Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. In response to the office action mailed on 11/25/2025, applicant filed an amendment on 03/25/2026, amending claims 1, 7, 11, 16, 17, and 20. The pending claims are 1-20.
Response to Arguments
3. With regard to prior art, applicant’s arguments with respect to the pending claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
With regard to the 35 U.S.C. 101 rejection, applicant argues that text embeddings stored in a vector database are not human-readable; and the amount of data that is necessary to query to identify a subset of matching embeddings in the vector database is beyond the scope of what could reasonably be performed by a human and thus requires processing circuitry.
The examiner notes that given the broadest reasonable interpretation to the claims’ language, the claimed steps could be performed by a human using observation, evaluation, judgment, and opinion. Neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1367 (Fed. Cir. 2015) (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
Applicant also argues that the current claims are comparable to the Enfish limitations.
The examiner notes that the Enfish limitations relate to an improvement over the relational database model that allows storing information describing a relation in a single table, as opposed to multiple tables used by relational databases, by making use of a “special row,” which “defines the characteristics of a column in that same table.” The claimed improvement provided for “faster searching of data than with the relational model,” “more efficient storage” of certain types of data, and “more flexibility in configuring the database.”. As to the current case, the claims do not show how the generative language model operate to input the prompt and generate the verbose tax category description. Yet, automation of manual processes, such as using a generic computer to process an application and speeding up a process neither adds structure to claims language, nor shows an improvement in computer-functionality. To show that the involvement of a computer assists in improving the technology, the claims (in addition to the specification) must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method.
Accordingly, the corresponding rejection is repeated below.
Claim Rejections - 35 USC § 101
4. 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 1-20 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
The claimed invention, at independent claims 1, 11, and 20, is directed to a method (process), and system (machine) for identifying a plurality of defined tax categories, and, for each defined tax category of the plurality of defined tax categories: extracting source text data associated with the defined tax category from a text source, generating respective source text embeddings representing the source text data, and storing the respective source text embeddings in a vector database; receiving an instruction requesting a verbose tax category description, the instruction including instruction text indicating a tax category; generating instruction text embeddings for the instruction text; querying the vector database with the instruction text embeddings to identify a subset of matching embeddings from among the respective source text embeddings stored in the vector database representing the source text data for each defined tax category; retrieving matching source text data associated with the matching embeddings; generating a prompt for a generative language model based on the matching source text data and the instruction; inputting the prompt to the generative language model to thereby generate verbose tax category description text for the verbose tax category description; and outputting the verbose tax category description text.
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Under the 35 U.S.C. 101 new guidelines, the broadest reasonable interpretation of the claims, the claimed steps fall within the “Mental Processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
The steps of for identifying a plurality of defined tax categories, extracting source text data, generating respective source text embeddings, and storing the respective source text embeddings in a vector database, may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, a person may evaluate tax documents and identify a plurality of tax categories and, for each category, extracts source text, converts the text into vectors or numerical representation, then stores the numerical representation in a file of record manually without using a machine.
The steps of receiving instruction text requesting a verbose tax category description and generating instruction text embeddings for the instruction text, could also be performed in a human mind using observation, evaluation, judgment, and opinion. A human could receive instruction and convert the text into numerical representation (vectors) representing the text.
The steps of querying the vector database with the instruction text embeddings …, retrieving matching source text data associated with the matching embeddings, and generating a prompt based on the matching source text data and the instruction, a human can mentally compare a query’s vector to the database vector and generate an answer. See MPEP 2106.04(a)(2), subsection III.
As to the step of inputting the prompt to the generative language model and outputting the verbose tax category description text, the claims do not provide any details about how the generative language model operates or how the verbose tax category description text is generated by the generative language model. The outputting of the verbose tax category description text is mere data gathering and manipulation, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Therefore, the claimed steps fall within the mental process grouping of abstract ideas
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recite the additional elements of “a processing circuitry”, “inputting the prompt to the generative language model” and “outputting the verbose tax category description text” are mere data gathering and manipulating recited at high level of generality, and thus are insignificant extra-solution activity The processor is recited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recitation of “generative language model” is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
As to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (Step 2B), as explained above in Step 2A, Prong 2, the use of “processor” and “a generative language model” is at high level of generality, and even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Accordingly, the claims are ineligible.
Dependent claims 2-10 and 11-19 further refer and describe the source text (claims 2-5, 12-15), the instruction text (claim 6), context from the vector database (claims 7, 8, 16, 17), and the prompt (claim 9, 18). Claims 10 and 19 recite updating the source tax category embeddings and revising product mapping according to the updated source text data embeddings, which encompasses a mental process that is practically performed in the human mind, as explained above in Step 2A, Prong 1. Accordingly, claims 1-20 are directed to an abstract idea, and are not patent eligible.
Claim Rejections - 35 USC § 103
5. 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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moyerman (US 2024/0013318) in view of Madisetti (US 12001462), and further in view of Santharam (US 20240037673).
As per claim 1, Moyerman teaches a computing device including processing circuitry configured to execute instructions using portions of associated memory (Fig. 8) to:
identify a plurality of defined tax categories ([0023], wherein one or more predetermined tax categories are identified), and, for each defined tax category of the plurality of defined tax categories: extract source text data associated with the defined tax category from a text source, generate respective source text embeddings representing the source text data, and store the respective source text embeddings representing the source text data associated with the defined tax category in a vector database ([0022]- [0024], [0050], [0053], [0059], wherein for each item listing, corresponding to a tax category, using a natural language processing model to extract corresponding text strings and generate embedded vectors representation of the text strings, and generating a database or a tax category prediction dataset. [0050], );
receive an instruction requesting a verbose tax category description, the instruction including instruction text indicating a tax category ([0039], [0041], receiving an item listing description request. The item listing provides description for an item which indicates the corresponding tax category);
generate instruction text embeddings for the instruction text ([0024], wherein a generated first text embedding from a first item listing of a first item is sent to the trained tax category prediction model for processing. [0051]- [0052], [0059], wherein said, received text strings are provided to natural language processing models, i.e. word2vector, to generate text embeddings which include numerical data describing the input in the vector space);
query the vector database with the instruction text embeddings to identify a subset of matching embeddings from among the respective source text embeddings stored in the vector database representing the source text data for each defined tax category ([0061], mapping component 216 maps the text embedding, identified by NLP engine 212, for the item listing 202 to one or more predetermined tax categories to generate a tax category prediction dataset 224 stored at data store 220);
retrieve matching source text data associated with the matching embeddings ([0061], the tax category prediction dataset is generated based on mapping the text embedding to one or more predetermined tax categories); and
output the verbose tax category description text ([0083], instruction is transmitted to a user device to display a graphical user interface including the tax category predictions for the received items along with corresponding descriptions). wherein the verbose tax category description text is a textual description of an imposed tax category that includes contextual information that determines what products are included in the defined tax category.
Moyerman may not explicitly disclose generate a prompt for a generative language model based on the matching source text data and the instruction text; and input the prompt to the generative language model, to thereby generate verbose tax category description text for the verbose tax category description. Madisetti in the same field of endeavor teaches a user enters a prompt in user interface. The prompt is sent to an AI Input Broker which generates multiple derived prompts for different categories. The prompts are converted into embeddings using multiple embedding models. The prompt embeddings are sent to a vector database, which returns a list of knowledge documents that are relevant to the prompt based on the similarity of their embeddings to the user's prompt. The knowledge documents are sent to an AI Input Broker which creates new context-aware prompts based on the user's initial prompt, derived prompts and the retrieved knowledge documents as context and sends it to multiple h-LLMs (generative language model). Then the best result is sent to the user along with citations from the knowledge documents (col. 7, line 55 – col. 8, line 6). Therefore, it would have been obvious at the time the application was filed to use Madisetti’s above features, in order to generate verbose tax category description text for the verbose tax category description, as claimed. This would improve response times, accuracies, and reduce computational load to save on both cost and scalability and expandability of existing AI models and their use (col. 2, lines 51-53).
Moyerman in view of Madisetti may not explicitly disclose wherein the verbose tax category description text is a textual description of an imposed tax category that includes contextual information that determines what products are included in the defined tax category. Santharam in the same field of endeavor teaches a system for processing natural language content comprising tax information, wherein a machine learning (ML) model is trained to output an indication of a type of tax expense information from multiple different types of tax expense information. Examples of different the types of tax expense information include charitable donations, child care expenses, rental home expenses, education costs, moving costs, home buying costs, business related tax expenses … ([0021]). Therefore, it would have been obvious at the time the application was filed to use Santharam’s above feature with the system of Moyerman in view of Madisetti, in order to improve tax compliance, enhance reporting accuracy, and minimize risks during audits.
As per claim 2, Moyerman teaches wherein the source text data associated with the defined tax category is stored in a legal definition database, at least one governing body for the defined tax category is identified, and the source text data includes at least one of jurisdictional rules, jurisdictional regulations, industry bodies, and industry standards for defining the tax category ([0066], [0077], wherein the used database comprises description or title of item products that enables the tax category prediction models to identify and distinguish between a cleaning product comprising alcohol and an alcoholic beverage) .
As per claim 3, Moyerman teaches wherein the source text data associated with the defined tax category is stored in a product database, and the source text data includes metadata and attributes for at least one sample product that is mapped to the defined tax category ([0053], text within the item listing or a document associated with the seller identifier or brand can be associated with metadata or can be indexed to indicate that the text represents the brand. In aspects, the metadata or index may indicate that the item listing is associated with a particular category for taxation corresponding to a particular geographical area).
As per claim 4, Moyerman teaches wherein the source text data associated with the defined tax category is stored in an internal research database, and the source text data includes at least one of notes relevant to the defined tax category, correspondence related to the defined tax category, and decision criteria for assigning the defined tax category to one or more products ([0052]- [0053], wherein item listing or a document associated with the seller identifier or brand can be associated with metadata or can be indexed to indicate that the text represents the brand. In aspects, the metadata or index may indicate that the item listing is associated with a particular category for taxation corresponding to a particular geographical area. That is, text within the item listing or a document associated with an item listing title or item listing description can be identified as associated with a particular tax based on a context of the textual data of the item listing. Text corresponding to a particular category can be associated with metadata or an index to indicate the relationship to the item with the particular category).
As per claim 5, Moyerman teaches wherein the metadata and attributes include at least one of a product description, a physical attribute, a product tree location, nutritional information, and a standard product code ([0054], wherein the language context of the item listing title or item listing description can be indicated using metadata).
As per claim 6, Moyerman teaches wherein the instruction text includes at least one of a tax category name, a tax category type, a product, and a jurisdiction ([0041], receiving an item listing description request. The item listing provides description for an item which indicates the corresponding tax category).
As per claim 7, Moyerman teaches wherein a context is selected from the vector database according to deterministic rules ([0054], [0067], [0077], wherein a context is selected from the vector database according to deterministic rules).
As per claim 8, Moyerman teaches selected from the vector database according to deterministic rules ([0054], [0067], [0077], wherein a context is selected from the vector database according to deterministic rules). Moyerman may not explicitly disclose the context is selected according to evaluation output by the generative language model during prompt engineering. Madisetti in the same field of endeavor teaches generating an inputting a prompt to a generative language model based on the matching source text data and the instruction text and generating a best result (col. 7, line 55 – col. 8, line 6). Therefore, it would have been obvious at the time the application was filed to use Madisetti’s above features, in order to select a context according to evaluation output by the generative language model during prompt engineering. This would improve response times, accuracies, and reduce computational load to save on both cost and scalability and expandability of existing AI models and their use (col. 2, lines 51-53).
As per claim 9, Moyerman teaches wherein user interaction history text between a user and the generative language model is included in the prompt ([0041], [0082]).
As per claim 10, Moyerman teaches wherein the processing circuitry is further configured to: monitor the at least one governing body for updates, update the source tax category embeddings representing the source text data associated with the defined tax category in the vector database, and revise product mapping according to the updated source text data embeddings ([0025], wherein said, the tax category prediction model may be further trained based on a detected change associated with an item listing used to generate the tax category prediction dataset, a detected change associated with the first item listing corresponding to the first text embedding, a detected change associated with the one or more predetermined tax categories, or a detected change ( e.g., a user-based selection) from the tax category prediction provided for display on the graphical user interface of the user device; and upon further training the tax category prediction model based on the detected change…).
As per claims 11-19, method claims 11-19 and apparatus claims 1-10 are related as method and apparatus of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 11-19 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1-10.
As per claim 20, Moyerman teaches a computing device including processing circuitry configured to execute instructions using portions of associated memory (Fig. 8) to:
identify a plurality of defined tax categories ([0023], wherein one or more predetermined tax categories are identified), and, for each defined tax category of the plurality of defined tax categories: extract source text data associated with the defined tax category from a text source, generate respective source text embeddings representing the source text data, and store the respective source text embeddings in a vector database ([0022]- [0024], [0059], wherein for each item listing, corresponding to a tax category, using a natural language processing model to extract corresponding text strings and generate embedded vectors representation of the text strings, and generating a database or a tax category prediction dataset);
receive an instruction requesting a verbose tax category description, the instruction including instruction text indicating a tax category ([0039], [0041], receiving an item listing description request. The item listing provides description for an item which indicates the corresponding tax category);
generate instruction text embeddings for the instruction text ([0024], wherein a generated first text embedding from a first item listing of a first item is sent to the trained tax category prediction model for processing. [0051]- [0052], [0059], wherein said, received text strings are provided to natural language processing models, i.e. word2vector, to generate text embeddings which include numerical data describing the input in the vector space);
query the vector database with the instruction text embeddings to identify a subset of matching embeddings from among the respective source text embeddings stored in the vector database representing the source text data for each defined tax category ([0061], mapping component 216 maps the text embedding, identified by NLP engine 212, for the item listing 202 to one or more predetermined tax categories to generate a tax category prediction dataset 224 stored at data store 220);
retrieve matching source text data associated with the matching embeddings ([0061], the tax category prediction dataset is generated based on mapping the text embedding to one or more predetermined tax categories); and
receive a response ([0083], instruction is transmitted to a user device to display a graphical user interface including the tax category predictions for the received items along with corresponding descriptions).
Moyerman may not explicitly disclose generate a prompt for a generative language model based on the matching source text data and the instruction text; and send the prompt to the generative language model. Madisetti in the same field of endeavor teaches a user enters a prompt in user interface. The prompt is sent to an AI Input Broker which generates multiple derived prompts for different categories. The prompts are converted into embeddings using multiple embedding models. The prompt embeddings are sent to a vector database, which returns a list of knowledge documents that are relevant to the prompt based on the similarity of their embeddings to the user's prompt. The knowledge documents are sent to an AI Input Broker which creates new context-aware prompts based on the user's initial prompt, derived prompts and the retrieved knowledge documents as context and sends it to multiple h-LLMs (generative language model). Then the best result (response) is received by the user along with citations from the knowledge documents (col. 7, line 55 – col. 8, line 6). Therefore, it would have been obvious at the time the application was filed to use Madisetti’s above features, in order to generate verbose tax category description text for the verbose tax category description, as claimed. This would improve response times, accuracies, and reduce computational load to save on both cost and scalability and expandability of existing AI models and their use (col. 2, lines 51-53).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELALI SERROU whose telephone number is (571)272-7638. The examiner can normally be reached M-F 9 Am - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659
05/07/2026
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