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 .
DETAILED ACTION
This action is issued in response to Application filed March 14, 2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 11, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 therefore, subject to the conditions and requirements of this title.
Claims 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an Abstract Idea without significantly more.
Claims 19 and 20 are method claims and directed to the process category of patentable subject matter.
Although claims 19 and 20 fall under at least one of the four statutory categories, it should be determined whether the claims recite a judicial exception.
Regarding Step 2A-1, Claims 19 and 20 recite a judicial exception. Exemplary independent claim 19 recites the limitations (struck-through limitations have been identified as additional elements and will be discussed in later sections):
receiving a user query
identifying a portion of a user-specific graph of enterprise data
prompting
With regard to the “receiving” limitation, a human may mentally obtain/accept a request from a user. This claim limitation has been identified as a recitation of a mental process.
With regard to the “identifying” limitation, a human may mentally find and recognize data of a graph or data structure. This claim limitation has been identified as a recitation of a mental process.
With regard to the “prompting” limitation, a human may mentally take action and request a response associated with the request and identified data. This claim limitation has been identified as a recitation of a mental process.
The examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. The limitations of “receiving”, “identifying”, and “prompting”; fall within the “Mental Processes” grouping of abstract ideas because this recites a mentally performable process of receiving and retrieving requested data. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all”. Specifically, the limitations as discussed above, as claimed, is a process that covers performance of the limitations in the mind, or with pen and paper, but for the recitation of generic computer components (i.e., processor, system, etc.) because a user can mentally, or with pen and paper, requesting data for retrieval. See Digitech (organizing and manipulating information through mathematical correlations), Electric Power Group (collecting information, analyzing it, and displaying certain results of the collection and analysis).
This judicial exception is not integrated into a practical application. The claim(s) includes additional elements which fall within the mental processing of requesting data for retrieval. In particular, the limitations of a computer system using a large language model (LLM); have been identified as recitations of generic computing functions. In particular, the claims only recite additional elements (i.e., processor, system, etc.) that are recited at a high-level of generality (e.g., as a generic computer or as a generic processor performing a generic computer function), such that it amounts to no more than mere instructions to apply the exception using generic computer components. See 2106.05(d) (II). Accordingly, the additional element(s) do not integrate the abstract idea into a practical application because it does not impose meaningful limits on practicing the abstract idea. The claims as a whole do not appear to integrate the mental process into a practical application and is thus directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claims recite additional elements of enterprise data and user-specific graph. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component or are merely drawn to insignificant extra-solution activity. Mere instructions to apply an exception using a generic computer component or insignificant extra-solution is not significantly more than the judicial exception.
The dependent claim 20, depends on a rejected parent claim and does not cure its deficiencies. Similar to the above discussion, each of the dependent claims are drawn to an abstract idea within the “Mental Processes” grouping of abstract ideas. The claims are drawn to subject matter that covers performance of the claimed limitations in the mind, or with pen and paper, but for the recitation of generic computer components as discussed above. The claims are not integrated into a practical application. The claims only recite additional elements that is/are recited at a high-level of generality (e.g., as a generic computer or as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component or are merely drawn to insignificant extra-solution activity.
The claim elements considered individually or in combination do not result in a new or improved method for data retrieved based on a user request.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-3, 6-7, 11-13, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bayless (U.S. Patent Application No. 2025/0112878).
Regarding Claim 1, Bayless discloses a computer implemented method, comprising:
receiving a user query for a generative artificial intelligence (AI) system (Fig.1; par [0037-0038], Bayless – submit a query into a chatbot user interface for an LLM of an AI system… par [0031], Bayless);
identifying a portion of a user-specific knowledge-based graph (user-specific KG) based on the user query (par [0030], [0066-0067], Bayless - the knowledge-graph system may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the query. For example, the knowledge-graph system may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the query… the query engine identifies relevant information from the knowledge graphs using one or more techniques to identify one or more nodes (i.e., entities) that have relevant information for the queries. The query engine utilizes pattern matching techniques to find subgraphs that match the structure of the query which may involve identifying nodes and edges that fit the query pattern… par [0064-0065], Bayless – curated knowledge graph are configured or built by a user that are specific to a particular subject matter … par [0108-0109]);
generating a prompt for the generative AI system including a representation of the user query and the identified portion of the user-specific KG (par [0068-0069], Bayless - the prompt-engineering component may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the formal-language query. For example, the query engine may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the formal-language query. The prompt-engineering component may then provide the most relevant information along with the formal-language query to the LLM in one or more prompts… par [0085] – the prompt-engineering component may then generate a prompt for the LLM);
sending the prompt to the generative AI system (Fig.5; par [0039], Bayless – submit a prompt to the LLM for the answer to the query… par [0069-0071], Bayless);
receiving a response to the prompt from the generative AI system (Fig.5; par [0039-0040], [0067], [0094], Bayless – the LLM may output the answer… The response from the LLM may include some additional answers for the query. The prompt-engineering component may continue to iteratively prompt the LLM until the LLM no longer has any more answers. As shown in prompt, the prompt -engineering component again asks for additional answers and provides the known answers for the query. The LLM may provide a response that indicates it provided all of the information it had related to the query); and
providing a response to the user query based on the response to the prompt (Fig.5; par [0039], [0067-0071], Bayless - the LLM may output the answer… The response from the LLM may include some additional answers for the query. The prompt-engineering component may continue to iteratively prompt the LLM until the LLM no longer has any more answers. As shown in prompt, the prompt -engineering component again asks for additional answers and provides the known answers for the query. The LLM may provide a response that indicates it provided all of the information it had related to the query).
Regarding Claim 2, Bayless discloses the computer implemented method of claim 1 wherein identifying a portion of a user-specific KG based on the user query comprises:
identifying content elements in the user query; and identifying subgraph portions of the user-specific KG that are relevant to the user query based on the content elements of the user query (par [0030], [0066-0067], Bayless - the knowledge-graph system may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the query. For example, the knowledge-graph system may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the query… the query engine identifies relevant information from the knowledge graphs using one or more techniques to identify one or more nodes (i.e., entities) that have relevant information for the queries. The query engine utilizes pattern matching techniques to find subgraphs that match the structure of the query which may involve identifying nodes and edges that fit the query pattern).
Regarding Claim 3, Bayless discloses the computer implemented method of claim 2 wherein identifying content elements in the user query comprises identifying entities in the user query and wherein identifying subgraph portions of the user-specific KG comprises:
identifying the subgraph portions of the user-specific KG that are relevant to the user query based on the entities in the user query (par [0020], Bayless - knowledge graphs may be graph-like data structures with nodes, edges, attributes, and labels. Nodes in the graphs represent entities or concepts (e.g., people places, objects, etc.)… par [0066], Bayless – the query engine identifies relevant information from the knowledge graphs using one or more techniques to identify one or more nodes (i.e., entities) that have relevant information for the queries. The query engine utilizes pattern matching techniques to find subgraphs that match the structure of the query which may involve identifying nodes and edges that fit the query pattern).
Regarding Claim 6, Bayless discloses the computer implemented method of claim 1 and further comprising: accessing user data (par [0073], Bayless – user may interact with an identity and access management (IAM) component that allows user to register and create user accounts to improve knowledge graphs that are being generated on their behalf. The different user accounts can assume different roles, or sets or permissions/credentials, that allow users to perform different actions and be restricted from performing some actions); generating the user-specific KG based on the user data (par [0064-0065], [0073], Bayless); and storing the user-specific KG in a data store (par [0073], Bayless – user may interact with an identity and access management (IAM) component that allows user to register and create user accounts to improve knowledge graphs that are being generated on their behalf. The different user accounts can assume different roles, or sets or permissions/credentials, that allow users to perform different actions and be restricted from performing some actions).
Regarding Claim 7, Bayless discloses the computer implemented method of claim 6 wherein accessing user data comprises: detecting that the user has logged in to a content system using a set of user credentials; and accessing user data available to the user based on the user credentials (par [0073], Bayless – user may interact with an identity and access management (IAM) component that allows user to register and create user accounts to improve knowledge graphs that are being generated on their behalf. The different user accounts can assume different roles, or sets or permissions/credentials, that allow users to perform different actions and be restricted from performing some actions).
Regarding Claim 11, Bayless discloses the computer implemented method of claim 1 and further comprising: detecting an intermittent trigger; accessing user data based on the detected trigger; updating the user-specific KG based on the user data; and storing the updated user-specific KG in a data store (par [0028], [0067], Bayless - The knowledge-graph system may then iteratively prompt the LLM to provide answers for the plurality of questions, and build or augment the knowledge graph using the answers. Thus, the knowledge-graph system may act or behave as a graph-as-a-service where it is able to generate, potentially from scratch, knowledge graphs using one or more LLMs. Additionally, the knowledge-graph system may be able to improve, or augment, curated domain-specific knowledge graphs by adding general-purpose knowledge to those knowledge graphs… par [0091], Bayless - The knowledge-graph component, and/or an administrator (admin 418) associated with the knowledge-graph system, may determine whether the answer is accurate, either modify or remove the answer if it is determined to be inaccurate or confusing and improve the knowledge graph.. par [0073-0079], [0108-0110], Bayless).
Claims 12 and 13 contain similar subject matter as claims 1 and 2 above; and are rejected under the same rationale.
Regarding Claim 19, Bayless discloses a method, comprising:
receiving a user query for a large language model (LLM) (Fig.1; par [0019], Bayless - a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users… par [0038], Bayless);
identifying a portion of a user-specific graph of enterprise data based on the user query (par [0030], [0066-0067], Bayless - the knowledge-graph system may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the query. For example, the knowledge-graph system may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the query); and
prompting the LLM based on the user query and the identified portion of the user-specific graph (par [0068-0070], Bayless - the prompt-engineering component may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the formal-language query. For example, the query engine may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the formal-language query. The prompt-engineering component may then provide the most relevant information along with the formal-language query to the LLM in one or more prompts… par [0085] – the prompt-engineering component may then generate a prompt for the LLM).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4, 5, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bayless (U.S. Patent Application No. 2025/0112878) in view of Sharma (U.S. Patent Application No. 2025/0265457).
Regarding Claim 4, Bayless discloses generating a prompt that includes the representation of the user query and the subgraph portions of the user-specific KG (par [0066-0067], Bayless - the query engine identifies relevant information from the knowledge graphs using one or more techniques to identify one or more nodes (i.e., entities) that have relevant information for the queries. The query engine utilizes pattern matching techniques to find subgraphs that match the structure of the query which may involve identifying nodes and edges that fit the query pattern… par [0068-0069], Bayless - the prompt-engineering component may provide the LLM with a portion of the knowledge graph, such as the most relevant information for the formal-language query. For example, the query engine may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the formal-language query. The prompt-engineering component may then provide the most relevant information along with the formal-language query to the LLM in one or more prompts); and Bayless discusses LLMs can understand and handle complex queries from users and respond. However, Bayless is not as detailed with respect to generating a complex prompt.
On the other hand, Sharma teaches generating a complex prompt (par [0054], Sharma - breaking large, complex prompts into smaller, comparatively more tailored, prompts that focus on individual segments).
Bayless and Sharma are analogous art because they are from the same field of endeavor of prompt engineering in systems for generative AI simulation and modeling. It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate Sharma’s teachings into Bayless’ knowledge graph assisted large language models. A skilled artisan would have been motivated to combine in order to produce more tailored prompts that focus on individual segments in a question chain, allowing for less computationally expensive responses and optimize the use of computational resources.
Regarding Claim 5, the combination of Bayless in view of Sharma, disclose the computer implemented method of claim 3 wherein generating a prompt comprises: generating a set of chained prompts, the chained prompts including the representation of the user query and the subgraph portions of the user-specific KG (par [0054], Sharma - par [0054], Sharma - breaking large, complex prompts into smaller, comparatively more tailored, prompts that focus on individual segments in the A-R-C-M chain can enable implementation of cascading, smaller AI models… also see par [0066-0069], Bayless).
Claims 14 and 15 contain similar subject matter as claims 4 and 5 above; and are rejected under the same rationale.
Claim(s) 8-10, 16-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bayless (U.S. Patent Application No. 2025/0112878) in view of Mui (U.S. Patent Application No. 2025/0278419).
Regarding Claim 8, Bayless discloses the computer implemented method of claim 7 wherein generating the user-specific KG comprises: selecting a content item from the user data (par [0041], [0065], Bayless – the knowledge -graph component may select a subject matter area… the curated knowledge graph are configured and built by the user for specific subject-matter).
While Bayless teaches generating a user-specific KG and the knowledge-graph system may determine which portion of the knowledge graph is semantically most relevant or has answers that are relevant for the query; as well as the curated knowledge graphs being determined to be used for queries that are semantically related to the selected domains (see par [0030], [0064]). However, Bayless is not as detailed with respect to generating an embedding of the content item as a representation of the content item; and identifying semantic relations.
On the other hand, Mui discloses generating an embedding of the content item as a representation of the content item (par [0013-0014], Mui – generate vector embeddings); and identifying semantic relations (par [0013], Mui – utilizing semantic vector augmentation).
Bayless and Mui are analogous art because they are from the same field of endeavor of large language prompt augmentation. It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate Mui’s’s teachings into Bayless’ knowledge graph assisted large language models. A skilled artisan would have been motivated to combine in order to provide accurate and relevant responses to user queries by helping to leverage complex algorithms and vast databases of information.
Regarding Claim 9, the combination of Bayless in view of Mui, disclose the computer implemented method of claim 8 wherein generating the user-specific KG comprises: generating a graph with the embeddings as nodes in the graph; and generating edges in the graph, connecting the nodes, based on the identified semantic relations (par [0013], Mui - A first pipeline utilizes semantic vector augmentation, a process that involves generating vector embeddings for a set of unstructured data (e.g., a set of documents). A second pipeline utilizes knowledge graph augmentation, a process that involves the extraction of triplets from a set of data and the storage of the triplets in a graph database. The graph database may store the triplets as nodes, edges, properties, or a combination thereof).
Regarding Claim 10, the combination of Bayless in view of Mui, disclose the computer implemented method of claim 9 wherein identifying semantic relations in the content items comprises: parsing the content item to generate semantic triples of a form comprising subject-predicate-object based on information in the content item (par [0013], Mui - A first pipeline utilizes semantic vector augmentation, a process that involves generating vector embeddings for a set of unstructured data (e.g., a set of documents). A second pipeline utilizes knowledge graph augmentation, a process that involves the extraction of triplets from a set of data (e.g., the unstructured data, structured data, or both) and the storage of the triplets in a graph database. A triplet may be an example of a data object or unit of information that includes three elements: a subject (or head) element, a predicate (or relation) element, and an object (or tail) element. In some examples, the graph database may store the triplets as nodes, edges, properties, or a combination thereof).
Claims 16-18 contain similar subject matter as claims 8-10 above; and are rejected under the same rationale.
Claim 20 contains similar subject matter as claims 8, 9, and 11 above; and is rejected under the same rationale.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Chelcie Daye
Patent Examiner
Technology Center 2100
September 1, 2026
/CHELCIE L DAYE/Primary Examiner, Art Unit 2161
Prosecution Insights