DETAILED ACTION
Status of the Claims
The following is a non-final Office Action in response to claims filed 15 October 2025.
Claims 1-23 are pending.
Claims 1-23 have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Note
Due to the nature of the independent claims’ scope varying from the generation of strategies and response (claims 1, 8, and 15) to improvements thereof (claims 21-23), some amendments to the independent claims could warrant future restriction(s).
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 1-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). The claims recite a system (with apparatuses) (claims 1 and 22), method (process) (claims 8 and 21) and apparatus (claims 15 and 23), however, the claim(s) recite(s) generating response strategies for office actions which is an abstract idea of organizing human activities as well as a mental process.
The limitations of:
In claim 1: “analyze the Office action and generate response strategies; ...generate text for sections of the Office action response template based on user selections of the response strategies”
In claim 8: “analyzing the Office action...to generate response strategies; generating text for sections of the Office action response based on user selections of the response strategies,”
In claim 15: “analyzing the Office action using an artificial intelligence (AI) module to generate response strategies; generating text for sections of the Office action response based on user selections of the response strategies”
In claim 21: “generating...response strategies for responding to one or more rejections or objections in the patent Office action”
In claim 22: “receive a placeholder identified from the client device; generate a prompt; received generated text from the LLM server; and transmit the generated text”
In claim 23: “transmitting a request to draft a selected section, the request including a placeholder identifier; receiving generated text for the selected section; and displaying the generated text”
...as drafted, is a process that, under its broadest reasonable interpretation, covers organizing human activities--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); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and/or a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion)but for the recitation of generic computer components (Step 2A Prong 1). That is, other than reciting “using an artificial intelligence (AI) module,” (or “A system for generating responses to patent Office actions, comprising: a server configured to...an artificial intelligence (AI) module configured to..., a processor configured to...” in claim 1 or “A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for generating responses to patent Office actions, the operations comprising: using an artificial intelligence (AI) module” in claim 15 or “A method for improving an efficiency and a performance of a system for drafting a response to a patent Office action by utilizing an artificial intelligence (AI) machine, comprising:” in claim 21 or “A system with improved efficiency and performance in drafting a response to a patent Office action by utilizing an artificial intelligence (AI) machine, comprising: a client device configured to display a user interface; a server communicatively coupled to the client device; and a large language model (LLM) server communicatively coupled to the server, wherein the server is configured to:” in claim 22 or “A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for improving an efficiency and a performance of a system for drafting a response to an Office action, the operations comprising:” in claim 23) nothing in the claim element precludes the step from the methods of organizing human interactions grouping or from practically being performed in the mind. For example, but for the “using an artificial intelligence (AI) module” or “using an AI machine” or ”large language model” language, “analyze/analyzing,” and “generate/generating” in the context of this claim encompasses the user manually reading office actions and formulating a response by hand which is a business relation/fundamental economic practice/commercial or legal interaction or mental process/judgement (i.e. legal analysis and writing). However, if possible, the Examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations are considered together as a single abstract idea for further analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations as a one of the methods of organizing human activities, while some of the limitations may be performed in the mind after certain limitations are performed, but for the recitation of generic computer components, then it falls within the grouping of abstract ideas. (Step 2A, Prong One: YES). Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Method claim 8 is devoid of structure whatsoever and thus cannot integrate the claims into a practical application. The “server configured to retrieve...patent Office database” “user interface configured to display....” in claim 1; “retrieving...patent Office database” in claim 8; the “retrieving...patent Office database” “...on a user interface,” in claim 15; the “user interface,” “inputting through a user interface...” “loading, through a server...” ’receiving....” in claim 21; “receive...” “communicate...” “transmit...” in claim 22; and “displaying...on a user interface” “receiving...” and “displaying...” are simply insignificant data gathering activities and pos solution output. Next, the claims only recites one additional element – using a server, processor, or AI module/machine to perform the steps. The processor (claims 1, 15, and 23), server (claims 1, 21, and 22), the artificial intelligence module (claims 1, 8, and 15), artificial intelligence machine (claims 21 and 22), large language model (claims and database (in claims 1 and 15), the artificial intelligence machine (claim 21) are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of electronic data, query, retrieval and display of results) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). The recitation of “using an artificial intelligence (AI) module” in the limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “artificial intelligence (AI) module” or “artificial intelligence (AI) machine” or “large language model (LLM)” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial intelligence, large language models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 8 is devoid of structure whatsoever and thus cannot amount to significantly more. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a server, processor, or AI module/machine to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Reevaluating here in step 2B, the “retrieve/retrieving” “display/displaying” (in claims 1, 8, and 15); the “inputting,” “loading” and “receiving” (claim 21); “receive” “communicate” “received” “transmit” (in claim 22); “displaying,” “receiving,” “transmitting,” “receiving,” and “displaying” step(s) which are insignificant extrasolution activities are also determined to be well-understood, routine and conventional activity in the field. The Symantec, TLI, and OIP Techs court decisions in MPEP 2106.05(d)(II) indicate that the mere receipt or transmission of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic manner (as is here). Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-7, 9-14, and 16-20 recite(s) the additional limitation(s) further limiting the actions, other documents (prior art, law, case law), and how the actions are analyzed and displayed, which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 8, and 15, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-23 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-20 and 22-23 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baird (US PG Pub. 2025/0384206).
As per claims 1, 8, and 15, Baird discloses a system, method and a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for generating responses to patent Office actions, the operations comprising (system, Baird ¶7; application, processor, ¶33 and ¶177; non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for generating responses to patent Office actions, the operations comprising:, Claim 17):
a server configured to retrieve an Office action from a patent Office database (USPTO office actions, Baird ¶64-¶66; server, API, ¶25; server with sophisticated AI processing, ¶180);
an artificial intelligence (AI) module configured to analyze the Office action and generate response strategies (Embodiments provided herein are directed to systems and methods for AI-powered intellectual property document generation and drafting assistance. The disclosed systems leverage artificial intelligence, including large language models (LLMs), to automate and enhance the creation of various intellectual property documents including patent applications, office action responses, claim charts, and litigation materials, Baird ¶5; detailed analysis, ¶64-¶65; artificial intelligence content generation, ¶40);
a user interface configured to display the generated response strategies and an Office action response template (response strategies, Baird ¶65; templates, ¶7; user interface, ¶41); and
a processor configured to generate text for sections of the Office action response template based on user selections of the response strategies (The output from document generator 114 is generated document 116, which comprises a completed intellectual property document ready for professional use, review, or filing. Generated document 116 may include fully formatted patent applications, office action responses, claim charts, litigation documents, or other intellectual property materials depending on the initial user requirements and document type selections. Generated document 116 maintains professional formatting standards and includes all necessary components for the specified document type, Baird ¶45; processor, ¶33).
As per claims 2, 9, and 16, Baird discloses as shown above with respect to claims 1, 8, and 15. Baird further discloses wherein the AI module is further configured to extract rejections and objections from the Office action (The prosecution-specific input and output selection capabilities of user interface (Prosecution) 404 enable users to specify precisely which prosecution documents should be generated while providing the AI system with extensive prosecution-specific contextual information through multiple types of supporting documents. This configuration ensures that prompt generator 106 (FIG. 1) can create highly tailored augmented prompts 108 (FIG. 1) that incorporate both the specific prosecution output requirements and the available case-specific input materials, enabling LLM 110 (FIG. 1) to generate contextually appropriate prosecution content that addresses the specific examiner rejections, prior art challenges, and strategic considerations relevant to the particular patent application under prosecution, Baird ¶68; addresses Examiner’s rejections and requirements, ¶64-¶65).
As per claims 3, 10, and 17, Baird discloses as shown above with respect to claims 2, 9, and 16. Baird further discloses wherein the AI module is configured to generate the response strategies based on the extracted rejections and objections (response strategies, addresses Examiner’s rejections and requirements, Baird ¶64-¶65; patent prosecution support and document generation strategies, ¶50).
As per claims 4, 11, and 18, Baird discloses as shown above with respect to claims 1, 8, and 15. Baird further discloses further comprising a prior art database, wherein the server is configured to retrieve prior art documents cited in the Office action from the prior art database (Summary of Cited Art 408 provides for generation of organized summaries of prior art references cited by patent examiners during prosecution. List of Distinguishing Features 410 enables generation of detailed analyses identifying novel features that distinguish the claimed invention from cited prior art. Summary of Office Action 412 provides for generation of structured summaries of USPTO office actions that organize examiner rejections and requirements, Baird ¶64; The system's advanced prior art integration and multi-jurisdictional capabilities provide sophisticated support for complex patent prosecution strategies and global intellectual property protection requirements. The prior art analysis functionality systematically compares invention disclosures against identified prior art references to automatically identify distinguishing features, suggest claim amendments that overcome potential rejections, and generate strategic prosecution recommendations that anticipate examiner challenges while maintaining appropriate claim scope and protection objectives. For multi-jurisdictional support, the system incorporates jurisdiction-specific legal requirements, formatting standards, and procedural guidelines that enable automatic adaptation of patent applications for filing in different countries and patent offices, including conversion between U.S. Patent and Trademark Office requirements and European Patent Office standards, while maintaining technical consistency across all jurisdictional versions. The system further includes deployment flexibility through multiple access mechanisms including standalone desktop applications for offline use, Microsoft Word plugin integration that enables direct AI assistance within existing document preparation workflows, and web-based interfaces that provide comprehensive functionality through standard browsers, ensuring that users can access sophisticated AI-powered patent drafting capabilities regardless of their preferred technical environment or existing software infrastructure while maintaining consistent functionality and professional quality standards across all deployment options, ¶191).
As per claims 5, 12, and 19, Baird discloses as shown above with respect to claims 4, 11, and 18. Baird further discloses wherein the AI module is configured to analyze the retrieved prior art documents and incorporate relevant information into the generated response strategies (Summary of Cited Art 408 provides for generation of organized summaries of prior art references cited by patent examiners during prosecution. List of Distinguishing Features 410 enables generation of detailed analyses identifying novel features that distinguish the claimed invention from cited prior art. Summary of Office Action 412 provides for generation of structured summaries of USPTO office actions that organize examiner rejections and requirements, Baird ¶64).
As per claims 6-7, 13-14, and 20, Baird discloses as shown above with respect to claims 1, 8, and 19. Baird further discloses wherein the user interface is configured to display relevant law citations related to the rejections in the Office action; wherein the processor is configured to incorporate the relevant law citations into the generated text for sections of the Office action response (Caselaw 1204 represents a comprehensive collection of judicial decisions, court opinions, and legal precedents that inform the AI system's understanding of patent law interpretation, claim construction principles, validity standards, and infringement analysis methodologies. Caselaw 1204 includes decisions from federal courts, the Court of Appeals for the Federal Circuit, the Supreme Court, and Patent Trial and Appeal Board proceedings that establish legal standards and interpretive frameworks for patent prosecution and litigation. The inclusion of Caselaw 1204 in the training data enables LLM 1218 to generate content that aligns with established legal precedents and incorporates appropriate legal reasoning in patent-related documents. Statutes 1206 represents the statutory framework governing intellectual property law, including 35 U.S.C. provisions, Patent Act requirements, USPTO regulations, and related legislative materials that define the legal requirements for patent validity, prosecution procedures, and enforcement mechanisms. Statutes 1206 provides LLM 1218 with foundational knowledge of legal requirements that must be satisfied in patent applications, prosecution documents, and litigation materials, ensuring that generated content complies with applicable statutory standards and regulatory requirements. Jurisdictional Guidelines 1208 represents comprehensive guidance from patent offices, courts, and regulatory bodies that establish procedural requirements, formatting standards, and practice conventions for intellectual property proceedings. Jurisdictional Guidelines 1208 includes USPTO examination guidelines, court rules, Patent Trial and Appeal Board procedures, and international patent office requirements that govern the preparation and submission of intellectual property documents. The incorporation of Jurisdictional Guidelines 1208 enables LLM 1218 to generate content that meets the specific procedural and formatting requirements applicable to different jurisdictions and proceeding types, Baird ¶139-¶141).
As per claim 22, Baird discloses a system with improved efficiency and performance in drafting a response to a patent Office action by utilizing an artificial intelligence (AI) machine, comprising (system, Baird ¶7; application, processor, ¶33 and ¶177; leverage artificial intelligence, ¶36; non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for generating responses to patent Office actions, the operations comprising:, Claim 17):
a client device configured to display a user interface (User input 102 is received and processed through user interface 104, which provides the primary interaction mechanism between users and the AI-powered document generation system. User interface 104 is configured to accept multiple types of input formats and present options for document generation workflows based on the type of intellectual property document being created, Baird ¶41);
a server communicatively coupled to the client device; and a large language model (LLM) server communicatively coupled to the server, wherein the server is configured to: receive a placeholder identified from the client device (placeholder elements, Baird ¶118-¶119; large language model, ¶36; server with sophisticated AI processing, ¶180);
generate a prompt (From user interface 104, user input 102 is transmitted to prompt generator 106, which represents a core component of the AI-powered document generation system. Prompt generator 106 is configured to process the received user input 102 and combine it with specialized system prompts, templates, and contextual information to create augmented prompt 108. Augmented prompt 108 comprises the original user input 102 enhanced with additional context, formatting instructions, legal requirements, technical guidelines, and other specialized prompts that guide the subsequent AI content generation process. The augmentation process performed by prompt generator 106 ensures that the AI-generated content will be appropriate for the specific type of intellectual property document being created and will comply with relevant legal and technical standards. Augmented prompt 108 is then transmitted to large language model (LLM) 110, which represents the artificial intelligence engine responsible for generating substantive content based on the enhanced prompts. LLM 110 comprises one or more artificial intelligence models, such as transformer-based neural networks, that have been trained on large datasets of intellectual property documents, legal precedents, technical literature, and patent prosecution materials. LLM 110 processes augmented prompt 108 and generates contextually appropriate content that addresses the specific requirements indicated in the prompt while maintaining compliance with intellectual property document standards and conventions, Baird ¶42);
communicate the prompt to the LLM server (From user interface 104, user input 102 is transmitted to prompt generator 106, which represents a core component of the AI-powered document generation system. Prompt generator 106 is configured to process the received user input 102 and combine it with specialized system prompts, templates, and contextual information to create augmented prompt 108. Augmented prompt 108 comprises the original user input 102 enhanced with additional context, formatting instructions, legal requirements, technical guidelines, and other specialized prompts that guide the subsequent AI content generation process. The augmentation process performed by prompt generator 106 ensures that the AI-generated content will be appropriate for the specific type of intellectual property document being created and will comply with relevant legal and technical standards. Augmented prompt 108 is then transmitted to large language model (LLM) 110, which represents the artificial intelligence engine responsible for generating substantive content based on the enhanced prompts. LLM 110 comprises one or more artificial intelligence models, such as transformer-based neural networks, that have been trained on large datasets of intellectual property documents, legal precedents, technical literature, and patent prosecution materials. LLM 110 processes augmented prompt 108 and generates contextually appropriate content that addresses the specific requirements indicated in the prompt while maintaining compliance with intellectual property document standards and conventions, Baird ¶42);
received generated text from the LLM server (From user interface 104, user input 102 is transmitted to prompt generator 106, which represents a core component of the AI-powered document generation system. Prompt generator 106 is configured to process the received user input 102 and combine it with specialized system prompts, templates, and contextual information to create augmented prompt 108. Augmented prompt 108 comprises the original user input 102 enhanced with additional context, formatting instructions, legal requirements, technical guidelines, and other specialized prompts that guide the subsequent AI content generation process. The augmentation process performed by prompt generator 106 ensures that the AI-generated content will be appropriate for the specific type of intellectual property document being created and will comply with relevant legal and technical standards. Augmented prompt 108 is then transmitted to large language model (LLM) 110, which represents the artificial intelligence engine responsible for generating substantive content based on the enhanced prompts. LLM 110 comprises one or more artificial intelligence models, such as transformer-based neural networks, that have been trained on large datasets of intellectual property documents, legal precedents, technical literature, and patent prosecution materials. LLM 110 processes augmented prompt 108 and generates contextually appropriate content that addresses the specific requirements indicated in the prompt while maintaining compliance with intellectual property document standards and conventions, Baird ¶42); and
transmit the generated text to the client device ((From user interface 104, user input 102 is transmitted to prompt generator 106, which represents a core component of the AI-powered document generation system. Prompt generator 106 is configured to process the received user input 102 and combine it with specialized system prompts, templates, and contextual information to create augmented prompt 108. Augmented prompt 108 comprises the original user input 102 enhanced with additional context, formatting instructions, legal requirements, technical guidelines, and other specialized prompts that guide the subsequent AI content generation process. The augmentation process performed by prompt generator 106 ensures that the AI-generated content will be appropriate for the specific type of intellectual property document being created and will comply with relevant legal and technical standards. Augmented prompt 108 is then transmitted to large language model (LLM) 110, which represents the artificial intelligence engine responsible for generating substantive content based on the enhanced prompts. LLM 110 comprises one or more artificial intelligence models, such as transformer-based neural networks, that have been trained on large datasets of intellectual property documents, legal precedents, technical literature, and patent prosecution materials. LLM 110 processes augmented prompt 108 and generates contextually appropriate content that addresses the specific requirements indicated in the prompt while maintaining compliance with intellectual property document standards and conventions, Baird ¶42)) and
display the generated text on the user interface (From user interface 104, user input 102 is transmitted to prompt generator 106, which represents a core component of the AI-powered document generation system. Prompt generator 106 is configured to process the received user input 102 and combine it with specialized system prompts, templates, and contextual information to create augmented prompt 108. Augmented prompt 108 comprises the original user input 102 enhanced with additional context, formatting instructions, legal requirements, technical guidelines, and other specialized prompts that guide the subsequent AI content generation process. The augmentation process performed by prompt generator 106 ensures that the AI-generated content will be appropriate for the specific type of intellectual property document being created and will comply with relevant legal and technical standards. Augmented prompt 108 is then transmitted to large language model (LLM) 110, which represents the artificial intelligence engine responsible for generating substantive content based on the enhanced prompts. LLM 110 comprises one or more artificial intelligence models, such as transformer-based neural networks, that have been trained on large datasets of intellectual property documents, legal precedents, technical literature, and patent prosecution materials. LLM 110 processes augmented prompt 108 and generates contextually appropriate content that addresses the specific requirements indicated in the prompt while maintaining compliance with intellectual property document standards and conventions, Baird ¶42; The output from document generator 114 is generated document 116, which comprises a completed intellectual property document ready for professional use, review, or filing. Generated document 116 may include fully formatted patent applications, office action responses, claim charts, litigation documents, or other intellectual property materials depending on the initial user requirements and document type selections. Generated document 116 maintains professional formatting standards and includes all necessary components for the specified document type, ¶45).
As per claim 23, Baird discloses a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for improving an efficiency and a performance of a system for drafting a response to an Office action, the operations comprising:
displaying one or more placeholders associated with sections of an Office action response on a user interface (placeholder elements, Baird ¶118-¶119);
receiving user input selecting a placeholder (placeholder elements, Baird ¶118-¶119);
transmitting a request to draft a selected section, the request including a placeholder identifier (The tag-based insertion system demonstrated by Tag 1004 enables Document Generator 914 (FIG. 9) to systematically identify appropriate locations for inserting specific types of AI-generated content from Generated Content 912A and Generated Content 912B (FIG. 9). Tag 1004 represents structured markup elements that correspond to different types of content generated by LLM 910 (FIG. 9) in response to specialized augmented prompts such as Augmented Prompt 908A and Augmented Prompt 908B (FIG. 9). The tag system ensures that content generated for specific purposes, such as background descriptions, claim summaries, or technical explanations, is inserted into the appropriate sections of the document template, Baird ¶119);
receiving generated text for the selected section (The tag-based insertion system demonstrated by Tag 1004 enables Document Generator 914 (FIG. 9) to systematically identify appropriate locations for inserting specific types of AI-generated content from Generated Content 912A and Generated Content 912B (FIG. 9). Tag 1004 represents structured markup elements that correspond to different types of content generated by LLM 910 (FIG. 9) in response to specialized augmented prompts such as Augmented Prompt 908A and Augmented Prompt 908B (FIG. 9). The tag system ensures that content generated for specific purposes, such as background descriptions, claim summaries, or technical explanations, is inserted into the appropriate sections of the document template, Baird ¶119); and
displaying the generated text through the user interface (The tag-based insertion system demonstrated by Tag 1004 enables Document Generator 914 (FIG. 9) to systematically identify appropriate locations for inserting specific types of AI-generated content from Generated Content 912A and Generated Content 912B (FIG. 9). Tag 1004 represents structured markup elements that correspond to different types of content generated by LLM 910 (FIG. 9) in response to specialized augmented prompts such as Augmented Prompt 908A and Augmented Prompt 908B (FIG. 9). The tag system ensures that content generated for specific purposes, such as background descriptions, claim summaries, or technical explanations, is inserted into the appropriate sections of the document template, Baird ¶119; The output from document generator 114 is generated document 116, which comprises a completed intellectual property document ready for professional use, review, or filing. Generated document 116 may include fully formatted patent applications, office action responses, claim charts, litigation documents, or other intellectual property materials depending on the initial user requirements and document type selections. Generated document 116 maintains professional formatting standards and includes all necessary components for the specified document type, Baird ¶45).
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baird (US PG Pub. 2025/0384206) and further in view of Walter (US PG Pub. 2025/0036884).
As per claim 21, Baird discloses a method for improving an efficiency and a performance of a system for drafting a response to a patent Office action by utilizing an artificial intelligence (AI) machine, comprising:
loading, through a server, the patent Office action and one or more documents related to the application number (The prosecution-specific input and output selection capabilities of user interface (Prosecution) 404 enable users to specify precisely which prosecution documents should be generated while providing the AI system with extensive prosecution-specific contextual information through multiple types of supporting documents. This configuration ensures that prompt generator 106 (FIG. 1) can create highly tailored augmented prompts 108 (FIG. 1) that incorporate both the specific prosecution output requirements and the available case-specific input materials, enabling LLM 110 (FIG. 1) to generate contextually appropriate prosecution content that addresses the specific examiner rejections, prior art challenges, and strategic considerations relevant to the particular patent application under prosecution, Baird ¶68; addresses Examiner’s rejections and requirements, ¶64-¶65);
generating, using an AI machine, response strategies for responding to one or more rejections or objections in the patent Office action (The prosecution-specific input and output selection capabilities of user interface (Prosecution) 404 enable users to specify precisely which prosecution documents should be generated while providing the AI system with extensive prosecution-specific contextual information through multiple types of supporting documents. This configuration ensures that prompt generator 106 (FIG. 1) can create highly tailored augmented prompts 108 (FIG. 1) that incorporate both the specific prosecution output requirements and the available case-specific input materials, enabling LLM 110 (FIG. 1) to generate contextually appropriate prosecution content that addresses the specific examiner rejections, prior art challenges, and strategic considerations relevant to the particular patent application under prosecution, Baird ¶68; addresses Examiner’s rejections and requirements, ¶64-¶65; leverage artificial intelligence, ¶36; server with sophisticated AI processing, ¶180); and
receiving, through the user interface, generated text affiliated with a response to the one or more rejections or objections in the patent Office action (The prosecution-specific input and output selection capabilities of user interface (Prosecution) 404 enable users to specify precisely which prosecution documents should be generated while providing the AI system with extensive prosecution-specific contextual information through multiple types of supporting documents. This configuration ensures that prompt generator 106 (FIG. 1) can create highly tailored augmented prompts 108 (FIG. 1) that incorporate both the specific prosecution output requirements and the available case-specific input materials, enabling LLM 110 (FIG. 1) to generate contextually appropriate prosecution content that addresses the specific examiner rejections, prior art challenges, and strategic considerations relevant to the particular patent application under prosecution, Baird ¶68; addresses Examiner’s rejections and requirements, ¶64-¶65; The output from document generator 114 is generated document 116, which comprises a completed intellectual property document ready for professional use, review, or filing. Generated document 116 may include fully formatted patent applications, office action responses, claim charts, litigation documents, or other intellectual property materials depending on the initial user requirements and document type selections. Generated document 116 maintains professional formatting standards and includes all necessary components for the specified document type, ¶45).
While Baird discloses the use of a large language model to draft documents and responses as shown above, Baird does not expressly disclose inputting, through a user interface, an application number associated with the patent Office action.
However, Walter teaches inputting, through a user interface, an application number associated with the patent Office action (The model component may be configured to provide the first case content and/or user-provided context values as input to a large language model. The context values may identify one or more of a document type, a document objective, and/or other information, Walter ¶7; An operation 204 may include obtaining first case content for a first case from electronic storage. The first case content may include factual information, legal information, and/or other information relevant to the first case. The first case content may further include one or more exemplary segments of legal documents. Operation 204 may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to content component 108, in accordance with one or more implementations, ¶41) (Examiner notes the context values as the case identification number such as an application number).
Both the Walter and Baird references are analogous in that both are directed towards/concerned with drafting legal documents. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Walter’s ability to identify case specific documents and related documents in Baird’s system to improve the system and method with reasonable expectation that this would result in a document drafting management system that is able to utilize document identification (application numbers, case numbers, attorney docket numbers etc.).
The motivation being that drafting legal documents may be a tiresome and time-consuming task. For example, in the context of litigation, an associate's work may consist of reviewing numerous, repetitive questions and document production demands then drafting lengthy repetitive responses to those questions and demands. Despite the routine nature of this process, associates must expend many hours to verify the documents are reviewed precisely and polished prior to delivery to opposing counsel. As such, there exists a need to streamline document drafting while ensuring the accuracy of the final product. By utilizing machine learning, attorneys may be able to automatically generate documents based on previously written documents and/or populate fields in document templates based on information inputted by associates (Walter ¶3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional art can be located on the PTO-892):
Lee et al. (US PG Pub. 2013/0144799) Computing device and method for extracting patent rejection information.
Walter (US Patent No. 12,430,502) Systems And Methods For Using Multiple Machine Agents To Generate Document Drafts.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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