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 .
Status of Claims
2. Claims 1 and 12 have been amended. Claims 6 and 19 have been canceled. Claims 1-5, 9-12, 15-18 and 20 are currently pending and have been rejected. Examiner Note: Applicant submitted initial claim set filed on 09/02/2026. Then Applicant submitted a Supplemental Response or Supplemental Amendment with a claim set filed on 09/25/2026. Examiner has taken the claimset filed on 09/25/2026 under consideration and has examined this latest claimset.
Foreign Priority
3. 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.
Status of the Application
4. Claims 1-5, 9-12, 15-18 and 20 are currently pending and have been examined in this application. This communication is the first action on the merits.
Response to Amendments
5. Applicant’s amendment filed on 09/25/2026 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
6. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/02/2026 has been entered.
Response to Arguments
7. Examiner adds a minor Specification Objection to the Title of the Disclosure. See below.
8. Applicant’s arguments, see page 7 filed on 09/25/2026, with respect to 35 U.S.C. § 112 (a) Claim Rejections for Claims 1-5, 9-12, 15-18 and 20 have been fully considered and is found to be persuasive. Therefore, the 35 U.S.C. § 112 (a) Claim Rejections have been withdrawn.
9. Applicant’s arguments, see page 7 filed on 09/25/2026, with respect to the Claim Objections for Claims 1, 3, 12 and 16 have been fully considered and is found to be persuasive.
Therefore, the Claim Objections for Claims 1, 3, 12 and 16 have been withdrawn.
Specification Objections to Title
10. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed to. Examiner has referred to Applicant’s Specification ¶ [0009] whereby the following title is suggested: “A System and Method for Federated Learning Models to Monitor Consumption Behavior of a Plurality of Customers to Purchase Products Using Services Between a Plurality of Business Operators”. See MPEP § 608.01.
Response to 35 U.S.C. § 101 Arguments
11. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-5, 9-12, 15-18 and 20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 7-10 dated 09/25/2026). Examiner respectfully disagrees. The amendments to claims 1 and 12 further specify how information from the respective local learning models is connected; however, the additional limitations continue to recite an abstract information-processing activity and do not integrate that activity into a practical application or provide significantly more.
Argument #1:
(A). Applicant argues that Claims 1-5, 9-12, 15-18 and 20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 7-9, dated 09/25/2026). Examiner respectfully disagrees.
Applicant argues that the amended claims do not recite a mental process because the claims require manipulation of computer data structures, including trained machine-learning models, and rely on Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859 (Fed. Cir. 2010). This argument is not persuasive.
The relevant inquiry is not whether a human could literally perform every computer operation recited in the claim, but whether the claim, viewed as a whole, recites an abstract mental process. MPEP § 2106.04(a)(2)(III) recognizes that a claim does not recite a mental process where its limitations cannot practically be performed in the human mind. However, the fact that an abstract analytical activity is implemented using computer data structures does not, by itself, remove the underlying activity from the abstract-idea inquiry.
Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
Also, with respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (B): “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.”
Moreover, with respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claims are an abstract idea because it could be "performed by humans without a computer").” “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see 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.”), or 2) in a computer environment (see Applicant’s Specification ¶ [0036]: “The data processing apparatus 110 can be configured by, for example, a computer, a server, or a dedicated device having a communication function. Note that in the following description, a “computer” may include any one of a server apparatus, a blade server, and a cloud computing system. The data processing apparatus 110 includes an input unit 111, a federated learning model 112, and an output unit 113 as main components.”), or 3) is merely using a computer as a tool to perform these concepts.” Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process.
Here, the substantive activity remains the organization, analysis, association, and evaluation of customer and consumption-behavior information. The claim identifies common customer groups, identifies relationships between the common customer groups and business-specific consumption behaviors, extracts information from the local models, associates corresponding information through the common customer groups, and generates estimated purchase indices indicating customer tendencies to perform a particular consumption behavior.
The recitation of a trained model, data structure, or feature extraction does not change the character of the information being processed. Unlike Research Corp., where the claimed manipulation of pixels and a blue-noise mask produced a modified digital image and was tied to a particular technological image-rendering process, the present claims uses the model data structures to organize and analyze customer information and generate information concerning prospective customers. See Research Corp., 627 F.3d at 868.
Moreover, the claims does not recite the particular internal operations of a neural network, decision tree, or other machine-learning architecture. Rather, "extracting one or more features" and "connecting" the respective data structures are recited at a high level of abstraction. The claims therefore does not establish that the alleged computer-data-structure manipulation itself constitutes a technological improvement.
Applicant's reliance on the division of data into a "common region" and a "unique region" is likewise unpersuasive. Those regions merely classify and organize customer information according to shared versus business-specific attributes. The claims do not require a particular memory architecture, encoding technique, indexing structure, or other technological mechanism for storing such regions. The claimed organization of information therefore remains part of the abstract information-processing activity.
Applicant further argues that the claims do not recite a certain method of organizing human activity. However, the claimed information is expressly directed to business operators, customers, consumption behaviors, prospective customers, and estimated purchase tendencies. The claimed analysis therefore concerns commercial/customer activity and the use of customer information for business decision-making. The fact that the claims performs that activity through machine-learning models does not remove it from the certain-methods-of-organizing-human-activity grouping.
Nor does limiting the claimed activity to machine learning render it non-abstract. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit rejected an eligibility argument based on applying machine learning to a particular business environment, explaining that an abstract idea does not become non-abstract merely by limiting it to a particular field of use or technological environment.
Applicant also argues that the claims do not recite mathematical concepts because no equation or mathematical formula is expressly recited. This argument does not establish eligibility.
With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
Although the absence of an express formula weighs against characterizing the entire claims as a mathematical concept, the claimed extraction of model features, learned relationships, model federation, and generation of estimated indices may involve mathematical operations underlying the machine-learning models. In any event, the rejection does not depend solely upon the mathematical-concepts grouping. The claimed organization and analysis of customer and consumption-behavior information independently support the mental-process and certain-methods-of-organizing-human-activity groupings.
Accordingly, Applicant has not demonstrated that the amended limitations remove the claims from the abstract-idea groupings identified in the rejection. In conclusion, therefore, at step 2a prong 1, Claims 1-5, 9-12, 15-18 and 20 are directed to the abstract idea and recited judicial exceptions under “Certain Methods of Organizing Human Activities” category or “Mental Processes” category or “Mathematical Concepts” category. Claims 1-5, 9-12, 15-18 and 20 are maintained as being patient ineligible under 35 U.S.C. 101 step 2a prong 1.
Argument #2:
(B). Applicant argues that Claims 1-5, 9-12, 15-18 and 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 09/25/2026). Examiner respectfully disagrees.
Applicant alternatively argues that the claims integrate any alleged abstract idea into a practical application because they define a specific distributed information-processing architecture and a specific manner of connecting local learning models. This argument is not persuasive.
The relevant question is whether the claimed additional elements, considered as a whole, reflect an improvement to the functioning of a computer or another technology or technical field, or otherwise integrate the exception into a practical application. The current USPTO guidance recognizes that a claimed improvement to a machine-learning model can support eligibility where the claims themselves reflect the asserted technological improvement. Ex parte Desjardins is distinguishable because the claims there were found to reflect a particular improvement in how the machine-learning model itself operated, including addressing catastrophic forgetting.
Here, the claimed "connecting" of the data structures through common customer groups does not improve the operation of the learning models themselves. The claims do not recite an improvement to model training, model architecture, feature-extraction technology, model convergence, computational efficiency, memory utilization, or another aspect of machine-learning operation.
Instead, the common customer groups serve as correspondence information for associating information from the respective business operators. Thus, the claimed connection specifies what information is associated and how the information is used, rather than improving how the computer or learning model operates.
Applicant relies on the Specification's statement that the system can associate data of unique regions of different businesses through common-region data. However, a stated benefit in the Specification is insufficient where the claims themselves do not recite a corresponding technological improvement. This distinction is expressly recognized in Ex parte Desjardins, where the ARP explained that an asserted improvement in the Specification must be reflected in the claims to support a practical-application determination.
The limitation that the federated-learning apparatus does not acquire the underlying customer data likewise does not provide the required integration. That limitation specifies that the raw data remains at the respective local apparatuses. It does not recite a particular privacy-preserving technology, encryption mechanism, communication protocol, distributed-computation technique, or other technical mechanism that improves the operation of the computer or network.
Also, for Independent Claims 1 and 12: Even if the steps of (e.g., “acquire, from a first local data processing apparatus of a first business operator, a first local learning model generated by the first local data processing apparatus using first customer data stored in a first storage of the first business operator” & “acquire, from a second local data processing apparatus of the second business operator, a second local learning model generated by the second local data processing apparatus using second customer data stored in a second storage of the second 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)).
The recited memory and processor merely provide generic computer components for performing the identified information-processing activities. Likewise, the first and second local data-processing apparatuses, local learning models, common and unique data regions, feature extraction, model federation, and generation of estimated purchase indices are recited as mechanisms for organizing, analyzing, associating, and predicting customer information. Similarly, connecting the data structures of the local learning models through common customer groups merely establishes relationships between information corresponding to customer groups. The claim does not identify a specific unconventional data structure or other technological improvement. The resulting prospective customer data and estimated purchase indices are informational outputs concerning customer behavior.
These claims use computer and machine-learning technology as a tool to perform the recited abstract information-processing activity rather than integrating the judicial exception into a practical application. The USPTO's AI guidance specifically instructs examiners to distinguish a technological improvement from merely using a computer to perform an abstract idea.
Thus, the claims effectively recite locally maintained customer information → local learning models → extraction/association of model information → federated model → customer-behavior predictions. The claimed result remains information concerning prospective customers and estimated purchase tendencies. The claims does not transform the abstract information-processing activity into a different technological process. Applicant's argument that the amended claims are more specific than merely stating a desired result is acknowledged. However, specificity alone does not establish a practical application. Recentive confirms that even detailed machine-learning implementations may remain abstract where the claimed technology is used to perform an abstract activity without a claimed technological improvement.
Accordingly, the amended limitations do not integrate the identified abstract idea into a practical application. 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 1-5, 9-12, 15-18 and 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 #3:
(C). Applicant argues that Claims 1-5, 9-12, 15-18 and 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, Page 10, dated 09/25/2026). Examiner respectfully disagrees.
Applicant further argues that the claimed elements, considered as an ordered combination, amount to significantly more. This argument is not persuasive.
The additional elements, considered individually and as an ordered combination, consist of generic computer components and information-processing operations for obtaining, organizing, analyzing, associating, and predicting information. The claim's memory and processor merely execute the instructions necessary to perform those operations. The local processing apparatuses generate local models, and the claimed federation operation combines information from those models according to common customer groups.
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, from a first local data processing apparatus of a first business operator, a first local learning model generated by the first local data processing apparatus using first customer data stored in a first storage of the first business operator” & “acquire, from a second local data processing apparatus of the second business operator, a second local learning model generated by the second local data processing apparatus using second customer data stored in a second storage of the second business operator” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflect 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 claims do not identify an unconventional processor, memory architecture, data-storage mechanism, communication protocol, model architecture, feature-extraction technique, or model-training technique. Nor does the claims establish that the ordered combination improves the operation of the computer or learning model itself.
The limitation that raw customer data is not acquired by the central apparatus merely maintains the respective datasets at the local businesses. The "common region" and "unique region" limitations organize information according to shared and business-specific attributes. The feature-extraction and model-connection limitations process information derived from the local models. Finally, the estimated purchase indices provide information regarding customer purchasing tendencies.
Thus, even when considered together, the limitations amount to the implementation of the identified abstract information-processing activity using computer and machine-learning technology. The combination does not supply an inventive concept beyond the abstract idea.
Applicant's reliance on the particularity of the ordered combination therefore does not establish significantly more. As explained in Recentive, applying machine learning in a particular environment or to achieve a particular business-related result does not itself supply an inventive concept.
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 1-5, 9-12, 15-18 and 20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-5, 9-12, 15-18 and 20 are ineligible with respect to the 35 U.S.C. § 101 analysis. For the foregoing reasons, Applicant's arguments have been considered but are not persuasive. The amendments further specify the information used to generate the federated model, but they do not change the character of the claimed activity from the organization, analysis, and prediction of customer and consumption-behavior information. Moreover, the Research Corp case is distinguishable because the present claim's “data structures” are being used to represent and associate customer/behavior information, while these claims do not recite a particular technological transformation or improvement of those structures. That distinction also better accommodates the current USPTO guidance following Desjardins. The 35 U.S.C. § 101 rejection of Claims 1-5, 9-12, 15-18 and 20 is maintained.
Claim Rejections - 35 USC § 101
12. 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.
13. Claims 1-5, 9-12, 15-18 and 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 1-5, 9-12, 15-18 and 20 are each focused to a statutory category namely, an “apparatus” or a “system” (Claims 1-5 and 9-11) and a “method” or a “process” (Claims 12, 15-18 and 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):
“” (see Independent Claim 1);
“” (see Independent Claim 1);
“acquire, of a first business operator, a first local learning model generated using first customer data stored of the first business operator” (see Independent Claim 1);
“acquiring, , of a first business operator, a first local learning model generated using first customer data stored of the first business operator” (see Independent Claim 12);
“wherein the first customer data is stored 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);
“wherein the first local learning model has been trained through a training step of learning relationships between a plurality of common customer groups included in the common region and a plurality of first consumption behaviors included in the unique region ” (see Independent Claims 1 and 12);
“acquire, of the second business operator, a second local learning model generated using second customer data stored of the second business operator” (see Independent Claim 1);
“acquiring, , of the second business operator, a second local learning model generated using second customer data stored of the second business operator” (see Independent Claim 12);
“wherein the second customer data is stored as divided into a corresponding common region having the attribute definitions and a unique region indicating consumption behaviors specific to the second business operator” (see Independent Claims 1 and 12);
“wherein the second local learning model has been trained through a training step of learning relationships between the plurality of common customer groups included in the corresponding common region and a plurality of second consumption behaviors included in the unique region ” (see Independent Claims 1 and 12);
“generate a federated learning model by federating one or more features extracted from the acquired first local learning model and one or more features extracted from the acquired second local learning model” (see Independent Claim 1);
“generating, , a federated learning model by federating one or more features extracted from the acquired first local learning model and one or more features extracted from the acquired second local learning model” (see Independent Claim 12);
“wherein the federating comprises connecting, via the plurality of common customer groups, of the acquired first local learning model and of the acquired second local learning model” (see Independent Claims 1 and 12);
“wherein, for generation of the federated learning model, the first customer data is not acquired from the , and the second customer data is not acquired by the from the ” (see Independent Claims 1 and 12);
“wherein the federated learning model is 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 prospective customer data including estimated purchase indices for at least a subset of the plurality of common customer groups, each estimated purchase index indicating a tendency of customers in a respective common customer group to perform the certain consumption behavior” (see Independent Claims 1 and 12).
Here, for Independent Claims 1 and 12, the abstract ideas in these claim limitations are directed to the collection, organization, analysis, and evaluation of customer and consumption-behavior information, including: (1) dividing customer information into common and business-specific regions; (2) identifying relationships between common customer groups and business-specific consumption behaviors; (3) acquiring local learning models representing those relationships; (4) extracting and connecting features from the local models through common customer groups; and (5) generating prospective customer information including estimated purchase indices indicating the tendency of customers in respective groups to perform a specified consumption behavior. More specifically, the abstract idea for Independent Claims 1 and 12 is directed to “Organizing and analyzing customer and consumption-behavior information from multiple businesses to identify relationships between customer groups and consumption behaviors and to generate information predicting customers' propensity to engage in a specified consumption behavior.”
These limitations fall within the certain methods of organizing human activity and mental processes groupings of abstract ideas. In particular, the claimed processing organizes and evaluates information concerning businesses, customers, and consumption behaviors for purposes of identifying prospective customers and predicting purchasing behavior, which concerns commercial activity and business/customer relationships. The claimed identification, evaluation, association, and prediction of relationships among customer groups and consumption behaviors also constitutes analysis and evaluation of information characteristic of a mental process. The abstract-idea groupings are not mutually exclusive.
To the extent the claimed extraction of model features, generation of the federated model, and estimated purchase indices necessarily involve mathematical calculations, such calculations may additionally implicate the mathematical-concepts grouping. However, the principal abstract idea is the organization and analysis of customer/consumption information to identify and predict prospective customer behavior.
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 “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) and (2) relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively covering performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) 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 “Mathematical Concepts” which pertains to (5) mathematical relationships.
That is, other than reciting (e.g., “at least one memory” & “a data structure” & “ & “first storage” & “second storage” & “federated learning model generation apparatus” & “a first local data processing apparatus” & “a second local data processing apparatus” & “at least one processor” & “a computer”), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) and (2) relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively covering performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, and additionally or alternatively covering performance of their limitations as “Mathematical Concepts” which pertains to (5) mathematical relationships.
Therefore, at step 2a prong 1, Yes, Claims 1-5, 9-12, 15-18 and 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” & “federated learning model generation apparatus” & “a first local data processing apparatus” & “a second local data processing apparatus” & “data structure” & “ & “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)).
The recited memory and processor merely provide generic computer components for performing the identified information-processing activities. Likewise, the first and second local data-processing apparatuses, local learning models, common and unique data regions, feature extraction, model federation, and generation of estimated purchase indices are recited as mechanisms for organizing, analyzing, associating, and predicting customer information. Similarly, connecting the data structures of the local learning models through common customer groups merely establishes relationships between information corresponding to customer groups. The claim does not identify a specific unconventional data structure or other technological improvement. The resulting prospective customer data and estimated purchase indices are informational outputs concerning customer behavior.
These claims use computer and machine-learning technology as a tool to perform the recited abstract information-processing activity rather than integrating the judicial exception into a practical application. The USPTO's AI guidance specifically instructs examiners to distinguish a technological improvement from merely using a computer to perform an abstract idea.
Also, for Independent Claims 1 and 12: Even if the steps of (e.g., “acquire, from a first local data processing apparatus of a first business operator, a first local learning model generated by the first local data processing apparatus using first customer data stored in a first storage of the first business operator” & “acquire, from a second local data processing apparatus of the second business operator, a second local learning model generated by the second local data processing apparatus using second customer data stored in a second storage of the second 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 amount 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 1-5, 9-12, 15-18 and 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” & “federated learning model generation apparatus” & “a first local data processing apparatus” & “a second local data processing apparatus” & “data structure” & “ & “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, from a first local data processing apparatus of a first business operator, a first local learning model generated by the first local data processing apparatus using first customer data stored in a first storage of the first business operator” & “acquire, from a second local data processing apparatus of the second business operator, a second local learning model generated by the second local data processing apparatus using second customer data stored in a second storage of the second business operator” (see Independent Claims 1 and 12)) are evaluated as additional elements, these activities reflect 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).
The additional elements, individually and in combination, do not amount to significantly more than the judicial exception. The memory, processor, local processing apparatuses, and associated model-processing operations provide generic computer implementation of the identified abstract idea. The division of customer data into common and unique regions, extraction of model features, connection of the local model data structures through common customer groups, and generation of estimated purchase indices merely specify how the customer information is organized, analyzed, and used to generate predictions.
The limitation that the underlying customer data is not acquired from either business operator may provide a privacy or business benefit, but the claim does not recite an unconventional technical mechanism that produces an improvement in computer functionality or another technology. Likewise, the claimed federation of the local models does not recite a particular unconventional computer architecture, training technique, communication protocol, or other technological improvement.
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 2-5, 9-11, 15-18 and 20 recite the same abstract ideas as Independent Claims 1 and 12 along with further steps/details that are performed as “Certain Methods of Organizing Human Activities” which pertains to (1) commercial interactions (including marketing or sales activities or behaviors or business relations) and (2) relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively covering performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, and additionally or alternatively covering performance of their limitations as “Mathematical Concepts” which pertains to (5) mathematical relationships.
Furthermore, Dependent Claims 2-5, 9-11, 15-18 and 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 networks.”. 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 1-5, 9-12, 15-18 and 20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-5, 9-12, 15-18 and 20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
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.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A /SARA GRACE BROWN/Primary Examiner, Art Unit 3625
