DETAILED ACTION
This is responsive to the RCE filed 07 January 2026.
Claims 1-20 remain pending and are 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 .
Response to Arguments
Applicant's arguments filed 07 January 2026 have been fully considered but they are not persuasive.
Regarding the 35 USC 101 rejection, Applicant argues:
The Applicant respectfully submits that the human mind cannot practically perform at least "receive, at a large language model, a prompt . . . generating, by the large language model in response to the prompt, a framework with sections and subsections for the document; writing, by the large language model using the framework and the prompt, the sections and the subsections of the document with natural language and references to the data sources that the large language model used in writing the document; and providing the document in response to the input query." The Applicant respectfully submits that the claimed features place the amended independent claims within a technological environment that is outside of what a human mind can do.
However, a person may generate, in response to a prompt, a framework with sections and subsections for a document (e.g. thinking and generating a format to prepare a report, e.g. having a title and different sections and sub-sections); write, using the framework and the prompt, the sections and the subsections of the document with natural language and references to data sources used in writing the document (e.g. writing, based on the format, the report with sections and sub-sections, the report also including a list of references used in generating it). Applying abstract ideas using generic processors performing generic computer functions does not integrate the abstract idea into a practical solution.
Applicant also argues:
The Applicant further submits that the features recited in the amended independent claims “provide benefits and/or solve problems associated with using LLMs to automatically generate documents.” See, Specification, paragraph [0025]. The Applicant submits that one “technical advantage of the system and methods of the present disclosure is using LLMs to automatically generate documents with references to the data source given a topic for the documents,” and that by “providing grounded documents 14 to the user with reference to the data sources 110, 112 used by the LLM(s) 108 in generating the document 14, the user 104 is able to easily identify the support for the information included in the document 14.” See, Specification, paragraphs [0026] and [0081].
However, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. Courts have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”)
Regarding the 35 USC 102(a) rejection, Applicant argues:
Applicant submits that there is no disclosure in the cited portions of Soubbotin of at least "generating, by a large language model in response to the prompt, a framework that provides an outline of the document with sections and subsections the large language model determined for the document and a description of each section and subsection; and writing by the large language model using the framework and the prompt, the sections and subsections of the document with natural language," as recited in amended independent claim 1. Instead, the cited portions of Soubbotin disclose the summary 200 of the user's query is presented as a list of sentences.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a framework that provides an outline of the document with sections and subsections the large language model determined for the document and a description of each section and subsection) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Further, Soubbotin explicitly discloses generating, by the large language model in response to the prompt, a framework with sections and subsections for the document; writing, by the large language model using the framework and the prompt, the sections and the subsections of the document with natural language and references to the data sources that the large language model used in writing the document (“Graphical User Interface (GUI) of summary 200 on a user's query, produced by a search system, in accordance with an embodiment of the present invention. In one interpretation of the present embodiment, explained in terms of search systems that are at least in part based on a generally one-to-one mapping of summary text fragment(s) and original source document(s), summary 200 may be presented as a list of sentences; however, in alternate embodiments, the summary may be displayed in different formats such as, but not limited to, a list of key concepts, a cluster hierarchy, a tag cloud, etc. The sentences or text fragments within summary 200 may originate from various sources, which may be listed below the summary in source list 205”, col. 21, lines 49-62, see related Fig. 2 where different sections SUMMARY: “causes of ocean tides”, summary sentences 200 and citations 205 include different subsections SUMMARY and “causes of ocean tides”, different sentences and different citations respectively).
Applicant also argues:
In addition, Applicant submits that the cited portions of Soubbotin do not disclose or suggest at least "the document is a grounded technical document written by the large language model on demand in response to the input query," as recited in amended independent claim 1. Instead, the cited portions of Soubbotin disclose presenting a summary of the search results of the user's query.
The Examiner respectfully disagrees. Soubbotin explicitly discloses providing the document in response to the input query, wherein the document is a grounded (i.e. source supported) technical document written by the large language model on demand in response to the input query (“an exemplary Graphical User Interface (GUI) of summary 200 on a user's query”, col. 21, lines 49-62, see also “the system core may display the summary to the user”, col. 26, lines 36-38. Note that the summary is supported by selected sources i.e. it is grounded. See also col. 33, lines 41-48 for technical queries which will lead to provision of grounded technical documents).
Therefore, all of Applicant’s arguments have been addressed and they are unpersuasive.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more. Further, this judicial exception is not integrated into a practical application.
In claims 1 and 14, the limitations document; and
That is, other than reciting a “large language model” (claims 1 and 14) and a “device, comprising: a memory to store data and instructions; and a processor operable to communicate with the memory, wherein the processor is operable to” (claim 14) nothing in the claims precludes the steps from practically being performed in the mind. For example, a person may generate, in response to a prompt, a framework with sections and subsections for a document (e.g. thinking and generating a format to prepare a report, e.g. having a title and different sections and sub-sections); write, using the framework and the prompt, the sections and the subsections of the document with natural language and references to data sources used in writing the document (e.g. writing, based on the format, the report with sections and sub-sections, the report also including a list of references used in generating it).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – a “large language model” (claims 1 and 14) and a “device, comprising: a memory to store data and instructions; and a processor operable to communicate with the memory, wherein the processor is operable to” (claim 14) which are recited at a high-level of generality (i.e., as generic processors performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components.
The claims also recite the additional elements “receiving an input query with a topic for a document” , “receiving, at a large language model, a prompt created based on the input query with instructions for writing sections and subsections of the document and identifying data sources to use in writing the document” and “providing the document in response to the input query, wherein the document is a grounded technical document written by the large language model on demand in response to the input query”. The claims do not impose any limits on how the input query and the prompt are received or how the claimed document is provided. These limitations therefore represent extra-solution activity because they are mere nominal or tangential addition to the claims. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As stated above, the claims recite the additional limitations of a “large language model” (claims 1 and 14) and a “device, comprising: a memory to store data and instructions; and a processor operable to communicate with the memory, wherein the processor is operable to” (claim 14). However, these are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications (see Applicant’s specification [0053], [0054] and [0091]). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The claims also recite the additional elements “receiving an input query with a topic for a document” , “receiving, at a large language model, a prompt created based on the input query with instructions for writing sections and subsections of the document and identifying data sources to use in writing the document” and “providing the document in response to the input query, wherein the document is a grounded technical document written by the large language model on demand in response to the input query”. The claims do not impose any limits on how the input query and the prompt are received or how the claimed document is provided. These limitations represent the extra-solution activities of data gathering and outputting which are well-understood, routine and conventional activities. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
The dependent claims recite:
further comprising: receiving a modification to the framework for the document; and generating an updated framework in response to the modification, wherein the large language model uses the updated framework to write the sections and the subsections of the document;
wherein the modification is an addition of a section, an addition of a subsection, a removal of a section, a removal of a subsection, editing a section, or editing a subsection;
wherein the input query further includes areas of focus for the topic and the sections and the subsections include additional content for the areas of focus.
wherein the input query further includes a set of data sources to use in providing the data for the document;
wherein the set of data sources are trusted data sources.
wherein the data sources includes a combination of publicly available data sources and private data sources;
further comprising: automatically generating, by the large language model, a list of references at an end of the document with citations to the data sources used in generating the document, wherein the references within the sections and the subsections correspond to the list of references;
further comprising: providing, to the large language model, a system prompt that includes a chain of thought for preparing the document, a goal for the document, and a length of the document, wherein the large language model uses the system prompt in writing the sections and the subsections of the document;
further comprising: providing, to the large language model, a preparation prompt that the large language model uses to identify information needed to prepare the document, wherein the large language model uses the preparation prompt to identify the information; and sending a retrieval request for the data to use in writing the document based on the information;
further comprising: providing, to the large language model, a section writing prompt that provides additional instructions to the large language model for writing the sections and the subsections of the document, wherein the large language model uses the section writing prompt to write the sections and the subsections of the document;
wherein the sections or the subsections further include figures or tables automatically created by the large language model; and
wherein the document is a report on the topic, a grounded technical report on the topic, a contract, a funding proposal, a clinical trial protocol, or product documentation.
The additional recited limitations further narrow the steps of the independent claims without however providing “a practical application of” or "significantly more than" the underlying “Mental Processes” abstract idea. Therefore, the dependent claims are also not patent eligible.
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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Soubbotin (US 12,038,958).
Claim 1:
Soubbotin discloses a method, comprising:
receiving an input query with a topic for a document (“a user's query for “causes of ocean tides” in query field 101”, col. 20, lines 43-53);
receiving, at a large language model, a prompt created based on the input query with instructions for writing sections and subsections of the document and identifying data sources to use in writing the document (“If the user clicks summary request button 100, a summary of the results that are displayed on this page may be generated by the system … Text fragments field 105 may indicate the number of text fragments or sentences to be included in the summary, and may be specified by the user in text fragments field 105. In this example, only six sentences are requested to allow for the display of the summary to fit on a single page. Checkboxes 110 may be located next to each search result in search results list 103, enabling the user to specify which of the search results are to be included in the summary … the present invention is agnostic as to the particular language model or AI that is employed to generate the summarization text fragments displayed … statistical AI language model frameworks (e.g., without limitation, Large language models (LLM) like ChatGPT)”, col. 20, line 48 to col. 21, line 36, note that the number of sentences and which sources to include for the summary read on the claimed instructions);
generating, by the large language model in response to the prompt, a framework with sections and subsections for the document; writing, by the large language model using the framework and the prompt, the sections and the subsections of the document with natural language and references to the data sources that the large language model used in writing the document (“Graphical User Interface (GUI) of summary 200 on a user's query, produced by a search system, in accordance with an embodiment of the present invention. In one interpretation of the present embodiment, explained in terms of search systems that are at least in part based on a generally one-to-one mapping of summary text fragment(s) and original source document(s), summary 200 may be presented as a list of sentences; however, in alternate embodiments, the summary may be displayed in different formats such as, but not limited to, a list of key concepts, a cluster hierarchy, a tag cloud, etc. The sentences or text fragments within summary 200 may originate from various sources, which may be listed below the summary in source list 205”, col. 21, lines 49-62, see related Fig. 2 where different sections SUMMARY: “causes of ocean tides”, summary sentences 200 and citations 205 include different subsections SUMMARY and “causes of ocean tides”, different sentences and different citations respectively); and
providing the document in response to the input query, wherein the document is a grounded (i.e. source supported) technical document written by the large language model on demand in response to the input query (“an exemplary Graphical User Interface (GUI) of summary 200 on a user's query”, col. 21, lines 49-62, see also “the system core may display the summary to the user”, col. 26, lines 36-38. Note that the summary is supported by selected sources i.e. it is grounded. See also col. 33, lines 41-48 for technical queries which will lead to provision of grounded technical documents).
Claim 2:
Soubbotin discloses the method of claim 1, further comprising: receiving a modification to the framework for the document; and generating an updated framework in response to the modification, wherein the large language model uses the updated framework to write the sections and the subsections of the document (col. 22, lines 19-24, see also col. 22, lines 57-63).
Claim 3:
Soubbotin discloses the method of claim 2, wherein the modification is an addition of a section, an addition of a subsection, a removal of a section, a removal of a subsection, editing a section, or editing a subsection (col. 22, lines 19-24, see also col. 22, lines 57-63).
Claim 4:
Soubbotin discloses the method of claim 1, wherein the input query further includes areas of focus for the topic and the sections and the subsections include additional content for the areas of focus (col. 21, lines 13-16, see also col. 23, lines 58-66).
Claim 5:
Soubbotin discloses the method of claim 1, wherein the input query further includes a set of data sources to use in providing the data for the document (col. 21, lines 13-16).
Claim 6:
Soubbotin discloses the method of claim 5, wherein the set of data sources are trusted data sources (col. 18, lines 1-6, see also col. 33, lines 7-14, note that municipal records are trusted sources).
Claim 7:
Soubbotin discloses the method of claim 1, wherein the data sources includes a combination of publicly available data sources (Internet) and private data sources (Intranet, an enterprise network, a standalone computer ) (col. 18, lines 1-6, see also col. 33, lines 7-14).
Claim 8:
Soubbotin discloses the method of claim 1, further comprising: automatically generating, by the large language model, a list of references at an end of the document with citations to the data sources used in generating the document, wherein the references within the sections and the subsections correspond to the list of references (col. 21, lines 49-62).
Claim 9:
Soubbotin discloses the method of claim 1, further comprising: providing, to the large language model, a system prompt that includes a chain of thought (coherence) for preparing the document, a goal (tone of the response) for the document (col. 26, lines 26-36), and a length of the document (col. 22, lines 22-26), wherein the large language model uses the system prompt in writing the sections and the subsections of the document (col. 26, lines 26-38).
Claim 10:
Soubbotin discloses the method of claim 1, further comprising: providing, to the large language model, a preparation prompt that the large language model uses to identify information needed to prepare the document, wherein the large language model uses the preparation prompt to identify the information; and sending a retrieval request for the data to use in writing the document based on the information (col. 21, lines 11-19).
Claim 11:
Soubbotin discloses the method of claim 1, further comprising: providing, to the large language model, a section writing prompt that provides additional instructions to the large language model for writing the sections and the subsections of the document, wherein the large language model uses the section writing prompt to write the sections and the subsections of the document (col. 21, lines 11-19, see also col. 26, lines 26-38).
Claim 12:
Soubbotin discloses the method of claim 1, wherein the sections or the subsections further include figures or tables automatically created by the large language model (col. 30, lines 16-20).
Claim 13:
Soubbotin discloses the method of claim 1, wherein the document is a report on the topic, a grounded technical report on the topic, a contract, a funding proposal, a clinical trial protocol, or product documentation (col. 17, lines 58-64).
Claims 14-20:
Soubbotin discloses a device, comprising: a memory to store data and instructions; and a processor operable to communicate with the memory, wherein the processor is operable (col. 13, lines 29-44, see also col. 29, lines 28-58) to perform the steps of process claims 1, 3-5 and 7-11 as shown above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gray et al. (US 2024/0220735) discloses selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SAMUEL G NEWAY/Primary Examiner, Art Unit 2657