DETAILED ACTION
This Non-Final Office Action in response to Application Serial 18/337,694. In response to the Examiner’s action mail dated October 01, 2025, Applicant submitted amendments and arguments mail dated December 30, 2025. The claims 1 - 20 are pending in this application and have been rejected below.
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 30, 2025 has been entered.
Information Disclosure Statement
The Applicant did not submit information disclosure statement (IDS) for consideration.
Response to Amendments
Claims 1-20 are pending in this application. Claim(s) 1, 7, 8, 14, 15, 16 and 20 are amended.
Claims 15 and 16 were objected for minor informalities. Applicant amended claim 15. Applicant marked claim 16 as (original). Applicant’s amendments are persuasive. Examiner withdraws the claim 15 and the claim 16 objection.
Regarding the 35 U.S.C. 101 rejection, the amendments are not persuasive. The claims 1-20 are rejected under 35 U.S.C. 101, see below.
Regarding the prior art rejection, the amendments to the claims are not persuasive. The claims 1-20 are rejected under 35 U.S.C. 103, see below.
Applicant is encouraged to request an interview.
Response to Arguments
Applicant’s arguments filed on December 30, 2025, have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
Art Rejections
On pages 10-13 of the Applicant’s prior art rejection, Applicant respectfully disagrees and traverses:
The Office Action does not identify any passage in Anand where model is trained on MPC data to learn to predict engagement scores.
Applicant asserts that the cited art fails to teach or suggest each and every element of claim 1, and thus, is/are allowable.
Dependent claims are allowable over the cited art for at least the reasons set forth with respect to the independent claims.
Examiner respectfully disagrees. Examiner submits, Applicant amendments to the claims are necessitate grounds for a new rejection. See prior art rejections below.
Rejections under 101
On pages 14- 18 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses the office rejects claims 1-20 as being directed to judicial exception without significantly more. Applicant submits:
The human mind cannot measure communications quality parameters (e.g., inbound/outbound audio timing) from an electronic communication session. Applicant submits the technical measurements can only be obtained through integration with electronic communication infrastructure capable of monitoring real-time data transmission.
Example 39, which addresses a method for training network for facial detection, includes the limitation “training the neural network for facial detection” does not describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols.” Amended claims 1 recites, “determining the engagement score by applying a trained engagement score model to the general MPC data and the participant-specific MPC data, where the trained engagement score model comprises a regression-based machine learning model.”
Example 47 where claims 2 recites “training , by computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN … includes a backpropagation algorithm and a gradient descent algorithm” requires specific mathematical calculations. Applicant argues amended claim 1 does not name specific mathematical algorithms such as backpropagation or gradient descent. It identifies a model type- a regression=based machine learning model.
The USPTO Appeals Review Panel in Ex parte Desjardins, Appeal 2024000567 (PTAB Sept. 26, 2025), provides directly applicable guidance on how to evaluate claims involving Step2A prong two. In Desjardins, the Panel found that claims reciting machine learning training methods integrated abstract elements into a practical application because they were “directed to an improvement to computer functionality versus being directed to an abstract idea.” Applicant cites Enfish turning on the whether the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Applicant submits the amended claim 1 recites a particular solution to the technical solution to the technical problem or a particular way to achieve a desired outcome. The solution involved capturing specific technical data types from an active electronic communication session and processing that data through a trained regression-based machine learning model to produce an engagement score. This not merely the idea of determining engagement; it is a specific technical approach that defines the data to be captured the source from which that data is captured, the source from which that data is captures, and the type of model used to process the data.
Applicant submits the claims do not recites a generic computer to automate a manual process that humans previously performed. Rather, the claims recite a technical system that captures data that can only be obtained from electronic communication infrastructure. The claims represent an improvement to electronic communication analysis technology, not the automation of a pre-existing manual process.
Applicant request the 35 U.S.C. 101 is withdrawn.
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 101 arguments. The claims recite the abstract idea of providing an engagement score corresponding to a multi-party communication (MPC).
The abstract idea is a mathematical calculation – regression model, and thus, the claim are a mathematical concept. The claims being a mathematical concept are directed to a judicial exception at Step 2A prong one.
The judicial exception is not integrated into a practical application at Step 2A prong two. The claims recite additional elements such as a regression-based machine learning model which is apply-it. See 2106.05(f). The mathematical step is based on mathematical relationships, formulas, or calculations. The claims do not rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims is more than a drafting effort designed to monopolize the judicial exception. The claims are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
Regarding an improvement. The Applicant is not improving the regression-model. The Applicant is using a computer to improve how the regression model is completed. Here, the model in completed on a computer using a regression-based machine learning model. The claims are not integrated into a practical application at Step 2A prong two.
At Step 2B, the claims when viewed as a whole do not amount to significantly more. As determined in the Step 2A prong two, the claims are using a computer to complete a regression-based machine learning model which is apply-it. See MPEP 2106.05(f).
Applicant’s arguments of Enfish, Desjardins, Example 39 (Facial Recognition) and Example 47 (Anomaly Detection) are not persuasive.
Examiner submits, Enfish and Desjardins are not persuasive. Regarding Applicant’s argument regarding Enfish, as discussed in the Step 2A prong two and the Step 2B argument, the claims are not directed to an improvement to computer functionality versus being directed to an abstract idea. Examiner submits the claims recite an abstract idea, and thus are directed to a judicial exception. Here, the judicial exception is a mathematical calculation using a computer. The communication session where the MPC data is collected further supports a computer is used to host the communication session, where data is collected for mathematical analysis. Applicant’s Enfish and Desjardins arguments are not persuasive.
Regarding Example 39, the claims are not similar to the facial detection. Example 39 used a an Articial Neural Network (ANN) to detect and improve facial detection. Here, the judicial exception is a mathematical calculation using a computer. Applicant’s Example 39 arguments are not persuasive.
Regarding Example 47, the Applicant’s arguments and amendments are not persuasive. In Example 47 claim 2, the claims analyze one or more detected anomalies and does not provide any detail that explain the analysis of detected anomalies. Like Example 47, the Applicant’s claims do not provide any detail that explain the trained engagement score model that is comprised of a regression-based machine learning model. Applicant’s Example 47 arguments are not persuasive.
The pending claims remain rejected under 35 U.S.C. 101 see below.
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 therefore, subject to the conditions and requirements of this title.
Claims 1- 7 is/are method.
Claims 8-14 are manufacture.
Claims 15-20 are machine.
The claims 1-20 recite, “receiving, … a request to provide an engagement score corresponding to … ; obtaining MPC data corresponding to the MPC while … is active, the MPC data including general MPC data and participant-specific MPC data captured during…, wherein the participant- specific MPC data comprises at least one of inbound/outbound audio timing, inbound/outbound video timing, or communications quality captured …; determining the engagement score by applying a … model to the general MPC data and the participant-specific MPC data, wherein the trained …model ; and transmitting, … the engagement score to be displayed to the user… ” is mathematical concept. The limitations determining the engagement score by applying a … model to the general MPC data and the participant-specific MPC data, wherein the trained …model are certain methods of organizing human activity –managing personal behavior. The claims are recite the abstract idea of providing an engagement score corresponding to a multi-party communication (MPC), which is a mathematical concept and certain methods of organizing human activity, therefore, the claims are directed to a judicial exception at Step 2A at prong one.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea include, “from an application on a user equipment (UE) of a user”, “a multi-party communication (MPC)”, “an ongoing electronic communication session”, “engagement score model comprises a regression-based machine learning model”, in claim 1; “A non-transitory computer-readable storage medium for storing instructions executable by a processor, the instructions comprising”, “ from an application on a user equipment (UE) of a user”, “an ongoing electronic communication session,” “from the electronic communication session, ” “the trained engagement score model comprises a regression-based machine learning model, ” “to the UE,” “to the user through the application”, in claim 8; “ A device comprising a processor configured to”, “from an application on a user equipment (UE) of a user,” “a multi-party communication (MPC),” “an ongoing electronic communication session,” “the trained engagement score model comprises a regression-based machine learning model” “the user through the application,” in claim 15; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea. – See MPEP 2106.05 (f).
The dependent claims do not recite additional elements that are not recited in the independent claims.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements when considered both individually and as an ordered combination do not amount to significantly more. (See MPEP 2106.05 f – mere instructions to Apply an Exception).
At step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 2-7 further narrow the abstract idea of independent claim 1. Dependent claims 9-14 further narrow the abstract idea of independent claim 8. Dependent claims 16-20 further narrow the abstract idea of independent claim 15. The claims 1-20 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-7, 9-14, & 16-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Examiner points the Applicant to instant specification [053]-[055].
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-4, 6-11, 13-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anand (US 2012/0,078,906 A1) in view of Karthikeyan (US 2018/0,191,606 A1).
Regarding Claim 8, [claim 1 and claim 15], (Currently Amended)
A non-transitory computer-readable storage medium for storing instructions executable by a processor, the instructions comprising: receiving, from an application on a user equipment (UE) of a user, a request to provide an engagement score corresponding to a multi-party communication (MPC); obtaining MPC data corresponding to the MPC while the MPC is active, the MPC data including general MPC data and participant-specific MPC data captured during an ongoing electronic communication session, …
Anand discloses process 200G gathers documents (e.g., e-mails and meetings) that have been sent to the user. … edges are created between a user and another user if both appear in dam document (operation 204), Anand [0092], [Figure 2G]
Anand discloses FIG 2K discloses search logs 268 are used to find queries 269 related to user queries and search strings 267. These are combined into an expertise query 270 using relatedness measurements., Anand [0098], [Figure 2K], [Figure 2A]
Anand teaches engagement index 305 which signifies the probability the user will respond to a request for contact., Anand [0106], [Figure 6C].
Anand teaches users' communications or interactions with other users, or any other information which signifies the user's expertise, interest, skills and behavior… Emails 62 represents electronic mail communication … one or more attachments, recipients list, sender information, etc. Chats 66 may include instant messages,…Call records 74 may contain information about the voice or video calls made from or to a user's device, including, but not limited to, Skype interactions. These records can also contain the actual data of the calls including recorded voice or video messages., Anand [037]
determining the engagement score by applying a trained engagement score model to the general MPC data and the participant-specific MPC data,… ;
Anand discloses building a knowledge base to find relevant persons through context -based search queries., Anand [0048], [Figure 2A] and Anand teaches note that engagement index score is not user-definable., Anand [0108], [Figure 6F] and Anand [0106], [Figure 6C],
Anand Figure 2A – 214 scoring component, Anand [Figure 2A], [089];
Anand discloses FIG 2K discloses a recommendation unit uses search logs 268 in conjunctions with user interface queries and search context 267 from users. … these are combined into an expertise query 270 using relatedness measurements from the distances 223, such as structural distances produced by the graph processing unit 225. The resulting expanded query 271 is then input into the recommendation service 272. Recommendation service 272 also receives as inputs determinations of user …, Anand [098]
and transmitting, to the UE, the engagement score to be displayed to the user through the application.
Anand discloses filtered text-based profile search is scored for its expertise and FIG 2K discloses output (ranked recommendation -290 based on aggregate score -280) to the user interface., Anand [Figure 2K], [099].
Although highly suggested, Anand does not explicitly teach:
wherein the participant- specific MPC data comprises at least one of inbound/outbound audio timing, inbound/outbound video timing, or communications quality captured from the electronic communication session… wherein the trained engagement score model comprises a regression-based machine learning model;
Karthikeyan teaches:
wherein the participant- specific MPC data comprises at least one of inbound/outbound audio timing, inbound/outbound video timing, or communications quality captured from the electronic communication session… wherein the trained engagement score model comprises a regression-based machine learning model;
Karthikeyan [050] collects quality of service data of a communication session. Karthikeyan [008], [050].
Karthikeyan [031] discloses the bandwidth available to a model on a particular route and the confidence vector of a model will be regularly updated based on traffic monitoring and predictive values (engagement score) can be computed based on historic data and a predictive model, for example, linear regression or a neural network.
Anand teaches a sharing system organized by context derived from competencies and interactions with others. Karthikeyan teaches methods of operating communications networks and in particular to the operation of networks whilst ensuring that quality of service provision is maintained. It would have been obvious to combine before the effective filing date, build and index profiles generated by context that is ranked to yield highly relevant results, as taught by Anand, with communication quality of service using learning algorithms, as taught by Karthikeyan, to improve the quality of service of communications session., Karthikeyan [010].
Within claim 8, Karthikeyan discloses a predictive model, for example, linear regression, and thus, Karthikeyan discloses supervised learning and unsupervised learning. Claim 8 a "Markush" claim recites model comprises a regression-based machine learning model. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
Regarding Claim 9, [claim 2 and claim 16], (Original)
The non-transitory computer-readable storage medium of claim 8, wherein the request further comprises a request to provide a visual representation of MPC keywords and wherein the instructions further comprise: obtaining a text component of the MPC data;
Anand discloses FIG. 6C includes a system-identified name, title, and contact information 302, engagement index 305, project titles 310, project keywords 315, Anand, [Figure 6C], [0106] and [Figure 2A]
parsing the text component to extract a first set of keywords, each keyword having a corresponding frequency;
Anand teaches projects are then broken down by contextual keywords 706, … The relevant keywords 715, and documents 720 are displayed …, and the engagement index 750 indicating the probability of a response. Profile B 714 is an example of an individual who is identified by the system as a relevant resource., Anand [0109], [Figure 6C], [Figure 2-203]- linguistic parsing.
Anand discloses keyword refers to a word or phrase relating to an atomic and relevant concept., Anand [030] … . Each keyword is allocated a weighting to rank profiles that match a particular context the user… The keyword can be used to create a context and also to match a user's profile against a specific context during a search. The process of generating user profiles uses a set of keywords that assists in indexing … A profile rank is specific to a particular context, and can be dynamically calculated if the context changes , Anand [031] and parsing Anand [043]
removing any non-verb, non-noun keywords from the first set of keywords; applying a keyword model to the first set of keywords to generate a second set of keywords;
Anand discloses operation 386 discloses concept mining and analytic service 56 of client application 76 … this operation considers various attributes and removes unwanted or common profile keywords from the profile., Anand [0100] and Anand Figure 3 discloses keywords and list 386, filtering and noise removal 390 and Figure 4 discloses profiles – which match key words (second keywords) are matched against search contact 402 and rank profiles are displayed 410., and Anand [Figure 2-203], [044], [039]- linguistic parsing.
ranking the second set of keywords based on frequency; selecting predetermined number of top keywords from the second set of keywords to generate the MPC keywords;
Anand [031] and [Figure 2A - 214]– see above.
Anand discloses the search results are returned by the profile service 48 to the client application 76 (operation 408) where they are displayed to the user in ranked order., Anand [0101], [Figure 4]
generating a visual representation including the MPC keywords; and transmitting, to the UE, the visual representation.
Anand [Figure 4 -410] and Figure 2A.
Regarding Claim 10, [claim 3 and claim 17],
The non-transitory computer-readable storage medium of claim 9, the instructions further comprising depicting, in the visual representation, each MPC keyword in a size corresponding to the MPC frequency and relative to the other MPC keywords.
Anand Figure 2A – 205, Anand teaches phrase pattern scoring, [086]-[091]
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Regarding Claim 11, [claim 4 and claim 18], (Original)
The non-transitory computer-readable storage medium of claim 8, wherein the trained engagement score model is a multi-class, regression-based….
Anand teaching statistical filtering and scoring of concepts., Anand [0053]-[080], [087]
Although highly suggested, Anand does not explicitly teach:
…. machine learning (ML) model
Karthikeyan teaches:
… regression-based machine learning (ML) model
Karthikeyan [031] a predictive model, for example, linear regression or a neural network.
Anand teaches a sharing system organized by context derived from competencies and interactions with others. Karthikeyan teaches methods of operating communications networks and in particular to the operation of networks whilst ensuring that quality of service provision is maintained. It would have been obvious to combine before the effective filing date, build and index profiles generated by context that is ranked to yield highly relevant results, as taught by Anand, with communication quality of service using learning algorithms, as taught by Karthikeyan, to improve the quality of service of communications session., Karthikeyan [010].
Regarding Claim 13, [claim 6] (Original)
The non-transitory computer-readable storage medium of claim 8, wherein the request further comprises a request to provide MPC metrics selected from the
Anand [036]- [037], [0041], [Figure 2A] – tracked keywords from documents (meetings, agenda, notes).
group comprising a number of attendees, a number of moderators, and a length of the MPC.
Anand [037] – see below, e-mails, skype interactions, call records 74, and user meeting s, agendas, attendees. (It would be obvious the meeting records include the length, number of attendees and moderators.)
Regarding Claim 14, [claim 7 and claim 20] (Currently Amended)
The non-transitory computer-readable storage medium of claim 8, wherein the general MPC data comprises a number of participants, dates and times associated with the MPC an overall length, and a ratio of invited participants versus actual participants.
Anand teaches users' communications or interactions with other users, or any other information which signifies the user's expertise, interest, skills and behavior… Emails 62 represents electronic mail communication … one or more attachments, recipients list, sender information, etc. Chats 66 may include instant messages, …Call records 74 may contain information about the voice or video calls made from or to a user's device, including, but not limited to, Skype interactions. These records can also contain the actual data of the calls including recorded voice or video messages., Anand [037]
Karthikeyan further teaches:
general MPC data comprises a number of participants, dates and times associated with the MPC an overall length, and a ratio of invited participants versus actual participants.
Karthikeyan [034] teaches the network gateway records all global statistics for each model in M, i.e. statistics across the whole network. The confidence vector and the number of flows (cluster support) indicate how reliable and how well supported by traffic a model.; Karthikeyan [035] teaches the number of traffic flows that were assigned to the model and their accumulated bandwidth.;
Karthikeyan [072] teaches an interface that negotiates on behalf of a number of clients or a sub-network represented with a subnet mask. Karthikeyan [074] teaches load-balancing. Karthikeyan [077] teaches a number of performance metrics associated.
Karthikeyan [032] teaches the training data table T contains entries representing the QoS of all end-to-end data transmissions within a given time period
(Karthikeyan teaches bandwidth, number of traffic flows, load balancing, number of routes.); (The labels general MPC data comprises a number of participants, dates and times associated with the MPC an overall length, and a ratio of invited participants versus actual participants., are merely names and labels indicating a particular function and do not alter the structure or function of the claimed invention, accordingly these limitations (dates and times associated with the MPC an overall length, and a ratio of invited participants versus actual participants …inbound/outbound audio timing, inbound/outbound video timing, inbound/outbound content timing, total inbound/outbound timing, total duration of participation, percentage of participation from overall MPC length do not patentability distinguish the claims from the cited prior art.)
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anand (US 2012/0,078,906 A1) in view of Karthikeyan (US 2018/0,191,606 A1) and in further view of Zuberi (US 2022/0,327,430 A1).
Regarding Claim 12, [claim 5 and claim 19], (Original)
The non-transitory computer-readable storage medium of claim 11, the instructions further comprising training the engagement score model by: obtaining annotated MPC data for a plurality of MPCs;
Anand teaches note that engagement index score is not user-definable., Anand [0108], [Figure 6F] and Anand [0106], [Figure 6C],
Anand Figure 2A – 214 scoring component, Anand [Figure 2A], [089];
selecting a training dataset and a testing dataset from the clustered MPCs through stratified sampling; training the engagement score model using the training dataset; clustering the MPCs based on the annotated MPC data, each cluster corresponding to an MPC type;
Anand discloses clustering – [Fig 2A -222], [046]-[048].
evaluating the engagement score model using… and generating the trained engagement score model based on whether the engagement score model evaluation is acceptable.
Anand discloses building a knowledge base to find relevant persons through context -based search queries., Anand [0048], [Figure 2A]
(In claim 8, Examiner relies on Karthikeyan [031] a predictive model, for example, linear regression or a neural network).
Although highly suggested, Anand does not explicitly teach:
… the testing dataset based on target performance metrics selected from the group comprising: accuracy, precision, and recall;
Zuberi teaches:
… the testing dataset based on target performance metrics selected from the group comprising: accuracy, precision, and recall;
Zuberi discloses computing system 130 may perform any of the exemplary processes described herein to compute a value of one or more metrics that characterize a predictive capability, and an accuracy, of the adaptively trained machine-learning, … the computed metrics may include, but are not limited to, one or more recall-based values (e.g., “recall@5,” “recall@10,” “recall@20,” etc.), one or more precision-based values for the adaptively trained, gradient-boosted, decision-tree process, and additionally, or alternatively, a computed value of an area under curve (AUC) for a precision-recall (PR) curve, a computed value of an AUC for a receiver operating characteristic (ROC) curve associated with the adaptively trained…, Zuberi [0123]
Anand teaches a sharing system organized by context derived from competencies and interactions with others. Zuberi teaches systems and methods that facilitate a prediction of future occurrences. It would have been obvious to combine before the effective filing date, build and index profiles generated by context that is ranked to yield highly relevant results, as taught by Anand, with engagement data 116A may include one or more data records that identify and characterize an engagement of one or more customers with the financial institution using machine learning, as taught by Zuberi, to identify one or more customers that represent “likely” candidates for involvement in future redemption events., Zuberi [017]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cerilles (WO 2007/104,075 A1) teaches keyword analysis.
Afram (WO 2022033804 A1) – machine learning.
Asenario (2020, MeetCues: Supporting online meetings experience.)
Carl (US 2021/0,377960 A1) – regression modeling and quality of service.
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/THEA LABOGIN/Examiner, Art Unit 3624
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