	TABLE 1

	Variable	Definition

	p	Customer Index
	y	Click Index
	c	Conversion Index
	e	Customer Experience Type
	ch5	Channels or devices
	RCV	Real-time Customer Journey Value
	Y	Contextual Signals Value
	t	Synaptic Weight
	Radians	Journey

In some embodiments, the Customer Index may be the number of profiles or personas or a unique customer corresponding to a micro-moment, the Click Index may be a statistical measure of changes of individual Data Points in the click rate or a primary key for a cookie or other piece of software that records a micro-moment, and the Conversion Index may be a statistical measure of changes of individual Data Points in the conversion rate or a particular number used to identify a micro-moment as a conversion. In some embodiments, the Customer Experience Type may be a measure of whether the customer experience is complete, where e=1, or an incomplete experience, where e=0. A conversion for a signal may indicate a complete customer experience or e=1 whereas anything other than a conversion may indicate a value of 0 for the customer experience type. The Channel or devices may be different vectors for customer interfacing; for example, mobile phones, smart phones, desktop or laptop computers, internet-capable television, etc. In some embodiments, the Real-time Customer Journey Value (RCV) may represent click through Domains; for example, the number of signals gathered from consumer behavior based on clicking behavior while browsing or using a mobile app. In other embodiments, the RCV includes a random number that thesystem100 uses to index the micro-moment. Contextual Signals Value may be synaptic signals through a graph of neurons; for example, as attained through an artificial neural network (ANN). In other embodiments, the Contextual Signals Value (Y) may be a value representing the context of the value associated with the micro-moment (i.e., a first value if the micro-moment was a click to complete a purchase, a second value if the micro-moment was a click to read an article or to send an email, delete an email, etc.). In some embodiments, the Synaptic Weight may be a number applied for weighting purposes in the ANN. The Journey, in some embodiments, may be expressed in radians and may be the value of the synaptic signals.

The micro-moment value (MMV) equation may be broken down into three components: the conversion rate, the signal of the click rate through domains, and the radian rate per channel. In some embodiments, the expression:

\sum_{e = 1}^{y} (\frac{RCV}{Y_{i, t}})

may represent the signal of the click rate through domains. In some embodiments, the expression:

\sum_{c = 1}^{p}

may represent the conversion rate, particularly when applied to the signal of the click rate through domains. In some embodiments, the expression:

(\frac{Π radians}{ch 5})

may represent the click rate across channels or click rate per channel.

In some embodiments, the artificial neural network model may be represented by the diagram200 illustrated inFIG. 2. In such embodiments, the inputs202 (e.g., x₁-x_n) may be any data points gathered from consumer behavior, as described in further detail with reference toFIG. 3. Weights204 (e.g., w_1j-w_nj) may be applied to the inputs depending on the particular design of the artificial neural network. The result may be combined into atransfer function206, the result of which may be a net input208 (e.g., net) fed into anactivation function210. Theactivation junction210 may output a threshold214 (e.g., Θ_j) and an activation212 (e.g., o_j). The output of the artificial neural network may, in some embodiments, be represented by a perceptron equation, as shown in Equation 2, below:

Output=(1,_Σ_t=0_n_w₁_*x₁_>c_b,−1otherwise Eq. 2

In Eq. 2, w may be determined from a Hebb Synapse simulated learning equation, an embodiment of which is shown inEquations 3 and 4, below:

w_j^k+1=w_j^k+Δw_j Eq. 3

Δw_j=α·(t−o)·i Eq. 4

- where: t=Target
- o=Output of the neural network
- α=Learning rate constants, between 0 and 1
- i=Input of the weight

As used herein, a “radian” is a data container used in the above algorithms to mathematically represent segments or “micro-moments” a user's journey. Past systems and methods of analyzing an individual's online presence to predict future online actions have used the individual's entire online history to make universal predictions about any particular moment of a user's internet presence. These past system have analyzed a customer's entire journey as the most effective way to market to a user based on a universal data set. In these past systems and methods, the accumulation of large amounts of data often leads to cumbersome calculations and inaccurate results as infinite data may lead to infinite results. The radian may provide definition to the limitless data related to web and other network interactions by providing a framework to calculate micro-moments as described herein. Rather than using all data that has been tracked for a customer to make a prediction about a future action, the radian contains a small amount of tracked data related to the time and duration of the action, a URL or other network location for the action, and a type of action. Framing each micro-moment or signal as a radian may allow predicting a follow-on action based on a set of signals corresponding to actions that precede the predicted action. Predicted events having the highest value for the MMVA, i.e., the highest duration for a particular action, will provide the best opportunity to reach the user for effective marketing.

In mathematics, a radian is an angle whose corresponding arc in a circle is equal to the radius of the circle. A circle has just more than six radians. A customer's complete “journey” may begin with an initial click or other tracked action related to a product or service at a domain or other “channel” and end with a purchase or other final action related to the domain, channel, good or service, etc. In some embodiments, a complete revolution of a circle is 2π radians and is analogous to a complete customer journey. In these embodiments, a customer's journey may be represented as a complete revolution of a circle and discrete events or signals related to the journey may be represented as radians. Values for each signal or “radian” may be assigned as degrees of a circle relative to the complete customer journey as shown in Table 1A, below. Where the final action In a series of actions or signals analyzed byEquation 1 and related to the domain, channel, good or service is a desired action (e.g., a conversion, purchase, etc.), then the value for the radian will reflect a complete journey or 360°. In further embodiments, where the final action related to the domain, channel, good or service is not a conversion or purchase, then the value for the radian will reflect something less than a complete journey or less than 360°. With brief reference toFIG. 4, each radian may include data for a single signal, action, or micro-moment. By stitching together several micro-moments based on knownactual customer journey412 for the user and anaudience trend414, the MMVA result may be aprediction416 of the user's journey. Table 1A indicates values for the radian as a number of degrees for a customer journey, where a complete customer journey ending in conversion having ten total “signals” corresponding to a user may include ten “radians” or, as shown in the table below, a complete journey including sixteen signals may have sixteen different radian values.

	TABLE 1A

	Radians	Degrees

	0, 2π	0°, 360°
	π/6	30°
	π/4	45°
	π/3	60°
	π/2	90°
	2π/3	120°
	3π/4	135°
	5π/6	150°
	π	180°
	7π/6	210°
	5π/4	225°
	4π/3	240°
	3π/2	270°
	5π/3	300°
	7π/4	315°
	11π/6	330°

FIG. 3 illustrates one embodiment of an example customerjourney use case300 that may result in signals that make up each radian to represent the data used as inputs for the MMVA. In this embodiment, thecustomer journey302 is represented by an arrow moving and may represent passage of time. Points along the arrow may be single points in time where a customer performs anaction301, represented by hash marks along the journey arrow, that results in a signal. This particular embodiment includes 14 actions and, therefore, 14 signals, but any number of signals may be gathered and used. Each signal may be interpreted by thedigital marketing platform112 as data forming the customer profile. In this example, two channels (browser304 and mobile app306) are used by the customer, though it should be understood that fewer or more than two channels. Thejourney302 includesmultiple browser actions308 conducted by the customer using an internet browser channel, such as on a desktop or laptop computer. In this embodiment, each of the browser signals are performed in the same domain. Some types of actions resulting in signals may include visiting social media sites, such as Facebook®).com, clicking on advertisements while on the social media site, viewing a product, adding a product to a shopping cart, and exiting the website. The browser signals308 may also include a sequence ofsignals312. In this embodiment, the sequence ofsignals312 is four consecutive signals grouped together after a customer has clicked on an advertisement. These signals may be 1) redirecting to the advertiser's site, 2) firing a cookie, 3) an impression, and 4) clicking on a promotion.

Thejourney302 also includesmobile signals314 taken by a customer on amobile app306 accessed on a mobile device, such as a smartphone or tablet. As used herein, the term “signals” may include data corresponding to a user and online activity such as a click (i.e., clicking on a URL), an impression (e.g., a user typing in a specific URL and causing a browser to initiate a redirect action to reach a domain), a pixel (e.g., selecting images including at least one pixel having a tag and/or other data for thesystem100 to track for a user), a cookie (e.g., a small piece of data sent from a website and stored on the user's computer by the user's web browser while the user is browsing to remember state information such as items added in the shopping cart in an online store or to record the user's browsing activity), etc. Some data included in asignal314 may include a beginning time and an ending time at a website (e.g., a time between mouse events or from landing at a URL to the browser initiating a redirect action) a duration (e.g., an indication of user activity, how much time and attention the user pays to web content corresponding to asignal312 at a particular domain and/or using a particular channel, mouse events, browser actions, etc.), a domain name, a tag number, a pixel name, a cookie name and value, and other data corresponding to the user's browsing activity. The cookie may allow thesystem100 to collect syntactic information that reflects a degree of probability for a future action by the user and also indicates how often the user will initiate a type of network action. Thus, the micro-moment value may predict a significant duration for a particular network action or Internet domain activity and, thus, the most optimal time to present advertising targeted to the user to influence the user's journey.

In some embodiments, each of themobile signals314 is performed in the same domain as one another. Some types of actions resulting inmobile signals314 may include searching for similar products on a search engine, such as Google, being redirected to the product website from the search engine, landing on the product description page, adding the product to the shopping cart, and checking out to purchase the product. Themobile signals314 may include a sequence ofmobile signals316 that, in this case, are three consecutive signals grouped together. In the example illustrated inFIG. 3, the result of the browser signals308 and themobile signals314 is a total of fourteen signals on thejourney302. These fourteen signals may be used as the RCV in the MMV Eq. 1, as shown in the example below.

The following is an example of a calculation made using the micro-moment value algorithm using example inputs that, in practice, would be the result of data gathering throughsystem100 ofFIG. 1 for use in thedigital marketing platform112. It should be understood that the input values used herein are for the sake of example only, and do not in any way limit the values available for use in the MMVA that can be gathered and used by thedigital marketing platform112. In this example embodiment, the data values for the MMVA are as follows in Table 2:

	TABLE 2

	Variable	Example Value

	p	3000
	y	10
	c	1
	e	1 or i
	ch5	1
	RCV	14
	Y	10
	t	Synaptic Weight
	Radians	360° or 2π

Using p=3000 and y=10, the conversion rate and the signal of the click rate through domains becomes:

\sum_{c = 1}^{p} \sum_{e = 1}^{y} (\frac{RCV}{Y_{i, t}}) = \sum_{c = 1}^{3000} \sum_{e = 1}^{10} (\frac{14}{10_{i, t}})

which can be expressed as:

\sum_{c = 1}^{p} \sum_{e = 1}^{y} (\frac{RCV}{Y_{i, t}}) = \sum_{c = 1}^{3000} (\frac{14}{10_{i, 1}} + \frac{14}{10_{i, 2}} + \frac{14}{10_{i, 3}} + \dots + \frac{14}{10_{i, 9}} + \frac{14}{10_{i, 10}})

Further, adding the click rate per channel, the MMVA may then be expressed as:

\begin{matrix} {MMV}_{1} = \sum_{c = 1}^{3000} \sum_{e = 1}^{10} (\frac{14}{10_{i, t}}) * (\frac{Π 2 π}{1}) & Eq . 5 \end{matrix}

FIG. 4 illustrates anexample graph400 of themicro-moment predictions402 over time, indicated on the horizontal axis as days of the week401. The results of the MMVA shown inFIG. 4 are plotted as themicro-moment predictions402 expressed in the example culminating in Equation 5, above. Thegraph400 inFIG. 4 additionally shows theactual customer journey404 and audience trends406. One goal of themicro-moments predictions402 calculated using the MMVA is to replicate theactual customer journey404. This allows thedigital marketing platform112 to most accurately predict the micro-moment for engagement with the customer. In some embodiments, these “key” micro-moments may be at the peaks of the MMVA plot. For example, inFIG. 4, themicro-moment predictions402 may have a first predictedkey moment408 at a peak in the micro-moment predictions plot. As shown in thegraph400, a plot of the micro-moment predictions may have a second predictedkey moment410 at another peak in the micro-moment predictions plot. In the illustrated embodiment ofFIG. 4, these peaks may correspond to an amount of time corresponding to a signal (e.g., signals312 with reference toFIG. 3) in the various plots of the graph400 (e.g., theactual customer journey412 plot, the audience trends414 plot, themicro-moments predictions plot416, etc.).

With further reference toFIG. 4, themethod500 generally and Eq. 1 in particular as described herein may result in values for micro moments to predict user trends for engaging a user at a favorable time. Thegraph400 includes a y-axis havingmicro-moment predictions402 and an x-axis having atime period403 corresponding to themicro-moment prediction402. In some embodiments, the inputs to Eq. 1 may result inmicro-moment predictions402 of a measure of time (e.g., milliseconds, etc.) of user behavior corresponding to a signal (e.g., signals312 ofFIG. 3) for theparticular time period403. For example, the first predictedkey moment408 may indicate a duration of about 500,000 milliseconds corresponding to Monday morning at a particular URL using a particular channel (i.e., a type of device and/or method for reaching the URL).

In some embodiments, thesystem100 may include a module to determine a value for each customer. For example, an algorithm may determine whether a customer is going to be profitable or not and for how long. Too, an algorithm may predict the monetary value associated with a customer relationship. Equation 6 illustrates one example of a customer lifetime value (“CLV”) algorithm:

\begin{matrix} CLV = \sum_{m = 1}^{t} (\frac{Future Contribution Margins}{Historical Lifetime Values * p}) - (\frac{Cost of Acquisition}{{CL}^{(m)}}) & Eq . 6 \end{matrix}

In Equation 6, m=the micro-moment index; c=Customer Index; p=total number of purchases in a period of time; t=number of time period the CLV is being calculated; and CL=the Customer Loyalty Index. The CLV algorithm of Equation 6 may allow observation of various individual-level buying patterns from the past and find the various customer stories in the data set. It may also allow understanding of which patterns correspond with valuable customers and which patterns correspond with customers who are opting out. As new customers join a system implementing the CLV algorithm of Equation 6, the system (e.g., system100) may match the new customer to patterns that are recognized by the CLV algorithm.

In some embodiments, thesystem100 may also include a module to determine which impressions will best meet the advertising performance metrics. For example, an algorithm may optimize timing for real-time bidding (“RTB”) on customer impressions within a website for a merchant. For example, advertising campaign budget constraints may be given as impression delivery goals q_j. An impression group i may be defined as a (placement, user) tuple, at which level both click-through-rate (“CTR”) prediction P_ijand inventory control represented by Equation 7 may be performed:

\begin{matrix} \sum_{j}^{1} x_{ij} \leq h_{i} & Eq . 7 \end{matrix}

Given the above, the cost term w_iwill be zero since impressions are from the inventory. Thus, the revenue lift and the CTR lift may be represented by Equations 8 and 9, below:

\begin{matrix} Revenue lift = \frac{y^{'}}{y} = \frac{Σ t, i, {jx}_{ij}^{'} (t) p_{ij} q_{j}}{Σ t, i, {jc}_{ij}^{'} (t) q_{j}} & Eq . 8 \\ CTR lift = \frac{{CTR}^{'}}{CTR} \frac{Σ t, i, {jx}_{ij}^{'} (t) p_{ij} / Σ t, i, {jx}_{ij}^{'} (t)}{Σ t, i, {jc}_{ij}^{'} (t) q_{j} / Σ t, i, {jx}_{ij}^{'} (t)} & Eq . 9 \end{matrix}

The timing for RTB may also be optimized by the following pseudo code:


	Input: q_j,g_j,α_j,∀j
	Output: x_ij,β_i,∀i,j
1	begin
2	\| G ← ∅;
3	\| foreach impression i from a stream do
4	\| \| p_ij= p(click\|i,j),∀j;
5	\| \| v_ij← p_ijq_j,∀j;
6	\| \| j* ← argmax_j∉G(v_ij− α_j);
7	\| \| if (v_ij− α_j) > 0 then
8	\| \| \| x_ij*← 1;
9	\| \| \| x_ij← 0,∀j ≠ j*;
10	\| \| \| β_i← v_ij− α_j;
11	\| \| \| if Σ_i′x_i′j= g_jthen
12	\| \| \| \| G ← G ∪j*;
13	\| \| \| end
14	\| \| end
15	\| \| α_j← UpdateAlpha(α_j),∀j;
16	\| end
17	end

In some embodiments, thesystem100 may also include a module to evaluate each customer impression based on its predicted probability to achieve a goal of the advertising campaign. For example the module may evaluateEquations 10 and 11, below.

\begin{matrix} mfb = \frac{ϵ + γ - P (B_{i *})}{γ} | \frac{γ - P (B_{i *})}{γ} & Eq . 10 \\ V_{cpm} = C_{explore} + C_{panic} + (n_{exploit}^{*}) T ? ? indicates text missing or illegible when filed & Eq . 11 \end{matrix}

Where C_panic=a number of impressions won during a panic; C_exploit=a number of impressions won during exploitation; and C_explore=a number of impressions won during exploration. Costs per thousand (“CPM”) may also be analyzed by the following pseudocode:


50:	If g_remain≤ 0 or j ≥ n then Terminate.
51:	B_final← B_i*.

52:	$A \leftarrow \frac{g_{remain}}{P_{i^{*}} (n - j)} .$

53:	if P_i(n − j) > g_remainand T_ig_remain> budget then
54:	Sort p ∈ S_i: define q_kto be the kth smallest p in S_i.

55:	$k_{s} \leftarrow ⌈ \frac{g_{remain}}{n - j} m ⌉$

56:	$for k = 1 to \langle S_{i^{*}} \rangle do g_{k} \leftarrow \frac{1}{k} \sum_{i = 1}^{k} q_{i} .$

57:	$t^{*} \leftarrow \frac{budget}{g_{remain}} .$

58:	k_p← min_k:g_k_≥t*k.
59:	k* ← max(k_s, k_p).
60:	B_final← q_k*.

61:	$A \leftarrow \frac{k^{*}}{m} .$

62:	$A \leftarrow \frac{g_{remain}}{A (n - j)} .$

63:	end if
64:	while More rounds and g_remain> 0 do
65:	Bid B_finalwith probability A, 0 otherwise.
66:	If Bid won then g_remain← g_remain− 1.
67:	end while

where optimized daily or other periodic budget updates may be calculated and posted, as needed, to meet each campaign goal.

FIG. 6 is a high-level block diagram of anexample computing environment600, for example, for linking the digital marketing platform to front end components that may run the customer digital content browser and/or the merchant digital content system. The computing device601 may include a server (e.g., the data processing server118), a mobile computing device (e.g.,computing device128, a cellular phone, a tablet computer, a Wi-Fi-enabled device or other personal computing device capable of wireless or wired communication), a thin client, or other known type of computing device. As will be recognized by one skilled in the art, in light of the disclosure and teachings herein, other types of computing devices can be used that have different architectures. Processor systems similar or identical to the example systems and methods for linking dynamic information, such as customer data, to a data record may be used to implement and execute the example systems ofFIG. 1. Although theexample system600 is described below as including a plurality of peripherals, interfaces, chips, memories, etc., one or more of those elements may be omitted from other example processor systems used to implement and execute the example system for linking dynamic information to a digital marketing platform. Also, other components may be added.

As shown inFIG. 6, the computing device601 includes aprocessor602 that is coupled to an interconnection bus. Theprocessor602 includes a register set or registerspace604, which is depicted inFIG. 6 as being entirely on-chip, but which could alternatively be located entirely or partially off-chip and directly coupled to theprocessor602 via dedicated electrical connections and/or via the interconnection bus. Theprocessor602 may be any suitable processor, processing unit or microprocessor. Although not shown inFIG. 6, the computing device601 may be a multi-processor device and, thus, may include one or more additional processors that are identical or similar to theprocessor602 and that are communicatively coupled to the interconnection bus.

Theprocessor602 ofFIG. 6 is coupled to achipset606, which includes amemory controller608 and a peripheral input/output (I/O)controller610. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to thechipset606. Thememory controller608 performs functions that enable the processor602 (or processors if there are multiple processors) to access asystem memory612 and amass storage memory614, that may include either or both of an in-memory cache (e.g., a cache within the memory612) or an on-disk cache (e.g., a cache within the mass storage memory614).

Thesystem memory612 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. Themass storage memory614 may include any desired type of mass storage device. For example, if the computing device601 is used to implement a module616 (e.g., the various modules link dynamic information, such as photographs, to a payment device transaction record and to create the dynamic transaction record and other modules as herein described). Themass storage memory614 may include a hard disk drive, an optical drive, a tape storage device, a solid-state memory (e.g., a flash memory, a RAM memory, etc.), a magnetic memory (e.g., a hard drive), or any other memory suitable for mass storage. As used herein, the terms module, block, function, operation, procedure, routine, step, and method refer to tangible computer program logic or tangible computer executable instructions that provide the specified functionality to the computing device601 and thesystem100. Thus, a module, block, function, operation, procedure, routine, step, and method can be implemented in hardware, firmware, and/or software. In one embodiment, program modules and routines are stored inmass storage memory614, loaded intosystem memory612, and executed by aprocessor602 or can be provided from computer program products that are stored in tangible computer-readable storage mediums (e.g. RAM, hard disk, optical/magnetic media, etc.).

The peripheral I/O controller610 performs functions that enable theprocessor602 to communicate with a peripheral input/output (I/O)device624, a network interface626, alocal network transceiver628, (via the network interface626) via a peripheral I/O bus. The I/O device624 may be any desired type of I/O device such as, for example, a keyboard, a display (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT) display, etc.), a navigation device (e.g., a mouse, a trackball, a capacitive touch pad, a joystick, etc.), etc. The I/O device624 may be used with themodule616, etc., to receive data from thetransceiver628, send the data to the backend components of thesystem100, and perform any operations related to the methods as described herein. Thelocal network transceiver628 may include support for a Wi-Fi network, Bluetooth, Infrared, cellular, or other wireless data transmission protocols. In other embodiments, one element may simultaneously support each of the various wireless protocols employed by the computing device601. For example, a software-defined radio may be able to support multiple protocols via downloadable instructions. In operation, the computing device601 may be able to periodically poll for visible wireless network transmitters (both cellular and local network) on a periodic basis. Such polling may be possible even while normal wireless traffic is being supported on the computing device601. The network interface626 may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 wireless interface device, a DSL modem, a cable modem, a cellular modem, etc., that enables thesystem100 to communicate with another computer system having at least the elements described in relation to thesystem100.

While thememory controller608 and the I/O controller610 are depicted inFIG. 6 as separate functional blocks within thechipset606, the functions performed by these blocks may be integrated within a single integrated circuit or may be implemented using two or more separate integrated circuits. Thecomputing environment600 may also implement themodule616 on aremote computing device630. Theremote computing device630 may communicate with the computing device601 over anEthernet link632. In some embodiments, themodule616 may be retrieved by the computing device601 from acloud computing server634 via theInternet636. When using thecloud computing server634, the retrievedmodule616 may be programmatically linked with the computing device601. Themodule616 may be a collection of various software platforms including artificial intelligence software and document creation software or may also be a Java® applet executing within a Java® Virtual Machine (JVM) environment resident in the computing device601 or theremote computing device630. The modeling module620 and the execution module622 may also be “plug-ins” adapted to execute in a web-browser located on thecomputing devices601 and630. In some embodiments, themodule616 may communicate withback end components638 such as thebackend components110 ofFIG. 1 via theInternet636.

Thesystem600 may include but is not limited to any combination of a LAN, a MAN, a WAN, a mobile, a wired or wireless network, a private network, or a virtual private network. Moreover, while only oneremote computing device630 is illustrated inFIG. 6 to simplify and clarify the description, it is understood that any number of client computers are supported and can be in communication within thesystem100.

FIG. 7 is a high-level block diagram of the various components of thesystem100.

Additionally, certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code or instructions embodied on a machine-readable medium or In a transmission signal, wherein the code is executed by a processor) or hardware modules. A hardware module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). These algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities.

Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

As used herein any reference to “some embodiments” or “an embodiment” or “teaching” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in some embodiments” or “teachings” in various places in the specification are not necessarily all referring to the same embodiment.

Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. For example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not In direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context.

Further, the figures depict preferred embodiments for purposes of illustration only. One skilled In the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein

Upon reading this disclosure, those of skill In the art will appreciate still additional alternative structural and functional designs for the systems and methods described herein through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the systems and methods disclosed herein without departing from the spirit and scope defined in any appended claims.