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 .
Status of Claims
Claims 1-20 are pending in this application.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 7, 8, 10, 11, 17, and 19-20 are rejected on the ground of nonstatutory double patenting over claims 1, 6, 7, and 10 of co-pending application No. 18/799,235. Although the claims at issue are not identical, they are not patentably distinct from each other because adding inherent and/or unnecessary limitations/step and rearranging the claims would be within the level of one of ordinary skill in the art. It is well settled that the insertion of an element, e.g. “receiving one or more data values are obfuscated in the result message based on the access level;”, and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Insertion of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
Instant Application No. 18/799,411
Co-pending Application No. 18/799,235
1. A computing system for processing a natural language question, the computing system comprising:
a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface;
a personalization large language model (LLM), a retrieval system, and a structured query language (SQL) LLM stored in the memory and executable by the processor;
the processor configured to:
receive the natural language question from a requesting entity;
obtain user profile data associated with the requesting entity;
generate a prompt that comprises the natural language question and a database schema corresponding to a database;
process, using the retrieval system, the prompt to identify one or more tables in the database, the one or more tables relevant to the natural language question;
generate, using the retrieval system, an augmented prompt that comprises the natural language question, the database schema, and one or more identities of the one or more tables;
generate, using the SQL LLM, a set of SQL code based on the augmented prompt;
initiate executing the set of SQL code on the database and receiving a result;
input the result and the user profile data into the personalization LLM to generate a personalized result message; and
output the personalized result message responsive to the natural language question.
from the natural language question to add to the user profile data.
7. The computing system of claim 1, wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM generates the augmented prompt.
8. The computing system of claim 1 further comprising a preliminary LLM in the memory, and the preliminary LLM generates the prompt that comprises the natural language question, the database schema and metadata of the database; wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM processes the prompt to identify the one or more tables in the database and a subset of the metadata that corresponds to the one or more tables; and wherein the retrieval LLM generates the augmented prompt that further comprises the metadata and the subset of the metadata.
10. The computing system of claim 1, wherein a chat user interface is stored in the memory, the chat user interface in communication with the personalization LLM and a user profile database that stores the user profile; and the processor is further configured to:
receive the natural language question via the chat user interface;
generate the personalized result message in a form of a natural language response that comprises the result; and
provide the natural language response via the chat user interface.
1. A computing system for processing a natural language question, the computing system comprising:
a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface;
a retrieval system and a structured query language (SQL) large language model (LLM) stored in the memory and executable by the processor;
the processor configured to:
receive the natural language question from a requesting entity;
obtain an access level associated with the requesting entity;
generate a prompt that comprises the natural language question and a database schema corresponding to a database;
process, using the retrieval system, the prompt to identify one or more tables in the database, the one or more tables relevant to the natural language question;
generate, using the retrieval system, an augmented prompt that comprises the natural language question, the database schema, and one or more identities of the one or more tables;
generate, using the SQL LLM, a set of SQL code based on the augmented prompt;
initiate executing the set of SQL code on the database and receiving a result that comprises one or more data values from the one or more tables;
generate a result message using the result, wherein the one or more data values are obfuscated in the result message based on the access level; and
provide the result message responsive to the natural language question.
6. The computing system of claim 1, wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM generates the augmented prompt.
7. The computing system of claim 1 further comprising a preliminary LLM in the memory, and the preliminary LLM generates the prompt that comprises the natural language question, the database schema and metadata of the database; wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM processes the prompt to identify the one or more tables in the database and a subset of the metadata that corresponds to the one or more tables; and wherein the retrieval LLM generates the augmented prompt that further comprises the metadata and the subset of the metadata.
10. The computing system of claim 1, wherein a chat user interface is stored in the memory; and the processor is further configured to:
receive the natural language question via the chat user interface;
generate the result message in a form of a natural language response that comprises the result; and
provide the natural language response via 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 and are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One: The independent claim 11 recites “receiving the natural language question from a requesting entity; obtaining user profile data associated with the requesting entity; generating a prompt that comprises the natural language question and a database schema corresponding to a database; processing, using a retrieval system, the prompt to identify one or more tables in the database, the one or more tables relevant to the natural language question; generating, using the retrieval system, an augmented prompt that comprises the natural language question, the database schema, and one or more identities of the one or more tables; generating, using a SQL large language model (LLM), a set of SQL code based on the augmented prompt; initiating executing the set of SQL code on the database and receiving a result; inputting the result and the user profile data into a personalization LLM to generate a personalized result message; and outputting the personalized result message responsive to the natural language question”.
Claims 1, 11 and 20 recite a workflow: receive a natural-language question, identify relevant database tables, generate SQL, execute it, and personalize the returned result using profile data. This relates to conceptually organizing data, translating a question into a query, performing retrieval, and formatting a personalized response.
Those tasks are similar to organizing information, lookup, routine data processing, and natural language conversion as abstract ideas when claimed at a high level. See Alice and Data Engine (abstract idea of storing, organizing, and retrieving information / manipulating data).
Accordingly, the claims are directed to the judicial exception of a mental process with a pen and paper.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. The computer is recited at a high-level of generality (i.e., as performing a generic computer function and being used as an applying) such that it amounts no more than mere instructions to apply the exception using a generic computer. Accordingly, there 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 claim is directed to an abstract idea.
Step 2B — Claims Do Not Recite an Inventive Concept That Transforms the Mental Process into Patent-Eligible Subject Matter
The claims add generic, well-understood computer components (memory and processor) and name LLMs (personalization LLM, SQL LLM) and a “retrieval system,” but does not explain how those components are specially configured, or how their interaction produces a technical improvement to the computer itself (e.g., reduced latency, improved correctness of generated SQL, new indexing/search data structures, memory savings, or improved security). Merely implementing an abstract idea on generic computer components or saying “use an LLM” is usually insufficient.
Because the claims lack limitations that tie the mental-process steps to a particular way of achieving a technological improvement (for example, a novel model architecture, specialized data representation, unique training regimen that yields demonstrable technical performance gains, a specialized streaming/decoding pipeline that reduces latency by a quantifiable amount, or hardware/software co-design), the additional elements do not transform the mental processes into significantly more.
Therefore, claims 1, 11 and 20 fail to recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
With respect to dependent claims 2-10 and 12-19, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Conclusion — Rejection
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (mental processes) and failing to recite additional elements that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7, 8, 11, 18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chakraborty (US Pat. 12,306,828).
Regarding claim 1, Chakraborty discloses a computing system for processing a natural language question, the computing system comprising:
a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface (Figs. 6 and 8, memory, application UI and processor);
a personalization large language model (LLM), a retrieval system, and a structured query language (SQL) LLM stored in the memory and executable by the processor (Figs. 6 and 8, Col. 1, lines 40-67, LLM, retrieval system, utilizing LLMs to converse with and/or search structured data);
the processor configured to:
receive the natural language question from a requesting entity (Fig. 7, step 702, Col. 17, lines 46-64, receiving a natural language question);
obtain user profile data associated with the requesting entity (Col. 3, lines 33-59, utilizing a user questions repository, UQR, which is persona specific and provides relevant metadata for responding to users);
generate a prompt that comprises the natural language question and a database schema corresponding to a database (Fig. 7, steps 704 and 706, Col 17, line 64- Col. 18, line 30, determining sub-questions and pre-generated questions corresponding to the received question);
process, using the retrieval system, the prompt to identify one or more tables in the database, the one or more tables relevant to the natural language question (Fig. 7, steps 704 and 706, Col 17, line 64- Col. 18, line 30, matching embeddings for each sub-question to pre-generated embeddings);
generate, using the retrieval system, an augmented prompt that comprises the natural language question, the database schema, and one or more identities of the one or more tables (Fig. 7, step 708, Col. 18, lines 31-48, generating a structured query to the structured databased using a list of the sub-questions);
generate, using the SQL LLM, a set of SQL code based on the augmented prompt (Fig. 2, Col. 11, line 59- Col. 12, line 12, Col. 17, lines 19-20, utilizing UQR and LLM for converting natural language inputs to the corresponding format and syntax of SQL or other structured data language; Col. 3, lines 49-59, using a retrieval augmented generation, RAG - based prompting);
initiate executing the set of SQL code on the database and receiving a result (Fig. 7, step 708, Col. 16, lines 38-43, Col. 18, lines 31-48, providing intelligent generation of structured queries based on syntax of SQL or other structured data language);
input the result and the user profile data into the personalization LLM to generate a personalized result message (Fig. 7, step 710, Col. 18, lines 49-62, querying the structured database using the structured query in order to receive a result; Col. 12, lines 10-16, integrating UQR and LLM); and
output the personalized result message responsive to the natural language question (Col. 4, lines 51-56, Col. 7, lines 21-45, returning data as a response to the user’s original natural language question based on user’s preferences and structured data system).
Regarding claim 7, Chakraborty discloses the computing system of claim 1, and Chakraborty further discloses:
wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM generates the augmented prompt (Col. 3, lines 33-59, “prompting strategies may be used, such as a retrieval augmented generation (RAG)-based prompting and questioning process”).
Regarding claim 8, Chakraborty discloses the computing system of claim 1, and Chakraborty further discloses:
a preliminary LLM in the memory, and the preliminary LLM generates the prompt that comprises the natural language question, the database schema and metadata of the database (Col. 11, lines 42-58, utilizing a UQR with an LLM and converting natural language to SQL or other structured data language for querying on structured databases, (e.g. databases or other data storages systems storing structured data in data tables and views including columns and rows that are searchable using structured data queries in a specific format);
wherein the retrieval system comprises a retrieval LLM, and the retrieval LLM processes the prompt to identify the one or more tables in the database and a subset of the metadata that corresponds to the one or more tables; and wherein the retrieval LLM generates the augmented prompt that further comprises the metadata and the subset of the metadata (Col. 16, lines 14-44, prompting LLM using the question and corresponding identified metadata for the match, and generating a SQL query which is used for structured data retrieval from a structured database system).
Regarding claims 11 and 17, Claims 11 and 17 are the corresponding method claims to system claims 1 and 7. Therefore, claims 11 and 17 are rejected using the same rationale as applied to claims 1 and 7 above.
Regarding claim 20, Claim 20 is the corresponding medium claim to system claim 1. Therefore, claim 20 is rejected using the same rationale as applied to claim 1 above.
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 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.
Claims 2-6, 10, 12-16 and 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Chakraborty (US Pat. 12,306,828) in view of Gruber et al, (US Pub. 2020/0327895).
Regarding claim 2, Chakraborty discloses the computing system of claim 1. Chakraborty does not explicitly teach however Gruber does explicitly teach:
wherein the processor is further configured to use the personalization LLM to derive personalization data from the natural language question to add to the user profile data (Gruber, Fig. 28, [0132]-[0141][0202]-[0210] using user-specific information and personal interaction history in the interpretation and execution of user requests such as that found in personal memory 1052 and 1054) .
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of the method of processing user’s query using a large language model as taught by Chakraborty with the method of engaging short and long term memory for personalizing user data as taught by Gruber so that user input can be interpreted in proper context given previous events and communications within a given session, as well as historical and profile information about the user (Gruber, [0011]).
Regarding claim 3, Chakraborty discloses the computing system of claim 1. Chakraborty does not explicitly teach however Gruber does explicitly teach:
wherein the processor is further configured to: receive a natural language response to the personalized result message; and use the personalization LLM to derive personalization data from the natural language response to add to the user profile data (Gruber, Fig. 28, [0132]-[0141][0202]-[0210] using user-specific information and personal interaction history in the interpretation and execution of user requests such as that found in short term and long term personal memory 1052 and 1054).
Regarding claim 4, Chakraborty discloses the computing system of claim 1. Chakraborty does not explicitly teach however Gruber does explicitly teach:
wherein the personalization LLM generates the personalized result message by incorporating a plurality of words from the user profile data (Gruber, [0132]-[0141] generating result using personal information and personal interaction history).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 5, Chakraborty discloses the computing system of claim 1. Chakraborty does not explicitly teach however Gruber does explicitly teach:
wherein the personalization LLM generates one or more graphics using the user profile data and the result, and the personalized result message comprises the one or more graphics (Gruber, Fig. 27, [0151]-[0159][0389][0390] generating graphical result using data source based on identifier for saving to personal memory and personal notes and etc.).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 6, Chakraborty discloses the computing system of claim 1. Chakraborty does not explicitly teach however Gruber does explicitly teach:
wherein the personalization LLM uses a digital voice associated with the user profile data to generate the personalized result message as audio speech data (Gruber, [0102][0151]-[0159] output synthesized speech from the assistant to the user in reply).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 10, Chakraborty discloses the computing system of claim 1.
wherein a chat user interface is stored in the memory, the chat user interface in communication with the personalization LLM and [a user profile database that stores the user profile]; and the processor is further configured to:
receive the natural language question via the chat user interface (Fig. 7, step 702, Col. 17, lines 46-64, receiving a natural language question);
generate the personalized result message in a form of a natural language response that comprises the result; and provide the natural language response via the chat user interface (Fig. 7, step 710, Col. 18, lines 49-62, querying the structured database using the structured query in order to receive a result; Col. 12, lines 10-16, integrating UQR and LLM; Col. 4, lines 51-56, Col. 7, lines 21-45, returning data as a response to the user’s original natural language question based on user’s preferences and structured data system).
Chakraborty does not explicitly teach however Gruber does explicitly teach: [a user profile database that stores the user profile] (Gruber, Fig. 28, [0132]-[0141][0202]-[0210] using user-specific information and personal interaction history in the interpretation and execution of user requests such as that found in short term and long term personal memory 1052 and 1054).
The previous motivation statement as in claim 2 is still applied.
Regarding claims 12-16 and 19, Claims 12-16 and 19 are the corresponding method claims to system claims 2-6 and 10. Therefore, claims 12-16 and 19 are rejected using the same rationale as applied to claims 2-6 and 10 above.
Claims 9 and 18 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Chakraborty (US Pat. 12,306,828) in view of Vu et al., (US Pub. 2026/0037505).
Regarding claim 9, Chakraborty discloses the computing system of claim 1, and Chakraborty further discloses:
wherein, [when the result comprises an error message], the processor is configured to: generate a new set of SQL code, using the SQL LLM, based on the augmented prompt; initiate executing the new set of SQL code on the database; and receive a new result comprising retrieved data from the database that is responsive to the new set of SQL code (Col. 17, lines 1-26, when user questions are not found in UQR 206, e.g., a very low matching score, an automated pipeline may generate variations of low scoring questions that are served on structured data and additional automated actions may be taken based on the results).
Chakraborty does not explicitly teach the bracketed limitation however Vu does explicitly teach the bracketed limitation:
when the result comprises an error message ([0041]-[0043] generating an error message corresponding to the dissimilarity between the inferred result and the gold result).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of the method of processing user’s query using a large language model as taught by Chakraborty with the method of providing error messages as taught by Vu to allow for the LLM to determine if there are any form of issues with the dataset by an error message and try to mitigate said messages.
Regarding claim 18, Claim 18 is the corresponding method claim to system claim 9. Therefore, claim 18 is rejected using the same rationale as applied to claim 9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/Primary Examiner, Art Unit 2659
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