DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 19/347,814, was filed on 10/02/2025, and claims foreign priority, based on Japanese Application JP2024-179344, filed on 10/11/2024.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant's claim for foreign priority, based on Japanese Application JP2024-179344, filed on 10/11/2024.
On 10/20/2025, the certified priority document was electronically retrieved by USPTO from WIPO. Acknowledgment is made of receipt of certified copies of papers required by 37 CFR 1.55.
Status of the Application
This Non-Final Office Action is in response to Applicant’s communication of 10/02/2025.
Claims 1-9 are pending, of which claims 1, 8, and 9 are independent.
All pending claims have been examined on the merits.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 10/02/2025 has been considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
Based on the flowchart in MPEP § 2106, Step 1 of the Alice/Mayo analysis is: “Is the claim to a process, machine, manufacture or composition of matter?”
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is an apparatus claim, claim 8 is a method claim, and claim 9 is an article of manufacture claim or product by process claim (“non-transitory computer readable medium”).
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
Step 2A, prong 1 of the Alice/Mayo analysis is: “Does the claim recite a law of nature, a natural phenomenon (product of nature), or an abstract idea?”
In regards to Step 2A, prongs 1 and 2 of the Alice/Mayo analysis, the abstract idea elements recited in independent claim 1 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
1. An information processing apparatus, comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to;
acquire condition information indicating a desired condition of the subject for insurance; and
extract a related description related to the desired condition from a document describing an insurance product that is a candidate recommended to the subject by using an extraction model with machine learning in such a way as to output a portion related to the data in the document using a set of the document and the data as an input.
More specifically, claims 1-9 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include:
“extract a related description related to the desired condition from a document describing an insurance product that is a candidate recommended to the subject”.
The “additional elements” include: “at least one memory”, “at least one processor”, and “an extraction model with machine learning”.
Moreover, “additional extra-solution elements” include: “storing instructions”, “acquire condition information indicating a desired condition of the subject for insurance”, “output a portion related to the data in the document”, and “using a set of the document and the data as an input”.
Step 2A, prong 2 of the Alice/Mayo analysis is “Does the claim recite additional elements that integrate elements that integrate the judicial exception into a practical application?”
In regards to Step 2A, prong 2 of the Alice/Mayo analysis, this abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“at least one memory”, “at least one processor”, and “an extraction model with machine learning”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, such as in the following feature: “using an extraction model with machine learning in such a way as to output a portion related to the data in the document using a set of the document and the data as an input”.
In regards to “apply it” (applying the abstract idea on a general purpose computer), the 35 USC § 101 rejections are based on the CAFC decision in Recentive Analytics, Inc. v. Fox Corp. April 18, 2025 (https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf).
The Recentive Analytics decision states (see page 10): “This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.”
The Examiner holds that Applicant’s description of the neural network merely describes “apply it” uses of a generic neural network. See the description in para. [0037] of the specification (or para. [0047] of the application’s US 2026/0105530 A1:
[0037] The determination unit 103A determines whether the insurance product described by the related description extracted by the extraction unit 102A satisfies the desired condition of the subject using a language model that has machine learned a natural language. Here, machine learning on natural language more specifically means learning of the arrangement of components (words and the like) in a sentence in a natural language and the arrangement of sentences in a text. Examples of the language model trained on natural language include bidirectional encoder representations from transformers (BERT), Robustly optimized BERT approach (RoBERTa), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and the like. Hereinafter, the language model used by the determination unit 103A is referred to as a language model M2.
The extra-solution activities (“storing instructions”, “acquire condition information indicating a desired condition of the subject for insurance”, “output a portion related to the data in the document”, and “using a set of the document and the data as an input”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo analysis is: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
In regards to Step 2B of the Alice/Mayo analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“at least one memory”, “at least one processor”, and “an extraction model with machine learning”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
Instead, they merely apply established methods of machine learning to a new data environment, as held to be unpatentable in the Recentive Analytics case.
In regards to the extra solution activities (“storing instructions”, “acquire condition information indicating a desired condition of the subject for insurance”, “output a portion related to the data in the document”, and “using a set of the document and the data as an input”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “storing” step, see the court cases Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (storing and retrieving information in memory).
More specifically, in regards to the “receiving”, “acquire condition information”, “output a portion related to the data in the document”, and “using a set of the document and the data as an input” steps, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claims 8 and 9 are rejected on the same grounds as independent claim 1. Independent claim 9 is also rejected on the grounds that it recites a computer-readable medium, which is merely another generic computer component.
All dependent claims are also rejected, because they merely further define the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 7-9 are rejected under 35 U.S.C. §102 (a)(2) as being anticipated by US 12,489,744 B1 to Krew et al. (“Krew”. Eff. Filed on Oct. 5, 2022).
In regards to claim 1,
1. An information processing apparatus, comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to;
(See Krew, col.2, line 58 to col.3, line 11: “The exemplary embeddable system of FIG. 1 includes memory coupled to one or multiple microprocessors 102 by a parallel circuit 108 or a local communication system such as a bus that transfers data between internal and external components. Some memory comprise a dynamic memory that improves microprocessor execution speed when the microprocessors 102 read and/or write to memory locations sequentially. In some systems, the dynamic memory is volatile and/or non-volatile memory 104 and 106, respectively. In other systems, the memory emulates a virtual disk drive that is read from and written to. The system's volatile memory 104 operates like a physical disk drive making the embeddable systems extremely fast without displacing the system's operating memory. Input and output interfaces 110 and local communication systems 108 connect peripherals and data pipelines to the embeddable system. The data pipelines ensure resource availability, manage inter-data dependencies, and minimize transient data delays that slows down executions. The data pipelines also allow embeddable systems to move and process data seamlessly that is usually locked up in local data silos.”)
acquire condition information indicating a desired condition of the subject for insurance; and
(See Krew, col. 3, lines 45-63: “Drawing upon a plurality of profiles stored in a parallel database 812, some embeddable systems deliver content to customized user profiles in use by identifying unique combinations of characteristics that differentiate users from one another through an advanced intelligence engine 208 (also referred to as an intelligent engine) resident to the intra-ware application 210; and in alternate systems, through comparisons to profile classifications retained in the parallel database system 812. By combining received data with contextual information about the user, such as, for example, the user's location (e.g., device proximity via global positioning) and address, a classification is executed by the intra-ware application 210 that classifies the user based on comparisons of characteristics. Intra-ware 210 comprises one or more software applications that sit between or interface two or more types of software; and in some applications, translates information between them. In some systems, it sits between a server's operating system and a network's operating system.”)
(See Krew, col.13, lines 14-32: “In FIG. 6 , the advanced intelligence engine 208 generates three estimated insurance rates based on a plurality of assumptions and defaults related to a plurality of insurance rating factors described above. In some systems, the assumptions and defaults are dynamically established by one or more machine learning engines 402, in other systems they are determined from profile classifications, in other systems they are based on statistical analytical defaults and/or on static and/or dynamic empiric and/or heuristic data comparisons, in other systems they are established by classifications with static values based on historical data, and in other systems they are based on combinations of these processes such as artificial intelligence and/or other factors. In some systems, the advanced intelligence engine 208 resides within an orchestration platform that leverages one or more predictive algorithms, including supervised learning models, unsupervised learning models, and/or combinations by processing data, provided, at least in part, by the host server 302, to render predictive insurance rates.”)
The Examiner interprets that Krew’s “classification” and“profile classifications” read upon the claimed “condition information indicating a desired condition of the subject for insurance”.
extract a related description related to the desired condition from a document describing an insurance product that is a candidate recommended to the subject by using an extraction model with machine learning in such a way as to output a portion related to the data in the document using a set of the document and the data as an input.
(See Krew, col.6, lines 23-50: “In return, the remote resource server 308 returns an insurance rate. The insurance rate includes a second redirect uniform resource link that specifies a protocol used to access a resource, the server where the resource resides, an address to that resource, and, optionally, the path to that resource (e.g., the route through the structured collection that defines the exact location). In the exemplary insurance rate/insurance quoting application, the second redirect uniform resource link transfers the user to a remote site that presents a user with a bindable insurance quote in the same computing session. In some exemplary insurance rate/insurance quoting applications, the remote resource server 308 serves insurance rates, insurance quotes including bindable insurance quotes for different users (e.g., different drivers, different insureds) and/or different products (e.g., coverages for different vehicles)—from one driver and one vehicle to two or more drivers and/or two or more vehicles, for example. Some exemplary insurance rate/insurance quoting applications serve insurance products covering many insurable scenarios including shared products (e.g., shared vehicles), short-term rentals, ridesharing, gig economy contracts, and/or multi-user scenarios (e.g., multi-driver scenarios, multi-owner scenarios, multi-property scenarios), etc. Further, each of the described insurance applications may also provide physical and/or virtual proof of insurance to users and/or third parties (e.g., third party sellers, service providers, object owners, etc.) when insureds accept rates and/or quotes and/or receive insurance”)
(See Krew, col.10, lines 4-28: “In FIG. 4, the second response returned by the resource server 308 is generated entirely, or in part, by one or more machine learning engines 402 (referred to as the machine learning engine) that, in some systems is a stand-alone system and in other systems comprises part of the advanced intelligence engine 208. The machine learning engine 402 processes the data transmitted by the second tier application programming interface 306. In the system of FIG. 4, data stored in the resource server's cache 204 and/or accessible to its delegated proxy harvested and/or was indexed by the intra-ware application 210 is processed by the machine learning engine to generate the machine learning response (ML-Response). In this system, one or more machine learning algorithms detect, analyze, classify, and generate responses that automatically interact with the system; and in some instances, train the machine learning engine 402 to emulate such intelligence. An exemplary machine learning training algorithm trains the machine learning engine to generate responses, such as providing insurance ratings, insurance rates, insurance quotes, insurance claim services, insurance policy services, insurance bundle services, and other services and/or tasks such as other financial services and/or tasks. Based on repeated training of prior user characteristics, the machine learning algorithms train the machine learning engines.”)
In regards to claim 2,
2. The information processing apparatus according to claim 1, the at least one processor is further configured to execute the instructions to determine whether the insurance product described in the related description satisfies the desired condition by using a language model that has machine learned a natural language; and
(See Krew, col.14, line 63 to col.15, line 9: ”Contextual adds comprise content that is dynamically adapted to the content displayed on and/or stored in the computer-mediated technology 310, which in FIG. 9 is a wireless device. A contextual targeting algorithm 902 analyzes content rendered on the computer-mediated technology 310 or within its cache to identify keywords, themes, sentiment, etc., and/or combinations. Some contextual targeting algorithms 902, such as the model based machine learning engines or rule-based machine learning engines described herein. The contextual targeting algorithms 902 leverage natural language processing engines 904 and/or computer vision engines 906 to scan and analyze text and images received by and/or stored on the computer-mediated technology 310.”)
present the insurance product determined to satisfy the desired condition to the subject as a recommended insurance product.
(See Krew, col.11, lines 24-44: “The first, the second, the third, etc., machine learning engines alone, in a parallel configuration, or in a serial configuration (e.g., MLE.sub.1+MLE.sub.2+MLE.sub.3 . . . MLE.sub.n) may be rule-based, may be modeled based, or may be a hybrid combination of one or more rule-based systems and one or more model-based systems, for example, depending on the application. In an exemplary rule based system, the rule comprises intelligent rules such as static rules created by domain experts that rely on encoded knowledge. For example, in an insurance context, exemplary intelligent rules may tailor insurance rates and/or bindable insurance quotes to user attributes and profiles to enhance personalization and provide a more accurate product and/or service in comparison to generic offers such as rate proposals and/or insurable quotes. The intelligent rules may be based on known patterns and criteria. For example, when a user under twenty-five years of age seeks insurance on a high risk sedan, for example, an exemplary intelligent rule may be applied that is expressed as the following exemplary statement. IF vehicle_type=“sports car” or “sedan” AND user_age<25 THEN select high risk table.”)
In regards to claim 3,
3. The information processing apparatus according to claim 2, the at least one processor is further configured to
execute the instructions to generate a prompt that includes the related description and the desired condition and instructs to infer a relationship between the related description and the desired condition; and
determine whether the insurance product described by the related description satisfies the desired condition based on an output obtained by inputting the generated prompt to the language model.
(See Krew, col.11, lines 38-49: “The intelligent rules may be based on known patterns and criteria. For example, when a user under twenty-five years of age seeks insurance on a high risk sedan, for example, an exemplary intelligent rule may be applied that is expressed as the following exemplary statement. IF vehicle_type=“sports car” or “sedan” AND user_age<25 THEN select high risk table. A logical engine then evaluates the rules against the input data to generate responses. In this exemplary system, the intelligent rules are updated in memory 804 as new patterns are established or are detected rendering new and/or modified responses.”)
(Also, see Krew, col.15, line 66 to col.16, line 20:” Some smart widgets 914 analyze user behavior data, demographic data, and user preference data through the natural language processing engines 904 and/or the computer vision engines 906 that scan and analyze text and images received by and/or stored on the computer-mediated technology 310 to render a dynamic personalization. Using generative models, decision trees, etc., and/or combinations, the smart widgets 914 generate unique content. Some exemplary generative models are transformer-based architectures that include generative pre-trained transformers and optional bidirectional encoder representations for transformers that process input, such as text input through natural language processing 904 and/or neural networks. In an exemplary transformer architecture, an attention mechanism weights an input sequence to understand the context of the input. Optional bidirectional encoder representations for transformers capture the context of the input; and the generative pre-trained transformers process the input sequentially to generate an output that responds to the input communicated through the application programming interfaces via the embeddable systems and processes described above and herein and further shown in FIGS. 9 and 10.”)
In regards to claim 4,
4. The information processing apparatus according to claim 3, the at least one processor is further configured to execute the instructions to
generate a prompt instructing to output a basis of the inference together with an inference result of the relationship between the related description and the desired condition; and
present the inference result output by the language model, a determination result by determined, or an insurance product determined to satisfy the desired condition together with the basis.
(See Krew, col.16, lines 45-65 : “The in-ad browsing enables users to interact with remote and local sites directly through the application programming interfaces and processes via the computer-mediated technology 310. Using a simplified interface 916 (e.g., a lightweight compact and efficient mini-browser) that allows user to view documents (such as Hypertext Markup Language files and software associated with them), users interact with ad content directly without leaving, browsing, or accessing more of a host page. The simplified interface 916 loads content dynamically and provides partial navigation functionality that allows users to interact with the content within the confines of the in-ad space on the hosting page without browsing other content on the page that is accessible via a Web browser. From the initial browsing, the user can click on content that provides access to a plurality of remote sources locally through the embedded system of tiered application programing interfaces described herein. The in-add browsing facilitates a high user engagement without an initial redirection which allows users to browse and shop directly in the advertisement that is framed within a site.”)
In regards to claim 5,
5. The information processing apparatus according to claim 4, the at least one processor is further configured to
execute the instructions to receive a correction instruction for the inference result; and redetermines whether the insurance product described by the related description satisfies the desired condition based on a correction instruction received.
(See Krew, col.10, line 55 to col.11, line 11:”When multiple machine learning engines are used, the machine learning algorithms may include supervised learning models, unsupervised learning models, and/or combinations that operate independently in stages and jointly as a unitary machine learning engine. Some integrations such as serial integrations, for example, are based on minimizing a measured machine learning engine residual, MLERES, which is the difference between a response (e.g., the predicted response of the machine learning engine) and the correct response. In the serial configuration, a second machine learning engine, MLE2, is generated to fit or minimize the residual of the prior machine learning engine, MLE1. The first and the second machine learning engines are combined (e.g., MLE1+MLE2) such that the input of the second machine learning engine, MLE2input, comprises the output of the first machine learning engine MLE1out, rendering a boosted version of the first machine learning engine MLE1. The boost generates a lower residual error than a single machine learning engine, MLE1 or MLE2. The residuals of the serially combined machine learning engines are lower than the sum of the residuals of the machine learning engines (e.g., equation 1); and in some systems, less than the residual of the individual machine learning engines alone (e.g., equation 2).”)
In regards to claim 7,
7. The information processing apparatus according to claim 1, the at least one processor is further configured to
execute the instructions to present history information regarding a desired condition of the subject for insurance;
receive an input of an explanatory sentence describing the presented history information; and
extract a related description related to the desired condition using a set of the history information and the explanatory sentence as one desired condition.
(See Krew, col.12, lines 58-65 : ”Some systems harvest additional information from motor vehicle reports and a loss underwriting exchange. An exemplary loss underwriting exchange comprises an exchange that provides insurance claim history that tracks the prior seven years of a driver's personal auto insurance claims. In response, the resource server 308 returns one or more estimated insurance rates and a redirect uniform resource link to the wireless device via the host server 302.”)
In regards to independent claim 8, it is rejected on the same grounds as independent claim 1.
In regards to independent claim 9, it is rejected on the same grounds as independent claim 1.
Conclusion
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
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.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
June 20, 2026
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