METHODS AND SYSTEMS FOR FIRST CONTACT RESOLUTION

§101 §103 §112

DETAILED ACTION Acknowledgement This non-final office action is in response to the request for continued examination (RCE) filed on 03/03/2026. Status of Claims Claims 11, 13, 15-16, 19-21, 23, 25-26, and 29-30 have been amended. Claims 11-30 are now pending. Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/03/2026 has been entered. Response to Arguments Applicant's arguments filed on 03/03/2026 regarding the 35 U.S.C. 101 and 103 rejections of claims 11-30 have been fully considered. The Applicant argues the following. (1) As per the 101 rejection, the Applicant argues, in summary, that (i) the claims are not directed to a judicial exception. Amended claims 11 recites a specific technical system that employs a first neural network AI model and a second neural network AI model to...These are not abstract concepts that can be performed in the human mind or that merely organize human activity. Rather, they define a concrete, technology-rooted solution to the technical problem of efficiently managing a multi-channel contact center computing infrastructure; (ii) the present claims integrate the alleged judicial exception into a practical application because the claims provide an improvement to technology and/or a technical field; and (iii) the pending claims recite additional elements that amount to significantly more than any alleged judicial exception. The Examiner respectfully disagrees. The Examiner maintains the position that amended claims 11 and 21 are directed to the abstract ideas of Mental Processes and Certain Methods of Organizing Human Activity because the claims describe a process of analyzing contact data via models to determine/predict an amount of contacts for resolving a customer issue, generate messages to an agent regarding the issue, and allocate resources to accommodate customer contacts. Analyzing contact data via models to determine/predict an amount of contacts for resolving an issue can be practically performed in the human mind via observation and evaluation. Generating a message about the issue to an agent and allocating resources (e.g. planning/scheduling) to accommodate customer contacts are considered certain methods of organizing human activity. As per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim. The Examiner acknowledges the technological components recited in the claims and characterizes these components as additional elements in Steps 2A(2) and 2B. However, these additional elements do not integrate the abstract idea into a practical application nor provide significantly more because the additional elements do not improve the functioning of the computer nor improve another technology or technological component beyond its original capabilities. The claims reflects the use of specific technology to perform analyses, predictions, generate messages, and allocate resources (i.e. abstract processes). Therefore, the additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use (e.g. call center) or technological environment (e.g. ML/AI) in which to apply a judicial exception. Applying an abstract idea on a computer and/or generally linking the use of the abstract idea to a particular technological environment does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05 (f) and (h)). Therefore, the 35 U.S.C. 101 rejection is maintained. (2) As per the 103 rejections, the Applicant argues, in summary, that the cited references Konig nor Mishra do not teach or suggest the following limitations of amended claims 11 and 21: (i) "determining... an amount of contacts for resolving one or more issues associated with the individual via one or more communication interfaces and at least one optimal communication interface of the one or more communication interfaces”, (ii) "wherein each message...is customized according to a communication modality for each optimal communication interface” and (iii) "dynamically adjusting...an allocation of computational resources of the one or more communication interfaces”. The Applicant also argues that the motivation to combine Mishra with Konig is not properly supported. The Examiner respectfully disagrees. The Examiner submits that based on the broadest reasonable interpretation of the claims that the combination of Konig and Mishra teach all of the limitations of amended claims 11 and 21 as shown in the updated mappings below. In summary, Konig teaches (i) determining an amount of contacts for resolving one or more issues with the prediction of customer interaction volume and call volume as recited in at least paragraphs [0065], [0066], and [0078]. Volume signifies an amount. Konig also teaches (iii) dynamically adjust an allocation of computational resources for example “based on predictions, generate new configuration settings for application such as routing, workforce optimization (WFO) or workforce management (WFM), and self-help” as referenced in paragraphs [0067], [0078], and [0080]. Konig in view of Mishra teach (ii) wherein each message...is customized according to a communication modality for each optimal communication interface as shown in at least paragraph [0108] that states “Such generative AI systems can generate text in response to inputs (e.g., language) from a customer device 705. Such inputs can be received via voice, text, or any electronic input. Generative AI outputs can then similarly be provided to a customer device 705 via audio (e.g., text to voice machine language outputs), text, or any such communication channel”. The updated claim mappings referenced above and provided in detail below supports a 103 rejection in view of Konig and Mishra. The Examiner submits that the combination of Konig and Mishra references is proper. The Examiner submits that for each 103 claim rejection where a combination of references were used, a proper motivation to combine statement was provided from the prior art references. The Examiner’s actions reflect example (G) of rationales that may support a conclusion of obviousness as stated in MPEP 2143. Example (G) rationale include some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. As per MPEP 2145, Applicants may argue that the examiner’s conclusion of obviousness is based on improper hindsight reasoning. However, "any judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant’s disclosure, such a reconstruction is proper." In re McLaughlin, 443 F.2d 1392, 1395, 170 USPQ 209, 212 (CCPA 1971). Applicants may also argue that the combination of two or more references is "hindsight" because "express" motivation to combine the references is lacking. However, there is no requirement that an "express, written motivation to combine must appear in prior art references before a finding of obviousness." See Ruiz v. A.B. Chance Co., 357 F.3d 1270, 1276, 69 USPQ2d 1686, 1690 (Fed. Cir. 2004). The Examiner submits that a proper motivation to combine was provided and does not reflect improper hindsight reasoning. The art references of Konig and Mishra are not disparate references as both the teachings are directed towards the use of ML and AI technology to plan and provide assistance and resources to help resolve customer issues efficiently. All of the claimed elements were known in the prior arts of Konig and Mishra and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Therefore, the 35 U.S.C. 103 rejections are proper and have been maintained. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. Claims 11-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 11 and 21 include the limitation of "dynamically adjusting, based on the amount of contacts for resolving the one or more issues associated with the individual, an allocation of computational resources of the one or more communication interfaces according to a plurality of individuals initiating a plurality of contacts”. Support for this limitation was not found in the Applicant’s specification. Paragraphs [0035], [0070], and [0072] recite "The prediction may be used to enable one or more entities to efficiently plan and allocate resources to accommodate large volumes of customers contacting the entities and resolve the customers' problems." These paragraphs do not state that the allocation of computational resources of the one or mor communication interfaces are dynamically adjusted. Therefore, claims 11 and 21 contain new matter and are rejected under 35 U.S.C. 112(a). Dependent claims 12-20 and 22-30 are also rejected under 35 U.S.C. 112(a). Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 11-30 are rejected under 35 U.S.C. 101 because the claimed invention, “Methods and Systems for First Contact Resolution”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer. Step 1: Claims 11-30 are directed to a statutory category, namely a process (claims 11-20) and a machine (claims 21-30). Step 2A (1): Independent claims 11 and 21 are directed to an abstract idea of Mental Processes and Certain Methods of Organizing Human Activity, respectively, based on the following claim limitations: “…determining a resolution of one or more issues associated with an individual within one or more contacts comprising: receiving,…, contact data associated with the individual , wherein the contact data comprises customer data and first contact resolution (FCR) metric data; determining,…, an amount of contacts for resolving one or more issues associated with the individual… and at least one optimal communication interface of the one or more communication interfaces for providing one or more alternative contacts for one or more ineffective contacts of the amount of contacts for resolving the one or more issues associated with the individual,…,wherein the FCR metric data associated with the plurality of entities is generated based on the customer data associated with the plurality of customers…; generating, …, one or more messages of the one or more issues to be output…, wherein each message of the one or more messages is customized according to a communication modality for each optimal communication interface of the one or more optimal communication interfaces,…;…; and dynamically adjusting, based on the amount of contacts for resolving the one or more issues associated with the individual, an allocation of computational resources of the one or more communication interfaces according to a plurality of individuals initiating a plurality of contacts.” (claims 11 and 21). Claims 11 and 21 describe a process of analyzing contact data via models to determine/predict an amount of contacts for resolving a customer issue, generate messages to an agent regarding the issue, and allocate resources to accommodate customer contacts. Analyzing contact data via models to determine/predict an amount of contacts for resolving an issue can be practically performed in the human mind via observation and evaluation. Generating a message about the issue to an agent and allocating computational resources (e.g. planning/scheduling) to accommodate customer contacts are considered certain methods of organizing human activity. Dependent claims 12-20 and 22-30 further describe the customer data (claims 12 and 22), contacts (claims 13 and 23), FCR metric data (claims 14 and 24), training of the AI model (e.g. determining contact data and features for the model, model training/testing, and model output) (claims 15-16 and 25-26), determining/generating the contact data (e.g. based on customer data, determining baseline features levels, and labeling) (claims 17-18 and 27-28), and determining features from the contact data for the predictive model (claims 19-20 and 29-30). These steps reflect data analysis and model training which can practically be performed in the human mind with pen and paper. Model training involves fitting a particular model to a dataset via the adjustment of coefficients or weights in the algorithm/formula to provide a specific output. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions and Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 11-30 are directed to an abstract idea and are not patent eligible. Step 2A (2): This judicial exception is not integrated into a practical application. In particular, claims 11, 15, 16, 21, and 25 recite additional elements of “a plurality of artificial intelligence (AI) models; a computing device; an application of a first neural network AI model; one or more communication interfaces; one or more optimal communication interfaces; wherein the first neural network Al model is trained…; wherein the one or more communication interfaces comprise one or more of a telephone interface, a voice over IP (VoIP) interface, a video conferencing interface, an interactive voice response (IVR) interface, a website, or a user device application; application of a second neural network AI model; wherein the one or more messages comprise one or more of an email message, a SMS message, or a chat message; causing each message to be output via each corresponding optimal communication interface (claims 11 and 21); further comprising training one or more of the first neural network Al model or the second neural network Al model (claims 15 and 25); wherein training the first neural network Al model comprises… (claims 16 and 26); and an apparatus for utilizing a plurality of artificial intelligence (AI) models…comprising one or more processors; and a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to… (claim 21)”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing components and devices that is used to perform the abstract process of analyzing contact data via models to determine/predict an amount of contacts for resolving a customer issue, generate messages to an agent regarding the issue, and allocating resources to accommodate customer contacts. The application and/or use of trained neural network AI models is considered instructions to apply or implement a model on a computer. The neural network AI models are also viewed as a tool to perform the abstract process of data analysis. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Also, limitations that amount to merely indicating a field of use or technological environment (e.g. contact center and machine learning) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 11-30 do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible. Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 11, 15, 16, 21, and 25 recite additional elements of “a plurality of artificial intelligence (AI) models; a computing device; an application of a first neural network AI model; one or more communication interfaces; one or more optimal communication interfaces; wherein the first neural network Al model is trained…; wherein the one or more communication interfaces comprise one or more of a telephone interface, a voice over IP (VoIP) interface, a video conferencing interface, an interactive voice response (IVR) interface, a website, or a user device application; application of a second neural network AI model; wherein the one or more messages comprise one or more of an email message, a SMS message, or a chat message; causing each message to be output via each corresponding optimal communication interface (claims 11 and 21); further comprising training one or more of the first neural network Al model or the second neural network Al model (claims 15 and 25); wherein training the first neural network Al model comprises… (claims 16 and 26); and an apparatus for utilizing a plurality of artificial intelligence (AI) models…comprising one or more processors; and a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to… (claim 21)”. These additional elements evaluated individually and in combination are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply a judicial exception. Applying an abstract idea on a computer and/or generally linking the use of the abstract idea to a particular technological environment does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05 (f) and (h)). Therefore, claims 11-30 do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 11-18 and 21-28 are rejected under 35 U.S.C. 103 as being unpatentable over Konig et al. (US 2017/0316438 A1) in view of Mishra (US 2023/0410801 A1). As per claims 11 (Currently Amended) and 21 (Currently Amended), Konig teaches a method for utilizing a plurality of artificial intelligence (AI) models for determining a resolution of one or more issues associated with an individual within one or more contacts comprising; An apparatus for utilizing a plurality of artificial intelligence (AI) models for determining a resolution of one or more issues associated with an individual within one or more contacts comprising: one or more processors; and a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to (Konig e.g. The present invention are directed to systems and methods for collecting customer experience analytics data within a contact center and to guide control of aspects of the contact center in accordance with the collected customer experience analytics data [0004]. According to one embodiment of the present invention, in a system for operating a contact center including a plurality of different applications, the system includes: a processor; and a memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to: supply one or more customer models to a predictor…[0019]. FIG. 1 is a schematic block diagram of a system for supporting a contact center in providing contact center services according to one exemplary embodiment of the invention [0027]. FIG. 6 is a flowchart illustrating a method for forecasting behavior using the predictors according to one embodiment of the present invention ([0034] and [0088]).): Konig teaches receiving/receive, by a computing device, contact data associated with the individual, wherein the contact data comprises customer data and first contact resolution (FCR) metric data; (Konig e.g. FIG. 3 is a flowchart illustrating a method for generating application settings based on collected data according to one embodiment of the present invention [0031]. Referring to FIG. 3, in operation 310, the CX analytics module 47 collects data from multiple applications. These multiple applications may include the various applications or modules running on various servers as shown in FIG. 1, such as the routing server 20, the stat server 22, the multimedia/social media server 24, the web servers 32, the IMR 34, and the voice analytics module 45 [0069]. In operation 320, the data collected from the various applications is aggregated [0070]. In operation 330, the CX analytics module 47 generates the customer models 232 from the aggregated data. All interactions associated with the particular customer (as identified by the customer id or as identified using other data such as telephone number or web browser configuration) are aggregated to form a customer model 232 for this particular customer [0072]. In operation 330, the CX analytics module 47 generates the agent models 234 from the aggregated data. The agent models 234 are aggregations of agent data for each of the agents. For example, each agent may be associated with a unique agent identifier (or agent id) and every interaction associated with that agent id may be aggregated into the agent model 234 for a particular agent [0074]. In operation 330 the CX analytics module 47 may also compute the aggregated agent models 234c from the individual agent models 234 by aggregating the performance metrics of the various agents across various groups of agents. This aggregation may be computed by, for example calculating mean performance metrics across the agents within the group (e.g., the mean first call resolution time of the group) [0075]. The customer models 232 store all available information about customers and enable the customer experience predictors 220 to make predictions of customer state and next actions. Similarly, the agent models 234 store all available information about agents to enable the customer experience predictors 220 to make predictions of agent performance characteristics [0059].) Konig teaches determining/determine, based on an application of a first neural network Al model to the contact data, an amount of contacts for resolving one or more issues associated with the individual via one or more communication interfaces and at least one optimal communication interface of the one or more communication interfaces for providing one or more alternative contacts for one or more ineffective contacts of the amount of contacts for resolving the one or more issues associated with the individual, (Konig e.g. Embodiments of the present invention are directed to predictive customer experience (CX) applications to perform personalized customer experiences to various users based on customer models and agent models (or profiles) that are constructed from analytics data collected from a range of different applications associated with the system [0042]. The behavior models may be used to predict behaviors of, for example, customers and/or agents, in a variety of situations, thereby allowing embodiments of the present invention to tailor interactions based on the predictions or to allocate resources in preparation for predicted characteristics of future interactions, and thereby improving overall performance, including improving the customer experience [0056]. Each of the individual customer models 232i includes all of the information about the customer's past interactions with the contact center or organization. A customer experience predictor (or prediction model) 220 may use the individual customer models 232i to compute probability distributions regarding... future interactions or the customer's future behavior ( e.g., will the customer renew his or her subscription next month, will the customer contact next week to purchase a new product, will the customer contact the support line tomorrow) [0063]. The third main module of the system, customer experience predictors 220, are used to compute predictions such as the probability that a customer will contact the organization in various timeframes for various reasons, including probabilities computed for every communication channel to the contact center (e.g., telephone, email, text chat) [0065]. The customer experience predictors 220 may compute these probability distributions based on a number of different circumstances such as the channel on which the offer is communicated (e.g., by a telephone, an email, or paper mail) [0065]. These predictors 220 can be used to predict an overall aggregated customer interaction volume (i.e. an amount of contacts), and agent models 234 can be used to predict first contact resolution rates for the various possible call reasons [0065]. Routing strategies can be tuned based on the predicted call volume in particular topics and based on agent performance characteristics [0066].The predictors 220 may correspond to deep neural networks (a deep neural network being a neural network that has more than one hidden layer) and the process of generating the predictors 220 may involve training the deep neural networks using the generated customer models 232, where the predictors 220 compute the probability that a particular customer will take a particular action (e.g., contact within a particular time frame over a particular channel or contact regarding a particular topic), based on the characteristics identified in the model 232 [0076].) Konig teaches wherein the first neural network Al model is trained based on customer data associated with a plurality of customers and FCR metric data associated with a plurality of entities, wherein the FCR metric data associated with the plurality of entities is generated based on the customer data associated with the plurality of customers (Konig e.g. The predictor may be a deep neural network, and training the predictor in accordance with the customer models may include: calculating, for each of the customer models, a plurality of features; identifying a target feature among the plurality of features; generating training data, the training data including a plurality of examples, wherein each of the examples may include: a plurality of input features corresponding to the plurality of features without the target feature; and at least one output feature corresponding to the target feature; and training the deep neural network in accordance with the training data by applying a back propagation algorithm [0006]. A customer experience analytics database 210 collects or stores all of the interaction data from all various applications 240 at the contact center and can deliver this data for use in training ( e.g., by applying machine learning techniques 260) the customer experience (CX) predictors 220 and the customer experience (CX) models 230 [0058]. This data may include interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center) [0058]. Data from applications, including historical customer interactions 252 and new customer interactions 254 are supplied to a machine learning process 260 (e.g., implemented in customer experience analytics module 47) to generate customer models 232 and agent models 234 [0067]. All interactions associated with the particular customer (as identified by the customer id or as identified using other data such as telephone number or web browser configuration) are aggregated to form a customer model 232 for this particular customer and therefore include information such as the timing of each of the customer's interactions with the organization, the timing of past purchases, the payment history, and amenability to sales offers [0072]. FIGS. 4A and 4B are flowcharts illustrating a method 330 for generating customer and agent models 232 and 234 and aggregated customer and agent models 232c and 234c according to one embodiment of the present invention [0082]. In operation 332, for the identified data for each customer, a set of features or attributes is computed from the aggregated data. The features may include, for example, time since last contact with the organization, total value of all purchases by the customer, the presence of an unresolved issue, the frequency of contact between the customer and the organization, the status of the customer (e.g., platinum tier support), etc. [0082]. In some embodiments, the features are not calculated at the time that the data is aggregated but instead are calculated at the time that the model is applied, such as when using the customer model 232 to train a predictor 220 or when predicting a feature of the customer model by supplying the features of the customer model as an input to a predictor 220 [0082].), Konig teaches wherein the one or more communication interfaces comprise one or more of a telephone interface, a voice over IP (VoIP) interface, a video conferencing interface, an interactive voice response (IVR) interface, a website, or a user device application; (Konig e.g. Interactions between contact center agents and outside entities (customers) may be conducted by speech voice (e.g., telephone calls or voice over IP or VoIP calls), video ( e.g., video conferencing), text ( e.g., emails and text chat), or through other media [0002]. Referring to FIG. 3, in operation 310, the CX analytics module 47 collects data from multiple applications. These multiple applications may include the various applications or modules running on various servers as shown in FIG. 1, such as the routing server 20, the stat server 22, the multimedia/social media server 24, the web servers 32, the IMR 34, and the voice analytics module 45 [0069]. In operation 320, the data collected from the various applications is aggregated. This aggregation operation may include, for example, converting the data from the native formats of the individual applications to an internal format of the CX analytics module 47 (e.g., using a data format conversion module tailored for each application), normalizing the units of the various data to a standard set of units (e.g., normalizing data to events per minute, where the original data may have been stored as events per day or minutes per event), accumulating or averaging values, identifying events (e.g., interactions tagged as being of a particular category or containing g a particular detected event such as an issue resolved event or a supervisor escalation event), and merging customer interaction data from interactions with different portions of the contact center (e.g., a customer has initial interactions to attempt to solve a problem using a self-help website, then emails technical support regarding the same problem, and finally calls technical support to speak to an agent to resolve the problem) [0070].) Konig teaches dynamically adjusting, based on the amount of contacts for resolving the one or more issues associated with the individual, an allocation of computational resources of the one or more communication interfaces according to a plurality of individuals initiating a plurality of contacts. (Konig e.g. The behavior models may be used to predict behaviors of, for example, customers and/or agents, in a variety of situations, thereby allowing embodiments of the present invention to tailor interactions based on the predictions or to allocate resources in preparation for predicted characteristics of future interactions, and thereby improving overall performance, including improving the customer experience [0056]. The customer models 232 and agent models 234 (e.g., including the aggregated models) are then used to make predictions that are then used to generate new configuration settings for applications such as routing, workforce optimization (WFO) or workforce management (WFM), and self-help [0067]. In operation 350, the generated predictors 220 may be used to forecast the behavior of individual customers and the customers as a whole [0077]. Based on determinations of the forecasted behavior of the customers and agents, the CX analytics module 47 may generate application settings in operation 360 [0078]. For example, predictions of call volume during a future shift may be used to calculate an appropriate agent staffing for that shift to handle the predicted call volume [0078]. In operation 370, the CX analytics module 47 may output the calculated application settings and supply the settings to the appropriate module of the contact center to automatically reconfigure the module (and possibly the entire contact center) based on the predicted behavior of customers and agents. For example, a workforce management module may be reconfigured to allocate additional agents to particular shifts. As another example, self-help systems may be modified to promote and to make more prominent articles that are predicted to be in higher demand [0080]. The CX analytics system 47 can control the routing system to smoothly transition agents from one activity to another, such as by allowing the agent to finish the interaction that he or she is currently handling, adjusting the pacing of the outbound contacts, adjusting the estimated wait time, and generally avoiding frequent changes at the agent level [0122].) Konig does not explicitly teach, however, Mishra teaches generating, based on an application of a second neural network Al model to the contact data, one or more messages of the one or more issues to be output via the at least one optimal communication interface, wherein each message of the one or more messages is customized according to a communication modality for each optimal communication interface of the at least one optimal communication interface, wherein the one or more messages comprise one or more of an email message, a SMS message, or a chat message; causing each message to be output via each corresponding optimal communication interface; and (Mishra e.g. The present disclosure relates generally to facilitating two-way communications. In some examples, automated transcripts of human agent, bot agent, and feedback portions of a two-way communication are generated as a merged transcript. In some examples, the merged transcript is used to automatically generate scripts, decision branches, training data for generative artificial intelligence, or other such data to update bot agent functionality [0002]. Communication systems that support such user interactions may allow users to speak or chat with a live representative of the client. Such communication systems may additionally or alternatively use automated bot systems for two-way interactions with users. Automated bot systems can use decision tree scripts to attempt to resolve customer issues [0003]. A communication system can be associated with many different bots to assist with the wide variety of user inquiries. Because bots can be complex to generate, a bot may not have responses for all customer issues that are presented to a bot. When an issue the bot is not capable of handling is presented, the bot can transfer the customer to a human agent, that can help to resolve the customer issue. Examples described herein can generate merged transcripts of human and bot (e.g., non-human) agent interactions with a customer. This merged data can then be used in automated identification of an issue, and details of the issue resolution. The merged data can be standardized across many different two-way communications, to allow new types of customer issues or new solutions to be dynamically and automatically identified by a system [0006]. FIG. 5 represents a multi-device communication exchange system 500 according to an embodiment. System 500 includes a network device 505 configured to communicate with a variety of types of terminal devices over a variety of types of communication channels [0078]. Automated script generation tools can include machine learning feedback systems that integrate various aspects of merged transcript analysis to dynamically improve bot operation and scripts available during system operations [0036]. In some implementations, a neural network can be used to analyze sets of merged transcript data to identify solutions [0106]. A neural network model can be used to analyze merged transcripts to identify combinations of actions that result in positive sentiment scores, and positive resolutions scores, or any set of target feedback results [0106]. The analysis can identify when human agent actions or combinations of bot agent and human agent actions result in above expected feedback. When the machine learning analysis of aggregated transcripts identifies such actions resulting in above expected scoring, such actions can either be presented to a script designer via a merged transcript user interface, or can be automatically processed to generate a script that can be dynamically included in a bot model via bot model training 730, and used in voice bot segments of subsequent system communications with customer devices [0106]. FIG. 7C illustrates aspects of a two-way communication session and associated input data used for merged transcripts and feedback training for large language models and associated generative artificial intelligence in accordance with examples described herein ([0018] and [0108]). Such generative AI systems can generate text in response to inputs (e.g., language) from a customer device 705. Such inputs can be received via voice, text, or any electronic input. Generative AI outputs can then similarly be provided to a customer device 705 via audio (e.g., text to voice machine language outputs), text, or any such communication channel [0108]. FIG. 8 shows a block diagram of a communication server 805 according to some embodiments. The communication server 805 can, in some examples, be implemented as a special purpose computer for transcription and automated script generation in accordance with aspects described herein [0112]. UI configuration engine 829 may include systems to implement an agent interface and/or a script management engine, or any other aspect of an interface for handling aspects of a communication in accordance with aspects described herein [0115]. In some implementations, the UI configuration engine 829 can be used to manage scripts and bots in a communication. For example, in some communications, a human agent can use bots or scripts to assist with issue resolution, and UI configuration engine 829 can be used to initiate use of bots or scripts from bot engine 833 [0116].) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Konig’s Customer Experience Analytics System’s use of ML/neural network prediction models with Mishra’s neural network AI bots/models to generate one or more messages of the one or more issues via communication interfaces in order to improve the efficiency of a system and the quality of information and services provided to a customer (Mishra e.g. [0149]). As per claims 12 and 22 (Previously Presented ), Konig in view of Mishra teach the method of claim 11 and the apparatus of claim 21, Konig teaches wherein the customer data comprises data associated with the individual communicating via a channel of an entity, wherein the data associated with the individual communicating via the channel of the entity comprises one or more of information associated with content of at least one type of contact associated with the individual, information associated with the channel of the entity, or time information associated with each contact of one or more contacts. (Konig e.g. A customer experience analytics database 210 collects or stores all of the interaction data from all various applications 240 at the contact center (Fig. 2A and [0058]). This data may include interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center) (Fig. 2A and [0058]). All interactions associated with the particular customer (as identified by the customer id or as identified using other data such as telephone number or web browser configuration) are aggregated to form a customer model 232 for this particular customer and therefore include information such as the timing of each of the customer's interactions with the organization, the timing of past purchases, the payment history, and amenability to sales offers [0072].) As per claims 13 and 23 (Currently Amended), Konig in view of Mishra teach the method of claim 12 and the apparatus of claim 22, Konig teaches wherein the contacts of the amount of contacts comprise one or more of billing and payments, credit and collections, or start/stop/move (SSM) (Konig e.g. T 1 , T 2, . . ., TY denote the various customer interaction reasons (or intent) that the customer can have for interacting with the contact center. These call reasons may include, for example, "technical support for product A," "purchase of product B," "billing questions," and "change of address." [0098].), and wherein the time information comprises one or more of a time of day, a day of a week, a time range of the day, or a range of days (Konig e.g. The features (or attributes or parameters) supplied to the predictor 220 may include: the number of times the customer contacted the organization regarding each call reason (T1, T2, . . . , Ty) in the past 48 hours; the number of times the customer contacted the organization regarding each call reason (T1, T2, . . . , Ty) in the past 30 days; the number of times the customer contacted the organization regarding any call reason in the past 30 days; the prior of customers in general contacting the organization about each call reason (T1, T2, . . . , Ty) (e.g., probability that any customer will contact the organization regarding each call reason); and whether the customer had a change in account or service over the past week [0103].). As per claims 14 (Original) and 24 (Previously Presented), Konig in view of Mishra teach the method of claim 11 and the apparatus of claim 21, Konig teaches wherein the FCR metric data is associated with an entity, wherein the FCR metric data comprises a percentage of customer contacts resolved on a first contact. (Konig e.g. Each of the plurality of agent models may include a first call resolution rate, an average handling time, and a customer satisfaction rating [0018]. In operation 330 the CX analytics module 47 may also compute the aggregated agent models 234c from the individual agent models 234 by aggregating the performance metrics of the various agents across various groups of agents. This aggregation may be computed by, for example calculating mean performance metrics across the agents within the group (e.g., the mean first call resolution time of the group) [0075].) As per claims 15 and 25 (Currently Amended), Konig in view of Mishra teach the method of claim 11, further comprising and the apparatus of claim 21, wherein the processor-executable instructions, when executed by the one or more processors, Konig teaches further cause the apparatus to training/train one or more of the first neural network AI model or the second neural network AI model. (Konig e.g. The predictor may be a deep neural network, and training the predictor in accordance with the customer models may include: calculating, for each of the customer models, a plurality of features; identifying a target feature among the plurality of features; generating training data, the training data including a plurality of examples, wherein each of the examples may include: a plurality of input features corresponding to the plurality of features without the target feature; and at least one output feature corresponding to the target feature; and training the deep neural network in accordance with the training data by applying a back propagation algorithm [0006]. The predictors 220 may correspond to deep neural networks (a deep neural network being a neural network that has more than one hidden layer) and the process of generating the predictors 220 may involve training the deep neural networks using the generated customer models 232, where the predictors 220 compute the probability that a particular customer will take a particular action (e.g., contact within a particular time frame over a particular channel or contact regarding a particular topic), based on the characteristics identified in the model 232 [0076]. Training a neural network based on historical data of the above input features and the actual historical results of whether or not those customers contacted the organization regarding each of those call reasons generates the above described predictor 220, which maps from input features describing a particular customer to probabilities of contacting the organization for each of interaction reasons [0117].) As per claims 16 and 26 (Currently Amended), Konig in view of Mishra teach the method of claim 15, wherein training the first neural network AI model comprises and the apparatus of claim 25, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to train the first neural network AI model, further cause the apparatus to: Konig teaches determining/determine contact data associated with the customer data and the FCR metric data (Konig e.g. the present invention are directed to systems and methods for collecting customer experience analytics data within a contact center…[0004]. A method for generating a predictor of customer behavior for a contact center includes: collecting, by a processor, data from a plurality of different applications of the contact center,... (Abstract and [0005]. Aspects of embodiments of the present invention are also directed to generating, updating, and modifying behavior models based on the collected interaction data. The behavior models may include models of customers and models of agents [0056]. All interactions associated with the particular customer (as identified by the customer id or as identified using other data such as telephone number or web browser configuration) are aggregated to form a customer model 232 for this particular customer and therefore include information such as the timing of each of the customer's interactions with the organization, the timing of past purchases, the payment history, and amenability to sales offers [0072]. The agent model 234 contains sufficient historical information to compute, for example, the agent's first call resolution rate, hold time, sales performance, customer satisfaction scores, and other performance metrics. Furthermore, these performance metrics may be computed based on various conditions such as the interaction topic and/or classes of customers [0074].), wherein the contact data associated with the customer data and the FCR metric data comprises one or more groups of contact characteristics, wherein each group of contact characteristics of the one or more groups of contact characteristics is labeled according to a predefined feature of a plurality of predefined features; (Konig e.g. This data may include interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center) (Fig. 2A and [0058]). FIG. 3 is a flowchart illustrating, in more detail, a method for generating application settings in based on collected data according to one embodiment of the present invention. The operations of FIG. 3 may be performed, for example, by the CX analytics module 47 shown in FIG. 1 [0069]. In operation 320, the data collected from the various applications is aggregated. This aggregation operation may include, for example, converting the data from the native formats of the individual applications to an internal format of the CX analytics module 47 (e.g., using a data format conversion module tailored for each application), normalizing the units of the various data to a standard set of units (e.g., normalizing data to events per minute, where the original data may have been stored as events per day or minutes per event), accumulating or averaging values, identifying events (e.g., interactions tagged as being of a particular category or containing a particular detected event such as an issue resolved event or a supervisor escalation event), and merging customer interaction data from interactions with different portions of the contact center (e.g., a customer has initial interactions to attempt to solve a problem using a self-help website, then emails technical support regarding the same problem, and finally calls technical support to speak to an agent to resolve the problem) [0070]. In operation 330, the CX analytics module 47 may also generate the aggregated customer models 232c by aggregating groups of customers based on one or more shared traits [0073]. These traits may relate to, for example, geography (e.g., customers from the same city), order frequency (e.g., regular customers versus infrequent customers), customer tier (e.g., platinum members versus regular members), types of customers (e.g., end users versus resellers), and users of different products offered by the organization (e.g., customers who own one product versus those who own a different product) [0073]. For each of the groups, the features or attributes of the individual customers is combined (e.g., an average value is calculated such as a mean) to generate an aggregate customer model 232c corresponding to that group [0084]. In operation 330 the CX analytics module 47 may also compute the aggregated agent models 234c from the individual agent models 234 by aggregating the performance metrics of the various agents across various groups of agents. This aggregation may be computed by, for example calculating mean performance metrics across the agents within the group (e.g., the mean first call resolution time of the group) [0075]. Examples of groups of agents include: agents who work together on a particular shift, agents who have received additional training, agents who work at a particular site, and agents who service particular product lines [0085]. For each of the groups, the features or attributes of the individual agents (such as their first call resolution rates, average handling times, and customer satisfaction scores) are combined (e.g., an average value is calculated such as a mean) to generate an aggregate agent model 234c corresponding to that group [0085]. FIG. 5 is a flowchart illustrating a method for generating predictors according to one embodiment of the present invention [0086]. A goal is to predict an event Ecustomer _call (e.g., whether the customer will call in the next 48 hours), then each of the customer corresponding to a relevant group for the predictor (e.g., all customers, or all customers of a particular tier, or all customers using a particular product line) may be used to provide in operation 344, examples that make up the training data, where, for each example, the input portion of the training data is all of the features of the model except Ecustomer_call which is the output portion or the label of the training data (i.e., for the given customer that is associated with the input features the historical truth of whether he called or not in the 48 hour period that occurred in the past. This is usually labeled as 1 for the event happened and 0 for had not happened) of the training data [0087].) Konig teaches determining/determine, based on the contact data associated with the customer data and the FCR metric data, a plurality of features for the first neural network AI model; (Konig e.g. Data from applications, including historical customer interactions 252 and new customer interactions 254 are supplied to a machine learning process 260 (e.g., implemented in customer experience analytics module 47) to generate customer models 232 and agent models 234 (Fig. 2B and [0067]).In operation 340, the CX analytics module may generate a set of predictors 220 to predict customer and agent behavior based on the generated customer and agent models 232 and 234 [0076]. The predictor may be a deep neural network ([0011] and [0020]).The customer models 232 may be thought of as containing a list or set of features (e.g., a feature vector) and one or more of the features may be supplied as input features to the predictor 220, where the predictor 220 predicts the probability of an event not included in the set of input features [0076]. In operation 332, for the identified data for each customer, a set of features or attributes is computed from the aggregated data. The features may include, for example, time since last contact with the organization, total value of all purchases by the customer, the presence of an unresolved issue, the frequency of contact between the customer and the organization, the status of the customer (e.g., platinum tier support), etc. [0082]. In operation 334, for the identified data for each agent, a set of features or attributes is computed from the aggregated data. These features may include, for example, first call resolution for each of a number of interaction reasons, average handling time for each of the interaction reasons, and customer satisfaction for each of the interaction reasons [0083]. FIG. 5 is a flowchart illustrating a method for generating predictors according to one embodiment of the present invention ([0033] and [0086]). The N features of a particular customer model can be represented as (x1, x2, . . . , xN), as identified by user input or as identified by customer experience analytics system 47 (e.g., automatically identifying features that appear relevant or generating predictors for all features) in operation 342, (Fig. 5 and [0087]). The predictors 220 are implemented using deep neural networks that are trained based on historical data [0087].) Konig teaches training/train, based on a first portion of the contact data associated with the customer data and the FCR metric data, the first neural network AI model according to the plurality of features; (Konig e.g. Data from applications, including historical customer interactions 252 and new customer interactions 254 are supplied to a machine learning process 260 (e.g., implemented in customer experience analytics module 47) to generate customer models 232 and agent models 234 [0067]. A predictor 220 may be trained for each group of customers (e.g., as divided by demographics as described above in the context of aggregated customer models 232 c) and/or a predictor 220 may be trained for all customers as a whole. This training of the predictors 220 may be performed using, for example, the back propagation algorithm [0076]. The training data is separated into a training set, a test set, and a validation set, as is well known in the art, and the back propagation algorithm may be used to train, in operation 346, a deep neural network (e.g., a neural network having an input layer, an output layer, and more than one hidden layer between the input layer and the output layer) (Fig. 5 and [0087]).) Konig teaches testing/test, based on a second portion of the contact data associated with the customer data and the FCR metric data, the first neural network AI model; and (Konig e.g. The training data is separated into a training set, a test set, and a validation set, as is well known in the art, and the back propagation algorithm may be used to train, in operation 346, a deep neural network (e.g., a neural network having an input layer, an output layer, and more than one hidden layer between the input layer and the output layer) (Fig. 5 and [0087]).) Konig teaches outputting/output, based on the testing, the first neural network AI model. (Konig e.g. The resulting trained neural network is a predictor for event Ecustomer_call (e.g., whether the given customer will call in the next 48 hours) and may then be output in operation 348 to be stored for later use in generating predictions (Fig. 5 and [0087]). In operation 350, the generated predictors 220 may be used to forecast the behavior of individual customers and the customers as a whole [0077]. For example, the customer model 232 generated for a particular customer can be supplied as an input to a generated predictor 220 (e.g., a deep neural network), which will generate an output probability based on the supplied input [0077]. Similarly, the agent models 234 may be used to predict the capacity of the agents to handle the predicted load [0077].) As per claims 17 and 27 (Previously Presented), Konig in view of Mishra teach the method of claim 16, wherein and the apparatus of claim 26, wherein the processor-executable instructions that, when executed by the one or more processors, Konig teaches cause the apparatus to determining/determine the contact data associated with the customer data and the FCR metric data comprises: determining, based on the customer data, one or more contact data sets that comprise one or more groups of one or more customer data characteristics and one or more FCR metric data characteristics; and generating, based on the one or more contact data sets, the contact data associated with the customer data and the FCR metric data. (Konig e.g. In operation 330, the CX analytics module 47 may also generate the aggregated customer models 232c by aggregating groups of customers based on one or more shared traits [0073]. These traits may relate to, for example, geography (e.g., customers from the same city), order frequency (e.g., regular customers versus infrequent customers), customer tier (e.g., platinum members versus regular members), types of customers (e.g., end users versus resellers), and users of different products offered by the organization (e.g., customers who own one product versus those who own a different product) [0073]. In operation 332, for the identified data for each customer, a set of features or attributes is computed from the aggregated data. The features may include, for example, time since last contact with the organization, total value of all purchases by the customer, the presence of an unresolved issue, the frequency of contact between the customer and the organization, the status of the customer (e.g., platinum tier support), etc. These features may form a portion of the customer model 232 for that particular customer [0082]. In operation 333, in a manner similar to that used for generating the customer models 232, the customer experience analytics module 47 computes a plurality of individual agent models 234 by identifying all data from the aggregated data that is associated with the identified agent. This may be, for example, ail recorded interactions between the agent and any of the customers of the organization (e.g., emails, conversation transcripts, chat histories, etc.) [0083]. In operation 334, for the identified data for each agent, a set of features or attributes is computed from the aggregated data. These features may include, for example, first call resolution for each of a number of interaction reasons, average handling time for each of the interaction reasons, and customer satisfaction for each of the interaction reasons. These agent specific features may form a portion of the agent model 234 for that particular agent [0083].) As per claims 18 and 28 (Previously Presented), Konig in view of Mishra teach the method of claim 16, wherein and the apparatus of claim 26, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determining/determine the contact data associated with the customer data and the FCR metric data comprises: Konig teaches determining baseline feature levels for each group of contact characteristics of the one or more groups of contact characteristics; (Konig e.g. The generating the plurality of customer models for the customers may further include, for each customer of the customers, generating a plurality of features in accordance with the identified data [0007]. This data may include interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center) [0058]. All interactions associated with the particular customer (as identified by the customer id or as identified using other data such as telephone number or web browser configuration) are aggregated to form a customer model 232 for this particular customer and therefore include information such as the timing of each of the customer's interactions with the organization, the timing of past purchases, the payment history, and amenability to sales offers [0072]. The customer experience analytics module 47 identifies all data from the aggregated data that is associated with the customer. This may be, for example, all recorded interactions between the customer and the organization (e.g., emails, sales orders, conversation transcripts, etc.) [0082]. Each agent may be associated with a unique agent identifier (or agent id) and every interaction associated with that agent id may be aggregated into the agent model 234 for a particular agent [0074].) Konig teaches labeling the baseline feature levels for each group of contact characteristics of the one or more groups of contact characteristics as at least one predefined feature of the plurality of predefined features; and (Konig e.g. A customer experience analytics database 210 collects or stores all of the interaction data from all various applications 240 at the contact center and can deliver this data for use in training ( e.g., by applying machine learning techniques 260) the customer experience (CX) predictors 220 and the customer experience (CX) models 230 [0058]. This data may include interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center) (Fig. 2A and [0058]). The customer models 232 store all available information about customers and enable the customer experience predictors 220 to make predictions of customer state and next actions. Similarly, the agent models 234 store all available information about agents to enable the customer experience predictors 220 to make predictions of agent performance characteristics [0059]. Each of the individual customer models 232i includes all of the information about the customer's past interactions with the contact center or organization [0063]. These aggregated customer models 232c may be grouped based on various characteristics such as living in particular geographic areas, customer loyalty program status (e.g., platinum members of the loyalty club), customer tier (e.g., paying extra for higher service levels), and product line (e.g., personal computers versus smartphones) [0064].The customer models 232 may be thought of as containing a list or set of features (e.g., a feature vector) and one or more of the features may be supplied as input features to the predictor 220, where the predictor 220 predicts the probability of an event not included in the set of input features [0076]. Each of the individual agent models 234i corresponds to one of the agents of the contact center and may contain information about that agent's performance such as first contact (or first call) resolution rate, average handling time, sales performance, and customer satisfaction across various call topics and across various classes of customers (e.g., based on customer call profile, customer model, and demographics). In some embodiments of the present invention, the individual agent models 234i also contain other information related to agent satisfaction such as agent business (or idle time or idle percentage), issue difficulty, and co-workers assigned on the agent's shift [0061]. The aggregated agent models 234c are aggregations of the individual agent models 234i, such as aggregations of the performance information of the agents. These aggregated agent models 234c may correspond to different groups of agents, such as the agents of a current shift, the agents at a particular location, agents having a particular combination of skills, or the entire population of agents of the organization [0062].) Konig teaches generating, based on the labeled baseline feature levels, the contact data associated with the customer data and the FCR metric data. (Konig e.g. In operation 320, the data collected from the various applications is aggregated. This aggregation operation may include, for example, converting the data from the native formats of the individual applications to an internal format of the CX analytics module 47 (e.g., using a data format conversion module tailored for each application), normalizing the units of the various data to a standard set of units ( e.g., normalizing data to events per minute, where the original data may have been stored as events per day or minutes per event), accumulating or averaging values, identifying events (e.g., interactions tagged as being of a particular category or containing g a particular detected event such as an issue resolved event or a supervisor escalation event), and merging customer interaction data from interactions with different portions of the contact center (e.g., a customer has initial interactions to attempt to solve a problem using a self-help website, then emails technical support regarding the same problem, and finally calls technical support to speak to an agent to resolve the problem) [0070]. In operation 332, for the identified data for each customer, a set of features or attributes is computed from the aggregated data. The features may include, for example, time since last contact with the organization, total value of all purchases by the customer, the presence of an unresolved issue, the frequency of contact between the customer and the organization, the status of the customer (e.g., platinum tier support), etc. These features may form a portion of the customer model 232 for that particular customer [0082]. In operation 334, for the identified data for each agent, a set of features or attributes is computed from the aggregated data. These features may include, for example, first call resolution for each of a number of interaction reasons, average handling time for each of the interaction reasons, and customer satisfaction for each of the interaction reasons. These agent specific features may form a portion of the agent model 234 for that particular agent [0083].) Claims 19-20 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Konig et al. (US 2017/0316438 A1) in view of Mishra (US 2023/0410801 A1) and in further view of Bayardo et al. (US 8,041,694 B1). As per claims 19 and 29 (Currently Amended), Konig in view of Mishra teach the method of claim 16, wherein and the apparatus of claim 26, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determining/determine, based on the contact data associated with the customer data and the FCR metric data, the plurality of features for the first neural network AI model comprises: Konig nor Mishra explicitly teach, however, Bayardo teaches determining, from the contact data associated with the customer data and the FCR metric data, features present in two or more contact data sets of a plurality of contact data sets as a first set of candidate contact characteristics; determining, from the contact data associated with the customer data and the FCR metric data, features of the first set of candidate contact characteristics that satisfy a first threshold score as a second set of candidate contact characteristics; and determining, from the contact data associated with the customer data and the FCR metric data, features of the second set of candidate contact characteristics that satisfy a second threshold score as a third set of candidate contact characteristics, wherein the plurality of features comprises the third set of candidate contact characteristics.(Bayardo e.g. Pairs of similar vectors in a set of vectors are identified. A comparison vector x is identified, and a set of candidate vectors corresponding to the vector x is identified. For each candidate vector y in the set, a similarity estimate between the comparison vector x and the candidate vector y is determined; if the similarity estimate meets a similarity threshold, a similarity score of the comparison vector x and the candidate vector y is determined; and if the similarity score meets the similarity threshold, the pair of vectors (x, y) is included in a list of similar pairs of vectors (Abstract). It is useful to search for similar pairs (or groups) of objects from a population of objects. The objects to be analyzed can be represented as vectors. Finding similar pairs of objects can involve computing similarity scores between pairs of vectors, and keeping track of which vectors (or which parts of the vectors) have already been compared. The similarity scores can be compared with a threshold, and pairs of vectors whose similarity scores above the threshold are provided as the search result. For example, given a dataset V of vectors, a similarity function sim(·, ·), and a threshold value t, one way to identify similar pairs of vectors is to compute sim(x, y) for all pairs of vectors x and y in the dataset V and identify the pairs of vectors x and y for which sim(x, y)≧t. (col. 1 lines 20-35). Identifying the pair of similar vectors (x, y) can include iteratively processing features of the comparison vector x, adding some of the vectors having features that have been processed to a set of candidate vectors, and stop adding new vectors to the set of candidate vectors when the upper bound meets the predetermined criterion (col. 2 lines 25-30). An index that includes features based on a comparison of the similarity contribution with a similarity threshold is generated; and another vector in the set of vectors that is similar to the vector x using the index is identified (col. 5 lines 5-10). FIG. 4 is a flowchart for an example process for searching and indexing a dataset. FIG. 5 is a flowchart for an example search and index processes (col. 8 lines 47-50).) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Konig in view of Mishra’s process for determining features for the first neural network AI model to include determining common or similar features within the contact data that satisfy thresholds and/or feature score as taught by Bayardo in order to increase the speed of computation and reduce the amount of data to be stored (cols. 8-9 lines 66-5). As per claims 20 and 30 (Currently Amended), Konig in view of Mishra and Bayardo teach the method of claim 19, wherein and the apparatus of claim 29, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determining/determine, based on the contact data associated with the customer data and the FCR metric data, the plurality of features for the first neural network AI model comprises: Konig nor Mishra explicitly teach, however, Bayardo teaches determining, for the third set of candidate contact characteristics, a feature score for each contact characteristic of a plurality of contact characteristics associated with the third set of candidate contact characteristics; and determining, based on the feature score, a fourth set of candidate contact characteristics, wherein the plurality of features comprises the fourth set of candidate contact characteristics. (Bayardo e.g. The similarity estimate can be determined based on a predetermined function such that the similarity estimate is greater than or equal to the similarity score between the comparison vector x and the candidate vector y. For each vector v different from x in a set of vectors, the vector v can be identified as a comparison vector, and a set of candidate vectors corresponding to v can be identified. For each candidate vector w in the set, a similarity estimate between the comparison vector v and the candidate vector w can be determined. If the similarity estimate meets the similarity threshold, a similarity score of the comparison vector v and the candidate vector w can be determined. If the similarity score meets the similarity threshold, the pair of vectors (v, w) can be included in the list of similar pairs of vectors. (cols. 1-2 50-4). Identifying the pair of similar vectors (x, y) can include iteratively processing features of the comparison vector x, adding some of the vectors having features that have been processed to a set of candidate vectors, and stop adding new vectors to the set of candidate vectors when the upper bound meets the predetermined criterion (col. 2 lines 25-30). An index that includes features based on a comparison of the similarity contribution with a similarity threshold is generated; and another vector in the set of vectors that is similar to the vector x using the index is identified (col. 5 lines 5-10).) The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Konig in view of Mishra’s process for determining features for the predictor models to include determining common or similar features within the contact data that satisfy thresholds and/or feature score as taught by Bayardo in order to increase the speed of computation and reduce the amount of data to be stored (cols. 8-9 lines 66-5). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include US: Osinski et al. (US 2022/0116793 A1) “Proactive Customer Care in a Communication System”, FOR: DAI, Li-xia (CN-117952624-A) "A Call Centre Data Analysis Method And System Based On Artificial Intelligence", and NPL: M. Begović, E. Avdagić-Golub, B. Memić and A. Kosovac, "Predicting Customer Behavior in Support Channels Using Machine Learning," 2023 46th MIPRO ICT and Electronics Convention (MIPRO), Opatija, Croatia, 2023, pp. 1275-1280. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.M./Examiner, Art Unit 3624 /Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624