DETAILED ACTION
This is responsive to the application filed 26 November 2024.
Claims 1-20 are pending and considered below.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, in lines 17-18, recites the limitation “the grid-based structure”. It is unclear if the limitation refers back to the grid-based structure of line 4 or the one of line 17. In order to overcome this rejection the limitation “a grid-based data structure” of line 17 will be interpreted as ‘[[a]] the grid-based data structure’.
Claim 11 suffers from the same deficiency and is therefore likewise rejected. The dependent claims are rejected for depending upon a rejected claim without providing a remedy.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art of record, Krishnan et al. (US 2025/0005050) discloses receiving a text query (input dialog portion 124) at a data analytics system from a client device associated with a user, wherein the text query describes an intent of the user with regards to generating a data structure (“receives an input portion of an online dialog 124 via a dialog-based information retrieval interface 122, e.g., an app or web page at a user device”, [0071], see also “analyze the user's input and determine the user's intent (e.g., “analyze the user's message to understand their question or the sub-topics they're interested in.”)”, [0101], see also [0047] for text based dialog); accessing a plurality of pre-generated prompts stored by the data analytics system; selecting a subset of the pre-generated prompts based on the text query (“search prompt generator 104 selects a search prompt template from e.g., prompt data store 556 of FIG. 6, combines the search prompt template with the input dialog portion 124 and, optionally, one or more pieces of dialog context data”, [0072]); generating a prompt (search prompt 106) for generating prompts based on the text query selected subset of the pre-generated prompts and the text query (“combines the search prompt template with the input dialog portion 124 and, optionally, one or more pieces of dialog context data, to formulate the search prompt 106”, [0072]), wherein the prompt for generating prompts comprises instructions for a large language model to generate prompts (search query) based on the text query and the selected subset of pre-generated prompts; transmitting the prompt for generating prompts to a large language model; receiving a response to the prompt for generating prompts from the large language model (“In response to search prompt 106, first large language model 108 generates and outputs search query 110. Search query 110 includes a query that can be executed by search system 112 to generate search result data 114. The search query 110 is determined, generated and output by first large language model 108 in response to the search prompt 106”, [0079]).
Krishnan, alone or in combination with the prior art of record, does not disclose
wherein the response comprises a plurality of column prompts for the grid-based data structure; generating a grid-based data structure based on the received response, wherein the grid-based data structure comprises the plurality of column prompts and a plurality of sources received by the data analytics system; generating contents for a plurality of cells of the grid-based data structure by applying each of the plurality of column prompts to each of the plurality of sources using the large language model; and transmitting the grid-based data structure to the client device.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Qazvinian et al. (US 2025/0013963) discloses intelligent people analytics from generative artificial intelligence. In one embodiment, the system: receives a prompt related to people analytics from a client device associated with a user; generates an embedding representation of the received prompt using a generative AI system including one or more generative AI models; performs a similarity search using the generated embedding representation to identify similar prompts that have been submitted before; obtains an executable expression for responding to the received prompt; executes the executable expression using a data warehouse comprising one or more data sources to obtain a response to the received prompt; determines a type of response based on the nature of the received prompt; generates a response output based on the determined type and the response to the received prompt; and provides the response output to the client device associated with the user.
Thomas et al. (US 12,147,758) discloses a method for the integration of spreadsheet environments with LLM services. In an implementation, an application receives a natural language input from a user associated with a spreadsheet hosted by the application. The application generates a prompt that includes metadata about the spreadsheet and identifies a required format for descriptions of pivot tables. The application sends a prompt to a large language model (LLM) service to elicit a reply that includes a description of the pivot table having the required format. The application receives a reply to the prompt from the LLM service that includes the description of the pivot table in the required format. The application generates pivot table according to the reply from the LLM service.
Amoateng et al. (US 2024/0320257) discloses technology generally directed to a personalized feed. In one example of the technology, selected key-value pairs from a profile associated with a user are provided. Based on a prompt that includes natural-language text instructions, the selected key-value pairs, and ranked content, a large language model is used to generate: pill prompts associated with the ranked content, such that the pill prompts are information requests that are unique and personalized to have particular relevance to the user based on selected key-value pairs, and a response to each pill prompt such that the response includes content corresponding to the requested information. A content feed is displayed to the user, including displaying selectable pills to the user as part of the displayed content feed such that each selectable pill includes a corresponding pill prompt. The response to the pill prompt that corresponds to the selection is displayed to the user.
Mace et al. (US 2024/0070270) discloses a method for generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SAMUEL G NEWAY/Primary Examiner, Art Unit 2657
Prosecution Insights