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 .
Introduction
The following is a non-final Office action in response to Applicant’s submission filed on 3/13/2025. Currently claims 1-17 are pending and claims 1, 12 are independent.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN202421096085, filed on 12/5/2024
Information Disclosure Statement
No information disclosure statement (IDS) submitted appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, no IDS 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 therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-17, following the guidance for 101 rejections contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-11) are directed to a statutory category, namely a system/machine. Claim 12 and its dependent claims (claims 13-17) are directed to a statutory category, namely a method.
Step 2A (Prong 1): Claims 1, 12, which are substantially similar claims to one another, are directed to the abstract idea of “Mental Processes”, or more particularly, “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system to gather business data and glean business insights from that enterprise data according to requests. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that a business analyst performs for a large organization. The abstract elements of claims 1, 12, recite in part “Receive query…Analyze query…Generate workflow…Assign processing an analysis…Coordinate analysis…Present insights…”. Dependent claims 2-11, 13-17 add to the abstract idea the following limitations which recite in part “Parse query…Identify required data…Map objectives…Define relationships…Select models…Categorize data…Determine order…create paths…establish checkpoints…Extract data…Validate data…Transform data…Select appropriate model…Perform analysis…Generate visuals…Identify patterns…Identify operations…Match operation…Incorporate data…Initiate agent multiple times…Monitor progress…Manage flows…Handle exceptions…Maintain logs…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12.
Step 2A (Prong 2): Independent claims 1, 12, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Processor…Memory…multi-agent framework…Agents…Orchestration engine…User interface…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Dependent claims 3, 4, 6, 7, 8, 9, 10, 14, 16, 17 add the additional element which recites in part “Trained ML model…Agents…Orchestration engine…” which again limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2, 5, 11, 13, 15 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 12, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Processor…Memory…multi-agent framework…Agents…Orchestration engine…User interface…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (gather business data and glean business insights from that enterprise data according to requests) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0025] – “the display unit 108 could be accessed via portable devices such as laptops, tablets, or smartphones...”
Dependent claims 3, 4, 6, 7, 8, 9, 10, 14, 16, 17 include additional elements, when considered both individually and as an ordered combination and in view of their respective independent claims, which are insufficient to amount to significantly more than the judicial exception. Specifically, dependent claims 3, 4, 6, 7, 8, 9, 10, 14, 16, 17 include the additional element which recites in part “Trained ML model…Agents…Orchestration engine…” These are the same additional elements that are addressed above in claims 1, 12, and are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (gather business data and glean business insights from that enterprise data according to requests) on a general purpose computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2, 5, 11, 13, 15 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-17 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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.
Claims 1, 2, 5-13, 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Batra et al. (WO 2005098652 A2) in view of Karri et al. (US 20250077263 A1)
Regarding claims 1, 12, Batra discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received), comprising: a processor; a memory storing instructions (Batra Fig. 39) that, when executed by the processor, cause the system to implement: a multi-agent framework comprising: a plurality of worker agents, wherein each worker agent is configured to execute one or more data processing and analysis operations on enterprise data (Batra ABS - Separate executable agents each perform tasks on associated information that is changing over time, to produce current information. Inputs and outputs are delivered among agents to enable assembly of a body of aggregated and summarized management information, based on the current information, to be used to manage at least a portion of an enterprise), the plurality of worker agents comprise at least a data engineer agent configured to extract and transform enterprise data, a data scientist agent configured to apply analytical models to transformed data, and a data analyst agent configured to generate analytical insights (Batra - Agents that participate in a BIN may have different types of functions, for example, extracting data from external web services or data sources, transforming data, analyzing and/or aggregating information, storing or retrieving information, and monitoring information changes); an orchestration engine operatively coupled to the plurality of worker agents, wherein the orchestration engine is configured to: receive, via a user interface, an analytical query from a user (Batra - The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received, for example, from dashboards 362 (which present the management information to users) or other processes 364. For the model to respond to queries with the required results, a hypercube agent receives the query and applies the hypercube model by invoking one or more data cubes, either directly, or indirectly through one or more virtual cubes); and present the business insights generated from the execution of the one or more data processing and analysis operations, via the user interface, to the user (Batra Fig. 6 – Batra - A dashboard designer (by dashboard 327 we mean the application that presents the management information to the manager) can then present any metrics from the virtual cube to the end user as tables or charts).
Batra lacks analyze the analytical query to determine one or more data processing and analysis operations; generate a computational workflow comprising a sequence of the one or more data processing and analysis operations to extract business insights; assign the one or more data processing and analysis operations to one or more of the worker agents based on specific operation requirements; coordinate sequential execution of the one or more data processing and analysis operations according to workflow dependencies
Karri, from the same field of endeavor, teaches analyze the analytical query to determine one or more data processing and analysis operations (Karri ¶29 - The user starts the workflow by interacting with the conversational planning system also known as the digital assistant via a query. Once the query is provided, the common AI service takes this information, runs a set of classification models to accurately identify the intent of the query. This query is classified into one of the many types of queries supported by the system. To accurately classify the query, a retrieval augmented generation scheme is employed that first searches for several of the queries that are semantically similar to the user query and this information is then supplied to the large language model. Based on this information, the LLM classifies the task into one of the several supported types of intent); generate a computational workflow comprising a sequence of the one or more data processing and analysis operations to extract business insights; assign the one or more data processing and analysis operations to one or more of the worker agents based on specific operation requirements (Karri ¶30 - Once the query intent is identified, the common AI then identifies the set of agents that can fulfill the query and then starts executing the agent chain one by one. In case of the update agent, this chain may include agents such as an entity resolution agent, an update data agent, and a report management agent); coordinate sequential execution of the one or more data processing and analysis operations according to workflow dependencies (Karri ¶39 - In an example, based on the identified intent, the orchestrator 155 selects and executes appropriate agents from the agent library 125. These agents perform specific tasks such as entity resolution, data querying, and update operations).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 2, 13, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches parsing natural language content of the analytical query; identifying required data elements and analytical objectives; determining data dependencies and processing requirements; and mapping the analytical objectives to the one or more data processing and analysis operations required for extracting business insights (Karri ¶29 - The user starts the workflow by interacting with the conversational planning system also known as the digital assistant via a query. Once the query is provided, the common AI service takes this information, runs a set of classification models to accurately identify the intent of the query. This query is classified into one of the many types of queries supported by the system. To accurately classify the query, a retrieval augmented generation scheme is employed that first searches for several of the queries that are semantically similar to the user query and this information is then supplied to the large language model. Based on this information, the LLM classifies the task into one of the several supported types of intent. Once the query intent is identified, the common AI then identifies the set of agents that can fulfill the query and then starts executing the agent chain one by one. In case of the update agent, this chain may include agents such as an entity resolution agent, an update data agent, and a report management agent); coordinate sequential execution of the one or more data processing and analysis operations according to workflow dependencies (Karri ¶39 - In an example, based on the identified intent, the orchestrator 155 selects and executes appropriate agents from the agent library 125. These agents perform specific tasks such as entity resolution, data querying, and update operations).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 5, 15, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches determining an order of the one or more data processing and analysis operations based on enterprise data dependencies and insight generation requirements; creating execution paths for parallel processing where the enterprise data dependencies allow (Karri ¶31 - The common AI service executes each agent and keeps the output in an ephemeral storage that every agent in the chain has access to. Each output is of a predefined schema, so every agent knows how to read and modify the output data of another agent in the chain. When an agent gets executed, it picks up available input information that would have either come from the user in the initial query or as part of a clarification question. For the first agent in the chain, this is just the information that the user provided in the original query but as the chain progresses, each agent will have information that gets enriched by the upstream agents); and establishing checkpoints for validation between sequential operations (Karri ¶71 - At decision 625, an access control check is performed to verify authorization of the user to access identified entities. If the access control check passes, the process 600 proceeds to operation 630 and an update policy validation is performed to check if the requested update complies with predefined data update policies. If the access control check or the update policy validation fail, an error message is transmitted to the user interface at operation 630).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 6, Batra in view of Karri discloses he data engineer agent is configured to: extract data from multiple enterprise data sources; perform data transformation operations to standardize data formats; validate data quality based on predefined rules; and maintain data lineage throughout processing operations (Batra - As shown in figure 32, one simple such pattern, called an extract-and-transform BIN 165, extracts information from a data source or web service 166 using a web service agent 168, performs a transformation using a transform agent 169, and makes the information available for immediate consumption on public channels 170. Extract-and- transform BINs can be directly used for just-in-time information retrieval or indirectly used as sub-networks in other BINs. Different data sources can be served by corresponding specialized extract-and- transform BINs and several such BINs can be used in an assembly network to produce information aggregated from multiple separate sources, including sources for which the data has formal and temporal inconsistencies).
Regarding claims 7, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches he data scientist agent is configured to: select appropriate analytical models based on data characteristics; apply the selected models to the transformed data (Karri ¶39 - In an example, based on the identified intent, the orchestrator 155 selects and executes appropriate agents from the agent library 125. These agents perform specific tasks such as entity resolution, data querying, and update operations); tune model parameters for optimal performance; and interpret model outputs for further analysis (Karri ¶31 - Each agent, as part of the execution, checks its policy and compares that with available data to decide whether any clarification is needed from the user as a follow up question which common AI takes and facilitates the conversation between the user and the agent).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 8, Batra in view of Karri discloses The system of claim 1, wherein the data analyst agent is configured to: perform statistical analysis on processed data; generate interactive data visualizations; identify key patterns and trends; and create comprehensive analytical reports (Batra Fig. 6 – Batra - A dashboard designer (by dashboard 327 we mean the application that presents the management information to the manager) can then present any metrics from the virtual cube to the end user as tables or charts. The information that contributes to the metrics is distributed and captured through BINs. The metrics modeling layer creates derived metrics, sets up thresholds and targets on metrics, and estimates the accuracy and reliability of metrics. The methodology model 328 has the function of organizing and managing metrics in an industry standard manner (like balanced scorecard, six sigma, etc.) to which end users are standardized on or accustomed. The presentation layer As shown in figure 6, metrics are presented to end users as a dashboard 330).
Regarding claims 9, 16, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received)
Karri further teaches identifying types of data processing and analysis operations for the workflow; matching each data processing and analysis operation with a corresponding worker agent based on the worker agent's configured capabilities; and incorporating the matched data processing and analysis operations into the workflow sequence (Karri ¶66 - At operation 525 LLM services (e.g., the LLM services 120 as described in FIG. 1, etc.) analyze the user input and intent candidate set to select an intent. At operation 530 the LLM services classify the query into one of a set of supported intent types. At operation 535 the classified intent matches are verified to ensure the intent matches the request of the user. At operation 540, the common AI service works in conjunction with an orchestrator (e.g., the orchestrator 155 as described in FIG. 1, etc.) generates an appropriate agent chain for execution based on the classified intent. At operation 545 the classified intent and generated agent chain are passed to relevant agent(s) for further processing).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 10, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches each worker agent can be instantiated multiple times within the workflow based on the specific operation requirements and different instances of a same worker agent can execute different data processing and analysis operations concurrently within the workflow (Karri ¶50 - At operation 220, an orchestrator (e.g., the orchestrator 155 as described in FIG. 1, etc.) selects and executes appropriate agents based on an identified intent. The orchestrator selects the agents from an agent library (e.g., the agent library 125 as described in FIG. 1, etc.) that contains specialized agents for various tasks such as, by way of example and not limitation, an entity resolution agent that extracts and maps entities from user utterances (e.g., requests, etc.), an update agent that creates update queries based on user input and entities, and other specialized agents (e.g., the numeric data query agent 130, the text query agent 135, the batch job agent 140, the ERP agent 145, and the CRM agent 150 as described in FIG. 1, etc.)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Regarding claims 11, 17, Batra in view of Karri discloses handling exceptions during execution (Batra - If a requestor makes a request for which the credentials don't match, the request is redirected to an authorization service 250 (figure 40). The authorization service can take one of the following steps: (a) reject the request and send it back to the requestor with an appropriate response with exception); and maintaining execution logs for audit purposes (Batra - In this example, the message Id is a globally unique Id that identifies the message for logging and trouble shooting purposes).
Karri further teaches monitoring progress of assigned data processing and analysis operations; managing data flow between worker agents (Karri ¶31 - The common AI service executes each agent and keeps the output in an ephemeral storage that every agent in the chain has access to. Each output is of a predefined schema, so every agent knows how to read and modify the output data of another agent in the chain. When an agent gets executed, it picks up available input information that would have either come from the user in the initial query or as part of a clarification question. For the first agent in the chain, this is just the information that the user provided in the original query but as the chain progresses, each agent will have information that gets enriched by the upstream agents. Each agent, as part of the execution, checks its policy and compares that with available data to decide whether any clarification is needed from the user as a follow up question which common AI takes and facilitates the conversation between the user and the agent).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Claims 3, 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Batra et al. (WO 2005098652 A2) in view of Karri et al. (US 20250077263 A1) further in view of Brende et al. (US 20240394251 A1)
Regarding claim 3, 14, Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches a semantic processing layer operatively coupled to the orchestration engine, the semantic processing layer comprising: domain-specific semantic data models defining relationships between enterprise entities for business insight extraction (Karri ¶48 - At operation 210, a common AI service (e.g., the common AI service 110 as described in FIG. 1, etc.)) processes the natural language input to classify the query type, generate an agent chain for execution, and facilitate conversations between agents and users. At operation 215, intents and example queries are identified. For example, a vector database (e.g., the vector database 115 as described in FIG. 1, etc.)) stores intents and example queries for semantic searching and LLM services (e.g., LLM services 120 as described in FIG. 1, etc.) assists identifying intents and assists in query processing based on LLMs for various domains based on the query type classification, etc.); he orchestration engine utilizes the semantic layer to interpret the analytical query, identify relevant data sources and relationships, and select appropriate machine learning models for analysis (Karri ¶48 - At operation 220, an orchestrator (e.g., the orchestrator 155 as described in FIG. 1, etc.) selects and executes appropriate agents based on an identified intent. The orchestrator selects the agents from an agent library (e.g., the agent library 125 as described in FIG. 1, etc.) that contains specialized agents for various tasks).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Batra in view of Karri lacks trained machine learning models configured for enterprise data analysis tasks.
Brende, from the same field of endeavor, teaches trained machine learning models configured for enterprise data analysis tasks (Brende ¶195 - In another embodiment, intelligence module 318 leverages machine learning algorithms to enhance its analytical capabilities. For instance, the module might use predictive modeling to forecast future sales based on historical data. This process involves training a model on past sales data and using it to predict future trends, helping the enterprise to plan inventory and marketing strategies more effectively).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the database querying techniques of Brende because Brende discloses “By leveraging these optimization techniques, the system ensures that the queries are executed in the most efficient manner possible, thereby reducing latency and improving overall query performance (Brende ¶12)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional database querying techniques that Brende discloses because they would improve the query performance for a user of Batra.
Regarding claim 4, Batra in view of Karri further in view of Brende discloses Batra in view of Karri discloses a system for processing analytical queries to extract business insights from enterprise data (Batra - A hypercube model is a multidimensional cube that models one or more measures or metrics m against multidimensional enterprise data dl, d2, d3, ... . Metrics or measures reflect the business management model of the enterprise and capture the relationship between the management metrics and the underlying data. The hypercube model forms the basis for other cubes in the system including data cubes and virtual cubes 354, both of which inherit properties of the hypercube. The hypercube model is used to supply management information (results 358) in response to queries (360) received).
Karri further teaches natural language processing models for query interpretation (Karri ¶56 - The common AI service 110 interacts with the orchestrator 155 to select and execute appropriate agents from the agent library 125 based on identified intent and interacts with the vector database 115 to provide semantic search capabilities for intents and queries. The common AI service 110 interacts with the LLM services 120 for assistance in intent identification and query processing).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the LLM database management techniques of Karri because Karri discloses “improved user productivity: The natural language interface and automated processing reduce the time and effort required for users to perform data updates, leading to tangible efficiency gains in business planning processes (Karri ¶34)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional LLM database management techniques that Karri discloses because they would make it easier for a user of Batra gather the required information.
Brende further teaches he trained machine learning models comprise classification models for categorizing enterprise data (Brende ¶166 - In one embodiment, machine learning model 306 employs decision tree algorithms to classify), prediction models for forecasting business metrics (Brende ¶195 - In another embodiment, intelligence module 318 leverages machine learning algorithms to enhance its analytical capabilities. For instance, the module might use predictive modeling to forecast future sales based on historical data. This process involves training a model on past sales data and using it to predict future trends, helping the enterprise to plan inventory and marketing strategies more effectively), clustering models for pattern identification (Brende ¶127 - For instance, machine learning models might analyze the frequency and types of queries executed against a particular dataset, identifying commonalities and bottlenecks in data access patterns. Based on this analysis, the models can generate query patterns that pre-optimize certain aspects such as join operations, data aggregation, and indexing strategies).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the enterprise data analysis methodology/system of Batra by including the database querying techniques of Brende because Brende discloses “By leveraging these optimization techniques, the system ensures that the queries are executed in the most efficient manner possible, thereby reducing latency and improving overall query performance (Brende ¶12)”. Additionally, Batra further details that it “providing appropriate mechanisms (which we sometimes refer to as a visibility solution or visibility system or a system 300) to acquire, store, process, and analyze selected raw enterprise information (Batra)” so it would be obvious to consider including the additional database querying techniques that Brende discloses because they would improve the query performance for a user of Batra.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yang et al. (EP 4614349 A1)
Tong et al. (US 20240419950 A1)
and
R. Shan, et al. “Enterprise LLMOps: Advancing Large Language Models Operations Practice," 2024 IEEE Cloud Summit, Washington, DC, USA, 2024, pp. 143-148 [online], [retrieved on 2026-06-27]. Retrieved from the Internet <https://ieeexplore.ieee.org/document/10630923?source=IQplus>
These pieces of prior art are cited because they disclose variations on enterprise data gathering and analysis using AI and machine learning.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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