DETAILED ACTION
This action is in response to the Amendment dated 02 February 2026. Claims 1, 5, 11, 13-16 and 20 are amended. Claims 4 and 12 have been cancelled. No claims have been added. Claims 1-3, 5-11 and 13-20 remain pending and have been considered below.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 21 November 2025 and 10 March 2026 have been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
Based on applicant’s amendment, the claim objection of claims 5 and 16 is withdrawn.
Based on applicant’s amendment, the 35 U.S.C. 101 rejection is withdrawn.
Claim Objections
Claim 20 is objected to because of the following informalities: claim 20 is indicated as being “Currently Amended”; however, there is no markup presented and there does not appear to be any changes from previous claim set. Appropriate correction is required.
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, 6-11, 15 and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Padmashali et al. (US 2025/0148020 A1, foreign priority (FP) date 08 November 2023).
As for independent claim 1, Padmashali discloses a method comprising:
determining, using at least one processor, a structural arrangement of one or more portions of each electronic document in a plurality of electronic documents, wherein one or more machine learning models determine the structural arrangement [(e.g. see Padmashali paragraphs 0039, 0047, 0184, FP: paragraphs 0034, 0129) ”The asset page 800 includes an “Upload” button 802 for uploading documents to the system. Additionally, the asset page 800 has a file list section 804 that displays agreements uploaded by users for analysis and summarization. Each file in the list is accompanied by a document title, a processing status, and the date it was last updated … artificial intelligence provide capabilities in natural language processing that can be applied to parse legal language automatically. Some examples of systems based on machine learning algorithms and neural networks that are trained to analyze the text of legal documents are described herein … The AI engine 102 analyzes the structure, formatting, and textual content of the input document 126 to determine if it is a legal document, research paper, novel, etc. Machine learning algorithms may be used to categorize the document type based on training data using supervised learning techniques”].
identifying, using the at least one processor, one or more parameters associated with each electronic document in the plurality of electronic documents [(e.g. see Padmashali paragraphs 0049, 0051, FP: 0043, 0045) ”textual content is extracted from the input document 126 through optical character recognition or other extraction techniques … the extracted text is programmatically separated into logical segments using visual and textual cues within the document itself. These cues include formatting elements like headers, section breaks, changes in text formatting, as well as textual dividers like “Section 1”, “Article II” etc. The spacing and positioning of text may also be algorithmically analyzed to detect the start and end of paragraphs and other semantic blocks”].
generating, using the at least one processor, one or more document generation rules based on the one or more parameters and the structural arrangement of the one or more portions, wherein the one or more document generation rules are generated for each type of electronic document in the plurality of electronic documents [(e.g. see Padmashali paragraphs 0071-0075, FP:0062, 0063) ”The database 208 may be implemented using technologies like MySQL, PostgreSQL, MongoDB, etc. to provide persistent storage and querying capabilities. The mapping data structure may consist of tables in the database 208 defining the relationship between document types and content categories: A “document_types” table would contain rows for each supported document type, such as “Services Agreement”, “NDA”, “Lease Agreement”, etc. A “content_categories” table would contain rows defining categories relevant to summarizing documents, such as “Parties”, “Term”, “Fees”, etc. A “document_type_map” join table would link each document type row to related content category rows, creating the many-to-many relationship … The backend 206 queries the database 208 to identify content categories for a given document type by joining the “document_types” and “document_type_map” tables. The identified content categories are passed to downstream engines and algorithms to customize the summarization for that document type”].
storing, using the at least one processor, the one or more document generation rules in a storage location [(e.g. see Padmashali paragraph 0071, FP: 0062) ”The database 208 may store a mapping data structure, making the mapping data structure accessible to the backend 206 … The mapping data structure may consist of tables in the database 208 defining the relationship between document types and content categories”].
receiving a request to generate an electronic document of a first type [(e.g. see Padmashali paragraphs 0183, 0186, 0256, FP: 0091, 0128, 0131) ”The landing page 700 includes an “Upload” button 702 which allows users to upload an input document 126 to the document assistant system 100 … the “Key Facts” tab 906 is active and displays summary data generated by the text summarization engine 222 … The summaries generated for the document may be saved for the user and accessed again at a later time”].
identifying, based on the first type of electronic document, at least one document generation rule in the one or more document generation rules [(e.g. see Padmashali paragraph 0075, FP: 0063) ”The backend 206 queries the database 208 to identify content categories for a given document type by joining the “document_types” and “document_type_map” tables. The identified content categories are passed to downstream engines and algorithms to customize the summarization for that document type”].
executing, using the at least one document generation rule, the one or more machine learning models associated with the first type of the electronic document to generate the electronic document of the first type [(e.g. see Padmashali paragraphs 0077, 0080, FP: 0065, 0068) ”This implementation allows the mapping data structure to be scaled, searched, and maintained efficiently while being leveraged by the backend 206 and algorithms to generate tailored summarization outputs … An AI engine 102 leverages AI/LLM models such as GPT-3.5 and GPT-4 to analyze the extracted text. The AI engine 102 runs tasks such as text summarization engines 222, metrics engines 224, question-answering engines 226, and section processing engines 228 in parallel using prompts customized for each document type and task”].
and generating the document of the first type in a graphical user interface of at least one computing device [(e.g. see Padmashali paragraphs 0082, 0186, 0256 and Fig. 9, FP: 0069, 0091, 0131 and Fig. 9) ”The text summarization engine 222 generates a high-level overview of the document. The metrics engine 224 extracts key facts and figures. The question-answering engine 226 generates common questions and answers about the document. The section processing engine 228 identifies and summarizes individual clauses within the document by type … The specific summary facts displayed can vary depending on the type of agreement and the assessed key facts … The summaries generated for the document may be saved for the user and accessed again at a later time”].
As for dependent claim 6, Padmashali discloses the method as described in claim 1 and Padmashali further discloses:
wherein the structural arrangement of the one or more portions of each electronic document in the plurality of electronic documents is generated using a generative artificial intelligence (AI) model [(e.g. see Padmashali paragraphs 0039, 0055, 0091, FP: 0034, 0048, 0076) ”artificial intelligence provide capabilities in natural language processing that can be applied to parse legal language automatically. Some examples of systems based on machine learning algorithms and neural networks that are trained to analyze the text of legal documents are described herein … Example implementations may leverage technologies like spaCy, OpenAI, and LangChain … incorporates the document type identified by the LLM engine 318 in operation 304. For example, if the input document was categorized as a “Services Agreement”, the prompt may be constructed to summarize the key points of a services agreement”].
As for dependent claim 7, Padmashali discloses the method as described in claim 1 and Padmashali further discloses:
wherein a type of at least one electronic document in the plurality of electronic documents includes at least one of the following: a legal document type, a non-legal document type, and any combinations thereof [(e.g. see Padmashali paragraph 0048, FP: 0041, 0042) ”At operation 108, if the input document 126 is determined to be a legal document, AI engine 102 categorizes the specific type of document, such as a non-disclosure agreement, services agreement, real estate contract, etc. A database of different agreement types, and their standard sections and clauses, is referenced to match the sections present in the input document 126. In some examples, if the input document 126 is determined not to be a legal document (e.g., research paper, novel)”].
As for dependent claim 8, Padmashali discloses the method as described in claim 7 and Padmashali further discloses:
wherein the one or more parameters include at least one of the following: the type of electronic document in the plurality of electronic documents, a position of each element in the structural arrangement in each electronic document, a type of each element in the structural arrangement in each electronic document, a function of each element in the structural arrangement in each electronic document, a content of each element in the structural arrangement in each electronic document, and any combination thereof [(e.g. see Padmashali paragraph 0051, FP: 0045) ”Specifically, the extracted text is programmatically separated into logical segments using visual and textual cues within the document itself. These cues include formatting elements like headers, section breaks, changes in text formatting, as well as textual dividers like “Section 1”, “Article II” etc. The spacing and positioning of text may also be algorithmically analyzed to detect the start and end of paragraphs and other semantic blocks”].
As for dependent claim 9, Padmashali discloses the method as described in claim 1 and Padmashali further discloses:
wherein each element in the structural arrangement includes at least one of the following: a text, an audio, an image, a table, and any combination thereof [(e.g. see Padmashali paragraphs 0049, 0051, FP: 0043, 0045) ”textual content is extracted from the input document 126 through optical character recognition or other extraction techniques … the extracted text is programmatically separated into logical segments using visual and textual cues within the document itself. These cues include formatting elements like headers, section breaks, changes in text formatting, as well as textual dividers like “Section 1”, “Article II” etc. The spacing and positioning of text may also be algorithmically analyzed to detect the start and end of paragraphs and other semantic blocks”].
As for dependent claim 10, Padmashali discloses the method as described in claim 1 and Padmashali further discloses:
wherein the one or more machine learning models includes at least one of the following: a large language model, at least one generative AI model, and any combination thereof [(e.g. see Padmashali paragraphs 0039, 0055, FP: 0034, 0048) ”artificial intelligence provide capabilities in natural language processing that can be applied to parse legal language automatically. Some examples of systems based on machine learning algorithms and neural networks that are trained to analyze the text of legal documents are described herein … Example implementations may leverage technologies like spaCy, OpenAI, and LangChain”].
As for independent claim 11, Padmashali discloses a system. Claim 11 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1. Further, Padmashali discloses wherein the at least one process is configured to select, based on the predetermined type, at least one machine learning model from a plurality of machine learning models and generate, using the at least one selected machine learning model, the one or more structural arrangements [(e.g. see Padmashali paragraphs 0080, 0086-0089, 0161, FP: 0068, 0073, 0074, 0107) ”An AI engine 102 leverages AI/LLM models such as GPT-3.5 and GPT-4 to analyze the extracted text. The AI engine 102 runs tasks such as text summarization engines 222, metrics engines 224, question-answering engines 226, and section processing engines 228 in parallel using prompts customized for each document type and task … In examples where multiple LLM engines are used, each of these engines may be fine-tuned and trained for a specific purpose or set of function … The prompt containing the context of the agreement type is provided as input to the LLM engine 520. The LLM engine 520 may be an optimized version of GPT-3 specialized for conversational tasks … multiple, fine-tuned models may be used, such as: … A conversation LLM engine 318 in the example form of OpenAI model GPT-4 … A text generation LLM engine 320 in the example form of OpenAI model text_davinci-003”] and present the one or more document generation rules on a graphical user interface of at least one computing device [(e.g. see Padmashali paragraphs 0075, 0186, 0187 and Figs. 9-10, FP: 0063, 0131, 0132 and Figs. 9-10) ”The backend 206 queries the database 208 to identify content categories for a given document type by joining the “document_types” and “document_type_map” tables. The identified content categories are passed to downstream engines and algorithms to customize the summarization for that document type … the “Key Facts” tab 906 is active and displays summary data generated by the text summarization engine 222. The top portion of the analysis section 904 presents key facts in cards, such as price per share data, increase in stock ownership data, rental rate for second lease amendment agreement data, promissory note duration data, and governing state law data. The specific summary facts displayed can vary depending on the type of agreement and the assessed key facts. Below the key facts cards, various summaries of sections or content of the document are displayed … Labels associated with specific clauses are displayed, and user selection of a label expands a summary description of the relevant clauses”].
As for dependent claim 15, Padmashali discloses the system as described in claim 11; further, claim 15 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for dependent claim 17, Padmashali discloses the system as described in claim 11; further, claim 17 discloses substantially the same limitations as claim 6. Therefore, it is rejected with the same rational as claim 6.
As for dependent claim 18, Padmashali discloses the system as described in claim 11; further, claim 18 discloses substantially the same limitations as claim 7. Therefore, it is rejected with the same rational as claim 7.
As for dependent claim 19, Padmashali discloses the system as described in claim 18; further, claim 19 discloses substantially the same limitations as claim 8. Therefore, it is rejected with the same rational as claim 8.
As for independent claim 20, Padmashali discloses a computer program product. Claim 20 discloses substantially the same limitations as claims 11 and 15. Therefore, it is rejected with the same rational as claims 11 and 15.
Claim Rejections - 35 USC § 103
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 2, 3, 5, 13, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Padmashali et al. (US 2025/0148020 A1, foreign priority (FP) date 08 November 2023) in view of Chua et al. (US 2020/0104353 A1).
As for dependent claim 2, Padmashali teaches the method as described in claim 1, but does not specifically teach wherein the one or more document generation rules are associated with one or more templates defining the structural arrangement and including the one or more portions for each type of electronic document in the plurality of electronic documents. However, in the same field of invention, Chua teaches:
wherein the one or more document generation rules are associated with one or more templates defining the structural arrangement and including the one or more portions for each type of electronic document in the plurality of electronic documents [(e.g. see Chua paragraphs 0031) ”the machine learning modeling 108 may be continuously adapted as new electronic documents (and associated signal data) are generated. For instance, a user may create a new type of electronic document where additional versions of that document may be subsequently re-created numerous times. As such, personalized content suggestions can be directed to such content to differentiate from traditional pre-canned suggestions for template creation. Pre-canned suggestions for template create may be surfaced in addition to the personalized content suggestions. In some instances, a larger amount of pre-canned suggestions may be presented when the user account first utilizes applications/services and processing of the present disclosure evolves those pre-canned suggestions into highly personalized content suggestions as the user interacts more with applications/services”].
Therefore, considering the teachings of Padmashali and Chua, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the one or more document generation rules are associated with one or more templates defining the structural arrangement and including the one or more portions for each type of electronic document in the plurality of electronic documents, as taught by Chua, to the teachings of Padmashali because it allows for improved efficiency and enhanced productivity by assisting users with recreating documents that they regularly use (e.g. see Chua paragraph 0011).
As for dependent claim 3, Padmashali teaches the method as described in claim 1, but does not specifically teach the following limitation. However, Chua teaches:
wherein the one or more document generations rules are generated based on historical versions of one or more electronic documents in the plurality of electronic documents [(e.g. see Chua paragraphs 0011, 0025, 0048) ”trained machine learning modeling, document-specific analytics are executed to match previously created documents with a document type that matches a user's intent for document creation. Execution of document-specific analytics enables improved efficiency and enhance productivity to assist users with recreating documents that they regularly work with. For instance, personalized content suggestions may be generated and presented, through a user interface, for a specific document type that enables efficient recreation of content for that document type. This may comprise content that a user (or associated group of users) has worked with in the past … Processing operations described herein may utilize this type of input to identify previously created electronic documents (e.g., prior versions of school newsletters) that may provide useful content for a user to create a new newsletter document … the machine learning modeling 108 may be trained to correlate previously created documents with specific inputs (e.g., derived from analysis of input and associated signal data) as well as identify specific content portions of previously created electronic documents that may be most relevant for the user. For instance, a user may create a business report, on a monthly basis, where the business report is the same document type created each month. Machine learning modeling 108 may be configured to identify similar types of documents for a user as well as content portions from those documents that the user may find most useful in creation a new version of an electronic document”].
The motivation to combine is the same as that used for claim 2.
As for dependent claim 5, Padmashali teaches the method as described in claim 1, but does not specifically teach the following limitations. However, Chua teaches:
further comprising: receiving at least one feedback from the at least one computing device [(e.g. see Chua paragraphs 0012, 0049) ”As indicated in the foregoing, processing operations described herein are configured to adapt an application/service to fit a context that a specific user may be working with. If the user's intent changes, the machine learning modeling is configured to adapt in real-time to tailor personalized content suggestions for a specific user during creation of a specific document type. As such, unique user experiences are created where one users experience may differ from that of another. Examples describe herein are applicable in scenarios where a user is creating an electronic document from scratch, as well as those in which a user is working with an existing electronic document. Processing operations described herein may be useful to adding content as well as modifying structure and layout of an electronic document. Real-time (or near real-time) processing enables application/services to continuously provide tailored user experiences, where user interfaces may be adapted to reflect a context that a specific user is working with … Continuing with the previous example where a school newsletter is being created, the personalized content suggestions 326 provide sections that may be personalized for the user. In further examples, a user interface may be adapted to provide a user interface features 325 enabling a user to add its own specific sections for document creation. User-created document content (e.g., sections) may be usable as personalized content suggestions for subsequent document creation of a specific document type (e.g., newsletter) by that specific user or other affiliated users. In some examples, user creation of document content may trigger an update to a listing of personalized content suggestions, where direct user input may add further context for creation of its electronic document”].
performing, based on the received at least one feedback, at least one of the following: modifying the electronic document of the first type; updating at least one document generation rule to generate at least one updated document generation rule, and executing, using the at least one updated document generation rule, the one or more machine learning models associated with the first type of the electronic document to generate an updated electronic document of the first type; updating the one or more machine learning models to generate one or more updated machine learning models and generating, using the one or more updated machine learning models, at least one of: the electronic document of the first type and the updated electronic document of the first type; and any combination thereof [(e.g. see Chua paragraphs 0049, 0050) ”Continuing with the previous example where a school newsletter is being created, the personalized content suggestions 326 provide sections that may be personalized for the user. In further examples, a user interface may be adapted to provide a user interface features 325 enabling a user to add its own specific sections for document creation. User-created document content (e.g., sections) may be usable as personalized content suggestions for subsequent document creation of a specific document type (e.g., newsletter) by that specific user or other affiliated users. In some examples, user creation of document content may trigger an update to a listing of personalized content suggestions, where direct user input may add further context for creation of its electronic document … a user interface callout 334 is presented, through the user interface, on behalf of the user. The user interface callout 334 provides context identifying why a specific personalized content suggestion is recommended. In the example shown, a personalized content suggestion for a section “teacher of the month” was recommended because the section was recently added to the last version of the school newsletter”].
The motivation to combine is the same as that used for claim 2.
As for dependent claim 13, Padmashali teaches the system as described in claim 11; further, claim 13 discloses substantially the same limitations as claim 2. Therefore, it is rejected with the same rational as claim 2.
As for dependent claim 14, Padmashali teaches the system as described in claim 11; further, claim 14 discloses substantially the same limitations as claim 3. Therefore, it is rejected with the same rational as claim 3.
As for dependent claim 16, Padmashali teaches the system as described in claim 15; further, claim 16 discloses substantially the same limitations as claim 5. Therefore, it is rejected with the same rational as claim 5.
Response to Arguments
Applicant's arguments, filed 02 February 2026, have been fully considered but they are not persuasive.
Applicant argues that [“Padmashali is entirely silent with regard to generating document generation rules and using such document generation rules to generate documents … No new documents are generated using any document generation rules in Padmashali.” (Page 11).].
Examiner respectfully disagrees. Examiner notes that there is no limitation in the independent claim that recites that a “new” document is created. Applicant’s specification defines an electronic document in paragraphs 0174, 0179 as “may comprise, for example, one or more audio components, text components, image components, or table components … that may be stored in any desired electronic format.” Therefore, the broadest reasonable interpretation of an electronic document, in light of the specification, is text that can be stored. Padmashali discloses generate an electronic document of a first type in paragraphs 0069, 0075, 0077, 0080, 0131, 0183, 0186, 0256 of Padmashali’s disclosure [(see rejection of claim 1 above)]. Padmashali shows the user requesting a summary of the document type that was uploaded to be created, this summary is text that can be stored, where the document type mapping is used to customize the summarization for that particular document type. Thus, Padmasahli discloses applicant’s claimed limitation.
Conclusion
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 CHRISTOPHER J FIBBI whose telephone number is (571)-270-3358. The examiner can normally be reached Monday - Thursday (8am-6pm).
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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