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 .
The application has been examined. Claims 1 – 20 are pending in this office action.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1 – 20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
The representative claim 1 (and other independent claims 19 and 20) recites receiving a user query for one or more items from a user; processing the user query by searching against at least one relational database associated with the query, wherein the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items; generating one or more query results based on the processing of the user query, wherein the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item; and transmitting the one or more query results to a user device for presentation to the user.
The claims recite a mental process and a certain method of organizing human activity. Before computers it would have been obvious for a person to be able to compare similarities in documents and also the differences in them. Additionally, this process would also be considered a method of organizing human activity as it relates to something comparable to checking papers or essays to see if they are similar or answered differently. Examples the courts have found recites an abstract idea includes filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016).
The claims additionally recite - Claim 1: a computer-implemented method, relational database, user device. - Claim 19: a system, a processor, a memory coupled to processor, relational database, user device. Claim 20: a non-transitory computer-readable medium, a processor, a memory coupled to processor, relational database, user device.
However, the limitations merely amount to 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, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). Furthermore, a method for transmitting, receiving, and processing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). 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.
As discussed above, the additional imitations amount to 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, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). It is well- understood, routine, and conventional to use a computer to gather, analysis, and present information to a plurality of users (also see court case A web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). See MPEP 2106.05(d) as well as USPTO Memorandum: Revising 101 Eligibility Procedure in view of Berkheimer v. HP, Inc. (April 19, 2018). And the following court cases (See MPEP 2106.05(d). Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015); Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
Claims 2 – 18 further narrow the abstract idea recited in the independent claims 1, 19 and 20 and are therefore directed towards the same abstract idea. The dependent claims are directed towards further narrowing the abstract idea of receiving query, finding similar information and presenting result.
Claims 2 – 18 do not recite any additional elements that have not already been analyzed above. Therefore, the claims do not direct the claims to recite a practical application.
Therefore, claims 1 – 20 are rejected under U.S.C. 101.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1, 4 – 8, 12, 13 and 17 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Dhar et al. (US 20240095268 A1) (‘Dhar’ herein after) further in view of Aggarwal et al. (US 20250252111 A1) (‘Aggarwal’ herein after).
With respect to claim 1, 19, 20,
Dhar discloses a computer-implemented method, comprising: receiving a user query for one or more items from a user (figure 2, paragraph 19, 47, teaches a query encoder wherein the query encoder receives a query statement and generates a query feature vector, Dhar); processing the user query by searching against at least one relational database associated with the query, wherein the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items (figure 2, paragraph 19, 47 teaches determining a document similarity score for each of the documents in the set of documents, wherein the document similarity score is calculated between the query feature vector and a document feature vector of each of the documents in the set of documents and ranking the set of documents based on the document similarity score to generate a ranked set of documents, Dhar); generating one or more query results based on the processing of the user query, wherein the one or more results include at least one item identified from the plurality of items and a justification for explaining at least one item (figure 2, paragraph 47 teaches determining at least one of a query explainability score and a document explainability score, wherein the query explainability score is determined for each word in the query statement and the document explainability score is determined for each word in each documents of the set of documents, provisioning a user to select at least one word from at least one from the query statement and the document, wherein the selection of the at least one word is based on at least one of the query explainability score and the document explainability score, Dhar) and transmitting the one or more query results to a user device for presentation to the user (figure 2, paragraph 47 teaches extracting the subset of document from the ranked set of documents based on the selection of the user and displaying the subset of document, Dhar).
Dhar does not explicitly teach a justification for explaining an irrelevance of the at least one item. However, Aggarwal teaches in paragraphs 44 – 45 stating the explanation agent can be configured to implement AI models to provide explanations for one or more of the query results, for example, the explanation for a given query result can include a breakdown of why the query result is relevant, a breakdown of how the query result was identified, a breakdown of where the query result was located. Additionally under some configurations, the explanation agent can also be configured to provide explanations for query results that were filtered out by the personalized ranking engine, that is considered irrelevant.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Aggarwal with the teachings of Dhar because besides being directed to retrieval of relevant search results, Aggarwal’s method furthermore explains in the AI environment why a result was considered irrelevant, such explanations can be utilized in any manner to improve the manner in which the system generates query results. For example, the explanations can be used to improve the intelligence of the various AI models to demonstrate to end-users that time is being saved by intelligently eliminating certain results for good/explainable reasons, paragraph 45.
With respect to claim 4,
Dhar as modified discloses the method according to claim 1, wherein the user query is a natural language user query, and processing the user query further includes converting the natural language user query into high-dimensional vectors that capture semantic meaning of the user query (paragraph 21, 21, Dhar and paragraph 49, 81, Aggarwal).
With respect to claim 5,
Dhar as modified discloses the method according to claim 4, wherein extracting the features from the electronic documents further includes converting one or more paragraphs and tables from an electronic document into vector embeddings (paragraph 21, 25 – 26, Dhar and paragraph 32, 55 – 56, Aggarwal).
With respect to claim 6,
Dhar as modified discloses the method according to claim 5, wherein generating the one or more query results further includes implementing a vector-based semantic search to determine a similarity between the vector embeddings associated with the electronic document and the high-dimensional vectors associated with the user query (figures 1, 2, paragraph 22, 27, 62 – 67, Dhar, figure 6, 7, 8, paragraph 41, 43 78 – 79, Aggarwal).
With respect to claim 7,
Dhar as modified discloses the method according to claim 6, wherein the similarity between the vector embeddings associated with the electronic document and the high-dimensional vectors associated with the user query is determined by using a dot product or a cartesian product calculation (paragraph 74, Dhar).
With respect to claim 8,
Dhar as modified discloses the method according to claim 1, wherein searching against the at least one relational database includes implementing a multi-method search, wherein the multi-method search includes a full-text-based search, a vector-based search, and an SQL-based search (paragraph 62 – 63, Dhar, paragraph 82, Aggarwal).
With respect to claim 12,
Dhar as modified discloses the method according to claim 11, wherein, when the page contains an image or block diagram, the page is fed into an API to extract textual information included in the image or block diagram (paragraph 30, 63, Dhar and paragraph 46, 76, Aggarwal).
With respect to claim 13,
Dhar as modified discloses the method according to claim 12, wherein remaining text or table from the page is extracted using PDF parsing libraries in combination with an artificial intelligence (AI) tool for extracting table structure (paragraph 30, 63, Dhar and paragraph 46, 76, Aggarwal).
With respect to claim 17,
Dhar as modified discloses the method according to claim 1, wherein generating the one or more query results based on the processing of the user query further includes excluding an item from the one or more query results when a justification for the item is unable to be generated (paragraphs 44 – 45, Aggarwal).
With respect to claim 18,
Dhar as modified discloses the method according to claim 1, wherein the justification is generated by using a multimodal large language model, and the generated justification is further passed back to the multimodal large language model with a new or modified prompt, instructing the multimodal large language model to evaluate the justification itself and determine a validity of the justification (paragraphs 44 – 45, Aggarwal).
Claims 2, 3, 9 and 15 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dhar et al. (US 20240095268 A1) further in view of Aggarwal et al. (US 20250252111 A1) as applied to claims 1, 4 – 8, 12, 13 and 17 – 20 above, and further in view of Sun et al. (US 20250061303 A1) (‘Sun’ herein after).
With respect to claim 2,
Dhar as modified discloses the method according to claim 1, wherein processing the user query further includes an extraction process where a PDF file or a website containing electronic document information is taken as an input (figure 2, paragraph 47 – 48 teaches extraction of data from documents, Dhar).
Dhar does not explicitly teach as claimed structured JSON file containing comprehensive extracted data is produced as an output.
However, Sun teaches structured JSON file containing comprehensive extracted data is produced as an output in paragraphs 51 and 77.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sun with the teachings of the combination of Dhar and Aggarwal because besides being directed to retrieval of relevant search results, Sun’s method furthermore explains in the AI environment automation of queries scaling the data to include various knowledge base and knowledge base scaling up can be done automatically without human interaction. In one example approach, past drilling records are stored in both human and machine-readable form. In one such example approach, the knowledge base has an application programming interface (API) for machines, and a natural language interface for humans. The knowledge base suggests optimal or near optimal operations and may respond to queries.
With respect to claim 3,
Dhar as modified discloses the method according to claim 2, wherein the extraction process is automated by fine-tuning a multimodal large language model with reinforcement learning with human feedback (paragraph 42, 65, 69, Aggarwal, paragraphs 41 and 63, Sun).
With respect to claim 9,
Dhar as modified discloses the method according to claim 1, wherein presenting the one or more query results to the user further includes generating a chat-based user interface to allow the user to ask contextual questions about the at least one item included in the one or more query results (paragraphs 63 and 68, Sun).
With respect to claim 15,
Dhar as modified discloses the method according to claim 1, wherein presenting the one or more query results to the user further includes generating a user interface to allow the user to compare two or more items included in the one or more query results (paragraph 42, 65, 69, Aggarwal, paragraphs 41 and 63, Sun).
With respect to claim 16,
Dhar as modified discloses the method according to claim 15, wherein the user interface is generated based on JSON files converted from electronic documents associated with the two or more items (paragraphs 51 and 77, Sun).
Claims 10, 11, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dhar et al. (US 20240095268 A1) further in view of Aggarwal et al. (US 20250252111 A1) as applied to claims 1, 4 – 8, 12, 13 and 17 – 20 above, and further in view of Sergiy Vasylyev (US 20240412720 A1) (‘Vasylyev’ herein after).
With respect to claim 10,
Dhar as modified further with Vasylyev discloses the method according to claim 9, wherein the chat-based user interface is generated based on a retrieval-augmented generation (RAG) approach (paragraph 639, Vasylyev).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sun with the teachings of the combination of Dhar and Aggarwal because besides being directed to retrieval of relevant search results, Vasylyev’s method furthermore extends to an AI Assistant environment where the main context window is immediately available to a language model of the AI assistant system, and the secondary context windows form a latent context. The AI assistant system may be configured to continuously update the contextual memory, recognize control signals, and generate appropriate responses using a transformer-based language model. Implementations of the AI assistant system may include features such as dynamic memory management, user hierarchy and prioritization, emergency shut-off, and long-term information management.
With respect to claim 11,
Dhar as modified discloses the method according to claim 10, wherein, when generating the chat-based user interface based on the RAG approach, an electronic document for an item included in the one or more query results is broken into pages, wherein each page is then converted into an image which is fed into a proprietary algorithm to determine if the page contains an image or block diagram, text or table (paragraph 30, 63, Dhar and paragraph 46, 76, Aggarwal, paragraph 639, Vasylyev).
With respect to claim 14,
Dhar as modified discloses the method according to claim 11, wherein the proprietary algorithm is a fine-tuned you-only-look-once (YOLO) model (paragraph 738, Vasylyev).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230132061 A1 teaches information extraction systems and computer-implemented methods for producing a searchable representation of information contained in a corpus of documents by generating a document structure graph for each document, the graph indicating a structural hierarchy of document items in that document based on a predefined hierarchy of predetermined item-types, and linking document items to a parent document item in the structural hierarchy, for each document, generating a knowledge graph.
US 20220237182 A1 teaches receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document.
US 20250054273 A1 teaches determining similarities between media, and systems and computer-readable media used to implement said method, includes determining an input image for comparison with a plurality of stored images in a media database; vectorizing the input image to determine an input image vector representation; and comparing the input image with each of the plurality of stored images in a media database. The method further comprises outputting a set of direct matches and a set of exact matches as a detected comparison output for the input image.
US 20250258847 A1 teaches improving computer functionality by retrieving answers/responses to questions/input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer.
US 20250061307 A1 teaches a multi-layer artificial intelligence system includes a foundation layer comprising at least one general-purpose large language model (LLM); an expert array layer comprising a plurality of specialized reasoning models; and a meta-reasoning model configured to coordinate operations between the foundation layer and the expert array layer to generate a reasoned analysis.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is (571)272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 5/7/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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