DETAILED ACTION
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 with respect to Double Patenting rejection of claims 19-36 have been considered and found persuasive due to Terminal Disclaimer filed on 3/30/2026, and the rejection has been withdrawn.
Applicant's arguments with respect to 35 U.S.C. 101 in regards to claims 19-38 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s arguments because the claims are directed to an abstract idea because the claims are mainly receiving a request, checking the user’s subscription level, limiting the information the user can access, retrieving responsive information, and displaying an answer with citations. At a high level, this is a business/access-control process for managing who may receive information, combined with collecting, selecting, and presenting information. Under the patent law, “commercial or legal interactions” and “business relations” fall within certain methods of organizing human activity, and evaluating/selecting information does also fall within metal-process type abstract ideas.
The claims do not integrate that abstract idea into a practical technological application. The processor, storage medium, prompt, query, large language model, retrieval step and display step are described functionally and at a high level. The claims do not recite a specific improvement to computer operation, LLM architecture, model training or access-control technology. The LLM is used as too to carry out the information process. Therefore, the claimed stand rejected.
Applicant's arguments with respect to 35 U.S.C. 102 in regards to claims 19 and 28 have been considered but are moot due to new grounds of rejection necessitated by amendments. See detailed rejection below.
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 19-38 are rejected under 35 U.S.C. 101 Abstract Idea.
Claims 19 and 28 are rejected under 35 U.S.C. § 101 because the claim is directed to a judicial exception, i.e., an abstract idea. The claim as a whole recites a process of receiving information (a user prompt), generating a request for information (a query), obtaining responsive information (retrieving relevant fact-based content), and presenting the results (displaying content with citations). This is fundamentally an information-processing and information-presentation concept that can be performed by humans (e.g., a researcher receiving a question, formulating a search query, retrieving sources, and presenting an answer with citations), and thus falls within the realm of mental processes and/or organizing and presenting information. The recited “fact-based language model” is used as a tool to perform the same abstract information-retrieval/synthesis task, and the claim is drafted primarily in functional/result-oriented terms (e.g., “generate,” “retrieve,” “provide,” “display”) without reciting a specific technical manner of achieving those results.
The claims do not recite additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself. The “computing device” and the “one or more fact-based language models trained with training data correlating prompts to content” are recited at a high level of generality and do not impose meaningful technological limitations (e.g., no specific model architecture, training technique, retrieval mechanism, citation-verification mechanism, or computer-performance improvement is claimed). Further, the requirement that the displayed content include “one or more citations identifying one or more fact based sources” is merely an output/presentation feature associated with the information provided, rather than a technical improvement to computer functionality. Accordingly, when considered as an ordered combination, the additional elements amount to no more than a generic computer implementation of the abstract idea of retrieving and presenting information (including source identifiers), and therefore Claims 19 and 28 lack an inventive concept and is not patent-eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 20-27 and 29-38 further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in data retrieval systems.
Claims 20 and 29: it is directed to the abstract idea of retrieving information (i.e., retrieving relevant fact-based content) from a specified repository (an electronic library).
Claims 21 and 30: it is directed to the abstract idea of selecting and using certain information sources (proprietary data such as textbooks, journals, whitepapers, or professor notes) to supply content.
Claims 22 and 31: it is directed to the abstract idea of analyzing information by determining or using a ratio (proprietary content to non-proprietary content) for the content provided.
Claims 23 and 32: it is directed to the abstract idea of presenting information, namely displaying a ratio relating content and fact-based content to a user.
Claims 24 and 33: it is directed to the abstract idea of responding to a request for information, where the user prompt is simply characterized as a request for fact-based content.
Claims 25 and 34: it is directed to the abstract idea of evaluating a user request and tailoring retrieved information based on an identified level of knowledge.
Claims 26 and 35: it is directed to the abstract idea of analyzing language by extracting linguistic data from a query.
Claims 27 and 36: it is directed to the abstract idea of providing and presenting information, merely specifying that the fact-based content may include text, images, video, or combinations thereof.
Claim 37: it is directed to the abstract idea of classifying information by categorizing content into one or more categories.
Claim 38: it is directed to the abstract idea of searching for and retrieving information from the internet based on criteria.
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.
Claims 19-21, 23-30 and 32-38 are rejected under 35 U.S.C. 103 as being unpatentable over Gray et al. (US 11,769,017) in view of Stuart et al. (US 2014/0172834).
Claims 19 and 28,
Gray teaches a system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising ([Fig. 2] [col. 11 lines 1-5] [Fig. 8] [col. 31 lines 31-45] Gray teaches the claimed computer system; Gray teaches a system having “one or more processors, memory” for the method):
receiving a user prompt from a user ([Fig. 2, block 252] [col. 11 lines 11-15] Gray teaches receiving the user prompt as a user query; Gray states that “the system receives a query.”; Gray further teaches the query may be based on typed input, voice input, image input or multimodal input);
generating a query requesting content based in response to the user prompt ([Fig. 1] [col. 8 lines 48-52] [Fig. 2, blocks 252/259] [col. 14 lines 27-35] Gray teaches generating supplementing, rewriting, and automatically generating queries based on user input/context; Gray teaches “supplementing or rewriting a query” formulated from user input; Gray also teaches an implied query can be “automatically generated” based on the query and context);
submit submitting the query to the one or more fact-based large language models trained with training data correlating prompts to content, wherein the one or more fact-based large language model are trained to receive prompts as input and provide fact-based content as output ([Fig. 1] [col. 5 line 62 to col. 6 line 5] [col. 10 lines 8-24] [col. 15 lines 35-60] Gray teaches generating LLM input from the query and processing that input using one or more LLMs; Gray states that the LLM input engine can “generate LLM input” to be processed using an LLM, and that the LLM response generation engine processes that input “using an LLM.”; Gray further teaches user of “one or more LLMs”; Gray teaches LLMs that receive prompts/content and generate fact-grounded summaries; Gray teaches a “summary prompt” processed with SRD content and teaches generated content influenced by “prior training” of the LLM; Gray also describes LLMs having hundreds of millions/billions of parameters and being Transformer-based),
wherein the one or more fact-based large language models are configured to: receive the query ([Fig. 1] [col. 10 line 8-14] Gray teaches that the LLM input includes query content; Gray states that LLM input is generated in response to receiving a query and may include “query content”);
retrieve relevant fact-based content based on the query from the accessible content ([Fig. 2, block 254/260] [col. 11 lines 50-60] [col. 15 lines 5-14] Gray teaches selecting relevant search-result-document content based on the query; Gray states that the system “selects one or more query-responsive SRDs” and may select a subset identified as responsive to the query; Gray further teaches processing corresponding content from each selected SRD using the LLM); and
provide the relevant fact-based content to the system ([Fig. 2, block 260] [col. 15 lines 5-14] [col. 16 lines 5-27] Gray teaches generating corresponding content from selected SRDs and using that content in the LLM/NL response system; Gray states that corresponding SRD content is processed using the LLM to generate the NL summary; Gray also teaches generating content such as a snippet from the SRD); and
displaying content to a user based on the user prompt, the content including at least a portion of the relevant fact-based content ([Fig. 2, block 262] [col. 15 lines 45-52] [col. 17 line 61 to col. 18 line 3] Gray teaches rendering/displaying the generated content to the user; Gray states that the system causes the NL based summary to be “rendered” in response to the query, including graphically in the interface of the application through which the query was submitted; Gray teaches that the generated summary may include portions of the retrieved SRD content, including “direct quotes” or paraphrases) and
one or more citations identifying one or more fact-based sources associated with the fact-based content ([col. 16 lines 38-65] [col. 18 lines 28-66] Gray teaches source identifiers and links to supporting source documents; Gray teaches include a “source identifier” for an SRD so LLM output reflects which portions are supported by which SRDs; Gray further teaches that a source identifier can indicate that the corresponding SRD verifies the portion and that a link to the SRD can be provided).
The difference between the prior art and the claimed invention is that Gray does not explicitly teach identifying an access status of the user, wherein the access status corresponds to a subscription level; restricting access of one or more fact-based large language models to a portion of available content based on the access status, resulting in accessible content and inaccessible content.
Stuart teaches identifying an access status of the user, wherein the access status corresponds to a subscription level ([0038] [0042] Stuart teaches subscription-level access status; Stuart teaches recognizing a user using credentials and a “subscription level”; Stuart further teaches that access may depend on their “subscription level”);
restricting access of one or more fact-based large language models to a portion of available content based on the access status, resulting in accessible content and inaccessible content ([0038] [0042] Stuart teaches subscription-based restriction of available content; Stuart states that, when the user connects, “only the data” for which the user is subscribed is available and search results are “filtered” so only appropriate data is delivered; Stuart teaches accessible and inaccessible content by subscription tier; Stuart teaches that the highest subscription level gives access to all premium channels, while “lesser subscription levels” provide access to only “a portion” of premium channels; Stuart also teaches private subscribers may not see other subscriber’ data).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Gray with teachings of Stuart by modifying the generative summaries for search results as taught by Gray to include identifying an access status of the user, wherein the access status corresponds to a subscription level; restricting access of one or more fact-based large language models to a portion of available content based on the access status, resulting in accessible content and inaccessible content as taught by Stuart for the benefit of providing access to at least a portion of the aggregated data set based upon a subscription level associated with a user (Stuart [Abstract]).
Claims 20 and 29,
Gray further teaches the system of claim 19, wherein the one or more fact-based language models retrieve relevant fact-based content from an electronic library ([col. 4 lines 6-53] [col. 10 lines 54-62] the input includes corresponding content from each of the SRDs of the set; the SRD selection engine 122 can generate a set of SRDs based on the query; the SRD engine 162 can, for example, utilize indices 172 and/or other resources in identifying search results documents that are responsive to queries as described herein; database).
Claims 21 and 30,
Gray further teaches the system of claim 19, wherein the electronic library includes proprietary data of one or more one of textbooks, journals, whitepapers, or professor notes ([Figs. 7A-7C] plurality of documents).
Claims 23 and 32,
Gray further teaches the system of claim 19, wherein the computing device is further configured to display a ratio of content to fact-based content to the user ([col. 19 lines 19-55] displaying veracity metrics; the system causes rendering of the NL based summary with confidence annotations; the confidence measure of a portion can be based on trust worthiness of the SRDs that verify that portion and/or a quantity of the SRDs that verify that portion).
Claims 24 and 33,
Gray further teaches the system of claim 19, wherein user prompt is a request for fact-based content ([Fig. 7A-7C] see example “is it good to drink coffee”).
Claims 25 and 34,
Gray further teaches the system of claim 19, wherein the one or more fact-based language models are further configured to identify a level of knowledge of the user prompt ([col. 26 line 57 to col. 27 line 8] it can be determined based on a profile associated with the query (a device profile of the client device via which the query was submitted and a user profile of the submitter; at block 654 the system determines based on a profile associated with the query whether the submitter of the query is already familiar with certain content that is responsive to the query) and retrieve the relevant fact-based content based on the level of knowledge of the user prompt ([col. 5 line 32-48] [col. 26 line 57 to col. 27 line 8] if so, additional content, that reflects familiarity of the user with the certain content, can be processed using the LLM in generating the NL based summary; for example, if it is determined that the user is not familiar with any content responsive to the query, a prompt of “answer [query]” can be processed using the LLM in generating the NL based summary; block 658 the system generates an NL based summary based on LLM processing of input that reflects familiarity with the certain content).
Claims 26 and 35,
Gray further teaches the system of claim 19, wherein the one or more fact-based language models are configured to extract linguistic data from the query ([Background] large language models (LLMs) have been developed that can be used to process NL content and/or other inputs; when the query includes content that is not in textual format; when the query includes content that is not in textual format, the system can convert the query to a textual format or other format; for example, if the query is a voice query the system can perform automatic speech recognition (ASR) to convert the query to textual format. As another example, assume the query is a multimodal query that includes an image of an avocado and a voice input of “is this healthy”; the system can perform ASR to convert the voice input to text form, can perform image processing on the image to recognize an avocado is present in the image, and can perform co-reference resolution to replace “this” with “an avocado”, resulting in a textual format query of “is an avocado healthy”).
Claims 27 and 36,
Gray further teaches the system of claim 19, wherein the fact-based content includes text, image, video, or a combination thereof ([col. 2 lines 28-58] a search result document, can include, for example, text content, image content, and/or video content).
Claim 37,
Gray further teaches the method of claim 28, further comprising classifying, by a classifier of the one or more fact-based large language models, the content into one or more categories ([col. 25 line 52 to col. 26 line 16] the system can process content of the query using a machine learning classifier to classify the query into one or more classifications).
Claim 38,
Gray further teaches the method of claim 28, wherein retrieving relevant fact-based content based on the query comprises: searching, by the one or more fact-based large language models, the internet for content based on criteria ([col. 3 lines21-55] [col. 11 line 32 to col. 12 line 34] the NL based response system 120 which utilizes the LLM, works in conjunction with search systems 160; a search can performed for the given query to obtain query-responsive search result documents; selection is based on features such as query-dependent measures, query-independent measures and/or user-dependent measures; a revised database query that facilitates a more focused retrieval of information from the database); and
retrieving, by the one or more fact-based large language models, content from the internet based on the criteria ([col. 3 lines21-55] [col. 11 line 32 to col. 12 line 34] the additional content that is processed, using the LLM includes content from query-responsive SRDs that are responsive to the query; the SRDs from which the content is from which the content is obtained, can be a subset of the SRDs that are responsive to the query; the subset can be selected based on features of the SRDs, such as query-dependent measures; the SRD engine 162 can utilize indices 172 in identifying SRDs).
Claims 22 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Gray et al. (US 11,769,017) in view of Stuart et al. (US 2014/0172834) and further in view of Evans et al. (US 2021/0004583).
Claims 22 and 31,
Gray and Stuart teach all the limitations in claim 21. The difference between the prior art and the claimed invention is that Gray nor Stuart explicitly teach wherein the content includes a ratio of proprietary content to non-proprietary content.
Evans teaches wherein the content includes a ratio of proprietary content to non-proprietary content ([0027] Jaccard scores express a ratio of overlapping content features between two documents).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Gray with teachings of Evans by modifying the generative summaries for search results as taught by Gray to include wherein the content includes a ratio of proprietary content to non-proprietary content as taught by Evans for the benefit of calculating for every document in the document set having a single number that indicates what ratio of overlap there is with every other document in the document set ([0027] Evans).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
Prosecution Insights