CTFR 18/864,085 CTFR 93087 DETAILED ACTION 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The following FINAL office action is in response to Applicant communication filed on 03/18/2026 regarding application 18/864,085. Claims 1-6 and 9-12 have been amended . Claims 7-8 and 13-14 have been canceled . Claims 15-20 have been added as new claims . Claims 1-6, 9-12 and 15-20 are pending and have been rejected . Response to Amendments 2. Applicant’s amendment filed on 03/18/2026 necessitated new grounds of rejection in this office action. IDS Statements 3. The 2 Information Disclosure Statements (IDS’s) filed on 12/23/2025 and 02/03/2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner. Foreign Priority 4. The Examiner has noted the Applicants claiming Foreign Priority from JP2022-089722 filed on 06/01/2022 and 371 of PCT/JP2023/019590 filed on 05/25/2023. Therefore, the earliest effective filing date considered for this case is of 06/01/2022 . Receipt is acknowledged of papers submitted under 35 U.S.C. § 119(a)-(d), which papers have been placed of record in the file. Response to Arguments 5. Applicant’s arguments, see page 8 filed on 03/18/2026 , with respect to Specification Objections to Title have been fully considered and is found to be persuasive . Therefore, the Specification Objections to Title have been withdrawn . Examiner annotated “ ok to enter ” with Examiner’s initials and date for OC case submission as acceptable . 6. Applicant’s arguments, see page 8 filed on 03/18/2026 , with respect to the Claim Objections for Claims 1, 3, 5 and 8-12 have been fully considered and is found to be persuasive . Therefore, the Claim Objections for Claims 1, 3, 5 and 8-12 have been withdrawn . Examiner Note : However, due to Applicant's proposed amendments here, Examiner adds minor Claim Objections for Claims 1, 3, 12 and 16 . See the Claim Objections Section shown below. 7. Applicant’s arguments, see page 9 filed on 03/18/2026 , with respect to Claims 1-10 and 12 are provisionally rejected on the ground of non-statutory obviousness type double patenting over Claims 1-11 of co-pending Application #18/854,990 (reference application) US PG Pub (US 2025/0245688 A1) have been fully considered and is found to be persuasive . Applicant submitted a Terminal Disclaimer (TD) which was filed on 03/18/2026 with a Terminal Disclaimer Review Decision filed on 03/23/2026 to obviate the non-statutory obviousness type double patenting rejections. Therefore, Claims 1-10 and 12 are provisionally rejected on the ground of non-statutory obviousness type double patenting over Claims 1-11 of co-pending Application #18/854,990 (reference application) US PG Pub (US 2025/0245688 A1) have been withdrawn . 8. Applicant’s arguments, see page 9 filed on 03/18/2026 , with respect to the 35 U.S.C. § 112 (a) rejections for Claims 10-11 under written description requirement have been fully considered and is found to be persuasive . Therefore, the 35 U.S.C. § 112 (a) rejections for Claims 10-11 have been withdrawn . Examiner Note : However, due to Applicant's proposed amendments here, Examiner adds 35 U.S.C. § 112 (a) rejections for Claims 1-6, 9-12 and 15-20. See the 35 U.S.C. § 112 (a) rejections Section shown below. 9. Applicant’s arguments, see page 9 filed on 03/18/2026 , with respect to the 35 U.S.C. § 112 (b) rejections for Claims 10-11 as allegedly being indefinite have been fully considered and is found to be persuasive . Therefore, the 35 U.S.C. § 112 (b) rejections for Claims 10-11 have been withdrawn . 10. Applicant’s arguments, see pages 11-12 filed on 03/18/2026 , with respect to the 35 U.S.C. § 102 (a) (2) Claim Rejections for Claims 1-5 and 12 have been fully considered and is found to be persuasive . Therefore, the 35 U.S.C. § 102 (a) (2) Claim Rejections for Claims 1-5 and 12 have been withdrawn . 11. Applicant’s arguments, see pages 12-13 filed on 03/18/2026 , with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 6-11 have been fully considered and is found to be persuasive . Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 6-11 have been withdrawn . See Examining Claims with Respect to Prior Art Section shown below . Response to 35 U.S.C. § 101 Arguments 12. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-6, 9-12 and 15-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 9-11 dated 03/18/2026) . Examiner respectfully disagrees . Argument #1 : (A). Applicant argues that Claims 1-6, 9-12 and 15-20 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-10, dated 03/18/2026). Examiner respectfully disagrees. Specifically, Applicant argues that amended claim limitations of Independent Claims 1 and 12 provide a technical improvement in distributed machine learning systems and integrate any alleged judicial exception into a practical application and are patent eligible under step 2a prong 2 of the Patent Subject Matter Eligibility Analysis (see Applicant Remarks, Page 9, dated 03/18/2026). Examiner respectfully disagrees. The Applicant’s arguments have been fully considered, but they are found to be not persuasive. Applicant contends that amended claim 1 recites a technical improvement in distributed machine learning systems because the claim allegedly integrates independently trained learning models, avoids transfer of raw customer data, reduces data transfer requirements, structurally controls federation and improves distributed learning systems. The claimed invention remains directed to an abstract idea because the focus of the claim is still the collection, organization, analysis and prediction of customer behavior information using mathematical models. The additional limitations cited by the Applicant merely describe the environment in which the abstract idea is implemented and does not improve the functioning of the computer itself or another technology. The claim recites results-oriented functional language such as “federating at least a part” of local learning models, “generate a federated learning model”, “maintaining the unique region locally” and outputting “prospective customer data” without reciting any specific technical mechanism for performing these operations. For example, the claim fails to recite a specific federated learning protocol, a particular parameter aggregation algorithm, any specialized synchronization technique, a technical communication protocol, bandwidth optimization mechanisms, distributed systems architecture improvements, memory-management improvements or any particular machine-learning architecture. Instead, the claim merely invokes generic computing components (“memory” and “processor”) to perform generalized data analysis and predictive modeling functions. As explained in Electric Power Group, LLC v. Alstom S.A. (Fed. Cir. 2016), collecting information, analyzing it, and displaying or using the results constitutes an abstract idea even when performed in a particular technological environment. Similarly, SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018) held that performing statistical analysis and predictive modeling on computers does not become patent eligible merely because the analysis is performed using computers. The alleged reduction in data transfer is likewise insufficient because the claims do not recite any technological mechanism by which network performance is improved. Rather, any reduction in transferred data is merely a consequence of the abstract business decision not to exchange customer data. Independent Claims 1 and 12 : With respect to reliance on (e.g., “ federated learning model ” & “ first local learning model ” & “ second local learning model ”) as additional elements shown in Independent Claims 1 and 12, when considered individually and in combination (as a whole) with these recited claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) limiting a field of use or technological environment for analyzing data to predict human behavior (specifically, customer consumption behaviors) for business purposes thru gathering data models, combining (federating) them, and using them to generate predictions ("prospective customer data") in a customer-business owner relationship environment (see MPEP § 2106.05 (h)). Also for Independent Claims 1 and 12: Even if the steps of (e.g., “ acquire a first local learning model and a second local learning model, the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflects mere data gathering which at most amounts to insignificant extra-solution activities (see MPEP § 2106.05 (g)). These claims do not integrate the judicial exception into a practical application because they involve a generic application of a mathematical concept (federated learning) to a business problem (customer prediction) without reciting a specific technical improvement to the computer or network technology itself. The claims describe using the concept of federated learning. However, the claim language focuses on the result (outputting prospective customer data) rather than a specific, technical solution that improves the functioning of a computer or the operation of the network used for the federation. The specification might describe such an improvement, but the claim language itself does not impose that meaningful limit. The claim language essentially amounts to "apply the abstract idea of federated learning to customer data to predict prospective customers" using generic computer components. Merely using a computer or a specific type of model (federated learning) as a tool to perform an abstract idea is insufficient to establish a practical application in Step 2A Prong 2 without further specific technical limitations on the implementation itself. The limitations primarily define a field of use (customer behavior prediction) rather than a specific, non-abstract implementation that meaningfully limits the scope of the abstract idea itself. The claims do not integrate the judicial exception into a practical application. The claim language is drafted at a high level of generality, covering the outcome or idea of a solution without detailing the specific how in a manner that constitutes a technical improvement to the underlying technology. In addition, these limitations 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 apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, 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. Therefore, in conclusion, Examiner maintains that Claims 1-6, 9-12 and 15-20 do not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis . Argument #2 : (B). Applicant argues that amended Independent Claim 1 of the instant claimed invention is analogous to Claim 1 of Example 40 of the USPTO Subject Matter Eligibility Examples issued January 2019 (hereinafter, “Example 40”) is directed to a particular improvement in the operation of distributed machine learning systems and thus is patent eligible (see Applicant Remarks, 2 nd ¶ of Page 10, dated 03/18/2026). Examiner respectfully disagrees. Applicant’s reliance on Example 40 of the USPTO Subject Matter Eligibility Examples issued January 2019 (hereinafter, “Example 40”) has been found to be not persuasive. Example 40 concerns a specific asserted improvement in network monitoring systems in which the claimed invention changed how data packets were collected in order to improve network performance itself. In contrast, the present claims do not improve the functioning of computer networks, distributed computing infrastructure or machine-learning systems themselves. Instead, the claims merely organize customer data into categories, train predictive models using customer behavior data and combine portions of models for business analytics purposes. The focus on the claims is not on improving computer functionality, but on improving the accuracy and scope of customer-behavior predictions. Any alleged “architectural constraints” recited in the claims merely describes how business information is partitioned and analyzed. Such limitations concern the content and organization of information rather than an improvement to computer technology itself. Unlike Claim 1 of Example 40 of the USPTO Subject Matter Eligibility Examples issued January 2019 (hereinafter, “Example 40”), the present claims do not recite a specific improvement to computer-resource utilization, improved packet processing, improved memory management, reduced computational complexity, improved network throughput or any concrete technological enhancement to system operations. According, Claim 1 of Example 40 of the USPTO Subject Matter Eligibility Examples issued January 2019 (hereinafter, “Example 40”) is non-analogous to Independent Claims 1 and 12 of the instant application and therefore distinguishable . Argument #3 : (C). Applicant argues that amended Independent Claim 1 of the instant claimed invention integrates any alleged judicial exception into a practical application, because the claims are directed to a specific technical means of achieving cross-entity model integration under enforced data-locality constraints (see Applicant Remarks, last ¶ of Page 10, dated 03/18/2026). Examiner respectfully disagrees. Specifically, Applicant further argues that the claims integrate any alleged abstract idea into a practical application because the claims allegedly recite specific region-based data structures, constrained federation processes, independently trained local models, and data-locality constraints. The arguments have been found to be not persuasive. Although the claims are performed in a distributed computing environment, merely limiting the use of an abstract idea to a particular technological environment does not integrate the abstract idea into a practical application. The claimed “common region” and “unique region” limitations merely organize and classify customer information into categories. Such data organization limitations constitute insignificant extra-solution activities and do not improve computer functionality. Similarly, the “training” and “federating” limitations merely recite mathematical relationships and predictive analytics operations at a high level of abstraction without reciting how such operations are technologically implemented. The claims recite the desired result of combining models while retaining local data, but does not recite any specific technical means for achieving the results. For example, the claims do not specify how portions of the models are selected, how model compatibility is ensured, how federation is technically performed, how updates are synchronized, how privacy is technically preserved or how distributed computation is technically improved. The Federal Circuit has repeatedly held that claims reciting functional results without specific technical implementation remains abstract. See Two-Way Media Ltd v. Comcast Cable Communications, LLC (Fed. Cir. 2017) and Affinity Labs of Texas, LLC v. DIRECTV, LLC (Fed. Cir. 2016). Accordingly, the additional limitations do not integrate the judicial exception into a practical application for Claims 1-6, 9-12 and 15-20 under step 2a prong 2 of the 35 U.S.C. § 101 analysis . Argument #4: (D). Applicant argues that Claims 1-6, 9-12 and 15-20 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. 101 analysis (see Applicant Remarks, Pages 10-11, dated 03/18/2026). Examiner respectfully disagrees. Applicant’s arguments also fail to demonstrate that the claims recite significantly more than the abstract idea itself. The additional claim elements consist of: generic processors, memory, data storage, machine-learning models and federated learning operations. These computer components when considered individually and in combination (as a whole) with these recited claim limitations, this additional element does not amount to significantly more than the judicial exceptions under step 2B due to: (1) limiting a field of use or technological environment for analyzing data to predict human behavior (specifically, customer consumption behaviors) for business purposes thru gathering data models, combining (federating) them, and using them to generate predictions ("prospective customer data") in a customer-business owner relationship environment (see MPEP § 2106.05 (h)). Examiner refers Applicant to Examiner’s 35 U.S.C. § 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below ) shown for step 2B particularly for Independent Claims 1 and 12 . The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception , (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer , and (3) generally linking the use of the judicial exception to a particular technological environment or field of use . Moreover, for Independent Claims 1 and 12 : Even if the steps of (e.g., “ acquire a first local learning model and a second local learning model, the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflects mere data gathering which at most amounts to insignificant extra-solution activities (see MPEP § 2106.05 (g)). Moreover, these activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Receiving or Transmitting Data over a Network , Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Further, federated learning techniques and distributed machine learning are known at the time of filing. The claims do not recite any unconventional implementation details that would amount to an inventive concept. The purported inventive concept identified by the Applicant – namely, combining portions of local learning models without sharing customer data – is itself part of the abstract idea and therefore cannot supply the required inventive concept under Step 2B. See BSG Tech LLC v. BuySeasons, Inc (Fed. Cir. 2018). Additionally, the claims merely automate business analytics processes using computer technology, which is insufficient for eligibility under Alice Corp v. CLS Bank International (Fed. Cir. 2014). Accordingly, when considered individually and as an ordered combination, the additional elements do not amount to significantly more than the recited abstract idea. 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. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-6, 9-12 and 15-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-6, 9-12 and 15-20 are ineligible with respect to the 35 U.S.C. § 101 analysis . Claim Rejections - 35 USC § 112 07-30-01 AIA 13. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. 07-31-01 14. Claims 1-6, 9-12 and 15-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The first paragraph of 35 U.S.C. 112 requires that the “specification shall contain a written description of the invention.” This requirement is separate and distinct from the enablement requirement. See, e.g., Vas-Cath, Inc. v. Mahurkar , 935 F.2d 1555, 1560, 19 USPQ2d 1111, 1114 (Fed. Cir. 1991). See also Univ. of Rochester v. G.D. Searle & Co ., 358 F.3d 916, 920-23, 69 USPQ2d 1886, 1890-93 (Fed. Cir. 2004) (discussing history and purpose of the written description requirement). To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc ., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar , 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc ., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002). (A). In this instance, Independent Claims 1 and 12 were amended on 03/18/2026 to include the new limitations of: “ generate a federated learning model, wherein the federated is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators , the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data .” The language in the Spec doesn’t appear to be enough to satisfy 35 U.S.C. § 112(a) in this instance because of the complete absence of the “ how ” to support the claimed result. Examiner refers to the following paragraphs in Applicant’s Specification. See Applicant’s Specification ¶ [0091]: “ The business customer data 221 includes a unique region in addition to the common region. The unique region includes unique data regarding the business of the business operator, and is associated with the customer statistical data D11 of the common region. For example, in the customer group #0001 illustrated in FIG. 9, the purchase index of a product type 1 is 20%, the purchase index of a product type 2 is 42%, and the like. The purchase index is statistics of consumption behaviors of customers belonging to a customer group .” See Applicant’s Specification ¶ [0093]: “ The local learning model generation unit 203 according to the present example embodiment generates the local learning model 210 by using the business customer data 221 as illustrated in FIG. 9. That is, the local learning model generation unit 203 generates a local learning model learned on the basis of business customer data including a common region commonly possessed by a plurality of business operators and a unique region possessed by each of the plurality of business operator s.” See Applicant’s Specification ¶ [0116]: “ The processing executed by the data processing system 1 has been described above. In the data processing system 1, the federated learning model 112 that has received the input regarding the consumption behavior outputs the prospective customer. In the federated learning model, personal information is not used, but information on customer groups grouped according to a predetermined standard is handled. Therefore, the data processing system 1 can associate data of unique regions of different business operators with each other via data of the common region by the federation learning. Therefore, the data processing system 1 can output, to each business operator, prospective customer data using data of a common region across a plurality of business operators. That is, the data processing system 1 can estimate the prospective customer data regarding the business of each business operator with high accuracy .” See Applicant’s Specification ¶ [0152]: “ The federated learning model generation apparatus according to any one of supplementary notes 1 to 6, wherein the local learning model acquisition unit acquires the local learning models learned on a basis of the business customer data including a common region commonly possessed by the plurality of business operators and a unique region possessed by each of the plurality of business operators .” None of these paragraphs from Applicant’s Specification nor from Applicant’s Drawings supports the claim limitation of “ generate a federated learning model, wherein the federated is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators , the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data ” with emphasis on “ while maintaining the unique region locally without transferring the first and second customer data between the business operators ”. Merely claiming the result without the corresponding algorithm or sequence of steps describing how the computer achieves this federation step results in a 35 U.S.C. § 112(a) rejection. Examiner points out that the quoted text in ¶ [0116] states the system can "associate data... via data of the common region," which implies some level of data alignment or sharing. It does not explicitly describe the structural or procedural steps required to perform federated learning without transferring the data between operators. Stating that the system outputs prospective customer data "with high accuracy" describes a desired result rather than the specific, required steps or structures to achieve federated learning locally. The specification leaves a person of ordinary skill in the art guessing at the exact architecture used to keep regional data private. The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement. “Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002). Thus, Independent Claims 1 and 12 fail to satisfy the written description requirement of §112(a) because there is no evidence of a complete specific application or embodiment to satisfy the requirement that the description is set forth “in such full, clear, concise, and exact terms” to show possession of the claimed invention. See Fields v. Conover , 443 F.2d 1386, 1392, 170 USPQ 276, 280 (CCPA 1971). Dependent Claims 2-6 and 9-11 depend from Independent Claim 1 and therefore inherit the 35 U.S.C. § 112(a) deficiency of Independent Claim 1 discussed above. Dependent Claims 15-20 depend from Independent Claim 12 and therefore inherit the 35 U.S.C. § 112(a) deficiency of Independent Claim 12 discussed above. Appropriate corrections are required . Claim Objections 15. Claims 1, 3, 12 and 16 are objected to because of the following informalities: (A). The last limitations of Independent Claims 1 and 12 recite the following: “ by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federated is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data .” There appears to be a minor claim informality regarding “the business operators” when referring back to the previous limitations of Independent Claims 1 and 12 which recite “the first business operator and a second business operator”. For the purposes of examination, Examiner suggests to Applicant to amend the last limitations of Independent Claims 1 and 12 to recite the following: “ by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federated is performed while maintaining the unique region locally without transferring the first and second customer data between the first business operator and the second business operator [[ business operators ]] , the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data .” (B). Dependent Claims 3 and 16 recite the following limitations: “ wherein the prospective customer data includes an estimated purchase index indicating a tendency of the certain consumption behavior of each of the one or more customer groups .” There appears to be a minor claim informality regarding missing the word “common” in which Examiner refers back to the previous limitations of Independent Claims 1 and 12 which recites “a plurality of common customer groups” whereby here recites “each of the one or more customer groups”. For the purposes of examination, Examiner suggests to Applicant to amend Dependent Claims 3 and 16 to recite the following: “ wherein the prospective customer data includes an estimated purchase index indicating a tendency of the certain consumption behavior of each of the plurality of common customer groups [[ one or more customer groups ]] .” Appropriate corrections are required . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 16. 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. 17. Claims 1-6, 9-12 and 15-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : Claims 1-6, 9-12 and 15-20 are each focused to a statutory category namely, an “ apparatus ” or a “ system ” (Claims 1-6 and 9-11) and a “ method ” or a “ process ” (Claims 12 and 15-20). Step 2A Prong One : Independent Claims 1 and 12 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough): “ at least one memory storing instructions ” (see Independent Claim 1); “ at least one processor configured to execute the instructions to ” (see Independent Claim 1); “ acquire a first local learning model and a second local learning model, the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claim 1); “ acquire, by a computer , a first local learning model and a second local learning model , the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claim 12); “ the first local learning model having been trained through a training step of learning relationships between a plurality of common customer groups included in the common region of the first storage and a plurality of first consumption behaviors included in the unique region of the first storage ” (see Independent Claims 1 and 12); “ the second local learning model being generated using second customer data owned by the second business operator, wherein the second customer data is stored in a second storage as divided into the common region and a unique region indicating consumption behaviors specific to the second business operator ” (see Independent Claims 1 and 12); “ the second local learning model having been trained through a training step of learning relationships between the plurality of common customer groups included in the common region of the second storage and a plurality of second consumption behaviors included in the unique region of the second storage ” (see Independent Claims 1 and 12); “ by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federation is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data ” (see Independent Claims 1 and 12). Here, for Independent Claims 1 and 12 , the abstract ideas in these claim limitations are directed to using federated mathematical models to analyze customer consumption behavior and generate marketing predictions. The first claim limitation step of “acquire a first local learning model and a second local learning model…” is directed to a mental process or a mathematical concept. Acquiring trained models is part of collecting and using information for predictive analytics. The “learning model” itself implicates statistical modeling, and mathematical correlations. Moreover, the second claim limitation step of “first customer data… divided into a common region… and a unique region…” is directed to certain methods of organizing human activities and mental processes. This is fundamentally categorizing information, organizing customer/business information and classifying customer behavior. Humans could conceptually perform such categorization using pen to paper as a physical aid. This resembles organizing commercial information, marketing segmentation and customer profiling. The third claim limitation step of “learning relationships between…customer groups…. and ... consumption behaviors…” is directed to mathematical concepts or certain methods of organizing human activities. Training relationships/correlations between variables is quintessential statistical analysis, regression/classification and mathematical optimization. The fourth claim limitation step of “by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model….” Is directed to mathematical concepts or mental processes. Federating models involves combining parameters, aggregating weights, statistical averaging and optimization. Absent technical implementation details, the claims recite the abstract result of combining models. Examiner notes as written, the claim appears functional/result-oriented rather than reciting specific communication protocols, bandwidth reduction techniques, security-preserving architectures, distributed systems improvements or specific aggregation mechanisms. Thus, under Prong One it still recites combining mathematical prediction models derived from business data. The step of “maintaining the unique region locally without transferring the first and second customer data” is characterized as managing information flow and organizing data access and is argued as certain methods of organizing human activities. Lastly, the step of “receive a certain consumption behavior… and output prospective customer data…” is directed to mathematical concepts or mental processes. This is predictive analytics in infer future customers from behavioral data and therefore is categorized as predicting prospective customers using behavioral correlations. Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “ Mental Processes ” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid , in order to help perform these mental steps does not negate the mental nature of these limitations. The use of " physical aids " in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C. Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “ Certain Methods of Organizing Human Activities ” which pertains to (3) commercial interactions (including marketing or sales activities or behaviors or business relations ) and (4) relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively covering performance of their limitations as “ Mathematical Concepts ” which pertains to (5) mathematical relationships . That is, other than reciting (e.g., “ at least one memory ” & “ first storage ” & “ second storage ” & “ at least one processor ” & “ a computer ”), nothing in the claim elements precludes the steps from being performed as “ Mental Processes ” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid , and additionally or alternatively as “ Certain Methods of Organizing Human Activities ” which pertains to (3) commercial interactions (including marketing or sales activities or behaviors or business relations ) and (4) relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “ Mathematical Concepts ” which pertains to (5) mathematical relationships . Therefore, at step 2a prong 1, Yes , Claims 1-6, 9-12 and 15-20 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2. Step 2A Prong Two : With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 recites additional elements directed to: (e.g., “ at least one memory ” & “ at least one processor ” & “ first storage ” & “ second storage ”). Independent Claim 12 recites additional elements directed to: (e.g., “ a computer ” & “ first storage ” & “ second storage ”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Independent Claims 1 and 12 : With respect to reliance on (e.g., “ federated learning model ” & “ first local learning model ” & “ second local learning model ”) as additional elements shown in Independent Claims 1 and 12, when considered individually and in combination (as a whole) with these recited claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) limiting a field of use or technological environment for analyzing data to predict human behavior (specifically, customer consumption behaviors) for business purposes thru gathering data models, combining (federating) them, and using them to generate predictions ("prospective customer data") in a customer-business owner relationship environment (see MPEP § 2106.05 (h)). Also for Independent Claims 1 and 12: Even if the steps of (e.g., “ acquire a first local learning model and a second local learning model, the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflects mere data gathering which at most amounts to insignificant extra-solution activities (see MPEP § 2106.05 (g)). These claims do not integrate the judicial exception into a practical application because they involve a generic application of a mathematical concept (federated learning) to a business problem (customer prediction) without reciting a specific technical improvement to the computer or network technology itself. The claims describe using the concept of federated learning. However, the claim language focuses on the result (outputting prospective customer data) rather than a specific, technical solution that improves the functioning of a computer or the operation of the network used for the federation. The specification might describe such an improvement, but the claim language itself does not impose that meaningful limit. The claim language essentially amounts to "apply the abstract idea of federated learning to customer data to predict prospective customers" using generic computer components. Merely using a computer or a specific type of model (federated learning) as a tool to perform an abstract idea is insufficient to establish a practical application in Step 2A Prong 2 without further specific technical limitations on the implementation itself. The limitations primarily define a field of use (customer behavior prediction) rather than a specific, non-abstract implementation that meaningfully limits the scope of the abstract idea itself. The claims do not integrate the judicial exception into a practical application. The claim language is drafted at a high level of generality, covering the outcome or idea of a solution without detailing the specific how in a manner that constitutes a technical improvement to the underlying technology. In addition, these limitations 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 apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, 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. 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. Therefore, at step 2a prong 2, Claims 1-6, 9-12 and 15-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application. Step 2B : (As explained in MPEP § 2106.05 ), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 recites additional elements directed to: (e.g., “ at least one memory ” & “ at least one processor ” & “ first storage ” & “ second storage ”). Independent Claim 12 recites additional elements directed to: (e.g., “ a computer ” & “ first storage ” & “ second storage ”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0059]: “ Each configuration of the data processing apparatus 110 and the federated learning model generation apparatus 100 may be implemented with dedicated hardware. Some or all of the constituent elements may be implemented by general-purpose or dedicated circuitry, a processor , or the like, or a combination thereof .”). Independent Claims 1 and 12 : With respect to reliance on (e.g., “ federated learning model ” & “ first local learning model ” & “ second local learning model ”) as additional elements shown in Independent Claim 1 and 12, when considered individually and in combination (as a whole) with these recited claim limitations, this additional element does not amount to significantly more than the judicial exceptions under step 2B due to: (1) limiting a field of use or technological environment for analyzing data to predict human behavior (specifically, customer consumption behaviors) for business purposes thru gathering data models, combining (federating) them, and using them to generate predictions ("prospective customer data") in a customer-business owner relationship environment (see MPEP § 2106.05 (h)). Moreover, for Independent Claims 1 and 12 : Even if the steps of (e.g., “ acquire a first local learning model and a second local learning model, the first local learning model being generated using first customer data owned by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator , and a unique region indicating consumption behaviors specific to the first business operator ” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflects mere data gathering which at most amounts to insignificant extra-solution activities (see MPEP § 2106.05 (g)). Moreover, these activities have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea . See MPEP § 2106.05(d) ii – Receiving or Transmitting Data over a Network , Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). 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. 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, as an ordered combination, amount to significantly more than the abstract idea itself. Dependent Claims 2-6, 9-11 and 15-20 recite the same abstract ideas as Independent Claims 1 and 12 along with further steps/details that could have concepts which can be performed in the human mind as “ Mental Processes ” (including observations or evaluations or judgments) or using pen to paper as a physical aid and additionally or alternatively as “ Certain Methods of Organizing Human Activities ” which pertains to commercial interactions (including marketing or sales activities or behaviors or business relations ) and relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “ Mathematical Concepts ” which pertains to mathematical relationships . Furthermore, Dependent Claims 2-6, 9 and 15-20 further narrows the abstract ideas with the same or similar additional elements identified in Independent Claims 1 and 12 , and are therefore ineligible for the same reasons previously provided in Step 2A Prong 2 and Step 2B . Dependent Claims 10-11 : With respect to reliance on (e.g., “ the federated learning model generation apparatus ” (see Dependent Claims 10-11) & “ a first local data processing apparatus ” (see Dependent Claim 10) & “ at least one memory ” see Dependent Claim 10) & “ at least one processor ” (see Dependent Claim 10) & “ a second local data processing apparatus ” (see Dependent Claim 10) as additional elements when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 and also secondly do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “ apply ” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a field of use or technological environment for analyzing data to predict human behavior (specifically, customer consumption behaviors) for business purposes thru gathering data models, combining (federating) them, and using them to generate predictions ("prospective customer data") in a customer-business owner relationship environment (see MPEP § 2106.05 (h)). The additional element of “ a plurality of local learning models ” in certain/particular claims do not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See for example; US PG Pub (US 2020/0027124 A1) hereinafter Knodel, et. al. Knodel at ¶ [0043]: “ The models 306 and 404 may include, but is not limited to, utilizing techniques such as least squares policy iteration, random forests, Q-learning, Bayesian models, support vector machines (SVM), federated learning , or neural network s.”. Knodel at ¶ [0063-0064]: “ The model 714 may include and/or utilize various machine learning techniques including, but not limited to techniques such as least squares policy iteration, random forests, Bayesian, support vector machines (SVM), federated learning , or neural networks. The models 306 and 404 may, in various embodiments, include, but are not limited to, utilizing techniques such as least squares policy iteration, random forests, Q-learning Bayesian models, support vector machines (SVM), federated learning , or neural networks .” See also US PG Pub (US 2021/0049628 A1) hereinafter Baird. Baird at ¶ [0070]: “ The machine learning model , for instance, may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, a federated learning model , and so on .” See also US PG Pub (US 2022/0156786 A1) hereinafter Collet, et. al. Collet at ¶ [0049]: “ The models 306 and 404 may include, but is not limited to, utilizing techniques such as least squares policy iteration, random forests, Q-learning, Bayesian models, support vector machines (SVM), federated learning , or neural networks .”). 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. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-6, 9-12 and 15-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-6, 9-12 and 15-20 are ineligible with respect to the 35 U.S.C. § 101 analysis . Examining Claims with Respect to Prior Art 18. Applicant’s arguments, see pages 11-13 filed on 03/18/2026 , with respect to the 35 U.S.C. § 102 (a) (1) Claim Rejections for Claims 1-5 and 12 and 35 U.S.C. § 103 Claim Rejections for Claims 6-11 have been fully considered and are found to be persuasive . Therefore, Claims 1-6, 9-12 and 15-20 have overcome the prior art rejections . Please note that the following issues still remain : (1) Claim Objections for Claims 1, 3, 12 and 16 and (2) 35 U.S.C. § 112 (a) Claim Rejections for Claims 1-6, 9-12 and 15-20 . Regarding Independent Claims 1 and 12 , there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of features either individually or in combination relating to: - by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federation is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data . The closest prior arts are as follows : 1) US PG Pub (US 2023/0377019 A1) – “Hyper-Segmented Personalization Using Machine Learning-Based Models in an Information Processing System”, hereinafter Kumar, et. al. 2) US PG Pub (US 2021/0174257 A1) – “Federated Machine-Learning Platform Leveraging Engineering Features Based on Statistical Tests”, hereinafter Pothula, et. al. 3) US PG Pub (US 2022/0156786 A1) – “Systems, Methods and Media for Automatic Prioritizer”, hereinafter Collet, et. al. Regarding Independent Claim 1 , Kumar system for a federated learning model generation apparatus teaches the following: - acquire a first local learning model and a second local learning model (see at least Kumar: ¶ [abstract] & Fig. 2 & Fig. 3A. Kumar teaches hyper-segmented personalization using machine learning-based models in an information processing system. For example, a method obtains one or more product experience recommendation data sets respectively from one or more product entities, and one or more purchase experience recommendation data sets respectively from one or more commerce entities.); - the first local learning model being generated using first customer data owed by a first business operator, wherein the first customer data is stored in a first storage as divided into a common region having attribute definitions common to the first business operator and a second business operator, and a unique region indicating consumption behaviors specific to the first business operator (see at least Kumar: ¶ [0002]. Kumar teaches that effective market segmentation, or the process of dividing a customer base into different groups based on behaviors on the e-commerce site, is a typical tool of such e-commerce sites . For example, such market segmentation is typically used to personalize the e-commerce experience to attract new customers and retain existing customers. See also Kumar at ¶ [0014]: The term “enterprise” as illustratively used herein is intended to be broadly construed, and may comprise, for example, one or more businesses, one or more corporations or any other one or more entities, groups, or organizations . See also Kumar at ¶ [0023]: Product company analysis engine 120 receives product usage data, support rating data, product upgrade data, and product ownership data from user 130 and performs an analysis of product 122, an analysis of support 124 , an analysis of region 126, and takes into account other user/similar product behavior 127 to generate a combination of recommendations 128 . Product upgrade data and product ownership data can comprise, for example, data describing the current upgrade state and ownership of a given product. Note that region analysis depends on whether or not the product company, e.g., an OEM of computing equipment, operates in multiple regions, e.g., geographic regions; in which case, recommendations may then depend on a geographic region. Examiner also points to Fig 2 noting “Product Company 1 with Product Experience Recommendation” 210-1, “Product Company 2 with Product Experience Recommendation” 210-2 and E-Commerce Site 1 212-1 and E-commerce Site 2 Purchase Experience Recommendation 212-2. In the middle of the flowchart shows a federated learning process.) - the first local learning model having been trained through a training step of learning relationships between a plurality of common customer groups included in the common region of the first storage and a plurality of first consumption behaviors included in the unique region of the first storage (see at least Kumar: ¶ [0022]. Kumar notes collaborative filtering 114 uses data for user 130 and other user data (other user behavior 117) to intelligently (i.e., utilizing machine learning technique(s)) identify relationships between data from multiple users , i.e., identify similarities between user data to make one or more recommendations. See also Kumar at ¶ [0037]: Federated ensemble knowledge distillation concept 300 provides, inter alia, data security, cross model training , and/or building of a model with available knowledge. Knowledge distillation trains a product and purchase personalized model 330 (also referred to as personalized model 330 which provides the final recommendation ) with a ground truth label, along with one or more purchase experience models 320. Accordingly, personalized model 330 is trained from both purchase experience and product/support experience .) However, neither Kumar and the other prior art of record do not reach or render obvious the sequence of limitations directed to: - by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federation is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data . Regarding Pothula reference, Pothula system for a federated learning model generation apparatus teaches the following: - the second local learning model having been trained through a training step of learning relationships between the plurality of common customer groups included in the common region of the second storage and a plurality of second consumption behaviors included in the unique region of the second storage (see at least Pothula: ¶ [0066-0067]. Pothula a cluster modeling module may also be used to group customers based on behavior into finite list for further processing . In some cases, the expected spend in one business may be correlated with expended spend in related businesses (say car and car loan, say airlines and hotel, say credit card payments and using airlines loyalty points). See also Pothula at ¶ [0067]: The cluster modeling module may be used to leverage specific groups of customers to only process and ingest more relevant data and focus on the situation at hand . The cluster modeling module may dynamically leverage the most relevant data (e.g. events and attributes) based on the current goals of an entity . This can reduce the computational load of learning about customers' behavior. See also Claim 8 of Pothula: Training , with one or more processors, a second machine-learning model on the second training dataset by adjusting parameters of the second machine-learning model to optimize a second objective function that indicates an accuracy of the plurality of object-orientation modelors in generating the library of classes; and storing the adjusted parameters of the trained second machine-learning model in memory .). However, neither Kumar and the other prior art of record do not reach or render obvious the sequence of limitations directed to: - by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federation is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data . Regarding the Collet reference, Collet teaches or suggests the following: - acquire a plurality of different local learning models (see at least Collet: ¶ [0049] & ¶ [0051] & ¶ [0070]. Collet teaches that the models 306 and 404 may, in various embodiments, include, but is not limited to, utilizing techniques such as least squares policy iteration, random forests, Q-learning, Bayesian models, support vector machines (SVM), federated learning , or neural networks. See also Collet at ¶ [0051]: This layer can be followed by one or more fine-grained models that make decisions taking more individual-level features (as explained further below and above, including, but not limited to, behaviors 610) into account, e.g., having a model reinforcement learning 606 make decisions for controlling a fine-grained gate 1 identified at 605, the fine-grained gate 605 being part of an overall gate 608 in the example in FIG. 6 . The final gate status (0/1 for closed/open) is simply the product of all individual pre-gates.) that have learned a relationship between a plurality of customer groups respectively generated (see at least Collet: ¶ [0035] & ¶ [0041] & ¶ [0064-0066] & ¶ [0106]. Collet notes that this learning from data for other campaigns and/or from the current campaign can be very powerful. For example, especially if an entity may have a hundred other campaigns already executed from which much can be learned. See also Collet: ¶ [0007]: “The audience including at least a particular customer or a potential customer; based at least on behavior data, training a model to learn a personalized frequency for sending the electronic communications to the particular customer or the potential customer.” See also Collet at ¶ [0035]: The “state” of each customer, given by the entirety of a customer's historical behavioral data ( behaviors 308 in the example in FIG. 3, which can also be “observe states” 410 in FIG. 4). See also Collet at ¶ [0041]: Customers (or groups of customers) are modeled , at least in part, by assigning them a state (“S”). This state may be in general determined by a customer's historical behavioral data. In exemplary operation (see especially FIG. 4), after taking an action (“A”), a reward (e.g., click/no click/unsubscribe) is observed , and, because the customer has had time to interact with the electronic communication or website, it is consequently found that the customer is in a new state S′. See also Collet at ¶ [0106]: Demographic and profile information for the particular customer may be used in the determination of the value function and resultant best send time . These additional online activities may be used to group people together and find a common value function for people that show similar behavioral patterns (e.g., browsing the same website at the same time during the day). from business customer data owned by a plurality of business operators and consumption behaviors corresponding to the business operators (see at least Collet: ¶ [0066] & ¶ [0072] & Fig. 7. Collet teaches that data is combined (both on a customer and a campaign level) across different entities (also referred to herein as partners) . Using the combined data, the model may still use campaign level data to predict how well campaigns will do and combine it with customer data to select the best frequency for each customer/potential customer. However, by using data from different partners, can transfer some of the learnings between different partners . For example, if a particular partner has never sent a Black Friday electronic communication before, but much data has been collected on other partners' Black Friday electronic communications and there is knowledge that this type of campaign tends to perform well, this insight can be used in some embodiments to predict that the given partner's campaign will also do well. See also Collet at ¶ [0072]: “ Behavior data can include, in addition to data regarding past campaigns, new data concerning actions or inaction of the current customer of the campaign, e.g., opening the electronic communication, past clicks on the electronic communication, and associated purchases made . In addition, the behavior data could also include what the customer is clicking on within a website, purchase actions, placing a product or service in a cart online, browsing from a general webpage having a number of products to a webpage for a particular product, putting an item in the cart but not purchasing, unsubscribing or otherwise blocking future electronic communications, and other available data .”). - receive a predetermined consumption behavior of a customer as input data by federating at least a part of the acquired local learning models (see at least Collet: ¶ [0004] & ¶ [0025] & ¶ [0094] & Fig. 7 (noting 716 behaviors). Collet notes that the entity may determine the recipients of their campaign by generating a target audience. This target audience may be defined by various conditional statements about customer's past behavior or attributes, for example, past open/click/purchase behavior, whether the customer added items to their cart, their predicted lifetime value, affinity to a certain product, etc. In some embodiments for the campaign, it can be determined that a certain electronic communication should only be sent to customers who have not received another electronic communication in a certain time frame . solutions are limited to handling one campaign in a predetermined time frame. For multi-campaigns, the client is typically asked to specify different non-overlapping time windows for each of the campaigns. See also Collet at ¶ [0004]: The resulting target frequency for each customer (or potential customer) is personalized, and can depend on the customer's or (or potential customer's) past behavioral data such as opens/views/clicks regarding the electronic communication, on-site interactions, purchases , and other relevant data. See also Collet at ¶ [0069-0070] noting “models 306, 404 and model 714.” See also Collet at ¶ [0094]: “Solutions are limited to handling one campaign in a predetermined time frame. For multi-campaigns, the client is typically asked to specify different non-overlapping time windows for each of the campaigns ; otherwise a given user will receive either only one of the communications or multiple electronic communications at the same time.”) , and generate a federated learning model (see at least Collet: ¶ [0049]. Collet teaches that the models 306 and 404 may include, but is not limited to, utilizing techniques such as least squares policy iteration, random forests, Q-learning, Bayesian models, support vector machines (SVM), federated learning , or neural networks. ) that outputs prospective customer data for the input data (see at least Collet: ¶ [0049] & ¶ [0066] & ¶ [0106]. Collet teaches that this value function may be determined using a machine learning model trained on historical data (e.g., past delivered times and other online behavior such as opens, clicks, purchases, etc.) and could be personalized (for instance taking into account customer-specific attributes and online/offline activity, to name just a few examples). Demographic and profile information for the particular customer may be used in the determination of the value function and resultant best send time . These additional online activities may be used to group people together and find a common value function for people that show similar behavioral patterns (e.g., browsing the same website at the same time during the day). See also Collet at ¶ [0027]: FIG. 1 sends electronic communications to customers/recipients/ audience 116, 118, 120 (which may include potential customers ). For the campaign, the user may manually input both the content for the electronic communications and the audience to receive the electronic communications . See also Collet at ¶ [0123]: The reinforcement learning model may be configured at the given point in time to perform a comparison of an output of the reinforcement learning model to an actual output generated from application of the output .). However, neither Collet and the other prior art of record do not reach or render obvious the sequence of limitations directed to: - by federating at least a part of the first local learning model and at least a part of the second local learning model, generate a federated learning model, wherein the federation is performed while maintaining the unique region locally without transferring the first and second customer data between the business operators, the federated learning model being configured to receive a certain consumption behavior included in the plurality of first consumption behaviors or the plurality of second consumption behaviors as input data, and to output prospective customer data for the input data . Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor do the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST. 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 on 571-270-5389 . The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853 . Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A /BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625 Application/Control Number: 18/864,085 Page 2 Art Unit: 3625A Application/Control Number: 18/864,085 Page 3 Art Unit: 3625A Application/Control Number: 18/864,085 Page 4 Art Unit: 3625A Application/Control Number: 18/864,085 Page 5 Art Unit: 3625A Application/Control Number: 18/864,085 Page 6 Art Unit: 3625A Application/Control Number: 18/864,085 Page 7 Art Unit: 3625A Application/Control Number: 18/864,085 Page 8 Art Unit: 3625A Application/Control Number: 18/864,085 Page 9 Art Unit: 3625A Application/Control Number: 18/864,085 Page 10 Art Unit: 3625A Application/Control Number: 18/864,085 Page 11 Art Unit: 3625A Application/Control Number: 18/864,085 Page 12 Art Unit: 3625A Application/Control Number: 18/864,085 Page 13 Art Unit: 3625A Application/Control Number: 18/864,085 Page 14 Art Unit: 3625A Application/Control Number: 18/864,085 Page 15 Art Unit: 3625A Application/Control Number: 18/864,085 Page 16 Art Unit: 3625A Application/Control Number: 18/864,085 Page 17 Art Unit: 3625A Application/Control Number: 18/864,085 Page 18 Art Unit: 3625A Application/Control Number: 18/864,085 Page 19 Art Unit: 3625A Application/Control Number: 18/864,085 Page 20 Art Unit: 3625A Application/Control Number: 18/864,085 Page 21 Art Unit: 3625A Application/Control Number: 18/864,085 Page 22 Art Unit: 3625A Application/Control Number: 18/864,085 Page 23 Art Unit: 3625A Application/Control Number: 18/864,085 Page 24 Art Unit: 3625A Application/Control Number: 18/864,085 Page 25 Art Unit: 3625A Application/Control Number: 18/864,085 Page 26 Art Unit: 3625A Application/Control Number: 18/864,085 Page 27 Art Unit: 3625A Application/Control Number: 18/864,085 Page 28 Art Unit: 3625A Application/Control Number: 18/864,085 Page 29 Art Unit: 3625A Application/Control Number: 18/864,085 Page 30 Art Unit: 3625A
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