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 Amendments
2. The action is responsive to the Applicant’s Amendment filed on 4/03/2026.
Claims 1-20 are pending in the application.
Response to Arguments
3. Applicant’s arguments with respect to the rejections previously made and the claims filed on 4/03/2026 have been fully considered but they are not persuasive.
In regards to independent claim 1, Applicant argued that “the Office Action fails to establish prima facie unpatentability of the claims because it does not show that the cited portions of Patil were disclosed before the priority date of this Application… the cited portions of Patil are only prior art if they are entitled to the benefit of Provisional Application No. 63/607,714 (the "Provisional"). But they are not.”
Examiner respectfully disagrees. In response to the arguments, it is submitted that the cited reference Patil qualifies as prior art because the specification of Patil provisional application 63/607,714 filed on 12/8/2023 provides the basis and support for the mapping in the Office Action for the cited limitations. Portions of the Patil application 18/972759 cited by the Office Action against various features of the claims are present in Patil provisional application 63/607,714. The reference still qualifies as prior art because the specification teaches the basis and support for the cited portions of the Patil application 18/972759.
Applicant argues that, “In particular, the cited portions of Patil do not appear in the Provisional. For example, the Provisional does not recite "knowledge graph constructor," "knowledge management system," or "GUI 610". Although Patil provisional application 63/607,714 does not recite "knowledge graph constructor," it teaches the basis and support for the term. The Patil application 18/972759 recites, “the knowledge graph constructor 235 is configured to generate a structured representation of entities and their relationships as a knowledge graph.” Likewise, Patil provisional application 63/607,714 recites, “Build knowledge graphs on trained sets… Automate knowledge graph generation… converting to code generating graphs… generate knowledge graph”. Therefore, the teachings of these terms are the same as the teaching of “the knowledge graph constructor” of Patil application 18/972759.
Also, "GUI 610" is recited in Patil application 18/972759 as, “the GUI 610 may include a prompt panel 612 located at the top of the interface, which allows users to input a prompt manually or utilize an automatically generated prompt based on project ideas.” Likewise, Patil provisional application 63/607,714 teaches a prompt panel, which allows users to input a prompt manually, teaching, “User asks questions and checks if answers are correct or if any ones are being missed… this level of human intervention is fine.” In this way, the Patil provisional application 63/607,714 specification teaches the basis and support for the cited portions of the Patil application 18/972759.
Applicant also argues that, “Likewise, the Provisional does not include any of the numbered paragraphs cited by the Office Action. Further still, Patil provisional application 63/607,714 does not include any of the figures cited by the Office Action.” Although the figures and paragraph numbers are different in Patil provisional application 63/607,714 filed on 12/8/2023 than in the Patil application 18/972759 filed 12/06/2024, portions of the Patil application cited by the Office Action against various features of the claims are present in Patil provisional application 63/607,714. The reference still qualifies as prior art because the specification teaches the basis and support for the cited portions of the Patil application 18/972759.
Therefore, Patil provisional application 63/607,714 substantially provides the basis and support for the mapping in the Office Action for the cited limitations. Portions of the Patil application 18/972759 cited by the Office Action against various features of the claims were present in Patil provisional application 63/607,714. Thus, for at least the reasons as set forth above, it is submitted that cited reference Patil qualifies as prior art under 35 U.S.C. § 102.
In regards to independent claims 11 and 12, the emphasized limitations that the Applicant argues in claims 11 and 12 are similar to the emphasized limitations of claim 1, which have been addressed above. See the response of claim 1 above for explanation.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Specification
4. In view of the amendment to the specification filed on 4/03/2026, the objections as set forth in the previous office action are hereby withdrawn.
Claim Rejections - 35 USC § 102
5. 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 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.
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Patil (US 20250190454, Earliest Priority Date: 2023-12-08).
Regarding Claim 1, Patil discloses a method comprising:
displaying, on a graphical user interface, a knowledge graph associated with a domain (Figs. 1-2; [0080]: In some embodiments, the knowledge graph constructor 235 is configured to generate a structured representation of entities and their relationships as a knowledge graph within the knowledge management system 110… [0099]: The front-end interface 255 may display a centralized platform in managing research… enabling users to efficiently access and interact with the knowledge graph),
wherein the knowledge graph comprises a plurality of nodes and a plurality of edges representing relationships between the plurality of nodes (Figs. 1-2; [0033]: In some embodiments, the knowledge management system 110 may construct a knowledge graph by representing entities as nodes and relationships among the entities as edges),
wherein the plurality of nodes comprise a plurality of leaf nodes, each of the plurality of leaf nodes being associated with respective metadata related to the domain ([0118]: FIG. 4D is a conceptual illustration of a large knowledge graph 440; [0028]: The knowledge graph may include nodes and relationships among the entities to facilitate efficient retrieval; [0062]: The stored data may include unprocessed documents, processed metadata, and structured representations such as vectors and entity relationships… The data library 215 may also manage knowledge graphs constructed from these entities, including relationships and metadata);
receiving, at the graphical user interface, one or more user inputs, wherein the one or more user inputs comprise a selection of a specific leaf node of the plurality of leaf nodes ([0147]: In some embodiments, identifying relevant prompts comprises selecting a node in the knowledge graph corresponding to a prompt-embedding that matches the query embedding);
displaying, on the graphical user interface, the respective metadata related to the domain that is associated with the specific leaf node ([0150]-[0155]: As such, the knowledge management system 110 retrieves specific documents or sections of documents that correspond to the user query… In some embodiments, the response may include an interactive visualization. For instance, the visualization may display nodes representing entities relevant to the query and edges indicating relationships between these entities; and
providing, on the graphical user interface, a search window configured to receive a search query related to the domain (Fig. 6A; [0157]-[0158]: the GUI 610 may include a prompt panel 612 located at the top of the interface, which allows users to input a prompt manually or utilize an automatically generated prompt based on project ideas… This prompt panel 612 may include a text input field).
Regarding Claim 2, Patil discloses the method of claim 1, further comprising:
receiving, at the search window, the search query (Fig. 6A; [0157]: the GUI 610 may include a prompt panel 612 located at the top of the interface, which allows users to input a prompt manually or utilize an automatically generated prompt based on project ideas… This prompt panel 612 may include a text input field… The summary panel 614 may also include interactive features, such as checkboxes or sliders, allow users to customize their query further);
executing, using a search engine, the search query against the respective metadata related to the domain that is associated with the specific leaf node ([0161]-[0163]: For example, a “Get Answer” button allows users to execute the query and retrieve data from the knowledge management system 110); and returning, in response to the search query, a subset of the respective metadata related to the domain that is associated with the specific leaf node (Fig. 7B; [0161]-[0163]: Upon receiving a user selection of one of the analytics, the knowledge management system 110 may generate an in-depth report using the analytics engine 250; [0138]: In some embodiments, metadata may be associated with the prompt-embedding clusters).
Regarding Claim 3, Patil discloses the method of claim 2, wherein the search engine is a keyword search engine (Fig. 1; [0109]: The knowledge management system 110… evaluates their co-occurrence with known keywords).
Regarding Claim 4, Patil discloses the method of claim 2, wherein the search engine is a structural search engine. ([0121]: The query may be converted into one or more structural queries such as SQL queries that retrieve relevant data to provide answers to the query).
Regarding Claim 5, Patil discloses the method of claim 2, wherein the search engine is a large language model (LLM) search engine ([0053] In some embodiments, the language models are large language models (LLMs) that are trained on a large corpus of training data to generate outputs).
Regarding Claim 6, Patil discloses the method of claim 1, further comprising generating a three-dimensional (3D) meta-profile for the respective metadata related to the domain that is associated with the specific leaf node ([0103]: AlphaFold, for example, uses transformer-based mechanisms to predict three-dimensional protein folding from amino acid sequences, providing valuable insights in the life sciences domain).
Regarding Claim 7, Patil discloses the method of claim 6, further comprising displaying, on the graphical user interface, the 3D meta-profile ([0103]: AlphaFold, for example, uses transformer-based mechanisms to predict three-dimensional protein folding from amino acid sequences, providing valuable insights in the life sciences domain; [0138]: In some embodiments, metadata may be associated with the prompt-embedding clusters.).
Regarding Claim 8, Patil discloses the method of claim 1, further comprising constructing the knowledge graph (Fig. 1; [0028]: The knowledge management system 110 employs an architecture that ingests unstructured data, identifies entities in the data, and constructs a knowledge graph that connects various entities).
Regarding Claim 9, Patil discloses the method of claim 8, wherein constructing the knowledge graph comprises:
initializing a structural hierarchy of the knowledge graph based, at least in part, on a user specification ([0034]: Additionally, the knowledge management system 110 supports enhanced user interaction by automatically analyzing the context of user queries and generating related follow-up questions. For example, when a query pertains to a specific topic); and
automatically fusing the respective metadata related to the domain to each of the plurality of leaf nodes ([0010]: [0010] FIG. 4C is a graphical illustration of a node and graph fusion process, in accordance with some embodiments; [0117]: Each fused node represents a consolidated entity that integrates all relevant information from its original components; [0138]: In some embodiments, metadata may be associated with the prompt-embedding clusters).
Regarding Claim 10, Patil discloses the method of claim 1, wherein the domain is cancer ([0127] In some embodiments, in generating responses, the knowledge management system 110 may apply text summarization techniques when appropriate… For a query about “biomarkers for cancer treatments,” the response might list the biomarkers and propose related queries).
Regarding Claim 11, Patil discloses a system comprising: a computing cluster comprising a plurality of computing devices, each computing device comprising at least one processor and a memory operably coupled to the at least one processor ([0005]: Figure (FIG.) 1 is a block diagram of an example system environment);
and a database operably coupled to the computing cluster ([0028]: In some embodiments, the knowledge management system 110 integrates knowledge from multiple sources, including research papers, Wikipedia entries, articles, databases), wherein the computing cluster is configured to:
display, on a graphical user interface, a knowledge graph associated with a domain (Figs. 1-2; [0080] In some embodiments, the knowledge graph constructor 235 is configured to generate a structured representation of entities and their relationships as a knowledge graph within the knowledge management system 110… [0099]: The front-end interface 255 may display a centralized platform in managing research… enabling users to efficiently access and interact with the knowledge graph),
wherein the knowledge graph comprises a plurality of nodes and a plurality of edges representing relationships between the plurality of nodes (Figs. 1-2; [0033]: In some embodiments, the knowledge management system 110 may construct a knowledge graph by representing entities as nodes and relationships among the entities as edges),
wherein the plurality of nodes comprise a plurality of leaf nodes, each of the plurality of leaf nodes being associated with respective metadata related to the domain ([0118]: FIG. 4D is a conceptual illustration of a large knowledge graph 440; [0028]: The knowledge graph may include nodes and relationships among the entities to facilitate efficient retrieval; [0062]: The stored data may include unprocessed documents, processed metadata, and structured representations such as vectors and entity relationships… The data library 215 may also manage knowledge graphs constructed from these entities, including relationships and metadata);
receive, at the graphical user interface, one or more user inputs, wherein the one or more user inputs comprise a selection of a specific leaf node of the plurality of leaf nodes ([0147]: In some embodiments, identifying relevant prompts comprises selecting a node in the knowledge graph corresponding to a prompt-embedding that matches the query embedding);
display, on the graphical user interface, the respective metadata related to the domain that is associated with the specific leaf node ([0150]-[0155]: As such, the knowledge management system 110 retrieves specific documents or sections of documents that correspond to the user query… In some embodiments, the response may include an interactive visualization. For instance, the visualization may display nodes representing entities relevant to the query and edges indicating relationships between these entities); and
provide, on the graphical user interface, a search window configured to receive a search query related to the domain (Fig. 6A; [0157]-[0158]: the GUI 610 may include a prompt panel 612 located at the top of the interface, which allows users to input a prompt manually or utilize an automatically generated prompt based on project ideas… This prompt panel 612 may include a text input field).
Regarding Claim 12, Patil discloses a non-transitory computer-readable storage medium, having instruction stored thereon that ([0050]: A data store 140 includes one or more storage units, such as memory, that take the form of a non-transitory and non-volatile computer storage medium), when executed by a processor, cause the processor to:
initialize a graph data structure using a seed to generate an initialized graph data structure ([0024]: The knowledge management system may generate a knowledge graph as a data structure to store the relationships among prompts, documents, and other entities);
train a machine learning (ML) model using a corpus to generate a hierarchical data structure comprising a subtree extracted from the corpus ([0031]: A set of documents (e.g., articles, papers, documents) that are used to construct a knowledge graph may be referred to as a corpus; [0051]: In some embodiments, the machine learning models deployed by the model serving system 145 are models that are originally trained to perform one or more NLP tasks); and
update the initialized graph data structure using the hierarchical data structure by adding at least one of (i) a node or (ii) an edge to the initialized graph data structure to generate an updated graph data structure, wherein the node or the edge is a representation of at least a portion of the subtree ([0033]: In some embodiments, the knowledge management system 110 may construct a knowledge graph by representing entities as nodes and relationships among the entities as edges… These values allow the knowledge graph to prioritize or rank connections; [0041]: the data sources 120 may support dynamic updates to ensure that the knowledge graph remains current).
Regarding Claim 13, Patil discloses the non-transitory computer-readable storage medium of claim 12, wherein generating the hierarchical data structure comprises analyzing the corpus using a large language model (LLM) to produce a topical table cluster, wherein the subtree is associated with a cluster of the topical table cluster ([0053]: In some embodiments, the language models are large language models (LLMs) that are trained on a large corpus of training data; [0090]: The searching of related entities may be based on the relationships and positions of nodes in the knowledge graph of a corpus… Alternatively, or additionally, the searching of related entities may also be based on the result of the analysis of a language model).
Regarding Claim 14, Patil discloses the non-transitory computer-readable storage medium of claim 13, wherein analyzing the corpus comprises:
generating at least two embedding vectors based on the corpus (Fig. 2; [0068]: In some embodiments, the vectorization engine 220 may generate embedding vectors using various methods and models);
generating a centroid vector based on the initialized graph data structure; and comparing the at least two embedding vectors to the centroid vector to identify an embedding vector of the at least two embedding vectors that is within a threshold degree from the centroid vector ([0075]: In some embodiments, embeddings may be compressed using clustering techniques, where similar embeddings are grouped together, and representative centroids replace individual embeddings).
Regarding Claim 15, Patil discloses the non-transitory computer-readable storage medium of claim 14, wherein the threshold degree is 18 degrees. ([0145]: In some embodiments, the knowledge management system 110 selects prompts with similarity scores above a predefined threshold to select prompts that are closely aligned with the meaning and context of the query).
Regarding Claim 16, Patil discloses the non-transitory computer-readable storage medium of claim 12, wherein the corpus is represented in a JavaScript Object Notation (JSON) format ([0040]: The datasets may be structured, semi-structured, or unstructured, encompassing formats such as articles in textual documents, JSON files).
Regarding Claim 17, Patil discloses the non-transitory computer-readable storage medium of claim 12, wherein updating the initialized graph data structure using the hierarchical data structure comprises identifying a node of the initialized graph data structure that corresponds to a node of the subtree ([0035]: The prompt-driven data structure enhances the precision of subsequent searches and allows the knowledge management system 110 to retrieve specific and relevant sections instead of entire documents).
Regarding Claim 18, Patil discloses the non-transitory computer-readable storage medium of claim 12, wherein the instructions further cause the processor to: receive a query for information; traverse the updated graph data structure to identify a node associated with the query; and transmit information associated with the identified node ([0121]: For example, a query asking for “phase 2 clinical trials for drug Y” is converted into a set of instructions to locate nodes labeled “drug Y,” traverse edges connected to “clinical trials,” and filter results based on attributes indicating “phase 2.”).
Regarding Claim 19, Patil discloses the non-transitory computer-readable storage medium of claim 18, wherein transmitting the information associated with the identified node comprises displaying a graphical user interface (GUI) that represents the identified node ([0157]: FIG. 6A is a conceptual diagram illustrating an example graphical user interface (GUI) 610… The visualization may enhance comprehension by summarizing relationships, trends, or metrics identified in the retrieved information).
Regarding Claim 20, Patil discloses the non-transitory computer-readable storage medium of claim 18, wherein receiving the query comprises performing natural language processing (NLP) on a string ([0036]: In some embodiments, the knowledge management system 110 may incorporate an advanced natural language processing (NLP) models).
Conclusion
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached on (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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