DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 18/377,403, was filed on 10/06/2023, and does not claim foreign priority or domestic benefit to any other application.
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.
Status of the Application
This Final Office Action is in response to Applicant’s communication of 01/22/2026.
Claims 1-6, 8-17, 19, 20 are pending, of which claims 1 and 12 are independent.
All pending claims have been examined on the merits.
Claim Objections
Claim 12 is objected to because of the following informalities: “identify at least one modified building property associated the unstructured data” should be amended to recite “identify at least one modified building property associated with the unstructured data”.
Appropriate correction is required.
Claim Interpretation
The term “embedding” in independent claims 1 and 12 is interpreted according to the special definition in para. [0046] of the specification (emphasis added):
[0046] An embedding is a representation of a discrete object, such as a word, a document, or an image, as a continuous vector in a multi-dimensional space. An embedding captures the semantic or structural relationships between the objects, such that similar objects are mapped to nearby vectors, and dissimilar objects are mapped to distant vectors. Embeddings are commonly used in machine learning and natural language processing tasks, such as language modeling, sentiment analysis, and machine translation.
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 1-6, 8-17, 19, 20 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 a method claim, and independent claim 12 is an apparatus claim.
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 12 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
12. (Currently Amended) A system comprising:
a storage configured to store instructions;
at least one processor configured to execute the instructions to cause the at least one processor to:
receive a document comprising unstructured data;
transform the unstructured data into one or more embeddings;
identify a property associated with the document based on geographical information extracted from the unstructured data;
identify at least one modified building property associated the unstructured data by inputting the one or more embeddings into a machine learning engine comprising a classifier trained to classify building modifications based on one or more classification taxonomies;
update a data structure corresponding to the property to include the at least one modified building property, wherein data structure comprises an immutable data structure that can only be appended to; and
update a loss model associated with the property based on the data structure by: generating, using a loss generation model trained with a federated learning technique, an updated model that estimates a potential loss based on at least one of: a weather patterns or an environmental effect.
More specifically, claims 1-6, 8-17, 19, 20 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Fundamental Economic Principles or Practices (including Hedging, Insurance, Mitigating Risk)”, “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, or “Managing Personal Behavior or Relationships or Interactions Between People (Including Social Activities, Teaching, and Following Rules or Instructions)” 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:
“identify a property associated with the document based on geographical information extracted from the unstructured data”. (This is equivalent to identifying a building based on an address extracted from unstructured data in a document).
“identify at least one modified building property associated the unstructured data by inputting the one or more embeddings into a machine learning engine comprising a classifier trained to classify building modifications based on one or more classification taxonomies”. (This is “apply it” – using a machine learning engine to classify building modifications, based on unstructured data from a document).
“update a loss model associated with the property based on the data structure by: generating, using a loss generation model trained with a federated learning technique, an updated model that estimates a potential loss based on at least one of: a weather patterns or an environmental effect”. (This is “apply it” – using a machine learning engine to estimate potential weather damage to a building, based on at least one of: a weather pattern data or environmental effect data).
Moreover, claims 1-6, 8-17, 19, 20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematical elements include:
“transform the unstructured data into one or more embeddings”. (According to the special definition in para. [0046] of the specification, an “embedding” is “a continuous vector in a multi-dimensional space”).
The “additional elements” include: “a storage configured to store instructions” and “at least one processor”.
The “additional extra-solution elements” include: “receive a document comprising unstructured data” and “update a data structure corresponding to the property to include the at least one modified building property, wherein data structure comprises an immutable data structure that can only be appended to”.
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 (“a storage configured to store instructions” and “at least one processor”) do not add a meaningful limitation to the abstract idea, because they amount to 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: “inputting the one or more embeddings into a machine learning engine comprising a classifier trained to classify building modifications” and “update a loss model … using a loss generation model trained with a federated learning technique”.
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 of the verdict): “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 the independent claims 1 and 12 merely recite “apply it” uses of a generic machine learning models.
The extra-solution activities (“receive a document comprising unstructured data” and “update a data structure corresponding to the property to include the at least one modified building property, wherein data structure comprises an immutable data structure that can only be appended to”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity (receiving a document, and updating a blockchain data structure).
The combination of the abstract idea with the additional elements (the generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
The independent claims 1 and 12 merely generally link the use of the abstract idea (i.e. the machine learning models and the blockchain) to a particular field of use (i.e. estimating weather insurance risks for buildings) - see MPEP 2106.05(h).
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" (“a storage configured to store instructions” and “at least one processor”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to 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, these elements merely apply well-established methods of machine learning to a new data environment (i.e. estimating weather insurance risks for buildings), which has been held to be unpatentable in the Recentive Analytics case.
In regards to the extra solution activities (“receive a document comprising unstructured data” and “update a data structure corresponding to the property to include the at least one modified building property, wherein data structure comprises an immutable data structure that can only be appended to”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “storing” / “update a data structure” 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” 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).
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Independent claim 1 is rejected on the same grounds as independent claim 12.
All dependent claims are also rejected, because they merely further define the abstract idea.
The dependent claims further refine and limit the abstract idea recited by the independent claims, from which these claims respectively directly or indirectly depend, where the abstract idea is described above.
Claims 2, 11 and 13 further refine the abstract idea by requiring that the modified property has a certain associated risk, or that that abstract idea requires an in-person inspection and further generates a list of properties to be inspected, which are risk mitigation steps for an insurance claim analysis, and these claims do not add any element or feature that provides an integration into a practical application by providing a technological solution to a technological problem or by technologically improving any recited additional element (which is being used to carry out the abstract idea as a “tool” under Step 2A, Prong 2), or include any element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B). (See MPEP §§2106.04, 2106.05)
Claims 3, 4, 14 and 15 further refine the abstract idea by requiring additional steps of identifying at least one improvement to the property, and generating and sending out certain education information corresponding to the at least one improvement, or further requiring that additional information associated with the education information is identified and a loss model is updated that is associated with the property, which are additional insurance property damage processing steps, and these claims do not add any element or feature that provides an integration into a practical application by providing a technological solution to a technological problem or by technologically improving any recited additional element (which is being used to carry out the abstract idea as a “tool” under Step 2A, Prong 2), or include any element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B). (See MPEP §§2106.04, 2106.05)
Claims 5 and 16 further refine the abstract idea by requiring that a security disbursement requirement is determined based on certain previous claim data, the insurance loss model and identity data of an entity associated with the property, which is data analysis for reducing a security requirement, and these claims do not add any element or feature that provides an integration into a practical application by providing a technological solution to a technological problem or by technologically improving any recited additional element (which is being used to carry out the abstract idea as a “tool” under Step 2A, Prong 2), or include any element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B). (See MPEP §§2106.04, 2106.05)
Claims 6, 8, 17 and 19 further refine the abstract idea by requiring that additional steps of receiving additional data regarding the modified property, processing that received data, and determining whether an in-person inspection is required, or that additional steps of receiving additional data regarding a certain property, mapping certain building lifecycle data to the property, and training a learning model using the mapped data, where these additional steps further refine process steps for insurance purposes, and these claims do not add any element or feature that provides an integration into a practical application by providing a technological solution to a technological problem or by technologically improving any recited additional element (which is being used to carry out the abstract idea as a “tool” under Step 2A, Prong 2), or include any element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B). (See MPEP §§2106.04, 2106.05)
Claims 9, 10 and 20 further refine the machine learning model additional element of by requiring that it be trained using certain data, where the data may be associated with various building standards, which is the use of certain data to train the additional element machine learning model, without bringing about any technological improvement. These claims do not add any element or feature that provides an integration into a practical application by providing a technological solution to a technological problem or by technologically improving any recited additional element (which is being used to carry out the abstract idea as a “tool” under Step 2A, Prong 2), or include any element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B). (See MPEP §§2106.04, 2106.05)
Thus, neither the independent claims nor the dependent claims, viewed individually and as a whole, including consideration of all the limitations of each claim viewed both individually and in combination, add any additional element or provide any subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter, nor do the claims provide something significantly more than the recited abstract idea to which the claims are directed.
Response to Amendments
Claim Rejections - 35 U.S.C. §112(a)
In response to Applicant’s amendments to independent claims 1 and 12, the 35 U.S.C. §112(a) rejection of claims 1-6, 8-17 and 19-20 is withdrawn.
Support for the amendment can be found para. [0044] and [0092] of the specification (emphasis added):
[0044] In some aspects, the ML training system 210 is configured to receive the information and process the information into different content and store the content in the storage system 220. The ML training system 210 can generate a plurality of ML models 221 associated with different functions of the climate risk assessment system 200. For example, the ML models 221 can include classifiers to classify a modification to a building, a transformer to understand the relationships of works in unstructured information (e.g., the transformer 700 of FIG. 7 ), and so forth. A non-limiting and illustrative example of ML model is a climate risk assessment model that is configured to receive different types of unstructured information, embeddings, structured content (e.g., maps, etc.), and assess a risk of damage to the building based on geographical information (e.g., terrain, etc.) and climate risk factors (e.g., humidity, rainfall, geography, etc.).
[0092] FIG. 9 is a block diagram of various classifiers that can be used to identify one or more classifications from one or more taxonomies in accordance with some aspects of the disclosure. In particular, FIG. 9 includes a binary classifier 910 and a multilabel classifier 950.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See CN 116977092 A, which teaches (emphasis added):
Wherein, the target deep learning model can be a trained model in the form of a "magnet model + classifier". Wherein, the solves model can be used as encoder, specifically can be a bert model (i.e., Bidirection Encoder Representation from Transformers, representing the language characterization model of pre-training), for performing coding and pool processing on the unstructured insurance data, obtaining the high-dimensional text vector corresponding to the unstructured insurance data, while the classifier can specifically be a multi-layer perceptron MLP (i.e., multilayer Perceptron), for mapping and classifying the high-dimensional text vector of the unstructured insurance data output by the bert model, That is, the multi-layer neural network is mapped to the specific classification type, specifically data such as insurance risk, or no insurance risk, or actual risk value for insurance, so as to obtain the second risk evaluation data corresponding to the unstructured insurance data.
Specifically, according to the second risk evaluation logic matched with the unstructured insurance data, determining the target depth learning model matched with the unstructured insurance data, through the target depth learning model matched with the unstructured insurance data, performing secondary risk evaluation processing on the unstructured insurance data. Wherein, the target deep learning model can be a trained model in the form of a "magnet model + classifier". Wherein, the Representation model may specifically be a bert model (i.e., a language representation model representing pre-training from Transformers), for encoding the unstructured insurance data, obtaining the high-dimensional text vector corresponding to the unstructured insurance data, and the classifier can be specifically a multi-layer perceptron MLP (i.e., Perceptron), for mapping and classifying the high-dimensional text vector of the unstructured insurance data output by the bert model, namely mapping to the specific classification type through the multi-layer neural network, specifically there is insurance risk, or there is no insurance risk, or performing data such as actual risk value of insurance to obtain the second risk evaluation data corresponding to the unstructured insurance data.
The person skilled in the art can understand that all or part of the processes in the method of the above embodiments can be implemented by a computer program instructing related hardware, the computer program can be stored in a non-volatile computer readable storage medium, when the computer program is executed, The flow of embodiments of the methods described above may be included. Any reference to a memory, database or other medium used in various embodiments provided by the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (Read-OnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high density embedded non-volatile memory, resistive random access memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory and so on. The volatile memory may include a Random Access Memory (RAM) or an external cache memory or the like. As an illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (Static Random Access Memory). SRAM) or Dynamic Random Access Memory (DRAM) and so on. The database involved in various embodiments provided by the present application may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a distributed database based on a blockchain. The processors involved in the various embodiments provided by the present application may be general purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, quantum-computing-based data processing logic devices, and the like, and are not limited thereto.
However, CN 116977092 A does not expressly teach either of the claimed features of “identify a property associated with the document based on geographical information extracted from the unstructured data” or “identify at least one modified building property associated the unstructured data by inputting the one or more embeddings into a machine learning engine comprising a classifier trained to classify building modifications based on one or more classification taxonomies”.
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 extension fee 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 date of this final action.
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.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
August 28, 2026
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