CTNF 18/945,097 CTNF 94100 DETAILED ACTION 12-151 AIA 26-51 12-51 Status of Claims The following is a Non-Final Office Action in response to applicant’s amendments and arguments received on 03/17/2026. Claims 6, 10, and 16 are cancelled. Claims 21-23 are newly added. Claims 1-3, 5, 7, 12-15, 17-20 are amended. Claims 1-5, 7-9, 11-15, and 17-23 are considered in this Office Action. Claims 1-5, 7-9, 11-15, and 17-23 are currently pending. Response to Arguments Response to §112(b) arguments: the examiner notes in light of applicant’s amendments, the 35 U.S.C. §112(b) rejection of claim 7 is withdrawn. Response to §101 arguments: Applicant's arguments with respect to the §101 rejection of claims has been considered, but are not persuasive. Applicant argues that independent claims 1, 12 and 17 have been amended to clarify that performing one or more automated actions based at least in part on the at least one generated system recommendation comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation. with respect to automatically training at least a portion of the one or more machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation, "these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field." Applicant further relies on Ex parte Desjardins as support. The examiner respectfully disagrees. The examiner notes that the Desjardins directed a specific continual-learning training techniques, the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. The enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Unlike Desjardins, the recitation of “automatically training” machine learning-based filtering technique based on feedback is functional and result-oriented. The claims do not recite a particular technical mechanism that improved the operation of the machine learning itself. the “using one or more machine learning-based filtering techniques” merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). The examiner further points to applicant’s specification “The processing device 1102-1 in the processing platform 1100 comprises a processor 1110 coupled to a memory 1112. The processor 1110 comprises a microprocessor, an ASIC, an SOC, an FPGA, a CPU, a GPU, an NPU, a DPU, a TPU, an ALU, a DSP, and/or other similar processing device components, as well as other types and arrangements of processing circuitry, in any combination. At least a portion of the functionality of at least one machine learning system and its associated machine learning algorithms provided by one or more processing devices as disclosed herein can be implemented using such circuitry.” Accordingly, Applicant's arguments with respect to the §101 rejection is not persuasive. An updated §101 rejection will address applicant’s amendments. Response to §103 arguments : Applicant's arguments with respect to the §103 rejection of claims has been considered, but are persuasive. An updated the 35 U.S.C. §103 rejection will address applicant’s amendments and arguments. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-5, 7-9, 11-15, and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. Claims 1-5, 7-9, 11-15, and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “ Patent Subject Matter Eligibility Guidance ” (as explained in MPEP 2106 ). With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106) , it is first noted that the method ( claims 1-5, 7-9, and 11), the non-transitory processor-readable storage medium (claims 12-15), and the apparatus (claims 17-23) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied. With respect to Step 2, and in particular Step 2A Prong One , it is next noted that the claims recite an abstract idea by reciting concepts of recommendation system pertaining to at least one resource-related task request for at least one given user which can be categorized as “Mental process” and “Certain Methods of Organizing Human Activity” (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). The abstract idea can be categorized as “mental process” because it is directed concept performed in the human mind (including an observation, evaluation, judgment, opinion) or by the aid of a pen and/or paper. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are: A computer-implemented method comprising: processing, into one or more data structures comprising resource-related task allocation data, information pertaining to at least one resource-related task request for at least one given user; generating filtered data by processing at least a portion of the one or more data structures using one or more machine learning-based filtering techniques, wherein generating filtered data comprises processing at least a portion of the one or more data structures using a combination of one or more collaborative filtering techniques and one or more content-based filtering techniques ; generating at least one system recommendation for allocating the at least one resource-related task request by processing at least a portion of the filtered data; and performing one or more automated actions based at least in part on the at least one generated system recommendation, wherein performing one or more automated actions comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation (recited at high level of generality amounts to extra-solution activity and “apply it”) ; wherein the method is performed by at least one processing device comprising a processor coupled to a memory . Claims 12 and 17 recite substantially the same limitations as claim 1, and therefore are directed to the same rational. With respect to Step 2A Prong Two , the judicial exception is not integrated into a practical application . The additional elements are directed to using one or more machine learning-based filtering techniques(recited at high level of generality amounts to “apply it”), generating filtered data comprises processing at least a portion of the one or more data structures using a combination of one or more collaborative filtering techniques and one or more content-based filtering techniques(recited at high level of generality); performing one or more automated actions based at least in part on the at least one generated system recommendation wherein performing one or more automated actions comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation (recited at high level of generality amounts to extra-solution activity) , wherein the method is performed by at least one processing device comprising a processor coupled to a memory, non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device, an apparatus comprising: at least one processing device comprising a processor coupled to a memory, the at least one processing device to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification disclose “at least portions of the information processing system 100 can be implemented using one or more processing platforms. A given processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines” describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. Further, the “using one or more machine learning-based filtering techniques” merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). The examiner further points to applicant’s specification “The processing device 1102-1 in the processing platform 1100 comprises a processor 1110 coupled to a memory 1112. The processor 1110 comprises a microprocessor, an ASIC, an SOC, an FPGA, a CPU, a GPU, an NPU, a DPU, a TPU, an ALU, a DSP, and/or other similar processing device components, as well as other types and arrangements of processing circuitry, in any combination. At least a portion of the functionality of at least one machine learning system and its associated machine learning algorithms provided by one or more processing devices as disclosed herein can be implemented using such circuitry.” Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. With respect to Step 2B of the eligibility inquiry , it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed using one or more machine learning-based filtering techniques(recited at high level of generality amounts to “apply it”), generating filtered data comprises processing at least a portion of the one or more data structures using a combination of one or more collaborative filtering techniques and one or more content-based filtering techniques(recited at high level of generality); performing one or more automated actions based at least in part on the at least one generated system recommendation wherein performing one or more automated actions comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation (recited at high level of generality amounts to extra-solution activity) , wherein the method is performed by at least one processing device comprising a processor coupled to a memory, non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device, an apparatus comprising: at least one processing device comprising a processor coupled to a memory, the at least one processing device to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification disclose “at least portions of the information processing system 100 can be implemented using one or more processing platforms. A given processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines” describe high-level general-purpose computer) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo . The examiner further notes “the one or more machine learning-based filtering techniques” are disclosed in the specification in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). Further, the “using one or more machine learning-based filtering techniques” merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). The examiner further points to applicant’s specification “The processing device 1102-1 in the processing platform 1100 comprises a processor 1110 coupled to a memory 1112. The processor 1110 comprises a microprocessor, an ASIC, an SOC, an FPGA, a CPU, a GPU, an NPU, a DPU, a TPU, an ALU, a DSP, and/or other similar processing device components, as well as other types and arrangements of processing circuitry, in any combination. At least a portion of the functionality of at least one machine learning system and its associated machine learning algorithms provided by one or more processing devices as disclosed herein can be implemented using such circuitry.” In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. In regard to dependent claims (2-5, 8-9, 13-15, and 17-23), the examiner notes the claims can be further categorized as “mathematical concept” because it is directed toward generating filtered data comprises predicting one or more preferences of at least one given user based at least in part on one or more preferences of one or more other users requesting resource-related tasks by processing the at least a portion of the one or more data structures using one or more collaborative filtering techniques, using matrix factorization, etc... The dependent claims recite the following additional elements: performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation(recited at high level of generality amounts to extra-solution activity), performing one or more automated actions comprises automatically initiating, using the system, completion of the at least one resource-related task request(recited at high level of generality amounts to extra-solution activity), and performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation (recited at high level of generality amounts to extra-solution activity), performing one or more automated actions comprises automatically initiating, using the system, completion of the at least one resource-related task request(recited at high level of generality amounts to extra-solution activity). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification disclose “at least portions of the information processing system 100 can be implemented using one or more processing platforms. A given processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines” describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification disclose “at least portions of the information processing system 100 can be implemented using one or more processing platforms. A given processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines” describe high-level general-purpose computer) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo . The examiner further notes “the one or more machine learning-based filtering techniques” are disclosed in the specification in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of certain method of organizing human activity, a mental process, and mathematical concept, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-3, 5, 7, 11, 12-15, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Ramaiah Radhakrishnan (US 20240212054 A1, hereinafter “Radhakrishnan”) in view of Zhichen Xu (US 8,260,117 B1, hereinafter “Xu”) in view of Yoky Matsuoka (US 2024/0273604 A1, hereinafter “Matsuoka”) . Claim 1/12/17 Radhakrishnan teaches: A computer-implemented ([0004] and [0038] A computer program product may include a non-transitory computer-readable storage medium storing applications, programs, program modules. Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non-volatile media). [0039] a non-volatile computer-readable storage medium may include memory. [0051] the processing element 205 may be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, coprocessing entity representations, application-specific instruction-set processors (ASIPs), microcontrollers, and/or controllers. [0052] the processing element 205 may be configured for a particular use or configured to execute instructions stored in volatile or non-volatile media or otherwise accessible to the processing element 205. As such, whether configured by hardware or computer program products, or by a combination thereof, the processing element 205 may be capable of performing steps or operations) method comprising: processing, into one or more data structures comprising resource-related task allocation data, information pertaining to at least one resource-related task request for at least one given user ([0082] and [0107] computing entity 106 receives a recommendation request. The recommendation may originate from various entities, such as any of client computing entities 102 a -N. The health insurance plan recommendation context, a recommendation request may be a request to recommend candidate health insurance plans originating from a client computing entity 102 a ) ; generating filtered data by processing at least a portion of the one or more data structures using one or more machine learning-based filtering techniques ([0083] at step/operation 502 of FIG. 5A, the collaborative filtering machine learning model identifies/determines entity representation feature values for selected entity representation features. [0084] at step/operation 503 of FIG. 5A, the collaborative filtering machine learning model generates an input entity representation for the input entity based on the combination of entity representation feature values. The predictive data analysis computing entity 106 may use the input entity representation to identify similar entity representations) ; generating at least one system recommendation for allocating the at least one resource-related task request by processing at least a portion of the filtered data ([0085] at step/operation 504 of FIG. 5A, the collaborative filtering machine learning model generates a predicted recommendation for the input entity representation) ; and performing one or more automated actions based at least in part on the at least one generated system recommendation ([0005] initiate the performance of a prediction-based action based at least in part on the predicted recommendation. [0091] At step/operation 505 of FIG. 5A, the predictive data analysis computing entity 106 initiates the performance of one or more prediction-based actions based on the predicted recommendation. [0092] Other examples of prediction-based actions may include automated recommendation notifications, automated appointment scheduling, automated implementation of precautionary actions, automated hospital preparation actions, automated workforce management operational management actions, automated server load balancing actions, automated call center preparation actions, automated insurance plan pricing actions, automated insurance plan update actions, and/or the like.) , wherein the method is performed by at least one processing device comprising a processor coupled to a memory ([0005] a computing apparatus comprising a processor and memory including computer program code is provided) . While Radhakrishnan teaches [0046] the predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model, where the collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. The user matrix may comprise values for a set of latent features for each user. The item matrix may comprise values for a set of latent features for each item. Collaborative filtering machine learning models can learn these latent features based on patterns in the initialization dataset. Further, collaborative filtering machine learning models can generate values for these latent features, such that they match/correspond as closely as possible to the existing ratings in the user-item matrix (e.g., initial entity-candidate matrix) as disclosed in [0027]. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Xu teaches: wherein generating filtered data comprises processing at least a portion of the one or more data structures using a combination of one or more collaborative filtering techniques and one or more content-based filtering techniques; (Col. 2 lines 53-60 a trained machine learning engine is fed both collaborative filtering (CF) parameter values and content-based filtering (CBF) parameter values. Based on the input parameter values, the machine learning engine generates a machine-learning score (an "ML score") that reflects the extent to which recommending the particular video/content to the user will satisfy a particular goal) ; It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan with Xu to include using a combination of one or more collaborative filtering techniques and one or more content-based filtering techniques as part of the machine learning model used in Radhakrishnan, because the judgments generated CF/CBF analyzers are exposed, together with the raw features from the CF/CBF, directly to the machine learning engine 102. Giving the machine-learning engine 102 access to the raw features may provide several benefits which will result in more accurate recommendation model. (column 5 lines 24-31). While Radhakrishnan teaches [0046] the predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model, where the collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. The user matrix may comprise values for a set of latent features for each user. The item matrix may comprise values for a set of latent features for each item. Collaborative filtering machine learning models can learn these latent features based on patterns in the initialization dataset. Further, collaborative filtering machine learning models can generate values for these latent features, such that they match/correspond as closely as possible to the existing ratings in the user-item matrix (e.g., initial entity-candidate matrix) as disclosed in [0027]. [0005] initiate the performance of a prediction-based action based at least in part on the predicted recommendation. [0091] At step/operation 505 of FIG. 5A, the predictive data analysis computing entity 106 initiates the performance of one or more prediction-based actions based on the predicted recommendation. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Matsuoka teaches: wherein performing one or more automated actions comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation ([0050] the task facilitation service may facilitate implementation of the task automatically.[0051] The information may be used to refine subsequent task recommendations (e.g., reinforcement learning of the machine-learning model, the representative and/or the like), third-party service selections for future tasks, machine-learning algorithms and/or models, and/or the like. [0277] Task recommendation system 112 may include or have access to one or more models/algorithms for generating tasks or recommended tasks, indicated as task recommendation models/algorithms 908 in FIG. 9. task recommendation system 112 and task recommendation models/algorithms 908 may be updated based on feedback received from member 118 and/or representative 106. For example, task recommendation system 112 and task recommendation models/algorithms 908 may be updated and refined based on whether member 118 or representative 106 approve a recommended task generated by task recommendation system 112) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan and Xu with Matsuoka to include performing one or more automated actions comprises automatically training at least a portion of the machine learning-based filtering techniques based at least in part on feedback related to the at least one generated system recommendation as part of the implementation phase of the recommendation system used in Radhakrishnan, because doing so would update and refined the recommendation models/algorithms which will result in an accurate recommendation. ([0277)]. Claim 2/13/18 Radhakrishnan further teaches: The computer-implemented method of claim 1, wherein generating filtered data comprises predicting one or more preferences of at least one given user based at least in part on one or more preferences of one or more other users requesting resource-related tasks by processing the at least a portion of the one or more data structures using one or more collaborative filtering techniques ([0017]-[0018] a collaborative filtering machine learning model generates model-predicted ratings from initial ratings when data is sparse. With the model-predicted data, the collaborative filtering machine learning model may be further trained for more accurate predictions. Additionally, each entity (e.g., input entity or reference entity) can be represented as a combination of relevant feature values to identify similar entities. When a reference entity is determined to be similar, the model-predicted ratings and initial ratings can be used to provide a predicted recommendation for the input entity. [0046] The predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model. The predictive data analysis computing entity 106 may further include an inference engine 121 configured to generate predicted recommendations using the collaborative filtering machine learning model. The inference engine 121 of the predictive data analysis computing entity 106 may be further configured to perform prediction-based actions based on the generated predicted recommendations) . Claim 3/14/19 Radhakrishnan further teaches: The computer-implemented method of claim 2, wherein processing the at least a portion of the one or more data structures using one or more collaborative filtering techniques comprises using matrix factorization to decompose at least one interaction matrix from the one or more data structures and identify one or more latent factors, associated with the at least one interaction matrix, that represent one or more relationships between the one or more other users requesting resource-related tasks and one or more systems available for task allocation ([0077] the training engine 122 trains the collaborative filtering machine learning model based on the initialization dataset. For example, the training engine 122 may generate or use a reference entity representation embedding for each reference entity representation (the one or more other users), a candidate embedding for each candidate, and/or combinations thereof from the initial entity-candidate matrix data object. Further, the candidate embeddings may be generated based on predicted values for one or more latent features for the candidates. For example, generating the reference entity representation embeddings and the candidate embeddings may include learning/identifying the one or more latent features for the reference entity representations and the one or more latent features for the candidates based on the initializing dataset (e.g., based on patterns in the dataset). [0079] For example, the collaborative filtering machine learning model may generate a model-predicted dataset comprising a model-predicted entity-candidate matrix with a plurality of rating data fields (comprising initial ratings and model-predicted ratings)) . Claim 5/15/20 While Radhakrishnan teaches in [0067] generating a collaborative filtering machine learning model configured to generate predicted recommendations for input entities. A collaborative filtering machine learning model may refer to a data construct that describes parameters, hyperparameters, and/or defined operations of a machine learning model. The collaborative filtering machine learning model may be configured to generate predicted recommendations for input entities based on rating associated with reference entity representations from a qualifying reference entity representation subset. Radhakrishnan and Matsuoka do not explicitly teach the following, however analogous reference, in the field of system recommendation, Xu teaches: using one or more content-based filtering techniques (column 4 line 29 describes using content-based filtering technique). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan with Xu to include using one or more content-based filtering techniques as part the predictive recommendation system of Radhakrishnan, because in doing so it will help giving the machine-learning engine 102 access to the raw features may provide several benefits which will result in more accurate recommendation model. (column 5 lines 24-31). Radhakrishnan teaches in [0067] generating a collaborative filtering machine learning model configured to generate predicted recommendations for input entities. A collaborative filtering machine learning model may refer to a data construct that describes parameters, hyperparameters, and/or defined operations of a machine learning model. The collaborative filtering machine learning model may be configured to generate predicted recommendations for input entities based on rating associated with reference entity representations from a qualifying reference entity representation subset. Radhakrishnan does not explicitly teach the following, however analogous reference, Matsuoka teaches: The computer-implemented method of claim 1, wherein generating filtered data comprises determining one or more task-specific attributes for the at least one resource-related task request by processing the at least a portion of the one or more data structures using ... filtering techniques ([0229] The feature vector may be generated by selecting features that are predictive of a particular task, task type, class of tasks, class of task types, and/or the like. [0192] If the feature vector includes an identification of a task type that corresponds to a predefined task type of the creation sub-system 302, then the feature vector may be passed to task templates 424. The task templates 424 may include a database of task templates. More specific task templates may include many fields (e.g., particular objects, vendors, timestamps, locations, third-party services, activities, etc.) that may be inserted into the task template. [0115] In some examples, a member can submit a request to the representative 106 to generate a project for which one or more tasks may be determined by the representative 106 and/or by the task recommendation system 112. task facilitation service 102 may include task recommendation system 112, which analyzes data available to task facilitation service 102 and provides recommendations regarding potential tasks for member 118 based on available task data (e.g., as stored in task datastore 110), user data (e.g., as stored in user datastore 108), a model of member 118 maintained by task facilitation service 102, and similar data and models. [0249] While a substantial proportion of data collected and used by task facilitation service 102 to recommend and generate tasks may originate from interactions between task facilitation service 102 and member 118, such data may also originate from various other sources and may be obtained by task facilitation service 102 using various mechanisms) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan and Xu with Matsuoka to include using generating filtered data comprises determining one or more task-specific attributes for the at least one resource-related task request by processing the at least a portion of the one or more data structures using ... filtering techniques as part the predictive recommendation system of Radhakrishnan, because in doing so it will accurately identifying appropriate and/or suitable (e.g., the best or most suitable) resources for one or more given tasks ([0319]). Claim 7 Radhakrishnan teaches: The computer-implemented method of claim 1, wherein generating at least one system recommendation comprises identifying one or more resource-related partner systems to which the at least one resource-related task request for the at least one given user should be allocated ([0027] the collaborative filtering machine learning model may be configured to generate predicted recommendations based on input entity. Generally, a collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. [0028] The term “candidate” may refer to an item, resource, and/or the like that may be recommended to an input entity. For example, a collaborative filtering machine learning model may be configured to recommend one or more candidates to an input entity based on the input entity representation for the input entity. In some embodiments, a candidate may include an identifier associated with the item, resource, and/or the like that may be recommended to input entities. For example, a candidate may include an item identifier associated with a particular item that may be recommended to an input entity. As another example, a candidate may include a resource identifier associated with a particular resource that may be recommended to an input entity. [0034] The terms “predicted recommendation” or “predicted recommendation data object” may refer to a prediction of one or more candidates. For example, a predicted recommendation may represent, describe, and/or comprise one or more candidates-such as computing resource identifiers, health plan identifiers, and/or the like.) . Claim 11 Radhakrishnan further teaches: The computer-implemented method of claim 1, wherein processing information pertaining to at least one resource-related task request for at least one given user comprises processing, into the one or more data structures, information related to one or more of user type associated with the at least one given user, resource type associated with the at least one resource-related task request, system type associated with one or more systems relevant to performance of the at least one resource-related task request, geographic information associated with the at least one given user, geographic information associated with the one or more systems, and historical performance data attributed to the one or more systems in connection with resource-related tasks ([0082] The recommendation may originate from various entities, such as any of client computing entities 102 a -N. In the health insurance plan recommendation context, a recommendation request may be a request to recommend candidate health insurance plans (resource type associated with the at least one resource-related task request) originating from a client computing entity 102 a. [0068] the process 400 begins at step/operation 401 when the predictive data analysis computing entity 106 receives or retrieves historical data associated with a plurality of reference entities. For example, in the health insurance plan recommendation context, the predictive data analysis computing entity 106 receives or retrieves data associated with small business Affordable Care Act (ACA) customers from one or more data sources, such as United States Pharmacopeia (USP), PRIME, UHOP/SAFES platforms that manage small business ACA customers. he combination that provides the optimal results may also uniquely identify each reference entity. In the health insurance plan recommendation context, the optimal reference entity features may include a combination of at least two of an industry code (e.g., sic code), a rating area, a first geographic identifier (e.g., state code), a first geographic identifier (e.g., county code), or employer size grouping. The employer size grouping may be defined by the number of employees associated with the employer entity representation (e.g., 5< employees, 5-10 employees, 10-20 employees, 20-30 employees, 30-50 employees, ≥50 employees, and/or the like). As will be recognized, depending on the context, any features and number of features may be used to adapt to various needs and circumstances. [0085] at step/operation 504 of FIG. 5A, the collaborative filtering machine learning model generates a predicted recommendation for the input entity representation. To do so, the collaborative filtering machine learning model determines, using the collaborative filtering machine learning model, the reference entity representations that are similar (e.g., or most similar) to the input entity representation) . Claim 21 While Radhakrishnan teaches [0005] initiate the performance of a prediction-based action based at least in part on the predicted recommendation. [0091] At step/operation 505 of FIG. 5A, the predictive data analysis computing entity 106 initiates the performance of one or more prediction-based actions based on the predicted recommendation. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Matsuoka teaches: The apparatus of claim 17, wherein performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation ([0041]For example, facilitating execution of a task may include executing a portion of the task (e.g., such as planning and/or acquisition activities) and transmitting instructions to one or more third-party service providers [0050] If more than one task recommendation is generated by the task facilitation service, then the representative of the task facilitation service (and/or the task facilitation service itself) may select a particular task recommendation from those generated by the task facilitation service. the task facilitation service may facilitate implementation of the task automatically. [0051] The information may be used to refine subsequent task recommendations (e.g., reinforcement learning of the machine-learning model, the representative and/or the like), third-party service selections for future tasks, machine-learning algorithms and/or models, and/or the like. [0277] Task recommendation system 112 may include or have access to one or more models/algorithms for generating tasks or recommended tasks, indicated as task recommendation models/algorithms 908 in FIG. 9. task recommendation system 112 and task recommendation models/algorithms 908 may be updated based on feedback received from member 118 and/or representative 106. For example, task recommendation system 112 and task recommendation models/algorithms 908 may be updated and refined based on whether member 118 or representative 106 approve a recommended task generated by task recommendation system 112) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan and Xu with Matsuoka to include performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation as part of the implementation phase of the recommendation system used in Radhakrishnan, because doing so would help improve the likelihood that the task will be completed in a timely and efficient manner. By doing so, implementations of this will minimize the duration of tasks and the resources necessary to track and manage active tasks. ([0251)]. Claim 22 While Radhakrishnan teaches [0005] initiate the performance of a prediction-based action based at least in part on the predicted recommendation. [0091] At step/operation 505 of FIG. 5A, the predictive data analysis computing entity 106 initiates the performance of one or more prediction-based actions based on the predicted recommendation. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Matsuoka teaches: The apparatus of claim 21, wherein performing one or more automated actions comprises automatically initiating, using the system, completion of the at least one resource-related task request ([0047] A machine-learning model may execute using the feature vector to generate a set of task recommendations that can be implemented by the task facilitation service [0050] If more than one task recommendation is generated by the task facilitation service, then the representative of the task facilitation service (and/or the task facilitation service itself) may select a particular task recommendation from those generated by the task facilitation service. the task facilitation service may facilitate implementation of the task automatically. [0101] the task coordination system 114 may utilize the selected proposal or parameters related to the task (e.g., if the member 118 has deferred to the representative for determination of how the task is to be performed), as well as historical task data from the task datastore 110 corresponding to similar tasks as input to the machine learning algorithm or artificial intelligence. The machine learning algorithm or artificial intelligence may produce, as output, a listing of one or more third-party services 116 that may perform the task with a high probability of satisfaction to the member 118. If the task is to be performed by the representative 106, the machine learning algorithm or artificial intelligence may produce, as output, a listing of resources (e.g., retailers, restaurants, brands, etc.) that may be used by the representative 106 for performance of the task with a high probability of satisfaction to the member 118. [0153] if the coordination with a third-party service 116 may be performed automatically (e.g., third-party service 116 provides automated system for ordering, scheduling, payments, etc.), the task coordination system 114 may interact directly with the third-party service 116 to coordinate performance of the task according to the selected proposal option) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan and Xu with Matsuoka to include performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation as part of the implementation phase of the recommendation system used in Radhakrishnan, because doing so would help improve the likelihood that the task will be completed in a timely and efficient manner. By doing so, implementations of this will minimize the duration of tasks and the resources necessary to track and manage active tasks. ([0251)]. Claim 23 While Radhakrishnan teaches [0046] the predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model, where the collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. The user matrix may comprise values for a set of latent features for each user. The item matrix may comprise values for a set of latent features for each item. Collaborative filtering machine learning models can learn these latent features based on patterns in the initialization dataset. Further, collaborative filtering machine learning models can generate values for these latent features, such that they match/correspond as closely as possible to the existing ratings in the user-item matrix (e.g., initial entity-candidate matrix) as disclosed in [0027]. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Matsuoka teaches: The apparatus of claim 17, wherein processing information pertaining to at least one resource-related task request for at least one given user comprises processing, into the one or more data structures, information related to one or more of user type associated with the at least one given user, resource type associated with the at least one resource-related task request, system type associated with one or more systems relevant to performance of the at least one resource-related task request, geographic information associated with the at least one given user, geographic information associated with the one or more systems, and historical performance data attributed to the one or more systems in connection with resource-related tasks ([0077] the representative may utilize a resource library maintained by the task coordination system 114 to identify one or more third-party services 116 and/or resources (e.g., retailers, restaurants, websites, brands, types of goods, particular goods, etc.) that may be used for performance the task for the benefit of the member 118 according to the one or more task parameters identified by the representative and the task recommendation system 112. A proposal may specify a timeframe for completion of the task, identification of any third-party services 116 (if any) that are to be engaged for completion of the task, a budget estimate for completion of the task, resources or types of resources to be used for completion of the task, and the like. [0158] The task creation sub-system 302 may maintain, in a task datastore 110, task templates for different task types or categories. Each task template may include different data fields for defining the task, whereby the different task fields may correspond to the task type or category for the task being defined. The member 118 may provide task information via these different task fields to define the task that may be submitted to the task creation sub-system 302 or representative 106 for processing. The task datastore 110, in some instances, may be associated with a resource library. This resource library may maintain the various task templates for the creation of new tasks. See [0077], [0084], [0085], [0141], [0144], [0145], and [0154]). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan and Xu with Matsuoka to include processing information pertaining to at least one resource-related task request for at least one given user comprises processing, into the one or more data structures, information related to one or more of user type associated with the at least one given user, resource type associated with the at least one resource-related task request, system type associated with one or more systems relevant to performance of the at least one resource-related task request, geographic information associated with the at least one given user, geographic information associated with the one or more systems, and historical performance data attributed to the one or more systems in connection with resource-related tasks as part of the recommendation system used in Radhakrishnan, because doing so would help improve the likelihood that the task will be completed in a timely and efficient manner . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan in view of Xu in view of Matsuoka, as applied in claims 1, 12, and 17, and further in view of Sheau Ng (US 20160371274 A1, hereinafter “Ng”) in view of Hyconwoo An (US 20230169330 A1, hereinafter “An”) . Claim 4 While Radhakrishnan teaches [0046] the predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model, where the collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. The user matrix may comprise values for a set of latent features for each user. The item matrix may comprise values for a set of latent features for each item. Collaborative filtering machine learning models can learn these latent features based on patterns in the initialization dataset. Further, collaborative filtering machine learning models can generate values for these latent features, such that they match/correspond as closely as possible to the existing ratings in the user-item matrix (e.g., initial entity-candidate matrix) as disclosed in [0027]. Radhakrishnan does not explicitly teach the following, however analogous reference, in the field of system recommendation, Ng teaches: The computer-implemented method of claim 3, wherein using matrix factorization to decompose at least one interaction matrix comprises using at least one […] singular value decomposition (SVD) technique to decompose the at least one interaction matrix into the one or more latent factors ([0046] The user-item interaction profiles 435 may correlate users with content items. [0050] generating item recommendations based on a decomposed (e.g., low-rank) similarity matrix 680. In particular, the transformed similarity matrix 520 may be decomposed (or otherwise reduced in size) to generate a decomposed similarity matrix 680, and the decomposed similarity matrix may be used to generate content recommendations. [0059] The dimensions of the similarity matrix 905 may be N×N, but it can be decomposed (or reduced in dimension) into multiple smaller matrices, such as matrix 910 (having dimensions of N×m) and matrix 915 (having dimensions of m×N). N may be the number of content items in the similarity matrix, and m may be the number of latent factors (the decomposed dimension). [0060] The size of the transformed similarity matrix may be reduced using singular value decomposition (SVD)) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan, Xu, and Matsuoka with Ng to include using matrix factorization to decompose at least one interaction matrix comprises using at least one truncated singular value decomposition (SVD) technique to decompose the at least one interaction matrix into the one or more latent factors as part of the filtering machine learning model, because in doing so it will provide significant advantages relative to conventional resource management techniques by effectively capture latent relationships which will allow accurate content recommendation([0061]). While Radhakrishnan teaches [0046] the predictive data analysis computing entity 106 may include a training engine 122 configured to generate trained machine learning models, such as a collaborative filtering machine learning model, where the collaborative filtering machine learning model uses relationship between users (e.g., entities) and items (e.g., candidates) to predict ratings for user-item pairs not associated with a rating. To do so, collaborative filtering machine learning models may perform approximate factorization on an initialization dataset (e.g., represented as a user-item matrix) to generate two matrices-a user matrix and an item matrix. The user matrix may comprise values for a set of latent features for each user. The item matrix may comprise values for a set of latent features for each item. Ng teaches [0046] The user-item interaction profiles 435 may correlate users with content items. [0050] generating item recommendations based on a decomposed (e.g., low-rank) similarity matrix 680. In particular, the transformed similarity matrix 520 may be decomposed (or otherwise reduced in size) to generate a decomposed similarity matrix 680, and the decomposed similarity matrix may be used to generate content recommendations. [0059] The dimensions of the similarity matrix 905 may be N×N, but it can be decomposed (or reduced in dimension) into multiple smaller matrices, such as matrix 910 (having dimensions of N×m) and matrix 915 (having dimensions of m×N). N may be the number of content items in the similarity matrix, and m may be the number of latent factors (the decomposed dimension). [0060] The size of the transformed similarity matrix may be reduced using singular value decomposition (SVD). Radhakrishnan and Ng do not explicitly teach the following, however analogous reference, in the field of system recommendation, An teaches: Using truncated singular value decomposition (SVD) technique ([0102] The matrix decomposition is performed through the Truncated-SVD (singular value decomposition) method, and the contents recommendation client 100 decomposes the weighted director matrix and the actor matrix) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan, Xu, Matsuoka, and Ng with An to include using truncated singular value decomposition (SVD) technique as part of the filtering machine learning technique, because in doing so it will provide significant advantages by filtering noise resulting in a cleaner low rank-approximation of a massive, sparse user-item matrix . 07-21-aia AIA Claim s 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Radhakrishnan in view of Xu in view of Matsuoka, as applied in claims 1, 12, and 17, and further in view of Rajesh Poornachandran (US 2023/0350722 A1, hereinafter “Poornachandran”) . Claim 8 While Radhakrishnan teaches in [0067] generating a collaborative filtering machine learning model configured to generate predicted recommendations for input entities. A collaborative filtering machine learning model may refer to a data construct that describes parameters, hyperparameters, and/or defined operations of a machine learning model. The collaborative filtering machine learning model may be configured to generate predicted recommendations for input entities based on rating associated with reference entity representations from a qualifying reference entity representation subset. Radhakrishnan does not explicitly teach the following, however analogous reference Poornachandran teaches: The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation ([0035] For instance, the processing circuitry 130 (e.g., a scheduler) may assign the resources 150, 160 to the execution of the task in a manner defined by the interdependency. For instance, the interdependency may be mapped to a schedule (e.g., a timeline of access, use and release of resources) and/or a configuration of the resources 150, 160 which may then be enforced by a resource control of the computing system 110. For scheduling execution of the task, the processing circuitry 130 may perform any scheduling technique, such as priority, multilevel, round-robin, pre-emptive and/or cooperative scheduling) . It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Radhakrishnan with Poornachandran to include performing one or more automated actions comprises automatically allocating the at least one resource-related task request to a system in accordance with the at least one system recommendation as part the predictive recommendation system of Radhakrishnan, because in doing so it will help in discovering all available resources and therefore improve the selection of the resources based on preferences of an entity and the recommendation engine may therefore increase the speed and improve the result of the decision making. ([00055] [0062]). Claim 9 Radhakrishnan teaches: The computer-implemented method of claim 8, wherein performing one or more automated actions comprises automatically initiating, using the system, completion of the at least one resource-related task request ([0091] At step/operation 505 of FIG. 5A, the predictive data analysis computing entity 106 initiates the performance of one or more prediction-based actions based on the predicted recommendation. [0092] Other examples of prediction-based actions may include automated recommendation notifications, automated appointment scheduling, automated implementation of precautionary actions, automated hospital preparation actions, automated workforce management operational management actions, automated server load balancing actions, automated call center preparation actions, automated insurance plan pricing actions, automated insurance plan update actions, and/or the like) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Vinay Avinash Dorle (US 20240028935 A1): Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model configured to generate a prediction and recommendation output from input data. The system obtains training data including a plurality of training examples, obtains context data, identifies one or more feature variables from the context data, constructs the machine-learning model based at least on the identified feature variables, generates feature variable training data by processing the training data based on the identified feature variables, and performs training and periodic update (if required) of the machine-learning model to generate model parameter data for the machine-learning model based at least on the generated feature variable training data. Alexandros Karatzoglou (US 20140180760 A1): Method for Context-Aware Collaborative Filtering comprising: a) performing collaborative filtering introducing a user-item-context interaction as a definition of the data and modelling them using tensor factorization (TF); b) generating one or more recommendations using said modelling; and c) displaying the recommendations to a user. said tensor used for tensor Factorization (TF) represent indirect indications of a user's preferences for an item, meaning implicit feedback data. Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 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, Brian Epstein can be reached at (571)-270-5389. 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. /REHAM K ABOUZAHRA/Examiner, Art Unit 3625 Application/Control Number: 18/945,097 Page 2 Art Unit: 3625 Application/Control Number: 18/945,097 Page 3 Art Unit: 3625 Application/Control Number: 18/945,097 Page 4 Art Unit: 3625 Application/Control Number: 18/945,097 Page 5 Art Unit: 3625 Application/Control Number: 18/945,097 Page 6 Art Unit: 3625 Application/Control Number: 18/945,097 Page 7 Art Unit: 3625 Application/Control Number: 18/945,097 Page 8 Art Unit: 3625 Application/Control Number: 18/945,097 Page 9 Art Unit: 3625 Application/Control Number: 18/945,097 Page 10 Art Unit: 3625 Application/Control Number: 18/945,097 Page 11 Art Unit: 3625 Application/Control Number: 18/945,097 Page 12 Art Unit: 3625 Application/Control Number: 18/945,097 Page 13 Art Unit: 3625 Application/Control Number: 18/945,097 Page 14 Art Unit: 3625 Application/Control Number: 18/945,097 Page 15 Art Unit: 3625 Application/Control Number: 18/945,097 Page 16 Art Unit: 3625 Application/Control Number: 18/945,097 Page 17 Art Unit: 3625 Application/Control Number: 18/945,097 Page 18 Art Unit: 3625 Application/Control Number: 18/945,097 Page 19 Art Unit: 3625 Application/Control Number: 18/945,097 Page 20 Art Unit: 3625 Application/Control Number: 18/945,097 Page 21 Art Unit: 3625 Application/Control Number: 18/945,097 Page 22 Art Unit: 3625 Application/Control Number: 18/945,097 Page 23 Art Unit: 3625 Application/Control Number: 18/945,097 Page 24 Art Unit: 3625 Application/Control Number: 18/945,097 Page 25 Art Unit: 3625 Application/Control Number: 18/945,097 Page 26 Art Unit: 3625 Application/Control Number: 18/945,097 Page 27 Art Unit: 3625 Application/Control Number: 18/945,097 Page 28 Art Unit: 3625 Application/Control Number: 18/945,097 Page 29 Art Unit: 3625 Application/Control Number: 18/945,097 Page 30 Art Unit: 3625 Application/Control Number: 18/945,097 Page 31 Art Unit: 3625 Application/Control Number: 18/945,097 Page 32 Art Unit: 3625 Application/Control Number: 18/945,097 Page 33 Art Unit: 3625
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