server100ain case it needs to communicate with anothervirtual agent server100brepresenting anexternal system404 to execute a user's actions, as depicted inFIG. 4. In this case, theoptimization processor module118 may be used to determine optimization options corresponding to the user's desired actions, communicating the optimization options with the othervirtual agent server100b, if allowed by the user. Further, theoptimization processor module118 may be used to determine which optimization option is to be executed.
• FIG. 2 depicts asystem200 comprising avirtual agent server100 which may communicate with primary and secondary users'mobile devices202aand202b; aweb application206, acustomer service representative208, a social network webpage210, and avirtual agent client212 as depicted.
• In an implementation, the primary and secondary user'smobile device202a,202bmay include mobile phones, palmtops, PDAs, tablet PCs, notebook PCs, laptops and computers, among other computing devices. In an embodiment, the primary and secondary user'smobile device202a,202bmay include any electronic device equipped with a browser to communicate with thevirtual agent server100. The user's mobile device202 may belong to a primary or a secondary user who may use it to communicate with thevirtual agent server100.
• In an implementation, the primary and/or secondary user'smobile device202a,202bmay include avirtual agent client212 which may communicate with thevirtual agent server100 and share inputs related to the primary or secondary user with thevirtual agent server100. Further, thevirtual agent client212 may execute one or more instructions in case communication with thevirtual agent server100 has failed, as described in embodiments above.
• In an implementation, thevirtual agent server100 may be implemented in the form of one or more processors with a memory coupled to the one or more processors with one or more communication interfaces. Thevirtual agent server100 may communicate with one or moreexternal systems404 and one or more primary and/or secondary user'smobile device202a,202bthrough a communication network. It may be noted that some of the functionality of thevirtual agent server100 may be implemented in the primary and/or secondary users'mobile devices202aand202b.
• In an implementation, the communication network may include a wired network, a wireless network, or a combination of wired network and wireless network. For example, the communication network may include local area network, wide area network, and metropolitan area network, among others.
• In an implementation, the social network webpage210 may comprise of any webpage related to a social networking application used by the primary and/or secondary users through their primary and secondary users'devices202aand202b.
• In an implementation, thevirtual agent server100 may communicate with a primary user through their primary user'smobile device202aand secondary users through their user'smobile devices202b. Thevirtual agent server100 may receive inputs from the primary and/or secondary users, and determine their desired action and a context of their desired action. Further, thevirtual agent server100 may determine preferences of the primary and/or secondary users, and may generate suggestions for the primary and/or secondary users. In case the primary and/or secondary users select a suggestion, thevirtual agent server100 may communicate with one or moreexternal systems404 such as theweb application206 or a natural person such as thecustomer service representative208 in order to execute the selected suggestion.
• In an implementation, thevirtual agent server100 may be required to contact thecustomer service representative208 in case options are not available. The customer service for the business may be handled by a human i.e. acustomer service representative208 or by avirtual agent server100. In both cases, thevirtual agent server100 may need to understand a natural language interaction wherein thevirtual agent server100 operating on behalf of the user may be required to answer questions about the user's desired action. In an implementation, this may be accomplished by parsing the natural language evaluating probabilities against configured slots and providing one or more answers to thecustomer service representative208 or thevirtual agent server100. Thevirtual agent server100 may ensure that the transaction is acknowledged. The acknowledgement may then be communicated to the user in the form of a message or notification. As an example, in case thevirtual agent server100 was required to contact thecustomer service representative208 of a hotel, thevirtual agent server100 may converse with thecustomer service representative208 using natural language to reserve bookings on behalf of the user as instructed, and receive an acknowledgement corresponding to the booking. Further,virtual agent server100 may communicate the following message to the user through the primary user'smobile device202a“The hotel bookings at Hilton for 2 days have been confirmed”.
• In an implementation, thevirtual agent server100 may receive inputs from a current conversation between a primary user and acustomer service representative208. Thevirtual agent server100 may convert the conversation to text to generate one or more metadata such as information about the speaker and emotional response of the speaker, among others. The annotated text may then be analysed for one or more actions such as placing an order/transaction, complaints, bad customer service etc. This analysis may be done using one or more of slot filling algorithms, manually configured rules, natural language analysis for pre-configured actions or a Machine Learning classifier. The actions may then be completed by avirtual agent server100 by calling external API's and/or making phone calls.
• FIG. 3 depicts amethod300 for avirtual agent server100 to determine one or more preferences of primary or secondary users to generate suggestions and execute the selected suggestions, in accordance with an implementation. Themethod300 may be implemented by a server or an application on a primary and/or secondary user'smobile device202a,202b.
• In an implementation, thevirtual agent server100 may be instructed by the primary user to include one or more secondary users in a conversation between the primary user and thevirtual agent server100. Thevirtual agent server100 may be configured to obtain input identifying the secondary user from one or more of a phone number of the secondary user, voice characteristics of the secondary user or metadata corresponding to the secondary user, among others. Thevirtual agent server100 may communicate with one or more external servers to obtain metadata corresponding to the secondary user.
• Atstep302, inputs from the primary and secondary users and their location information may be received or determined by the various modules included in thevirtual agent server100. Further, using previous and present inputs, thevirtual agent server100 may determine one or more preferences of the primary and secondary users as depicted atstep304. The user inputs and their preferences may be used by thevirtual agent server100 to determine one or more desired actions and the context of the actions desired by the primary and/or secondary users.
• The method atstep306 comprises of receiving inputs from contacts of the primary user, regarding their advice or opinions related to the preferences of the primary user. Atstep308, thevirtual agent server100 may process the preferences of the primary and/or secondary users to generate a broad query. The result of this broad query may result in one or more suggestions that may correspond to the preferences of the primary and/or secondary users. Further, as shown instep310, thevirtual agent server100 may share one or more multimedia files with the primary and/or secondary users, wherein the shared multimedia files may be related to the generated suggestions and/or preferences of the primary and/or secondary users.
• After sharing the generated suggestions, thevirtual agent server100 may receive feedback related to the suggestions from the primary and/or secondary users as shown atstep312. Thevirtual agent server100 may process the received feedback to determine whether any of the suggested generations were selected by the primary and/or secondary users. In case the primary and/or secondary users did not agree on finalizing a suggestion, thevirtual agent server100 may generate further suggestions based on the feedback of the primary/secondary users as shown instep308. In case one or more suggestions were selected by the primary and/or secondary users, thevirtual agent server100 may execute one or more actions based on the received feedback as shown atstep314.
• FIG. 4 depicts a system400 including avirtual agent server100aassisting a user and avirtual agent server100bassisting an organization, in accordance with an embodiment. In an embodiment, a user's actions may be executed by avirtual agent server100aand anexternal system404's actions may be executed by avirtual agent server100b. Thevirtual agent server100amay communicate with thevirtual agent server100bthrough an external application programming interface. In an embodiment, this communication may be in the form of one or more of natural speech interaction or machine understandable language, among others.
• In an implementation, thevirtual agent server100amay receive the user's speech or chat402 as an input from the user or other modules of thevirtual agent server100. Subsequently, thevirtual agent server100amay use theNLU module102 to process the input to determine an action desired by the user and context of the action. The context and user's desired action may be communicated to thevirtual agent server100band may be incorporated during execution of actions.
• In an implementation, thevirtual agent server100amay determine one or more plans of action that may optimize the desired actions of the user. These optimization options may be communicated with thevirtual agent server100brepresenting anexternal system404. The user may determine which information may be shared by using thesecurity module112.
• In an implementation, thevirtual agent server100bmay determine one or more optimization options related to theexternal system404 and communicate them tovirtual agent100a. Thevirtual agent server100aof the user may contact thevirtual agent server100bof theexternal system404 through an external application programming interface call. Thevirtual agent server100amay communicate the user's desired actions to thevirtual agent server100b. The twovirtual agent servers100aand100bmay communicate with each other to execute the desired action of the user while implementing one or more optimization options.
• In an implementation, the twovirtual agent servers100aand100bmay communicate using natural language speech which may be understood by the twovirtual agent servers100aand100b. The user's context and desired actions may be included as one or more parameters in the speech. Alternatively, thevirtual agent servers100aand100bmay communicate via one or more application programming interfaces, wherein the user's context and user's desired action are shared as parameters in the application programming interface call.
• In an implementation, thevirtual agent servers100aand100bmay communicate with one or more databases to receive further information corresponding to the user and theexternal system404.
• In an implementation, the user's speech may be in the form of natural language and may comprise textual words from the user received from the current conversation. In an implementation, the user's input may be taken from previous “m” conversations, where “m” is manually configured or tuned using Machine Learning for an application. TheNLU module102 may assign one or more weights to the tokens (individual words) in the speech context using Term Frequency Inverted Document Frequency (tfidf) and the recency of the communication session. The speech context may also include explicit inputs or inferences from previous speech interaction sessions decayed using recency of occurrence.
• In an implementation, thevirtual agent server100bmay be configured to receive and process the action desired by the customer and the context of the action to obtain input for executable action.
• In an implementation, thevirtual agent servers100aand100bmay be configured to exchange optimization options of the user and theexternal system404, respectively, with each other, where the optimization options correspond to the execution of the action. Further, thevirtual agent servers100aand100bmay identify if a common optimization option exists between the optimization options shared by thevirtual agent servers100aand100b. Subsequently, thevirtual agent servers100aand100bmay execute one or more actions as per the common optimization option, in case it exists.
• In an implementation, the system400 may be configured to execute the action as per the preference of the customer in case a common preference does not exist.
• In an implementation, as an example, consider avirtual agent server100b(hereafter called V_B) handling the customer service of a business. A customer may order food using their ownvirtual agent server100a(hereafter called V_U). V_Umay determine to place an order for one or more dishes on behalf of the user. V_Umay contact V_Band initiate a communication session through an application programming interface call or by conversing through dialogs. The virtual agent servers V_Band V_Umay use natural language understanding techniques to understand each other and to determine further actions from the conversation.
• The payment for the order may be transferred through an online bank transaction using one of the customer's bank accounts. V_Bmay request payment information from V_Uto complete the order. V_Umay identify one or more manually configured rules to optimize the payment on behalf of the user. As an example, V_Umay identify a rule to optimize for one or more parameters such as credit score, cash rewards and offers, among others, which may be applicable to the user's transaction. The manual rule may be configured either by taking the user's feedback or through V_U, which may configure default rules for the users. V_Bmay comprise a rule which instructs it to prefer a credit card that charges the least transaction fees. V_Bmay configure the default rule for the merchant. In case the user allows freedom to and V_Uagent to complete the transaction, V_Umay optimize the transaction for the user. Similarly, V_Bmay optimize for the business.
• In an implementation, V_Bmay process one or more information corresponding to the user to determine whether they are flexible on the type of card to use to complete the transaction. In case V_Band V_Ubelong to the same entity, they might coordinate a transaction which is optimized for both the user and the business, after optimizing on their respective customers. For example, V_Umay decide to use any one of two credit cards C₁, C₂belonging to the user in case they both comprise the same cash rewards and don't show any adverse impact on the credit score of the user. V_Bmay prefer to complete the transaction with credit card C₁in case it charges less on the transaction fees. Hence, V_Bmay negotiate with V_Uto determine whether credit card C₁may be used instead of C₂. V_Bmay accomplish this by using a manual rule configured against payment transaction and requesting V_Ufor information regarding all the credit card types owned by the customer. V_Umay share the user's information about the credit cards that may be use to complete the transaction after getting an approval from the user to share the information. V_Bmay ask V_Uto use C₁over C₂for completing the transaction, by passing a list of cards it would prefer to use.
• In an implementation, this may be done by using an application programming interface wherein the preferred list is passed as a list ordered by preference of credit cards. V_Umay then determine whether to accept V_B's request regarding the preferred credit card. In an embodiment, avirtual agent server100 may represent V_Uand V_Band may do global optimization for both V_Band V_Uafter optimizing on their respective customers.
• The discussed optimization principles may be applied to one or more types of actions including transactions such as merchandising on e-commerce platform, negotiating charges for a spa service, and negotiating hotel stay rates, among others.
• FIG. 5 may depict a flowchart of anexemplary method500 for optimizing a user's desired actions, in accordance with an embodiment. This method may be used in case a firstvirtual agent server100 and a secondvirtual agent server100 that represent a user and a business respectively. Atstep502, the user's desired actions may be determined by the firstvirtual agent server100 from one or more inputs corresponding to the user. Further, the firstvirtual agent server100 may contact the secondvirtual agent server100 to communicate the user's desired actions with the secondvirtual agent server100.
• Atstep504, the firstvirtual agent server100 may determine one or more optimization options preferred by the user, and the secondvirtual agent server100 may determine one or more optimization options preferred by the business. Subsequently, one or more of the optimization options may be communicated between the first agent and the secondvirtual agent server100 which may determine whether any optimization options are present that are common between the optimization options determined by the first and the secondvirtual agent server100 as shown atstep506.
• In case no common optimization options are present, the firstvirtual agent server100 and the secondvirtual agent server100 may proceed to execute the user's desired actions according to the instructions or context of the user, as shown atstep508. In case there are one or more common optimization options present, the firstvirtual agent server100 and the secondvirtual agent server100 may determine which common optimization option to execute. Further, the firstvirtual agent server100 and the secondvirtual agent server100 may execute the user's desired action according to the selected common optimization as shown atstep510.
• FIG. 6 may depict asystem600 including thevirtual agent server100 which may share one or multimedia files with users, in accordance with an embodiment. The system may include a primary user'smobile device202a, a secondary user'smobile device202b, and amultimedia database602.
• The primary user may initiate communication with a secondary user. In an implementation, there may be more than one secondary user using a secondary user'smobile device202bto communicate with the primary user. Themultimedia database602 may comprise one or more multimedia files, with one or more tags associated with one or more of the multimedia files. Further, thevirtual agent server100 may receive input from the primary and the secondary user, and may process the received input to understand a context of the input. Further, the virtual agent server may identify one or more multimedia files based on the context and the tags. Subsequently, thevirtual agent server100 may share one or more of the identified multimedia files with one or more of the primary and secondary user to enable better understanding between the primary and secondary user.
• In an implementation, the tags may be natural language tags. Further, a convolutional neural network may be used to tag multimedia files. Alternatively, multimedia files may be tagged using one or more crowdsourcing platforms. The textual tags and meta-data corresponding to the multimedia files may be stored in a database or an inverted index to optimize the retrieval performance of thevirtual agent server100.
• In an implementation, themultimedia module114 may receive one or more of conversation context, user intent, dialog summary and speech of the user converted into text, among other information, from theNLU module102 through the primary and/or secondary user'smobile devices202aand202b. The conversation context and the dialog summary may then be used to identify tagged multimedia files related to the user's intent by using information retrieval techniques. Further, thevirtual agent server100 may share the identified multimedia with one or more of the primary and/or secondary users by sharing the multimedia files through their user'smobile device202a,202bas explained below.
• FIG. 7 depicts a flowchart of anexemplary method700 for sharing multimedia files with primary and/or secondary users through their user'smobile device202a,202b, in accordance with an embodiment.
• Thevirtual agent server100 may receive one or more inputs from the first user and the second user as depicted instep702. Further, thevirtual agent server100 may process the received input to determine a context of the input atstep704.
• Atstep706, thevirtual agent server100 may identify one or more multimedia files from amultimedia database602 by matching the context of the user to tags assigned to one or more multimedia files. Subsequently, thevirtual agent server100 may share at least one of the identified multimedia files with one of the two users atstep708.
• Atstep710, thevirtual agent server100 may receive feedback corresponding to at least one of the identified multimedia files from the user with whom it is shared. The feedback may contain a selection or ranking of the identified multimedia files. Further, as shown atstep712, thevirtual agent server100 may share one or more of the selected or ranked multimedia files with the other user among the two users based on the feedback received.
• In an embodiment, the first user may be a customer and the second user may be acustomer service representative208. Further, the identified multimedia files may be shared with the customer who may share their feedback regarding the identified multimedia files with thevirtual agent server100. The identified multimedia files may have been ranked for natural language context by the customer and may be shared with thecustomer service representative208 who did not receive any multimedia files yet.
• In an embodiment, the identified multimedia files may be shared with thecustomer service representative208 who may share their feedback regarding the identified multimedia files with thevirtual agent server100. The identified multimedia files may have been ranked for natural language context by thecustomer service representative208 and may be shared with the customer who did not receive any multimedia files yet. The sharing of the relevant multimedia files may aide in better understanding between the user and thecustomer service representative208.
• In an implementation, thevirtual agent server100 may be further configured to share at least one of the identified multimedia files in a current conversation between the first user and the second user.
• In another implementation, a single image may be selected from the search results to stream to the users. Thevirtual agent server100 may use one or more of a predictive algorithm or machine learning to share one or more multimedia files.
• In another implementation, the multimedia file may include at least one of an image, an audio file, a video file or a document.
• FIG. 8 depicts an exemplary user interface which may show thevirtual agent client212 in the primary user'smobile device202a. Thevirtual agent client212 may include one or more options to be selected by the user. The options may include ‘Add a friend’ wherein a primary user may choose to add one or more secondary users to a conversation. An ‘Execute action’ function may be used by the user to instruct thevirtual agent client212 about one or more actions desired by the user. InFIG. 8, a primary user has selected the ‘Add a friend’ option. The primary user may view a pop-up window with two options as shown. The primary user may choose the ‘Select friend’ option in case the primary user chooses to add a friend to the conversation by using thevirtual agent client212. Alternatively, the primary user may choose the ‘Select friend’ option in case the primary user chooses to add a friend to the conversation manually.
• FIG. 9 depicts an exemplary user interface which may show thevirtual agent client212 present in the primary user'smobile device202aafter a friend (secondary user) of the primary user has been added to a conversation.
• FIG. 10 depicts an exemplary user interface which may show a conversation between the primary user and secondary user on the primary user'smobile device202a. In this conversation, the two users are discussing where to have lunch. The primary user and secondary user may decide to eat burgers at a restaurant near the primary user's house as shown.
• Thevirtual agent client212 may determine the context, user's desired actions and preferences from the conversation between the primary and secondary user's conversation as follows. The user's desired action may be to locate a restaurant, the context may be to have lunch, and the preferences may be a restaurant close to the primary user's house where burgers are available.
• FIG. 11 depicts an exemplary user interface showing avirtual agent client212 that has determined a suggestion for the primary and secondary user. The suggestion was created using the preferences of the primary and secondary user. This suggestion may be a restaurant “Burger Prince”. A multimedia file comprising an image depicting the restaurant may be shared with the primary user'smobile device202a. Additionally, thevirtual agent client212 may communicate with an external map application to share a map depicting a route from the primary user's house to the suggested restaurant, as shown.
• FIG. 12 depicts a system including thevirtual agent server100 for assisting users in a conversation with acustomer service representative208, in accordance with an embodiment. The system depicts a user using their user's mobile device202 in a conversation with acustomer service representative208. Thevirtual agent server100 may receive the conversation as an input. Further, the virtual agent may process and comprehend the input in order to send one or more multimedia files related to the conversation. In this case, the user may have a doubt regarding what to order. Consequently, the user may ask thecustomer service representative208 about the best burger available at “Burger Prince” restaurant. Thevirtual agent server100 may comprehend this query and conduct an appropriate search. Further, thevirtual agent server100 may share one or more multimedia files with thecustomer service representative208, who may select one image representing the most popular burger at Burger Prince. Further, thecustomer service representative208 may share the image with the user on their user's mobile device202 as depicted inFIG. 12.
CONCLUSION
• The present invention overcomes the drawbacks of virtual assistant systems, by taking into consideration preferences of multiple users and generating suggestions which may be suitable to most the users. Further, the system helps to enable better understanding between a customer and customer service representative. Additionally, the system helps in selection and sharing of multimedia files between two users or between a customer service representative and a user. Thus, the present invention as discussed in this document with respect to different embodiments will be advantageous at least in optimizing the process of event coordination and execution of actions selected by multiple users. Further, it is advantageous in providing better user experience and decreasing time and effort required by users in coordinating an event. It is also advantageous in helping the user in the process of conversing with a customer representative. Additionally, the system may optimize actions for the user while trying to execute actions on behalf of the user. Additional advantages not listed may be understood by a person skilled in the art considering the embodiments disclosed above.
• It shall be noted that the processes described above are described as sequence of steps; this was done solely for the sake of illustration. Accordingly, it is contemplated that some steps may be added, some steps may be omitted, the order of the steps may be re-arranged, or some steps may be performed simultaneously.
• Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the system and method described herein. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
• Many alterations and modifications of the present invention will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. It is to be understood that the description above contains many specifications; these should not be construed as limiting the scope of the invention but as merely providing illustrations of some of the personally preferred embodiments of this invention. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents rather than by the examples given.

Claims (21)

I/We claim:

1. A system configured to assist users of a software application, the system comprising a virtual agent server configured to:

receive input identifying a primary user and at least one secondary user, wherein the primary user and the secondary user are parties to an event;

receive input identifying one or more preferences of the primary user and the secondary user corresponding to one or more categories of preferences;

assign relative weightage to preferences within a category among the one or more categories of preferences; and

generate one or more suggestions corresponding to the event based on the relative weightage.

2. The system according toclaim 1, wherein the virtual agent server is configured to obtain the input identifying the secondary user from at least one of phone number of the secondary user, voice characteristics of the secondary user or metadata corresponding to the secondary user.

3. The system according toclaim 1, wherein the virtual agent server is configured to consider the secondary user to be party to the event based on input received from the primary user.

4. The system according toclaim 1, wherein the virtual agent server is configured to receive location information corresponding to at least one of the primary user or the secondary user, wherein the location information is one of the preferences.

5. The system according toclaim 4, wherein the virtual agent server is configured to determine one or more locations of consensus, wherein the one or more locations of consensus are determined based on the location information corresponding to the primary user or the secondary user, wherein the suggestions are also based on the based on the locations of consensus.

6. The system according toclaim 4, wherein the location information is a current location information of at least one of the primary user or the secondary user.

7. The system according toclaim 4, wherein the location information is a preferred location information of at least one of the primary user or the secondary user.

8. The system according toclaim 1, wherein the virtual agent server is configured to identify at least one of the preferences of each of the primary user and the secondary user based on conversation between at least two of, the primary user, the secondary user and the virtual agent server.

9. The system according toclaim 8, wherein the conversation is a current conversation.

10. The system according toclaim 8, wherein the conversation is one or more previous conversations.

11. The system according toclaim 8, wherein the at least one of the preferences is identified based on frequency of words used in the conversation.

12. The system according toclaim 8, wherein weightage is assigned to the at least one of the preferences based on frequency of words used in the conversation.

13. The system according toclaim 8, wherein the at least one of the preferences is identified based on tone of the conversation.

14. The system according toclaim 8, wherein weightage is assigned to the at least one of the preferences based on tone of the conversation.

15. The system according toclaim 1, wherein the virtual agent server is configured to identify at least one of the preferences of each of the secondary user based on a social profile of the secondary user.

16. The system according toclaim 1, wherein the virtual agent server is configured to identify at least one of the preferences of each of the primary user and the secondary user based on history of the primary user and the secondary user.

17. The system according toclaim 1, wherein the virtual agent server is configured to, at least one of:

identify at least one of the preferences of the primary user by receiving input from one or more contacts of the primary user; or

identify at least one of the preferences of the secondary user by receiving input from one or more contacts of the secondary user.

18. The system according toclaim 17, wherein the virtual agent server is configured to, at least one of:

rank the contacts of the primary user and select one or more contacts based on the ranking, to receive input corresponding to the at least one of the preferences of the primary user; or

rank the contacts of the secondary user and select one or more contacts based on the ranking, to receive input corresponding to the at least one of the preferences of the secondary user.

19. The system according toclaim 1, wherein the virtual agent server is further configured to:

receive feedback from the primary user corresponding to the suggestions; and

execute one or more actions based on the feedback received from the primary user.

20. The system according toclaim 1, wherein the virtual agent server is configured to execute at least one of the actions by communicating with an external system, wherein the external system is at least one of a software application or a natural person.

21-40. (canceled)

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