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 .
Priority
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Acknowledgment is made of Applicant's claim for foreign priority based on an application filed in India on July 6, 2023. It is noted, however, that Applicant has not filed a certified copy of Application IN202311045331 in either this Application or parent Application 18/764446 as required by 37 CFR 1.55.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-5, 8-10, 12-17, 19, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-4, 6-15, and 17 of US Patent No. 12,306,845 B2 (‘845 Patent). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the instant application are broader than the claims in the ‘845 Patent and are thus anticipated by the ‘845 Patent.
Regarding Claim 1, ‘845 Patent discloses a data processing system comprising:
a processor (see ‘845 Patent, Claim 1, a processor); and
a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations comprising (see ‘845 Patent, Claim 1, a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations comprising):
accessing content items from a plurality of data sources (see ‘845 Patent, Claim 1, accessing content items from a plurality of data sources);
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items (see ‘845 Patent, Claim 1, generating a knowledge graph by analyzing each of the content items with a first language model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items);
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph (see ‘845 Patent, Claim 1, receiving a query and an indication of a format for results of the query from a first client device, the query identifying one or more categories of information to search for using the knowledge graph);
generating query embeddings for the query using the knowledge graph builder model (see ‘845 Patent, Claim 1, generating query embeddings for the query using the first language model);
searching the knowledge graph based on the query embeddings to obtain results of the query (see ‘845 Patent, Claim 1, searching the knowledge graph based on the query embeddings to obtain the results of the query);
generating a representation of the results of the query (see ‘845 Patent, Claim 1, generating a representation of the results of the query according to the indication of the format for the results of the query); and
causing the client device to present the representation of the results of the query on a user interface of the client device (see ‘845 Patent, Claim 1, causing the first client device to present the representation of the results of the query on a user interface of the first client device).
Regarding Claim 2, ‘845 Patent discloses the data processing system of Claim 1, wherein the content items include one or more of press releases, news articles, documents submitted to regulatory agencies both domestically and internationally, journal articles and/or other publications, abstracts of publications, published patent applications and issued patents, financial filings, and analyst call transcripts (see ‘845 Patent, Claim 2, the content items include one or more of press releases, news articles, documents submitted to regulatory agencies both domestically and internationally, journal articles and/or other publications, abstracts of publications, published patent applications and issued patents, financial filings, and analyst call transcripts).
Regarding Claim 3, ‘845 Patent discloses the data processing system of Claim 1, wherein the knowledge graph builder model is a Large Language Model (LLM) or Small Language Model (SLM) (see ‘845 Patent, Claim 3, wherein the first language model is a Large Language Model (LLM) or Small Language Model (SLM), the first language model having an encoder-decoder architecture).
Regarding Claim 4, ‘845 Patent discloses the data processing system of Claim 1, wherein the knowledge graph builder model has an encoder-decoder or other architecture (see ‘845 Patent, Claim 3, wherein the first language model is a Large Language Model (LLM) or Small Language Model (SLM), the first language model having an encoder-decoder architecture).
Regarding Claim 5, ‘845 Patent discloses the data processing system of Claim 1, wherein the representation of the results of the query comprises a graphical representation of results of the query providing a visualization of the results of the query (see ‘845 Patent, Claim 4, wherein the representation of the results of the query comprises a graphical representation of results of the query providing a visualization of the results of the query).
Regarding Claim 8, ‘845 Patent discloses the data processing system of Claim 1, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of, prior to generating the knowledge graph, extracting information from the content items and converting the information to a standard format used to train the knowledge graph builder model (see ‘845 Patent, Claim 6, the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of, prior to generating the knowledge graph, extracting information from the content items and converting the information to a standard format used to train the first language model).
Regarding Claim 9, ‘845 Patent discloses the data processing system of Claim 1, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
automatically checking the plurality of data sources for new content items, updated content items, or both (see ‘845 Patent, Claim 7, automatically checking the plurality of data sources for new content items, updated content items, or both); and
automatically updating the knowledge graph by analyzing the new content items, the updated content items, or both using the knowledge graph builder model (see ‘845 Patent, Claim 7, automatically updating the knowledge graph by analyzing the new content items, the updated content items, or both using the first language model).
Regarding Claim 10, ‘845 Patent discloses the data processing system of Claim 9, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
causing the client device to present a source configuration user interface that includes a control for configuring a frequency at which automatic checks for a selected data source of the plurality of data sources is checked for new content items, updated content items, or both (see ‘845 Patent, Claim 8, causing the first client device to present a source configuration user interface that includes a control for configuring a frequency at which the automatic checks for a selected data source of the plurality of data sources is checked for new content items, updated content items, or both);
receiving an indication of a first frequency from the client device (see ‘845 Patent, Claim 8, receiving an indication of a first frequency from the first client device); and
automatically checking the selected data source for new content items, updated content items, or both according to the first frequency (see ‘845 Patent, Claim 8, automatically checking the selected data source for new content items, updated content items, or both according to the first frequency).
Regarding Claim 12, ‘845 Patent discloses the data processing system of Claim 1, wherein generating the knowledge graph includes associating each content item with content item source information that provides an indication of a data source from the plurality of data sources from which each content item can be obtained (see ‘845 Patent, Claim 9, wherein the generating the knowledge graph includes associating each content item with content item source information that provides an indication of the data source from the plurality of data sources from which the content item can be obtained).
Regarding Claim 13, ‘845 Patent discloses the data processing system of Claim 1, wherein the representation of the results of the query include controls, which when activated, cause the client device to present content source information associated with each of the content items from which the representation is derived (see ‘845 Patent, Claim 10, the representation of the results of the query include controls, which when activated, cause the client device to present content source information associated with each of the content items from which the representation is derived).
Regarding Claim 14, ‘845 Patent discloses the data processing system of Claim 1, wherein generating the knowledge graph further comprises generating connection information indicating connections between the content items and known concepts or categories of entities (see ‘845 Patent, Claim 11, generating the knowledge graph further comprises generating connection information indicating connections between the content items and known biomedical entities).
Regarding Claim 15, ‘845 Patent discloses the data processing system of Claim 14, wherein generating the connection information further comprises:
generating a final confidence score by combining a similarity score associated with a first content item and a first known concept or category generated by a search algorithm and with an output of a last neural layer of the search algorithm (see ‘845 Patent, Claim 12, generating a final confidence score by combining a similarity score associated with a first content item and a first known biomedical entity generating by a vector search algorithm and with an output of a last neural layer of the vector search algorithm); and
creating a connection between the first content item and the first known concept or category in response to the final confidence score indicating that a confidence score matrix indicating that the first content item includes a reference to the first known concept or category (see ‘845 Patent, Claim 12, creating a connection between the first content item and the first known biomedical entity in response to the final confidence score indicating that a confidence score matrix indicating that the first content item includes a reference to the first known biomedical entity).
Regarding Claim 16, ‘845 Patent discloses the data processing system of Claim 1, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
receiving indication of a format for the results of the query that indicates a selected format in which results of the query are to be presented (see ‘845 Patent, Claim 1, indication of the format for the results of the query indicating a format in which results of the query are to be presented),
wherein generating a representation of the results of the query further comprises generating the representation of the results of the query according to the indication of the format for the results of the query (see ‘845 Patent, Claim 1, generating a representation of the results of the query according to the indication of the format for the results of the query).
Regarding Claim 17, ‘845 Patent discloses a method implemented in a data processing system for analyzing content items, the method comprising:
accessing content items from a plurality of data sources (see ‘845 Patent, Claim 13, accessing content items from a plurality of data sources);
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items (see ‘845 Patent, Claim 13, generating a knowledge graph by analyzing each of the content items with a first language model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items);
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph (see ‘845 Patent, Claim 13, receiving a query and an indication of a format for results of the query from a first client device, the query identifying one or more categories of information to search for using the knowledge graph);
generating query embeddings for the query using the knowledge graph builder model (see ‘845 Patent, Claim 13, generating query embeddings for the query using the first language model);
searching the knowledge graph based on the query embeddings to obtain results of the query (see ‘845 Patent, Claim 13, searching the knowledge graph based on the query embeddings to obtain the results of the query);
generating a representation of the results of the query (see ‘845 Patent, Claim 13, generating a representation of the results of the query according to the indication of the format for the results of the query); and
causing the client device to present the representation of the results of the query on a user interface of the client device (see ‘845 Patent, Claim 13, causing the first client device to present the representation of the results of the query on a user interface of the first client device).
Regarding Claim 19, ‘845 Patent discloses a data processing system comprising:
a processor (see ‘845 Patent, Claim 15, a processor); and
a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations comprising (see ‘845 Patent, Claim 15, a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations comprising):
obtaining, using a data access unit, known entity information for a plurality of known concepts or categories of information (see ‘845 Patent, Claim 15, obtaining, using a data access unit, known biomedical entity information for a plurality of known biomedical entities, the known biomedical entities comprising one or more of disease information, biomarker information, or mechanisms of action information);
selecting, using the data access unit, information for a first entity from among the plurality of known concepts or categories of information as a first query parameter (see ‘845 Patent, Claim 15, selecting, using the data access unit, information for a first biomedical entity from among the plurality of known biomedical entities as a first query parameter);
querying, using the data access unit, content items from one or more data sources (see ‘845 Patent, Claim 15, querying, using the data access unit, content items from one or more data sources associated with pharmaceutical development, medical device development, or both);
compressing, using a data compression unit, the first query parameter and each of the content items using a first compression algorithm to create compressed content entries (see ‘845 Patent, Claim 15, compressing, using the data compression unit, the first query parameter and the each of the content items using a first compression algorithm to create compressed content entries);
analyzing, a candidate entity selection unit, the compressed content entries to select candidate content entries to be added to a knowledge graph (see ‘845 Patent, Claim 1,5 analyzing, a candidate entity selection unit, the compressed content entries to select candidate content entries to be added to a knowledge graph);
constructing, using a graph construction unit, the knowledge graph using the candidate content entries (see ‘845 Patent, Claim 15, constructing, using a graph construction unit, the knowledge graph using the candidate content entries); and
validating, using a graph validation unit, the knowledge graph to identify and remove discrepancies from the knowledge graph (see ‘845 Patent, Claim 15, validating, using a graph validation unit, the knowledge graph to identify and remove discrepancies from the knowledge graph).
Regarding Claim 20, ‘845 Patent discloses the data processing system of Claim 19, wherein the first compression algorithm is one of a lossless compression algorithm or a lossy compression algorithm (see ‘845 Patent, Claim 17, the first compression algorithm is one of a lossless compression algorithm or a lossy compression algorithm).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 4 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Regarding dependent Claim 4, the limitation “or other architecture” renders the claim indefinite in that it fails to point out what is included or excluded by the claim language.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 4 and 20 are rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends.
Regarding dependent Claim 4, the claim recites every possible architecture and thus fails to further limit Claim 1.
Regarding depednent Claim 20, the claim recites every possible compression algorithm, and thus fails to further limit Claim 19.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-11, 13, 14, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (PG Pub. No. 2020/0159867 A1), and further in view of Jang (PG Pub. No. 2026/0120887 A1) and Berglund (PG Pub. No. 2024/0403341 A1).
Regarding Claim 1, Sharma discloses a data processing system comprising:
a processor (see Sharma, paragraph [0020], server 135 includes … at least one processor 130) and
a machine-readable medium storing executable instructions (see Sharma, paragraph [0020], where server 135 includes … memory 140) that, when executed, cause the processor alone or in combination with other processors to perform operations comprising:
accessing content items from a plurality of data sources (see Sharma, paragraph [0022], where document fetching module 145 may search one or more repositories for new documents).
Sharma does not disclose:
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items;
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph;
generating query embeddings for the query using the knowledge graph builder model;
searching the knowledge graph based on the query embeddings to obtain results of the query;
generating a representation of the results of the query; and
causing the client device to present the representation of the results of the query on a user interface of the client device.
Sharma in view of Jang discloses:
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model (see Sharma, paragraph [0028], where knowledge graph module 160 may assemble an initial knowledge graph using conventional or other machine learning techniques) to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items (see Jang, paragraph [0193], where the prediction model may categorize each information in the form of embedding and perform operations required for generating output results according to the assigned categories; as an example, the category embedding of family history information, medical history information, and genetic information).
Sharma and Jang contemplate building knowledge graphs. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the content item categorization in Jang with the knowledge graph in Sharma as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Sharma in view of Jang does not disclose:
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph;
generating query embeddings for the query using the knowledge graph builder model;
searching the knowledge graph based on the query embeddings to obtain results of the query;
generating a representation of the results of the query; and
causing the client device to present the representation of the results of the query on a user interface of the client device.
Sharma in view of Jang and Berglund discloses:
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph (see Berglund, paragraph [0014], where the techniques described herein include receiving a user query associated with a plurality of content items stored in a content repository maintained by the content management system);
generating query embeddings for the query using the knowledge graph builder model (see Berglund, paragraph [0014], where a set of text chunks corresponding to the plurality of content items are retrieved, where each text chunk has an associated text embedding. From the set of text chunks, a subset of relevant text chunks is identified based at least in part on similarity between the associated text embeddings and a query embedding that represents the user query);
searching the knowledge graph based on the query embeddings to obtain results of the query (see Berglund, paragraph [0014], where a set of text chunks corresponding to the plurality of content items are retrieved, where each text chunk has an associated text embedding. From the set of text chunks, a subset of relevant text chunks is identified based at least in part on similarity between the associated text embeddings and a query embedding that represents the user query);
generating a representation of the results of the query (see Berglund, paragraph [0014], where at least a portion of the subset of relevant text chunks are sent to a large language model (LLM) to cause the LLM to generate an answer description for the user query based on the text chunks. The answer description can be returned to the user, optionally with the content items themselves that contained the relevant text chunks); and
causing the client device to present the representation of the results of the query on a user interface of the client device (see Berglund, paragraph [0014], where at least a portion of the subset of relevant text chunks are sent to a large language model (LLM) to cause the LLM to generate an answer description for the user query based on the text chunks. The answer description can be returned to the user, optionally with the content items themselves that contained the relevant text chunks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma and Jang with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 2, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein the content items include one or more of press releases, news articles, documents submitted to regulatory agencies both domestically and internationally, journal articles (see Sharma, paragrpah [0031], where database 180 of repository 175 may include scientific or scholarly publications) and/or other publications, abstracts of publications, published patent applications and issued patents, financial filings, and analyst call transcripts.
Regarding Claim 3, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose the knowledge graph builder model is a Large Language Model (LLM) or Small Language Model (SLM). Berglund discloses the knowledge graph builder model is a Large Language Model (LLM) or Small Language Model (SLM) (see Berglund, paragraph [0025], where Fig. 2A is an interaction diagram illustrating a process 200 for generating content item embeddings, according to some implementations; as shown in Fig. 2A, the process can include interactions between the content repository 150, the content management system 110 and the LLM 140).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 4, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose the knowledge graph builder model has an encoder-decoder or other architecture. Berglund discloses the knowledge graph builder model has an encoder-decoder or other architecture (see Berglund, paragraph [0068], where a type of neural network architecture, referred to as a “transformer,” can be used for language models. For example, the Bidirectional Encoder Representations from Transformers (BERT) model [emphasis added by Examiner]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 5, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein the representation of the results of the query comprises a graphical representation of results of the query providing a visualization of the results of the query (see Sharma, paragraph [0019], where a user may provide a query comprising one or more entities, and query module 115 may send the query to server 135, which processes the query and sends the results back to query module 115 for presentation to the user … query module 115 enables a user to visually explore a knowledge graph, which may be presented on display 120 of client device 105 as a map of entities and entity associations).
Regarding Claim 6, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose the representation of the results of the query comprises a graphical representation of the query. Berglund discloses the representation of the results of the query comprises a graphical representation of the query (see Berglund, paragraph [0032], where an example user interface is illustrated in Fig. 3A; in this example, the user has input a search query for ‘sales pitch for life science company’).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 7, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose searching the knowledge graph based on the query embeddings to obtain the results of the query comprises searching the knowledge graph using search algorithm that generates similarity scores representing similarities between the query embeddings and embeddings associated with the content items. Berglund discloses searching the knowledge graph based on the query embeddings to obtain the results of the query comprises searching the knowledge graph using search algorithm that generates similarity scores representing similarities between the query embeddings and embeddings associated with the content items (see Berglund, paragraph [0038], where the search engine 114 generates an embedding similarity score between a query embedding that represents the search query and a respective text embedding associated with a text chunk. A text chunk can be identified as relevant to the query when its embedding similarity score satisfies a similarity threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 8, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of, prior to generating the knowledge graph, extracting information from the content items and converting the information to a standard format used to train the knowledge graph builder model. Sharma in view of Berglund discloses the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of, prior to generating the knowledge graph, extracting information from the content items and converting the information to a standard format (see Sharma, paragraph [0004], where the set of documents and the new document are processed by applying text normalization) used to train the knowledge graph builder model (see Berglund, paragraph [0024], where LLM 140 can include commercially available, fine-tuned, or custom language models that are configured to perform language analysis and generation tasks [it is the position of the Examiner that the broadest reasonable interpretation of ‘training’ includes fine-tuning]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 9, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
automatically checking the plurality of data sources for new content items, updated content items, or both (see Sharma, paragraph [0003], where present invention embodiments enable a self-evolving knowledge graph to be updated automatically to encompass information found in new documents in the knowledge graph’s domain of study, thereby eliminating the need for researchers to manually discover and add entity associations to the knowledge graph); and
automatically updating the knowledge graph by analyzing the new content items, the updated content items, or both using the knowledge graph builder model (see Sharma, paragraph [0003], where present invention embodiments enable a self-evolving knowledge graph to be updated automatically to encompass information found in new documents in the knowledge graph’s domain of study, thereby eliminating the need for researchers to manually discover and add entity associations to the knowledge graph).
Regarding Claim 10, Sharma in view of Jang and Berglund discloses the data processing system of Claim 9, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
causing the client device to present a source configuration user interface (see Sharma, paragraph [0052], present invention embodiments may employ any number of any type of user interface (e.g., Graphical User Interface (GUI), command-line, prompt, etc.) for obtaining or providing information (e.g., document text data, vector space model data, entity data, entity association data, confidence score data, etc.), where the interface may include any information arranged in any fashion. The interface may include any number of any types of input or actuation mechanisms (e.g., buttons, icons, fields, boxes, links, etc.) disposed at any locations to enter/display information and initiate desired actions via any suitable input devices (e.g., mouse, keyboard, etc.). The interface screens may include any suitable actuators (e.g., links, tabs, etc.) to navigate between the screens in any fashion) that includes a control for configuring a frequency at which automatic checks for a selected data source of the plurality of data sources is checked for new content items, updated content items, or both (see Sharma, paragraph [0012], where in some embodiments, the new document is selected from a second set of documents, and fetching the second set of documents from one or more repositories is performed according to a predefined schedule; by scheduling the fetching of documents, knowledge graphs can be updated according to a schedule that matches the publication schedules of publication sources, thereby ensuring that the knowledge graph is always up-to-date, and reducing processing requirements by updating the knowledge graph only when there are new documents to ingest);
receiving an indication of a first frequency from the client device (see Sharma, paragraph [0012], where in some embodiments, the new document is selected from a second set of documents, and fetching the second set of documents from one or more repositories is performed according to a predefined schedule; by scheduling the fetching of documents, knowledge graphs can be updated according to a schedule that matches the publication schedules of publication sources, thereby ensuring that the knowledge graph is always up-to-date, and reducing processing requirements by updating the knowledge graph only when there are new documents to ingest); and
automatically checking the selected data source for new content items, updated content items, or both according to the first frequency (see Sharma, paragraph [0012], where in some embodiments, the new document is selected from a second set of documents, and fetching the second set of documents from one or more repositories is performed according to a predefined schedule; by scheduling the fetching of documents, knowledge graphs can be updated according to a schedule that matches the publication schedules of publication sources, thereby ensuring that the knowledge graph is always up-to-date, and reducing processing requirements by updating the knowledge graph only when there are new documents to ingest).
Regarding Claim 11, Sharma in view of Jang and Berglund discloses the data processing system of Claim 10, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of updating the knowledge graph by analyzing each of the new content items, updated content items, or both with the knowledge graph builder model (see Sharma, paragraph [0012], where in some embodiments, the new document is selected from a second set of documents, and fetching the second set of documents from one or more repositories is performed according to a predefined schedule; by scheduling the fetching of documents, knowledge graphs can be updated according to a schedule that matches the publication schedules of publication sources, thereby ensuring that the knowledge graph is always up-to-date, and reducing processing requirements by updating the knowledge graph only when there are new documents to ingest).
Regarding Claim 13, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose the representation of the results of the query include controls, which when activated, cause the client device to present content source information associated with each of the content items from which the representation is derived. Berglund discloses the representation of the results of the query include controls, which when activated, cause the client device to present content source information associated with each of the content items from which the representation is derived (see Berglund, paragraph [0055], where in response to this query, the content management system identifies one or more content items 310 that are semantically related to the query).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regaring Claim 14, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein generating the knowledge graph further comprises generating connection information indicating connections between the content items and known concepts or categories of entities (see Sharma, paragraph [0012], where self-evolving knowledge graphs can ensure that researchers are provided with up-to-date information relating to the relationships between entities in any domain of study, while eliminating the need to perform time-consuming manual discovery of entity associations).
Regarding Claim 17, Sharma discloses a method implemented in a data processing system for analyzing content items, the method comprising:
accessing content items from a plurality of data sources (see Sharma, paragraph [0022], where document fetching module 145 may search one or more repositories for new documents).
Sharma does not disclose:
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items;
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph;
generating query embeddings for the query using the knowledge graph builder model;
searching the knowledge graph based on the query embeddings to obtain results of the query;
generating a representation of the results of the query; and
causing the client device to present the representation of the results of the query on a user interface of the client device.
Sharma in view of Jang discloses:
generating a knowledge graph by analyzing each of the content items with a knowledge graph builder model (see Sharma, paragraph [0028], where knowledge graph module 160 may assemble an initial knowledge graph using conventional or other machine learning techniques) to obtain embedding vectors representing each first content items, the embedding vectors representing one or more categories of information associated with each of the content items (see Jang, paragraph [0193], where the prediction model may categorize each information in the form of embedding and perform operations required for generating output results according to the assigned categories; as an example, the category embedding of family history information, medical history information, and genetic information).
Sharma and Jang contemplate building knowledge graphs. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the content item categorization in Jang with the knowledge graph in Sharma as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Sharma in view of Jang does not disclose:
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph;
generating query embeddings for the query using the knowledge graph builder model;
searching the knowledge graph based on the query embeddings to obtain results of the query;
generating a representation of the results of the query; and
causing the client device to present the representation of the results of the query on a user interface of the client device.
Sharma in view of Jang and Berglund discloses:
receiving a query from a client device, the query identifying one or more categories of information to search for using the knowledge graph (see Berglund, paragraph [0014], where the techniques described herein include receiving a user query associated with a plurality of content items stored in a content repository maintained by the content management system);
generating query embeddings for the query using the knowledge graph builder model (see Berglund, paragraph [0014], where a set of text chunks corresponding to the plurality of content items are retrieved, where each text chunk has an associated text embedding. From the set of text chunks, a subset of relevant text chunks is identified based at least in part on similarity between the associated text embeddings and a query embedding that represents the user query);
searching the knowledge graph based on the query embeddings to obtain results of the query (see Berglund, paragraph [0014], where a set of text chunks corresponding to the plurality of content items are retrieved, where each text chunk has an associated text embedding. From the set of text chunks, a subset of relevant text chunks is identified based at least in part on similarity between the associated text embeddings and a query embedding that represents the user query);
generating a representation of the results of the query (see Berglund, paragraph [0014], where at least a portion of the subset of relevant text chunks are sent to a large language model (LLM) to cause the LLM to generate an answer description for the user query based on the text chunks. The answer description can be returned to the user, optionally with the content items themselves that contained the relevant text chunks); and
causing the client device to present the representation of the results of the query on a user interface of the client device (see Berglund, paragraph [0014], where at least a portion of the subset of relevant text chunks are sent to a large language model (LLM) to cause the LLM to generate an answer description for the user query based on the text chunks. The answer description can be returned to the user, optionally with the content items themselves that contained the relevant text chunks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sharma and Jang with Berglund for the benefit of using LLMs to help users interact with content (see Berglund, paragraph [0013]).
Regarding Claim 18, Sharma discloses the method of Claim 17, wherein the content items include structured documents, unstructured documents, tabular data sources, or a combination thereof (see Sharma, paragrpah [0031], where database 180 of repository 175 may include scientific or scholarly publications).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Jang, and Berglund as applied to Claims 1-11, 13, 14, 17, and 18 above, and further in view of Das (PG Pub. No. 2019/0034500 A1).
Regarding Claim 12, Sharma in view of Jang and Berglund discloses the data processing system of Claim 1, wherein:
Sharma does not disclose generating the knowledge graph includes associating each content item with content item source information that provides an indication of a data source from the plurality of data sources from which each content item can be obtained. Das discloses generating the knowledge graph includes associating each content item with content item source information that provides an indication of a data source from the plurality of data sources from which each content item can be obtained (see Das, paragraph [0097], where metadata fields may, for example, provide information related to the data block as a whole and may apply to each event that is subsequently derived from the data in the data block … a source field may contain a value identifying a source of the data, such as a pathname of a file or a protocol and port related to received network data).
Sharma and Das contemplate building knowledge graphs. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the data source metadata tags in Das with the knowledge graph in Sharma as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Jang, and Berglund as applied to Claims 1-11, 13, 14, 17, and 18 above, and further in view of Glass (PG Pub. No. 2024/0330264 A1).
Regarding Claim 15, Sharma in view of Jang and Berglund discloses the data processing system of Claim 14, wherein generating the connection information further comprises:
Sharma does not disclose:
generating a final confidence score by combining a similarity score associated with a first content item and a first known concept or category generated by a search algorithm and with an output of a last neural layer of the search algorithm; and
creating a connection between the first content item and the first known concept or category in response to the final confidence score indicating that a confidence score matrix indicating that the first content item includes a reference to the first known concept or category.
Sharma in view of Glass discloses:
generating a final confidence score by combining a similarity score associated with a first content item and a first known concept or category generated by a search algorithm and with an output of a last neural layer of the search algorithm (see Glass, paragraph [0049], where query table 108C and the result (or retrieved) table are provided to a reader transformer 804 that works with a cell relevance classifier 804 to generate Retrieval Scores and a Combined Cell Score. The Retrieval Score is the inner product (dot product) between the query vector (the table query projected to a vector) and the retrieved table-part vector (the table-part projected to a vector). The score from the cell relevance classifier 804 is the token vectors (the transformer produces vectors for each token) for the start and end token concatenated together and multiplied (inner product) by a learned weight vector. Then these two scores are added for the Combined Cell Score); and
creating a connection between the first content item and the first known concept or category (see Sharma, paragraph [0026], where evidence identification module 155 may identify documents, referred to as evidences, that support a particular entity association between two or more entities; in partiular, evidence identification module 155 analyzes a vector space model by comparing the vectors of new documents to the vectors of pre-existing documents that support a particular entity association … if the new document exceeds a predefined similarity threshold when compared to the pre-existing document, the new document is considered to be evidence for the entity association … evidence identification module 155 compares the vectors to documents by calculating a cosine similarity between the two vectors) in response to the final confidence score indicating that a confidence score matrix indicating that the first content item includes a reference to the first known concept or category (see Glass, paragraph [0049], where query table 108C and the result (or retrieved) table are provided to a reader transformer 804 that works with a cell relevance classifier 804 to generate Retrieval Scores and a Combined Cell Score. The Retrieval Score is the inner product (dot product) between the query vector (the table query projected to a vector) and the retrieved table-part vector (the table-part projected to a vector). The score from the cell relevance classifier 804 is the token vectors (the transformer produces vectors for each token) for the start and end token concatenated together and multiplied (inner product) by a learned weight vector. Then these two scores are added for the Combined Cell Score).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the knowledge graph builder in Sharma with the verification techniques in Glass for the benefit of automated data wrangling techniques that facilitate the analysis efforts of end-users, e.g., data analysts, in performing data analysis tasks (see Glass, paragraph [0006]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Jang, and Berglund as applied to Claims 1-11, 13, 14, 17, and 18 above, and further in view of Jain (PG Pub. No. 20120271604 A1).
Regarding Claim 16, Sharma in view of Jang and Berglund discloses the data procesing system of Claim 1, wherein the machine-readable medium includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:
Sharma does not disclose receiving indication of a format for the results of the query that indicates a selected format in which results of the query are to be presented, wherein generating a representation of the results of the query further comprises generating the representation of the results of the query according to the indication of the format for the results of the query. Jain discloses receiving indication of a format for the results of the query that indicates a selected format in which results of the query are to be presented, wherein generating a representation of the results of the query further comprises generating the representation of the results of the query according to the indication of the format for the results of the query (see Jain, paragraph [0009], where system provides a utility to query the various tables in response to a request from a user, format query results according to user preferences or parameters, and provide the data to the requesting user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the user defined query results format in Jain with the knowledge graph query results in Sharma as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Lewis (PG Pub. No. 2023/0350931 A1), and further in view of Schmidt (PG Pub. No. 2014/0074829 A1) and Kotnis (PG Pub. No. 2023/0267338 A1).
Regarding Claim 19, Lewis discloses a data processing system comprising:
a processor (see Lewis, paragraph [0042], where the present disclosure provides an apparatus comprising a processor unit, a memory unit); and
a machine-readable medium storing executable instructions (see Lewis, paragraph [0042], where the present disclosure provides an apparatus comprising a processor unit, a memory unit) that, when executed, cause the processor alone or in combination with other processors to perform operations comprising:
obtaining, using a data access unit, known entity information for a plurality of known concepts or categories of information (see Lewis, paragraph [0031], where the expansion engine or process comprises one or more entity expansion process(es) from the group of: an entity expansion process configured to extract additional entities of interest from or filter an existing graph of entities of interest and relationships thereto based on data representative of a set of entity concepts; an entity expansion process configured to input data representative of a set of entity concepts to an ML model trained for predicting or identifying additional entities of interest and relationships thereto from a corpus of text; an entity expansion process configured to search for additional entities of interest from a corpus of text based on inputting data representative of a search query associated with a set of entity concepts to a search engine coupled to the corpus of text; an entity expansion process configured to retrieve additional entities of interest from a lexicon dictionary associated with a set of entity concepts; and any other entity expansion process configured to retrieve additional entities from a database, dictionary system and/or search engine and the like in relation to a set of entity concepts);
selecting, using the data access unit, information for a first entity from among the plurality of known concepts or categories of information as a first query parameter (see Lewis, paragraph [0031], where the expansion engine or process comprises one or more entity expansion process(es) from the group of: an entity expansion process configured to extract additional entities of interest from or filter an existing graph of entities of interest and relationships thereto based on data representative of a set of entity concepts; an entity expansion process configured to input data representative of a set of entity concepts to an ML model trained for predicting or identifying additional entities of interest and relationships thereto from a corpus of text; an entity expansion process configured to search for additional entities of interest from a corpus of text based on inputting data representative of a search query associated with a set of entity concepts to a search engine coupled to the corpus of text; an entity expansion process configured to retrieve additional entities of interest from a lexicon dictionary associated with a set of entity concepts; and any other entity expansion process configured to retrieve additional entities from a database, dictionary system and/or search engine and the like in relation to a set of entity concepts);
querying, using the data access unit, content items from one or more data sources (see Lewis, paragraph [0008], where receiving a search query corresponding to entities of interest, the search query comprising data representative of a first set of entities); and
constructing, using a graph construction unit, the knowledge graph using the candidate content entries (see Lewis, paragraph [0032], where creating a graph of entities of interest and relationships thereto further comprises: generating a graph based on the retrieved sets of entities and relationships thereto; and updating an existing graph associated with the one or more entities of interest based on the generated graph. As an option, creating a graph further comprises generating a graph based on the retrieved sets of entities and relationships thereto).
Lewis does not disclose:
compressing, using a data compression unit, the first query parameter and each of the content items using a first compression algorithm to create compressed content entries;
analyzing, a candidate entity selection unit, the compressed content entries to select candidate content entries to be added to a knowledge graph; and
validating, using a graph validation unit, the knowledge graph to identify and remove discrepancies from the knowledge graph.
Lewis in view of Schdmit discloses:
compressing, using a data compression unit, the first query parameter and each of the content items using a first compression algorithm to create compressed content entries (see Schmidt, paragraph [0066], where an SR system executing process 200 can use co-compression distance between two data models to determine a rough metric for their similarity at 206); and
analyzing, a candidate entity selection unit, the compressed content entries (see Schmidt, paragraph [0066], where an SR system executing process 200 can use co-compression distance between two data models to determine a rough metric for their similarity at 206) to select candidate content entries to be added to a knowledge graph (see Lewis, paragraph [0103], where relevant documents may be selected a number of ways. In one example, the search engine comprises one or more ML search model(s) is configured for identifying, predicting, ranking and/or scoring the plurality of documents associated with the expanded search query for determining the set of relevant documents).
Schmidt discloses that compression distance based querying is well known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the compression distance based querying in Schmidt with the querying in Lewis as it amounts to simple substitution of one known element for another to yield predictable results (see MPEP 2143(I)(B)).
Lewis in view of Schdmit does not disclose validating, using a graph validation unit, the knowledge graph to identify and remove discrepancies from the knowledge graph. Kotnis discloses:
validating, using a graph validation unit, the knowledge graph to identify and remove discrepancies from the knowledge graph (see Kotnis, paragraph [0038], where OpenKG pruning and classification module 18 can filter triples that are irrelevant and/or error prone).
Lewis and Kotnis are both directed to knowledge graphs. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the knowledge graph in Lewis with the knowledge graph pruning techniques in Kotnis as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Lewis, Schmidt, and Kotnis as applied to Claim 19 above, and further in view of Bird (US Patent No. 9,143,393 B1).
Regarding Claim 20, Lewis in view of Schdmit and Kotnis discloses the data processing system of Claim 19, wherein:
Lewis does not disclose the first compression algorithm is one of a lossless compression algorithm or a lossy compression algorithm. Bird discloses the first compression algorithm is one of a lossless compression algorithm or a lossy compression algorithm (see Bird, Claim 2, where processor is further adapted to compute said normalized compression distance using a lossy compression).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the lossy compression distance algorithm in Bird with the compression distance based search in Schmidt as it amounts to simple substitution of one known element for another to yield predictable results (see MPEP 2143(I)(B)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
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/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
