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 .
This Office Action is in response to correspondence filed 12 February 2025 in reference to application 19/051,826. Claims 1-20 are pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-4, 9-14, 19, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 10, 11, 13, and 20 of U.S. Patent No. 12,271,707. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of 12,271,707 anticipate the instant claims as laid out in the chart below.
Instant Application
US Patent 12,271,707
Claim 1: A method comprising:
Claim 1: A method comprising:
receiving, by a data analytics system, a text query from a client device associated with a user through a user interface;
receiving a chat query from a client device associated with a user through a chat user interface, wherein the chat query comprises a text query;
accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model;
accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model;
identifying a subset of the set of cells that relate to the received text query;
identifying a subset of the set of cells that relate to the received chat query;
generating a response to the text query using a large language model by applying the large language model to the text query and contents of the identified subset of cells; and
transmitting the final prompt to the large language model; receiving a response from the large language model, wherein the response comprises text for a chat response to the chat query;… wherein the final prompt comprises text instructions for a large language model to generate a response to the chat query from the user based on contents of the identified subset of the set of cells
transmitting text for the response to the text query to the client device for display in the user interface.
transmitting the text for the chat response to the client device for display in the chat user interface.
Claim 2: The method of claim 1, wherein generating the response to the text query comprises: generating a prompt for input to the large language model, wherein the prompt comprises the text query and the contents of the identified subset of cells
Claim 1: generating a final prompt based on contents of the identified subset of cells and the text query, wherein the final prompt comprises text instructions for a large language model to generate a response to the chat query from the user based on contents of the identified subset of the set of cells;
Claim 3: The method of claim 2, wherein generating the response to the text query comprises: transmitting the generated prompt to a third-party system operating the large language model.
Claim 1: transmitting the final prompt to the large language model.
Claim 4: The method of claim 1, further comprising: generating the grid-based data structure based on the text query.
Claim 3: The method of claim 1, further comprising: generating the grid-based data structure based on the chat query.
Claim 9: The method of claim 1, wherein receiving the text query comprises: receiving the text query through a chat user interface on the client device.
Claim 1: receiving a chat query from a client device associated with a user through a chat user interface, wherein the chat query comprises a text query;
Claim 10: The method of claim 9, wherein generating the response to the text query comprises: generating the response to the text query based on a chat history in the chat user interface on the client device.
Claim 1: generating a final prompt based on contents of the identified subset of cells and the text query, Claim 10: wherein the final prompt further comprises a chat history in the chat user interface
Claim 11: A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:
Claim 11: A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:
receiving, by a data analytics system, a text query from a client device associated with a user through a user interface;
receiving a chat query from a client device associated with a user through a chat user interface, wherein the chat query comprises a text query;
accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts ,and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model;
accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model;
identifying a subset of the set of cells that relate to the received text query;
identifying a subset of the set of cells that relate to the received chat query;
generating a response to the text query using a large language model by applying the large language model to the text query and contents of the identified subset of cells; and
transmitting the final prompt to the large language model; receiving a response from the large language model, wherein the response comprises text for a chat response to the chat query;… wherein the final prompt comprises text instructions for a large language model to generate a response to the chat query from the user based on contents of the identified subset of the set of cells
transmitting text for the response to the text query to the client device for display in the user interface.
transmitting the text for the chat response to the client device for display in the chat user interface.
Claim 12: The computer-readable medium of claim 11, wherein generating the response to the text query comprises: generating a prompt for input to the large language model, wherein the prompt comprises the text query and the contents of the identified subset of cells.
Claim 11: generating a final prompt based on contents of the identified subset of cells and the text query, wherein the final prompt comprises text instructions for a large language model to generate a response to the chat query from the user based on contents of the identified subset of the set of cells;
Claim 13: The computer-readable medium of claim 12, wherein generating the response to the text query comprises: transmitting the generated prompt to a third-party system operating the large language model.
Claim 11: transmitting the final prompt to the large language model.
Claim 14: The computer-readable medium of claim 11, the operations further comprising: generating the grid-based data structure based on the text query.
Claim 13: The computer-readable medium of claim 11, the operations further comprising: generating the grid-based data structure based on the chat query.
Claim 19: The computer-readable medium of claim 11, wherein receiving the text query comprises: receiving the text query through a chat user interface on the client device.
Claim 11: receiving a chat query from a client device associated with a user through a chat user interface, wherein the chat query comprises a text query;
Claim 20: The computer-readable medium of claim 19, wherein generating the response to the text query comprises: generating the response to the text query based on a chat history in the chat user interface on the client device
Claim 11: generating a final prompt based on contents of the identified subset of cells and the text query, Claim 20: wherein the final prompt further comprises a chat history in the chat user interface
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1 and 11 recite receiving, by a data analytics system, a text query from a client device associated with a user through a user interface; accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model; identifying a subset of the set of cells that relate to the received text query; generating a response to the text query using a large language model by applying the large language model to the text query and contents of the identified subset of cells; and transmitting text for the response to the text query to the client device for display in the user interface.
The limitation of receiving, by a data analytics system, a text query from a client device associated with a user through a user interface, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a data analytics system,” “a client device” and additionally “a non-transitory computer readable medium” in claim 11, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for computer components, “receiving” in the context of this claim encompasses a person reading a text query that has been typed into a computing device.
The limitation of accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a large language model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for computer components, “accessing” in the context of this claim encompasses a person information in a spreadsheet arranged in the claim manner.
The limitation of identifying a subset of the set of cells that relate to the received text query, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for computer components, “identifying” in the context of this claim encompasses the person deciding that cells in the spreadsheet are relevant to the query.
The limitation of generating, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for computer components, “generating” in the context of this claim encompasses the person thinking of a response to the query given the relevant information in the spreadsheet.
The limitation of transmitting text for the response to the text query to the client device for display in the user interface, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for computer components, “transmitting” in the context of this claim encompasses the person typing the response into a word processing program that displays text on a screen.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only additionally recite “a data analytics system,” “a client device” “a large language model” and additionally “a non-transitory computer readable medium” in claim 11. These computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element computer components amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2 and 12 additionally recite generating a prompt for input to the large language model, wherein the prompt comprises the text query and the contents of the identified subset of cells. Similar to above these steps encompass a mental process as the person could perform the steps by writing a prompt for an LLM to generate the response. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 3 and 13 additionally recite transmitting the generated prompt to a third-party system operating the large language model. Similar to above these steps encompass a mental process as the person could perform the steps by typing the prompt into the user interface of a language model. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 4 and 14 additionally generating the grid-based data structure based on the text query. Similar to above these steps encompass a mental process as the person could perform the steps by writing out a spreadsheet. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 5 and 15 additionally recite generating a prompt for generating column prompts based on the text query, wherein the prompt for generating column prompts comprises instructions for a large language model to generate column prompts based on the text query. Similar to above these steps encompass a mental process as the person could perform the steps by writing out a prompt to generate the grid structure. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 6 and 16 additionally recite the prompt for generating column prompts further comprises a set of pre-generated column prompts stored by the data analytics system. However this added limitation does not prevent the recited step from being performed as a mental process because the person could still write out a prompt using previously stored prompts. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 7 and 17 additionally recite wherein the generated grid-based data structure comprises a set of columns with a set of pre-generated column prompts. However this added limitation does not prevent the recited step from being performed as a mental process because the person could still write out a prompt using previously stored prompts. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 8 and 18 additionally recite accessing an initial grid-based data structure with an initial set of column prompts; and adding a plurality of column prompts to the initial grid-based data structure. Similar to above these steps encompass a mental process as the person could perform the steps by reading a spreadsheet, and writing in additional column prompts. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 9 and 19 additionally recite receiving the text query through a chat user interface on the client device. Similar to above, these steps encompass a mental process as the person could perform the steps by reading the query in a chat interface. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Claims 10 and 20 additionally recite generating the response to the text query based on a chat history in the chat user interface on the client device. Similar to above these steps encompass a mental process as the person could perform the steps by typing a response in a chat interface while considering a chat history. Similar to above, no additional limitations are recited that amount to significantly more than the abstract idea or provide a practical application. The claim is not patent eligible.
Allowable Subject Matter
Claims 1-20 are allowed. Claims 1-20 would be allowable if rewritten or amended and/or a terminal disclaimer filed to overcome the rejection(s) under 35 U.S.C. 101 and double patenting doctrine set forth in this Office action. The following is a statement of reasons for the indication of allowable subject matter:
Consider claim 1, Thomas et al. (US Patent 12,147,758) teaches a method (abstract) comprising:
receiving a chat query from a client device associated with a user through a chat user interface, wherein the chat query comprises a text query and a set of sources (col 7 lines 4-14, natural language query from user regarding associated spreadsheet.);
accessing a grid-based data structure comprising a set of rows and a set of columns (col 8 lines 4-11, spreadsheet data, rows and columns);
identifying a subset of the set of cells that relate to the received chat query (col 8 lines 15-20, a portion of the spreadsheet data may be used);
generating a response to the text query using a large language model by applying the large language model to the text query and contents of the identified subset of cells (col 8 lines 12-40, generating a prompt based on user query and data for LLM model; sending prompt to LLM; col 8 lines 35-40, receiving reply from LLM);
transmitting text for the response to the text query to the client device for display in the user interface (col 8 lines 45-50 displaying generated data on user interface).
However the prior art of record does not teach or fairly suggest the limitations of “accessing a grid-based data structure comprising a set of rows and a set of columns, where each row is associated with a source of a plurality of sources and each column is associated with a column prompt of a plurality of column prompts, and wherein the set of rows and set of columns define a set of cells for the grid-based data structure, and wherein each of the set of cells comprises contents from applying a column prompt of a corresponding column to a source of a corresponding row using a large language model;” when combined with each and every other limitation of the claim. Rather Thomas is concerned with manipulating generic spreadsheet data, and not necessary data involving responses from LLMs. Therefore claim 1 is allowable.
Claim 11 contains similar limitations as claim 1 and therefore is allowable as well.
Claims 2-10 and 12-20 depend on and further limit claims 1 and 11 and therefore are allowable as well.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Coate et al. (US PAP 2024/0378375) and Gillman et al. (US PAP 2024/0028312) both teach a similar method of using LLMs to manipulate spreadsheet data through a chat interface.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
