DETAILED ACTION
This action is in response to the RCE dated 21 August 2026 which incorporates the amendment dated 03 August 2026. Claims 1, 11 and 20 are amended. No claims have been added or cancelled. 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 .
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 1-3, 5-11 and 13-20 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 Titemore et al. (US 2007/0011608 A1).
As for independent claim 1, Padmashali teaches 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”].
Padmashali does not specifically teach wherein each document generation rule in the one or more document generation rules is represented by an executable code, which, upon being triggered, causes generation of data for inclusion into the electronic document. However, in the same field of invention, Titemore teaches:
wherein each document generation rule in the one or more document generation rules is represented by an executable code, which, upon being triggered, causes generation of data for inclusion into the electronic document [(e.g. see Titemore paragraphs 0009, 0012, 0031, 0035) ”the computer code mechanism is electronically connected to one or more third databases in which the one or more third databases contain predetermined auto-reuse of document text rules corresponding to the predetermined document types … The rules may determine the placement location for reuse data in a document depending on the type of the document. For example, if the type of document is a medical chart, then the pre-existing business rules may instruct the system to insert reuse data from other medical charts relating to a certain patient and/or the patient's conditions, allergies, or medications … the computer-implemented steps which may be performed by the system of the present invention using … a plurality of computer code instructions. With reference to box 15, a user activates the auto reuse process, which may be done by clicking on a mouse or keyboard button or by entering a command … the application automatically selects document text from the one or more preexisting documents in accordance with the predetermined rules for the predetermined document type corresponding to the first document. The application then automatically inserts the selected document text into the first document”].
Therefore, considering the teachings of Padmashali and Titemore, 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 each document generation rule in the one or more document generation rules is represented by an executable code, which, upon being triggered, causes generation of data for inclusion into the electronic document, as taught by Titemore, to the teachings of Padmashali because it allows for the efficient creation of documents by reusing or inserting portions of document text from archived documents thereby allowing documents to be created in a more expedient manner (e.g. see Titemore paragraph 0023).
As for dependent claim 2, Padmashali and Titemore teach the method as described in claim 1, but Padmashali does not specifically teach the following limitation. However, Titemore 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 Titemore paragraphs 0045, 0052, 0053, 0059) ”the selected work type template and all the current set of rules for it … The list of available work types and sections are derived from a combination of known templates and documents using those work types previously dictated. The likelihood of a section being part of a document's template is used to generate the list … quickly add rules to the template by using the "matching sections" process which creates multiple "one section" rules based on sections that exist in both the current template and the existing document … This creation process makes one or more "one section" rules based on sections which exist in both the current template and the document work type chosen from the list”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 3, Padmashali and Titemore teach the method as described in claim 1, but Padmashali does not specifically teach the following limitation. However, Titemore 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 Titemore paragraphs 0012, 0052, 0057) ”determine whether the user has previously requested a document text reuse process during dictation of the first document … The list of available work types and sections are derived from a combination of known templates and documents using those work types previously dictated … a rule that retrieves all the sections (and included text) from an existing document”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 5, Padmashali and Titemore teach the method as described in claim 1, but Padmashali does not specifically teach the following limitations. However, Titemore teaches:
further comprising: receiving at least one feedback from the at least one computing device [(e.g. see Titemore paragraph 0046) ”existing rules can be modified by clicking the rule label”].
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 Titemore paragraphs 0046, 0050) ”the Work Type Rule Set Editor 130 is used to add, remove, and edit rules for an individual work type … the appropriate rule editor is opened with the rule's specifications. This allows a user to quickly edit an existing rule”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 6, Padmashali and Titemore teach the method as described in claim 1 and Padmashali further teaches:
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 and Titemore teach the method as described in claim 1 and Padmashali further teaches:
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 and Titemore teach the method as described in claim 7 and Padmashali further teaches:
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 and Titemore teach the method as described in claim 1 and Padmashali further teaches:
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 and Titemore teach the method as described in claim 1 and Padmashali further teaches:
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 and Titemore teach 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 teaches 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 Titemore teaches present the one or more document generation rules on a graphical user interface of at least one computing device [(e.g. see Titemore paragraphs 0017, 0054 and Figs. 3, 4A-C) ”FIG. 3 is a view of a dialog box for auto-reuse rule sets … Referring to FIG. 4A, there is shown a dialog box 150 for a document rule where a user can create a rule that retrieves the section text from an existing document, based on the criteria specified. The retrieved text is placed in the section of the dictation based on the rule's location in the template rule set”]. The motivation to combine is the same as that used for claim 1.
As for dependent claim 13, Padmashali and Titemore teach 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 and Titemore teach 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 15, Padmashali and Titemore teach 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 16, Padmashali and Titemore teach 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.
As for dependent claim 17, Padmashali and Titemore teach 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 and Titemore teach 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 and Titemore teach 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 and Titemore teach 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.
Response to Arguments
Applicant's arguments, filed 21 August 2026, have been fully considered but they are not persuasive.
Applicant argues that [“Padmashali fails to disclose ‘storing, using the at least one processor, the one or more document generation rules in a storage location, wherein each document generation rule in the one or more document generation rules is represented by an executable code, which, upon being triggered, causes generation of data for inclusion into the electronic document’ as recited in [amended] claim 1.” (Page 10).].
The argument described above, in paragraph number 5, with respect to the newly added limitations to the independent claims has been considered, but is moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPub 2020/0371647 A1 issued to Gerges et al. on 26 November 2020. The subject matter disclosed therein is pertinent to that of claims 1-3, 5-11 and 13-20 (e.g. automatically generating document sections based on a document type).
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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