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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to independent claims 1 and 13:
At Step 1:
The claims are directed to a “system” and “method” and thus directed to a statutory category.
At Step 2A, Prong One:
The claims recite the following limitations directed to an abstract idea:
“generating a content item using private enterprise data items” as drafted recites a mental process. One can write private enterprise data on paper using a pen.
“generating a template containing a table of contents section, which includes a plurality of nested subsections, for the content item to be generated” as drafted recites a mental process. One can create a template containing a table of content with nested subsections using pen and paper.
“identifying a section from the generated template for performing a write operation” as drafted recites a mental process. One can mentally evaluate or judge sections of a template to identify and make changes.
At Step 2A, Prong Two:
The claims recite the following additional elements:
That the method and system are performed by a “communication circuitry configured to access a user device” and “control circuitry” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“receiving a request from a user device for generating a content item using private enterprise data items” is insignificant extra-solution activity. This limitation is recited as receiving data (i.e. mere data gathering). This does not provide integration into a practical application.
“querying a semantic graph for accessing private enterprise data items for the identified section, wherein the semantic graph indexes only those private enterprise data items that are authorized for the user device to access” is insignificant extra-solution activity. This limitation is recited as receiving data (i.e. mere data gathering). This does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and do not provide significantly more.
Looking at the claims as a whole does not change this conclusion and the claim is ineligible.
As to dependent claims 2-12 and 14-20:
At Step 1:
The claims are directed to a “method” and “system” and thus directed to a statutory category.
At Step 2A, Prong One:
The claims recite the following limitations directed to an abstract idea:
“generating an initial template; verifying the initial template via user input; and generating the template containing the table of contents based on the verified user input” as drafted recites a mental process. One can generate a template with pen and paper and mentally evaluate/verify a template based on user input.
“determining a change in a private enterprise data item from a first data source, from the plurality of data sources” as drafted recites a mental process. One can mentally evaluate or judge text to identify a change.
“automatically determining that a task is to be performed by the user device; and suggesting, to the user device, one or more templates that correlate to the task to be performed” as drafted recites a mental process. One can mentally evaluate or judge a task and suggest a template.
“the task to be performed is determined by analyzing a plurality of communications associated with the user device” as drafted recites a mental process. One can mentally evaluate or judge communications associated with a user.
“identifying the section from the generated template for performing a write operation comprises: identifying a bottom most sub section from the plurality of nested subsections for a particular section; and selecting the bottom most sub section for performing the write operation” as drafted recites a mental process. One can mentally evaluate or judge sections of a template to identify and make changes.
“determining that a first nested subsection and a second nested subsection, from the plurality of nested subsections, are on a same layer” as drafted recites a mental process. One can mentally evaluate or judge if two sections are similar.
“” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”). Applicant’s specification teaches semantic embedding converts claims into embedding vectors. Semantic similarity matching may be performed between the claim embeddings and one or more of the product embeddings (e.g., by computing the cosine similarity between the claim embeddings and a given product embedding, where the higher the computer similarity the better the match) (See [0019] and [0020]).
At Step 2A, Prong Two:
The claims recite the following additional elements:
That the method and system are performed by a “communication circuitry configured to access a user device” and “control circuitry” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“detecting establishing of a connection between a user device and a plurality of data sources; performing an automatic initial synchronization of private enterprise data items from the plurality of data sources in response to detecting the establishing of the connection; and generating the semantic graph based on the initial synchronization of private enterprise data items from the plurality of data sources” is insignificant extra-solution activity. This limitation is recited as receiving data (i.e. mere data gathering). This does not provide integration into a practical application.
“the semantic graph provides associations between private enterprise data items from a plurality of data sources” is insignificant extra-solution activity.
“performing a subsequent synchronization with the first data source to obtain the changed private enterprise data item; and regenerating a portion of the semantic graph effected by the changed private enterprise data item” is insignificant extra-solution activity. This limitation is recited as receiving data (i.e. mere data gathering). This does not provide integration into a practical application.
“simultaneously writing content for the first nested subsection and the second nested based on both subsections being on the same layer” is insignificant extra-solution activity. This limitation is recited as providing/presenting data. This does not provide integration into a practical application.
“querying the semantic graph for obtaining private enterprise data items for all remaining sections in the generated template; performing a write operation for all the remaining sections starting with a section at a bottom of a section hierarchy to a section at the top of the hierarchy; and providing a completed content item upon completion of the write operation for all the remaining sections” is insignificant extra-solution activity. This limitation is recited as gathering and providing/presenting data. This does not provide integration into a practical application.
“the semantic graph indexes data sources that contain the private enterprise data items that are to be used to perform the write operation for the identified section” is insignificant extra-solution activity. This limitation is recited as gathering and providing/presenting data. This does not provide integration into a practical application.
“generating the semantic graph using a large language model (LLM)” is insignificant extra-solution activity in the form of pre-solution activity. The limitation merely defines a well-understood or conventional learning model as source of data gathering and does not amount to an inventive concept.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and do not provide significantly more.
Looking at the claims as a whole does not change this conclusion and the claim is ineligible.
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) 1-9 and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over HUGHES (US 20250053899 A1) in view of OJHA (US 20240303263 A1).
As to claims 1 and 13, HUGHES teaches A method comprising:
receiving a request from a user device for generating a content item using private enterprise data items (HUGHES [0015]-[0018] discloses receiving user inputs (i.e. requests) for generating deliverables (i.e. content items) using AI agents with character reference data such as, real-world examples in the form of case studies, financial statements, contracts, marketing materials, demographics, and interview transcripts, from data sources comprising Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, web servers, social media APIs, Electronic Content Management (ECM) systems, and public financial databases (i.e. private enterprise data).);
generating a template containing a table of contents section, which includes a plurality of nested subsections, for the content item to be generated (HUGHES [0139] discloses executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file.);
identifying a section from the generated template for performing a write operation (HUGHES [0139] discloses preparing the structure for content writing.);
performing a write operation in the identified section based on the private enterprise data items obtained by (HUGHES [0104], [0131], [0139], and [0141]-[0144] discloses the AI agent in combination with one or more additional AI agents using the characteristic reference data may edit the section of the business plan (i.e. deliverable/content item) depicted in the pop-up window).
HUGHES fails to teach querying a semantic graph for accessing private enterprise data items for the identified section, wherein the semantic graph indexes only those private enterprise data items that are authorized for the user device to access.
However, OJHA teaches querying a semantic graph for accessing private enterprise data items for the identified section, wherein the semantic graph indexes only those private enterprise data items that are authorized for the user device to access (OJHA [0033]-[0046] discloses a user is able to call up data which he/she has access to. A set of data points associated with data such as user relevant data may be stored using a semantic architecture that includes multiple dimensions associated with the set of data points. The semantic architecture may be implemented by using a semantic layer that functions as an index of and/or on top of an organization's enterprise data assets. The machine learning models guided using the semantic graph may better understand the terminology and vocabulary of a particular enterprise, to better generate documents and reports. Accordingly, the generative machine learning model may be trained to analyze the user input query in view of a given organizations data including the given organizations vocabulary, data, semantic graph, trends, metrics, and/or the like.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of HUGHES to incorporate the providing predictive outputs and key drivers as taught by OJHA for the purpose of providing autonomous self-optimization, as the system learns which models are best to use for different situations.
As to claims 2 and 14, HUGHES teaches generating an initial template (HUGHES [0139] discloses executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file.); verifying the initial template via user input (HUGHES [0139] discloses synthetic samples may then be reviewed by a human user.); and generating the template containing the table of contents based on the verified user input (HUGHES [0139] discloses executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file.).
As to claims 3 and 15, OJHA teaches detecting establishing of a connection between a user device and a plurality of data sources; performing an automatic initial synchronization of private enterprise data items from the plurality of data sources in response to detecting the establishing of the connection; and generating the semantic graph based on the initial synchronization of private enterprise data items from the plurality of data sources (OJHA [0042] discloses a machine learning model can be trained to receive a data catalog as input and to generate metadata or semantic graph data as an output. Receiving data inherently means a connection has been established and it would have been obvious to synchronize the data in order to have the latest information.).
As to claims 4 and 16, OJHA teaches the semantic graph provides associations between private enterprise data items from a plurality of data sources (OJHA [0042] discloses the model can use a data catalog as the source of the terminology for a schema, metadata repository, and/or semantic graph. In some implementations, the generated schema or metadata repository is an intermediate representation that is used by a database system or by machine learning models for interacting with the data set).
As to claims 5 and 17, OJHA teaches determining a change in a private enterprise data item from a first data source, from the plurality of data sources; performing a subsequent synchronization with the first data source to obtain the changed private enterprise data item; and regenerating a portion of the semantic graph effected by the changed private enterprise data item (OJHA [0043] and [0044] discloses forecasts (e.g., forecasted changes, trends, etc.) associated with user relevant data and/or user relevant data types (user relevant data) may be generated. The forecasts may be based on trends and/or seasonality associated with user relevant data. The forecasts may further be based on a semantic architecture (e.g., one or more semantic graphs) associated with a given entity. For example, user relevant data trends and/or seasonality may be analyzed in view of the semantic architecture and associated data. A user relevant data forecast may be generated based on the trends, seasonality, and/or semantic architecture. A key driver analysis may be conducted to determine key data drivers that effect changes to user relevant data. The key driver analysis may identify such key drivers and the semantic architecture may be used to apply such key drivers to generate user relevant data analysis (e.g., for forecasting). The forecasting suggests that the semantic architecture is changed over time causing a regeneration to take effect in order to implement the changes.).
As to claims 6 and 18, HUGHES teaches automatically determining that a task is to be performed by the user device; and suggesting, to the user device, one or more templates that correlate to the task to be performed (HUGHES [0035] discloses divide the provision of management consulting into a plurality of tasks, the plurality of tasks defined as a plurality of respective workflows. Furthermore, the processor is enabled to assign a plurality of Artificial Intelligence (AI) agents to the plurality of tasks with each one of the plurality of tasks assigned at least one AI agent. HUGHES [0139] discloses executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file.).
As to claim 7, HUGHES teaches the task to be performed is determined by analyzing a plurality of communications associated with the user device (HUGHES [0030] discloses the plurality of interfaces utilizes Application Programming Interfaces (APIs) (i.e. user device interactions), data translation protocols, messaging formats, and object models to enable communication between the plurality of AI agents. HUGHES [0035] discloses divide the provision of management consulting into a plurality of tasks, the plurality of tasks defined as a plurality of respective workflows. Furthermore, the processor is enabled to assign a plurality of Artificial Intelligence (AI) agents to the plurality of tasks with each one of the plurality of tasks assigned at least one AI agent.).
As to claim 8, HUGHES teaches identifying the section from the generated template for performing a write operation comprises: identifying a bottom most sub section from the plurality of nested subsections for a particular section; and selecting the bottom most sub section for performing the write operation (HUGHES [0139] discloses The template creation essentially involves two steps. At Step 572, the processor 122 chunks each section of the report into a JSON schema. Furthermore, the processor 122 maps the chunks to the questionnaire sources, creating few-shot learning examples. At Step 574, the processor 122 executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file. Furthermore, the processor 122 then maps JSON field names to the previous template JSON, preparing the structure for content writing. It would have been obvious to create subsections for a particular section and select a particular section for data entry.).
As to claims 9 and 19, HUGHES teaches determining that a first nested subsection and a second nested subsection, from the plurality of nested subsections, are on a same layer; and simultaneously writing content for the first nested subsection and the second nested based on both subsections being on the same layer (HUGHES [0139] discloses The template creation essentially involves two steps. At Step 572, the processor 122 chunks each section of the report into a JSON schema. Furthermore, the processor 122 maps the chunks to the questionnaire sources, creating few-shot learning examples. At Step 574, the processor 122 executing a template creation agent, which may be an AI-based agent or a user-defined code segment, develops a table of contents and outline within a Google Workspace file. Furthermore, the processor 122 then maps JSON field names to the previous template JSON, preparing the structure for content writing. It would have been obvious to create subsections nested under a related parent section and write data into similar subsections in order to streamline the data input.).
As to claim 11, OJHA teaches the semantic graph indexes data sources that contain the private enterprise data items that are to be used to perform the write operation for the identified section (OJHA [0033]-[0046] discloses a user is able to call up data which he/she has access to. A set of data points associated with data such as user relevant data may be stored using a semantic architecture that includes multiple dimensions associated with the set of data points. The semantic architecture may be implemented by using a semantic layer that functions as an index of and/or on top of an organization's enterprise data assets. The machine learning models guided using the semantic graph may better understand the terminology and vocabulary of a particular enterprise, to better generate documents and reports. Accordingly, the generative machine learning model may be trained to analyze the user input query in view of a given organizations data including the given organizations vocabulary, data, semantic graph, trends, metrics, and/or the like.).
As to claims 12 and 20, OJHA teaches generating the semantic graph using a large language model (LLM) (OJHA [0038]).
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Specifically, claim 10 recites “querying the semantic graph for obtaining private enterprise data items for all remaining sections in the generated template; performing a write operation for all the remaining sections starting with a section at a bottom of a section hierarchy to a section at the top of the hierarchy; and providing a completed content item upon completion of the write operation for all the remaining sections”, which is not found in the cited prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cheng et al (US 20120233209 A1) - A unified search service may collect information related to an enterprise from at least one of publicly available data and private enterprise data. In some implementations, crowd sourcing may be used to determine a source list of one or more sources of information. Authored content can be generated, such as by combining one or more items of information from the public data with one or more items of information from the private enterprise data. Further, in some implementations, a public index may be generated from the public data, and one or more affiliation indexes may be generated from the private enterprise data. For example, a first affiliation index may contain confidential enterprise information, while a second affiliation index may contain non-confidential enterprise information. A user's affiliation to the enterprise may be taken into consideration when determining which indexes to use when responding to a search request from the user.
TRIPATHI et al (US 20250292109 A1) - A user-specific knowledge-based graph (KG) is generated from enterprise data corresponding to a user. The user-specific KG is stored in a data store. A query processing system receives a user query that is to be used in prompting a large language model (LLM) in an LLM service. The query processing system identifies portions of the KGs in the data store that relate to the query. A prompt generator generates a prompt to the LLM service using the identified portions of the KG and the query. A response processor receives a response from the LLM service and generates a response to the query based on the response received from the LLM service.
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/JARED M BIBBEE/ Primary Examiner, Art Unit 2161
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