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 .
DETAILED ACTION
Application 19/316,673 filed 09/02/2025 (with provisional application priority of 9/16/2024) has been examined.
In this Office Action, Claim 1-20 are currently pending.
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 9 and 10 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.
The term “tailoring” in claims 9-10 is/are a relative term which renders the claim indefinite. The term “tailoring” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because independent claim(s) 1 does not recite statutory computer hardware/processors (only generic methods/”A method comprising” without even generically described computer processing elements, like a “computer-implemented method” for example) without limitation and thus the claim(s) is/are directed to a signal per se and/or mere information in the form of data, and dependent claims 2-15 do not correct this deficiency.
See generally guidance on the New Form Paragraphs for Subject Matter Eligibility Rejections
under the 2019 Revised Patent Subject Matter Eligibility Guidance (¶ 7.05.01 Rejection, 35
U.S.C. 101, Nonstatutory (Not One of the Four Statutory Categories);
Available via:
https://www.uspto.gov/sites/default/files/documents/form_para_for_2019peg_20190108.pdf
Additionally, as to Claim 20, the recited machine storage medium is interpreted as a non-transitory medium per Applicant’s explicit definition per specification para. [0072]: “The terms machine-storage medium or media, computer-storage medium or media, and device-storage medium or media 622 specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term "signal medium" discussed
below. In this context, the machine-storage medium is non-transitory.”
Additionally, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more.
Claim 1 recites: (Step 2a, Prong One)
storing a SQL result in a topic of a storage layer.
The limitation of storing a SQL result in a topic of a storage layer, 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 generic method/platform/generative AI, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the method/platform with a generic “generative artificial intelligence (Al) system” language, “storing” in the context of this claim encompasses the user manually storing generic “results” using generic “topics” and “storage layers” steps.
Additionally, note the recent and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining that “we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”).
Similarly, the limitation(s) of receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting, 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 the method/platform language, receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting in the context of this claim encompasses the user manually receiving generic “natural language input” and performing generic prompt “generating” steps. 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 (concepts performed in the human mind (including an observation, evaluation,
judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic storing steps of generic results using generic natural language input and generic “generating” steps is a method of human activity in commercial or legal interactions (such as record keeping, for example).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a method with a platform/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. The method with a platform/generative AI in both steps is recited at a high
level of generality (i.e., as a generic processor performing a generic computer function of
“storing”) such that it amounts no more than mere instructions to apply the exception using a
generic computer component.
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 claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does 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 of a method with a platform/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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
claim(s) is/are not patent eligible.
Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating, by the generative AI system, a summary of the SQL result prior to
transmitting the response to the client device, the response being based on the summary of
the SQL result”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating, by the generative AI system, a summary of the SQL result prior to transmitting the response to the client device, the response being based on the summary of the SQL result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “generating, by the generative AI system, a summary of the SQL result prior to transmitting the response to the client device, the response being based on the summary of the SQL result” steps to perform both the
aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “storing the summary of the SQL result in the topic; and converting the summary of the SQL result into an audio file, wherein the response comprises the audio file of the summary of the SQL result”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “storing the summary of the SQL result in the topic; and converting the summary of the SQL result into an audio file, wherein the response comprises the audio file of the summary of the SQL result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “storing the summary of the SQL result in the topic; and converting the summary of the SQL result into an audio file, wherein the response comprises the audio file of the summary of the SQL result” steps to perform both the
aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “converting the response from text to audio prior to transmitting the response to the client device”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “converting the response from text to audio prior to transmitting the response to the client device” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “converting the response from text to audio prior to transmitting the response to the client device” steps to perform both the
aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the natural language input comprises a verbal input, the method further comprising: storing the verbal input as an audio file in the topic; and converting, by a text/audio converter, the audio file into the text request”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the natural language input comprises a verbal input, the method further comprising: storing the verbal input as an audio file in the topic; and converting, by a text/audio converter, the audio file into the text request” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein the natural language input comprises a verbal input, the method further comprising: storing the verbal input as an audio file in the topic; and converting, by a text/audio converter, the audio file into the text request” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 6, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the natural language input comprises the text request”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the natural language input comprises the text request” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein the natural language input comprises the text request” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein: the generative AI system comprises an external AI system; and
the transmitting of the prompt to the generative AI system comprises making a direct call by the processing engine to the generative AI system”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein: the generative AI system comprises an external AI system; and the transmitting of the prompt to the generative AI system comprises making a direct call by the processing engine to the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein: the generative AI system comprises an external AI system; and the transmitting of the prompt to the generative AI system comprises making a direct call by the processing engine to the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 8, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the prompt describes fields in tables, includes business descriptions for each field, provides an example of an expected SQL query, and specifies an expected result”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the prompt describes fields in tables, includes business descriptions for each field, provides an example of an expected SQL query, and specifies an expected result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein the prompt describes fields in tables, includes business descriptions for each field, provides an example of an expected SQL query, and specifies an expected result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 9, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein generating the prompt comprises tailoring the prompt based on a location associated with the client device”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein generating the prompt comprises tailoring the prompt based on a location associated with the client device” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein generating the prompt comprises tailoring the prompt based on a location associated with the client device” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 10, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein generating the prompt comprises tailoring the prompt based on the SQL database being targeted”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein generating the prompt comprises tailoring the prompt based on the SQL database being targeted” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein generating the prompt comprises tailoring the prompt based on the SQL database being targeted” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 11, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “performing A/B testing of different prompts by replaying events in the real-time
streaming platform to improve accuracy and relevance of SQL queries generated by the
generative AI system”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “performing A/B testing of different prompts by replaying events in the real-time streaming platform to improve accuracy and relevance of SQL queries generated by the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “performing A/B testing of different prompts by replaying events in the real-time streaming platform to improve accuracy and relevance of SQL queries generated by the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 12, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “further comprising: replaying, by the real-time streaming platform, a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at a regular interval”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “further comprising: replaying, by the real-time streaming platform, a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at a regular interval” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “further comprising: replaying, by the real-time streaming platform, a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at a regular interval” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 13, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein each of the receiving, storing, generating, triggering, and transmitting is a microservice”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein each of the receiving, storing, generating, triggering, and transmitting is a microservice” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein each of the receiving, storing, generating, triggering, and transmitting is a microservice” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 14, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein transmitting the response comprises generating and transmitting a user interface or dashboard that displays the SQL result or a summary of the SQL result”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein transmitting the response comprises generating and transmitting a user interface or dashboard that displays the SQL result or a summary of the SQL result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein transmitting the response comprises generating and transmitting a user interface or dashboard that displays the SQL result or a summary of the SQL result” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 15, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “continuously training a prompt model within a prompt component of the processing engine based in part on replayed events to improve prompt accuracy and relevance”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “continuously training a prompt model within a prompt component of the processing engine based in part on replayed events to improve prompt accuracy and relevance” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “continuously training a prompt model within a prompt component of the processing engine based in part on replayed events to improve prompt accuracy and relevance” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Claim 16 recites: (Step 2a, Prong One)
storing a SQL result in a topic of a storage layer.
The limitation of storing a SQL result in a topic of a storage layer, 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 generic processor/memory/generative AI, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor/memory/generative AI language, “storing” in the context of this claim encompasses the user manually storing generic “results” using generic “topics” and “storage layers” steps.
Additionally, note the recent and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining that “we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”).
Similarly, the limitation(s) of receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting, 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 the processor/memory/generative AI language, receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting in the context of this claim encompasses the user manually receiving generic “natural language input” and performing generic prompt “generating” steps. 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 (concepts performed in the human mind (including an observation, evaluation,
judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic storing steps of generic results using generic natural language input and generic “generating” steps is a method of human activity in commercial or legal interactions (such as record keeping, for example).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor/memory/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. The processor/memory/generative AI in both steps is recited at a high
level of generality (i.e., as a generic processor performing a generic computer function of
“storing”) such that it amounts no more than mere instructions to apply the exception using a
generic computer component.
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 claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does 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 of a processor/memory/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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
claim(s) is/are not patent eligible.
Referring to claim 17, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “performing A/B testing of different prompts by replaying events to improve
accuracy and relevance of SQL queries generated by the generative AI system”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “performing A/B testing of different prompts by replaying events to improve accuracy and relevance of SQL queries generated by the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “performing A/B testing of different prompts by replaying events to improve accuracy and relevance of SQL queries generated by the generative AI system” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 18, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “replaying a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at
a regular interval”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “replaying a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at a regular interval” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “replaying a previously executed SQL query to obtain updated SQL results based on newly received data, wherein the replaying is performed at a predetermined later time or at a regular interval” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Referring to claim 19, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the operations further comprise: continuously training a prompt model within a prompt component of a processing engine based in part on replayed events to improve prompt accuracy and relevance”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the operations further comprise: continuously training a prompt model within a prompt component of a processing engine based in part on replayed events to improve prompt accuracy and relevance” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. 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.
(Step 2b)
The claim does 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 of using “wherein the operations further comprise: continuously training a prompt model within a prompt component of a processing
engine based in part on replayed events to improve prompt accuracy and relevance” steps to perform both the aforementioned receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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 claim(s) is/are not patent eligible.
Claim 20 recites: (Step 2a, Prong One)
storing a SQL result in a topic of a storage layer.
The limitation of storing a SQL result in a topic of a storage layer, 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 generic processor/medium/generative AI, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor/medium/generative AI language, “storing” in the context of this claim encompasses the user manually storing generic “results” using generic “topics” and “storage layers” steps.
Additionally, note the recent and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining that “we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”).
Similarly, the limitation(s) of receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting, 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 the processor/medium/generative AI language, receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting in the context of this claim encompasses the user manually receiving generic “natural language input” and performing generic prompt “generating” steps. 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 (concepts performed in the human mind (including an observation, evaluation,
judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic storing steps of generic results using generic natural language input and generic “generating” steps is a method of human activity in commercial or legal interactions (such as record keeping, for example).
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor/medium/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps. The processor/medium/generative AI in both steps is recited at a high
level of generality (i.e., as a generic processor performing a generic computer function of
“storing”) such that it amounts no more than mere instructions to apply the exception using a
generic computer component.
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 claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does 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 of a processor/memory/generative AI to perform both the receiving; storing; generating; transmitting; receiving; storing; triggering; and transmitting; and storing steps amounts 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
claim(s) is/are not patent eligible.
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) 1-2, 5-8, 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mathew et al., US Pub. No. 2026/0072902, in view of Chandrasekarasatry et al., US pub. No. 2026/0072909 A1.
As to claim 1, (and substantially similar claim 16 and claim 20) Mathew discloses
a method
(Mathew abstract; see also [0016-0019])
comprising:
receiving, by a real-time streaming platform,
(Mathew [0231] In certain aspects, communications subsystem 624 may be configured to receive data in the form of continuous data streams, which may include event streams 628 of
real-time events and/or event updates 630, that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, sensor data applications, financial tickers, network performance measuring tools ( e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.
See also [0188] In some implementations, server 414 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client
computing devices 402, 404, 406, 408, and/or 410. As an example, data feeds and/or event updates may include, but are not limited to, blog feeds, Tbreads® feeds, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream)
a natural language input from a client device, the natural language input indicating a query to be performed on a SQL database;
(Mathew [0006] The computer-implemented method further includes accessing a natural language request from the particular user.;
see also [0066] In one embodiment, information is provided to enable a large language model to generate an application query or a structured query language (SQL) query or other query to obtain information to answer a query)
generating, by a processing engine of the real-time streaming platform, a prompt based on the text request, the prompt being used to prompt a generative artificial intelligence (Al) system to generate a SQL query;
(Mathew [0066] information is provided to enable a large language model to generate an application query or a structured query language (SQL) query or other query to obtain information to answer a query;
See also [0175] In one embodiment, the LLM provides a response that includes an application query or a structured query language (SQL) query or other query that may be executed
to obtain information to answer a query.;
See also [0047] As shown, user 202 submits a natural language request to application 204. Application 204 uses prompt assembler and LLM manager 208 to select context specific information 206 to be included in a prompt. Prompt assembler and LLM manager 208 then prompts large language model 216 of large language model service 214. A result is received from the large language model 216 by application 204, which causes execution by a query execution engine 210 of an executable query determined from the result (e.g., constructed based on the result or extracted from the result). The executable query accesses database 212 to retrieve results that are displayed on a user interface of application 204.)
transmitting, by the processing engine, the prompt to the generative AI system;
(Mathew [0047] As shown, user 202 submits a natural language request to application 204. Application 204 uses prompt assembler and LLM manager 208 to select context specific information 206 to be included in a prompt. Prompt assembler and LLM manager 208 then prompts large language model 216 of large language model service 214.)
receiving, by the processing engine, an SQL query generated by the generative AI
system based on the prompt;
(Mathew [0175] In one embodiment, the LLM provides a response that includes an application query or a structured query language (SQL) query or other query that may be executed
to obtain information to answer a query.;
see also [0047] A result is received from the large language model 216 by application 204, which causes execution by a query execution engine 210 of an executable query determined from the result (e.g., constructed based on the result or extracted from the result). The executable query accesses database 212 to retrieve results that are displayed on a user interface of application 204.;
see also [0173] In various embodiments, any set of data and/or operation may be identified in the prompt to the LLM, along with instructions about how to change the set of data to trigger downstream operations and/or how to execute the operation directly.)
triggering, by an SQL executor of the real-time streaming platform, execution of the
SQL query on a cloud SQL to obtain an SQL result;
(Mathew [0018] Cloud services, microservices, or other machine hosted services may be offered that perform part or all of one or more methods disclosed herein.;
See also [0171] In one example, Oracle Cloud Infrastructure REST APIs use standard HTTP requests and responses. Each may contain headers for pagination, entity tags, and other information. Each response may include a unique request ID in the response header so the requesting entity may associate the response with the corresponding request;
See also [0196] Cloud infrastructure system 502 may include a suite of databases, middleware,
applications, and/or other resources that enable provision of the various cloud services.)
and
transmitting, by the real-time streaming platform, a response to the client device
based on the SQL result;
(Mathew [0047] A result is received from the large language model 216 by application 204, which causes execution by a query execution engine 210 of an executable query determined from the result (e.g., constructed based on the result or extracted from the result). The executable query accesses database 212 to retrieve results that are displayed on a user interface of application 204.;)
Mathew does not explicitly disclose:
storing a text request corresponding to the natural language input in a topic of a
storage layer, each topic comprising an unbounded sequence of serialized events;
storing the SQL query to the topic in the storage layer;
storing the SQL result in the topic of the storage layer;
However, Chandrasekarasatry discloses:
storing a text request corresponding to the natural language input in a topic of a storage layer,
each topic comprising an unbounded sequence of serialized events;
(Chandrasekarasatry teaches a storage layer for NL queries/utterances from event streams for labels/target domains, i.e. “storing a text request corresponding to the natural language input in a topic of a storage layer, each topic comprising an unbounded sequence of serialized events;”
See [0107] At its core, the architecture is comprised of two primary components: the storage layer for vector embeddings and the similarity search index. The vector embeddings are numeric representations of data ( e.g., text, images, or audio such as the NL utterances in the <text, sql>pairs) that encode their semantic or contextual meaning in a continuous, high-dimensional space;
[0039] Additionally, in See also accordance with various embodiments, one or more memory banks are provided to and/or obtained by the Text-to-SQL system. The one or more memory banks include various candidate example <NL query, sql, schema> tuples of the form shown Table 2 that can be used for in-context learning
see also [0011] natural language utterance, and an illustrative database schema, and the searching comprises: determining a similarity score as a metric of similarity between the key and the illustrative natural language text pattern corresponding to each of the in-context examples, comparing the similarity scores to a predetermined threshold, and identifying one or
more in-context examples having a similarity score greater than or equal to the predetermined threshold as the one;
see also input from event/data streams see [0218] Additionally, communications subsystem 1124 may also be configured to receive data in the form of continuous data streams, which may include event streams 1128 of real-time events and/or event updates 1130, that may be continuous or unbounded in nature with no explicit end.;
see also Chandrasekarasatry [0090] The training and testing flows start at either a training schemas and NL question versus (vs) gold SQL query pairs block 424 or a testing schemas and NL question versus gold SQL query pairs block 426, respectively, where training and testing data is collected (e.g., acquired or accessed). The data collection can include exploring various data sources such as public datasets, private data collections, or real-time data streams, depending on a project's needs… In other instances, a data source is a private repository of information or examples pertinent to a general or target domain space. For example, a data source can be a storage device that stores various schemas and natural language questions (including labels for corresponding gold SQL queries 403 , 413).)
storing the SQL query to the topic in the storage layer;
(Chandrasekarasatry teaches an SQL agent with memory store for storing queries/results/conversations see [0052-0053] [0052] The chat 206 can include one or more inputs from the user 204 and one or more responses from the SQL agent 202. The chat 206 may correspond to one or more chat sessions between the user 204 and the SQL agent 202. During the chat 206, the user 204 provides a natural language utterance that can be processed by the SQL agent 202. The natural language utterance can include a question related to a database or SQL generation. [0053] One or more user inputs provided by the user 204 via the chat 206 are provided to the SQL agent 202. Included in the SQL agent 202 are a routing model 208, a memory store 210 and tools 212. The routing model 208 and memory store 210 receive user inputs such as natural language utterances from the chat 206. The memory store 210 can store a chat history for the user 204 and contextual information related to the user 204, the chat 206, and/or other pieces of information relevant to the NL2SQL operations such as in-context examples, APis, external knowledge, and the like. The tools 212 can include functions, APis, and trained machine learning models that can be used by the SQL agent 202 to interact with external systems (e.g., database 226, external knowledge bases) and/or generate SQL statements.;)
storing the SQL result in the topic of the storage layer; and
(Chandrasekarasatry teaches an SQL agent with memory store for storing queries/results/conversations see [0052-0053] [0052] The chat 206 can include one or more inputs from the user 204 and one or more responses from the SQL agent 202. The chat 206 may correspond to one or more chat sessions between the user 204 and the SQL agent 202. During the chat 206, the user 204 provides a natural language utterance that can be processed by the SQL agent 202. The natural language utterance can include a question related to a database or SQL generation. [0053] One or more user inputs provided by the user 204 via the chat 206 are provided to the SQL agent 202. Included in the SQL agent 202 are a routing model 208, a memory store 210 and tools 212. The routing model 208 and memory store 210 receive user inputs such as natural language utterances from the chat 206. The memory store 210 can store a chat history for the user 204 and contextual information related to the user 204, the chat 206, and/or other pieces of information relevant to the NL2SQL operations such as in-context examples, APis, external knowledge, and the like. The tools 212 can include functions, APis, and trained machine learning models that can be used by the SQL agent 202 to interact with external systems (e.g., database 226, external knowledge bases) and/or generate SQL statements.;)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply storage layer for vector embeddings for event streams as taught by Chandrasekarasatry, to the system of Mathew, since it was known in the art that search/query systems provide machine learning techniques with pattern-based retrieval for the task of converting NL to SQL, enabling the improvement of large language models' (LLMs) SQL generation and database query efficiency and accuracy. (Chandrasekarasatry [0007]).
As to claim 2, Chandrasekarasatry as modified discloses the method of claim 1, further comprising:
generating, by the generative AI system, a summary of the SQL result prior to transmitting the response to the client device, the response being based on the summary of the SQL result(Chandrasekarasatry teaches SQL result(s) reported back to the user as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models, i.e. “generating, by the generative AI system, a summary of the SQL result prior to transmitting the response to the client device, the response being based on the summary of the SQL result”
see [0050] The SQL result(s) 110 can be provided back to the user 101 by the NL2SQL tool 104. In some instances, the SQL result(s) 110 are reported back to the user 101 as raw output. In other instances, the SQL result(s) 110 are reported back to the user 101 as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models in response to the natural language utterance 102. In other instances, the SQL result(s) 110 are reported back to the user 101 as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models and/or with a visualization (e.g., a bar chart, pie chart, table, or the like) generated by one or more generative artificial intelligence models and/or analytic subsystems in response to the natural language utterance 102. The user 101 may receive the SQL result(s) 110 through the client device(s) 103. Additionally or alternatively, the NL2SQL tool 104 may provide the SQL query 106 to the user(s) via some other means such as an email communication, SMS message, or other type of notification receivable on one or more other computing devices ).
As to claim 5, Chandrasekarasatry as modified discloses the method of claim 1, wherein the natural language input comprises a verbal input, the method further comprising: storing the verbal input as an audio file in the topic; and converting, by a text/audio converter, the audio file into the text request
(Chandrasekarasatry teaches natural language utterance can be in speech form, which may be converted to text form and provided to the NL2SQL tool see [0048] The client devices(s) 103 can be configured to communicate with the NL2SQL tool 104, provide the natural language utterance 102 to the NL2SQL tool 104 and receive outputs from the NL2SQL tool 104. In some implementations, the natural language utterance 102 can be in speech form, which may be converted to text form and provided to the NL2SQL tool 104. As an example, a natural
language utterance 102 such as 102a "Show me all the students who got an A in math" can be spoken by the user 101 and the NL2SQL tool 104 may be configured as a standalone or via a plug-in, or make use of some other audio-to-text translator, configured to translate the audio into
text for further processing. See also [0046] For example, an end user may send one or more messages to the agent system in order to achieve a desired goal. A message may include certain content, such as natural language text, audio, image, video, or other method of conveying a message. In some embodiments, the agent system may convert the content into a standardized logical form (e.g., a SQL query).;
see also [0207] subsystem 1118 may also provide a repository for storing data used in accordance with the present disclosure.; and [0107] storage layer for vector embeddings and the similarity search index. The vector embeddings are numeric representations of data ( e.g., text, images, or audio such as the NL utterances in the <text, sql>pairs) that encode their semantic or contextual meaning in a continuous, high-dimensional space. These embeddings
are stored in the database, often alongside metadata for contextual reference).
As to claim 6, Chandrasekarasatry as modified discloses the method of claim 1, wherein the natural language input comprises the text request
(Chandrasekarasatry [0048] As illustrated in FIG. 1, a user 101 provides a user input to the NL2SQL tool 104. The user input can be or can include a natural language utterance 102. The natural language utterance can be in text form, such as when the user types a sentence, a question, a text fragment, or phrase and provides it as an input to the NL2SQL tool 104 via client device(s) 103. The client devices(s) 103 can be configured to communicate with the NL2SQL tool 104, provide the natural language utterance 102 to the NL2SQL tool 104 and
receive outputs from the NL2SQL tool 104).
As to claim 7, Chandrasekarasatry as modified discloses the method of claim 1, wherein:
the generative AI system comprises an external AI system; and
(Chandrasekarasatry teaches various generative Ais external to the client see
Fig. 3 item 350: “Tool Routing LLM Module”
see also [0062] The agent core 308 may access a tool routing LLM module 350 in
order to identify, select, utilize, and/or train one or more LLM(s) that may suitably apply to the utterance received from the client device(s) 303.; see also [0065] To do this, the NL2SQL tool
310 may access one or more generative artificial intelligence models such as LLMs (e.g., SQL LLM 375) that may have been trained on generating SQL queries.)
the transmitting of the prompt to the generative AI system comprises making a direct call by the processing engine to the generative AI system
(Chandrasekarasatry [0008] transmitting the prompt to a first pretrained generative artificial intelligence model).
As to claim 8, Mathew as modified discloses the method of claim 1, wherein the prompt describes fields in tables,
(Mathew teaches generating a prompt that identifies the subset of fields [0045] In block 106, the computer system determines a context based at least in part on content of the natural language request. In block 108, the computer system selects, from a plurality of fields available, a subset of fields that are associated with the particular role and/or with the context. In block 110, the computer system generates a prompt that identifies the subset of fields and requests a
query executable to answer the natural language request.;
see also [0062] In various embodiments, the role of the user may be used to filter, constrain, or select which fields, corresponding values, or other information is presented in the prompt. For example, prompt templates may be selected according to the corresponding user roles, and, even within the selected prompt template, selected fields may be customized based on various roles of varying users executing queries that use the selected prompt template.).
includes business descriptions for each field, (Mathew teaches domain specific knowledge/term definitions, i.e. business descriptions
see [0162] In various embodiments, the resource management system includes, in the prompt, domain-specific knowledge that extends beyond table names and resource types to
include term definitions, interactions or relationships between variables and/or calculations based on variables, data constraints or expectations for default values in different scenarios, and/or any other information not inherent to the LLM or to the schema itself, such as the types of information provided in the examples. This additional domain-specific knowledge improves the quality of the response from the LLM.;
see also [0068] Current default views, objects, and other metadata about the user session may also be included in the prompt as contextual information to guide the LLM to generate a result
that is more applicable to the user's intent of the user request. For example, default business units, ledgers, or other information most frequently accessed by a user may be passed in as default in case the user request refers to an object without naming which object is intended to be accessed or used.)
and Chandrasekarasatry as modified discloses:
provides an example of an expected SQL query, and specifies an expected result
(Chandrasekarasatry [0039] The one or more memory banks include various candidate example <NL query, sql, schema> tuples of the form shown Table 2 that can be used for in-context learning.)
As to claim 10, Chandrasekarasatry as modified discloses the method of claim 1, wherein generating the prompt comprises tailoring the prompt based on the SQL database being targeted
(Chandrasekarasatry teaches improving chatbot responses when the answer can be
found in different databases or tables with different schemas/searching for schemas/context examples based on natural language queries
see [0033] The converted SQL could also enable digital assistants such as chatbots and
others to improve their responses when the answer can be found in different databases or tables with different schemas.
See also [0035] the generative artificial intelligence model is also provided with a database or system that provides the context for answering the NL queries. For example, given a Sales-database and the NL query such as "What is the total advertising expenditure in 2024", the task
of the generative artificial intelligence model is to automatically generate the SQL statement of the form "select sum (amount) from expenditure where year=2024 and expense type='advertising"'. A pre-trained generative artificial intelligence model such as a pre-trained LLM, further fine-tuned on Text-to-SQL pairs is fed with a suitable prompt that contains the NL query to generate the desired SQL statement.;
see also [0039] The one or more memory banks include various candidate example <NL query, sql, schema> tuples of the form shown Table 2 that can be used for in-context learning.;
see also [0013] In some embodiments, the computer-implemented method further includes: comparing the database schema to the illustrative database schemas of the in-context examples; determining, based on the comparing, the database schema is not the same as any of the illustrative database schemas; responsive to determining the database schema is not the
same as any of the illustrative database schemas, transmitting the natural language utterance to a second pretrained generative artificial intelligence model ; and receiving, from the second pretrained generative artificial intelligence model, a natural language text pattern based at least in part on the natural language utterance, wherein: the natural language text pattern comprises a portion of the natural language utterance, each of the in-context examples comprises an illustrative natural language utterance, an illustrative natural language text pattern corresponding to the illustrative natural language utterance, an illustrative logical form corresponding to the illustrative natural language utterance, and an illustrative database schema, the memory bank is searched for the one or more in-context examples that are relevant to the key using the natural language text pattern as the key;
see also [0041] In an exemplary embodiment, a method associated with the N-shot ICL approach comprises acquiring a natural language utterance and a database schema, and searching, using at least a portion of the natural language utterance as a key, a memory bank for one or more in-context examples that are relevant to the key. The memory bank comprises
in-context examples, and each of the in-context examples comprises an illustrative natural language utterance, an illustrative logical form corresponding to the illustrative
natural language utterance, and an illustrative database schema. The method further comprises generating a prompt comprising the natural language query, the database schema, and the one or more in-context examples, transmitting the prompt to a pretrained generative artificial intelligence model, receiving, from the first pretrained generative artificial intelligence model, a logical form corresponding to the natural language utterance based at least in part on the
prompt, executing the logical form on a database to obtain a query result, and providing the query result to a user.).
As to claim 11, Chandrasekarasatry as modified discloses the method of claim 1, further comprising:
performing A/B testing of different prompts by replaying events in the real-time streaming platform to improve accuracy and relevance of SQL queries generated by the generative AI system.
(Chandrasekarasatry teaches ground truth comparison testing i.e. A/B testing see [0089-0090]
[0089] The prompt example can then be sent to the LLM model to generate the SQL query during training and testing phases. The gold (ground truth) SQL Query: "SELECT
Tl .name FROM employee AS Tl JOIN certificate AS T2 ON Tl.eid=T2.eid JOIN aircraft AS T3 ON T2.aid=T3.aid WHERE T3.distance >5000 AND Tl.salary >100000 GROUP BY Tl .eid ORDER BY count (*) DESC LIMIT 1" is used to evaluate the generated SQL query using a loss
function such as cross-entropy loss (e.g., using cross-entropy loss module 402) in training and a performance metric such as execution match in testing. For execution match, both gold and generated SQL queries are executed on the database using the SQL engine. Their result sets are compared to check if they are matched. [0090] The training and testing flows start at either a
training schemas and NL question versus (vs) gold SQL query pairs block 424 or a testing schemas and NL question versus gold SQL query pairs block 426, respectively, where
training and testing data is collected (e.g., acquired or accessed).).
As to claim 12, Chandrasekarasatry as modified discloses the method of claim 1, further comprising:
replaying, by the real-time streaming platform, a previously executed SQL query to
obtain updated SQL results based on newly received data, wherein the replaying is
performed at a predetermined later time or at a regular interval
(Chandrasekarasatry teaches period updating see [0104] Therefore, maintaining a machine learning model in a production environment often involves setting up mechanisms for performance monitoring, regular evaluations against new test data, and potentially periodic updates and retraining of the model to ensure it remains effective and accurate in making predictions.)
As to claim 13, Mathew as modified discloses the method of claim 1, wherein each of the receiving, storing, generating, triggering, and transmitting is a microservice
(Mathew [0018] Cloud services, microservices, or other machine hosted services may be offered that perform part or all of one or more methods disclosed herein.).
As to claim 14, Chandrasekarasatry as modified discloses the method of claim 1, wherein transmitting the response comprises generating and transmitting a user interface or dashboard that displays the SQL result or a summary of the SQL result
(Chandrasekarasatry teaches SQL result(s) reported back to the user as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models, i.e. “generating, by the generative AI system, a summary of the SQL result prior to transmitting the response to the client device, the response being based on the summary of the SQL result”
see [0050] The SQL result(s) 110 can be provided back to the user 101 by the NL2SQL tool 104. In some instances, the SQL result(s) 110 are reported back to the user 101 as raw output. In other instances, the SQL result(s) 110 are reported back to the user 101 as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models in response to the natural language utterance 102. In other instances, the SQL result(s) 110 are reported back to the user 101 as part of a natural language response (e.g., a summary) generated by the one or more generative artificial intelligence models and/or with a visualization (e.g., a bar chart, pie chart, table, or the like) generated by one or more generative artificial intelligence models and/or analytic subsystems in response to the natural language utterance 102. The user 101 may receive the SQL result(s) 110 through the client device(s) 103. Additionally or alternatively, the NL2SQL tool 104 may provide the SQL query 106 to the user(s) via some other means such as an email communication, SMS message, or other type of notification receivable on one or more other computing devices;
See also [0062] analytical use cases using unique software packages (e.g., Oracle™ Analytics Cloud (OAC), Tableau™, etc.), a single analytical dashboard may generate multiple SQL queries using output from previous inputs ( e.g., by way of Chum analysis, Funnel analysis, cohort analysis, etc.). The agent core 308 may access a tool routing LLM module 350 in
order to identify, select, utilize, and/or train one or more LLM(s) that may suitably apply to the utterance received from the client device(s) 303.).
As to claim 15, Chandrasekarasatry as modified discloses the method of claim 1, further comprising:
continuously training a prompt model within a prompt component of the processing
engine based in part on replayed events to improve prompt accuracy and relevance
(Chandrasekarasatry teaches period updating/retraining see [0104] Therefore, maintaining a machine learning model in a production environment often involves setting up mechanisms for performance monitoring, regular evaluations against new test data, and potentially periodic updates and retraining of the model to ensure it remains effective and accurate in making predictions.;
See also [0104] To manage and maintain its performance, a deployed model such as the NL2SQL model may be continuously monitored to ensure it performs as expected over
time. This involves tracking the model's inference accuracy, response times, and other operational metrics. Additionally, the model may require retraining or updates based on new
data or changing conditions in the environment it is applied in. This can be useful because machine learning models can drift over time due to changes in the underlying data).
Referring to claim 17, this dependent claim recites similar limitations as claim 11;
therefore, the arguments above regarding claim 11 are also applicable to claim 17.
Referring to claim 18, this dependent claim recites similar limitations as claim 12;
therefore, the arguments above regarding claim 12 are also applicable to claim 18.
Referring to claim 19, this dependent claim recites similar limitations as claim 15;
therefore, the arguments above regarding claim 15 are also applicable to claim 19.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mathew et al., US Pub. No. 2026/0072902, in view of Chandrasekarasatry et al., US pub. No. 2026/0072909 A1, in view of Kamble et al., US Pub. No. 2025/0370996 A1.
As to claim 3, Chandrasekarasatry as modified discloses the method of claim 2, further comprising:
storing the summary of the SQL result in the topic; and
(Chandrasekarasatry teaches results in a conversation that may be stored in a chat history, i.e. storing the summary see [0147] At step 635, the query result is provided to a user. For example, the execution engine can obtain the query result from the database and forward said query result to the user. Alternatively, the execution engine can obtain the query result from the database and forward said query result to the SQL agent. The SQL agent may then forward said query result to the user or further process the query result and provide the query result as part of a natural language conversation with the user ( e.g., incorporate the query result into a conversation with the user and/or generate analytics or visuals concerning the query result and provide said analytics, visual, query result, or any combination thereof to the user as part of the conversation).;
See also [0053] The memory store 210 can store a chat history for the user 204 and contextual information related to the user 204, the chat 206, and/or other pieces of information relevant to the NL2SQL operations such as in-context examples, APis, external knowledge, and
the like.; See also [0056] In some examples, context may include contextual information related to the user 204 and/or chat 206 history and may be retrieved from the memory store 210 by the routing model 208).
Mathew/ Chandrasekarasatry do not disclose:
converting the summary of the SQL result into an audio file, wherein the response
comprises the audio file of the summary of the SQL result;
However, Kamble discloses:
converting the summary of the SQL result into an audio file, wherein the response
comprises the audio file of the summary of the SQL result
(Kamble teaches output natural language response (from an NL SQL query) can be voice see [0131] At block 780, the computing system outputs the response to the client device. For example, the response may be returned to the client device via the web-based API to cause the response to appear in the chat-like interface of example GUI 600. The chat-like interface may invoke a user experience including elements indicative of a dialog with the LLM. Other means for displaying the output response can be used including voice, email, notifications, and so on.;
see also [0025] The non-technical stakeholder's natural language question from the example above about customers' experience with the network in the New York area can thus be
answered in natural language. A complete example exchange may be begin with the stakeholder asking, "Which customers in the New York area have been having network problems recently?" The computing system can respond, using the techniques above, "Customer A and Customer B, both in the New York area, experienced low network latency and throughput in the past month."
see also [0083] The data querying subsystem 420 includes a number of components that can be used by the LLM 455 to generate the response 465.;
see also [0018] In the example method, a computing system accesses a database with a number of collections, such as relational database tables. The database may be, for instance,
a data warehouse based on a relational database with a number of tables that can be queried using SQL. The computing system then generates one or more documents based on one of the tables in the database. The generated documents may be a representation of the table contents in natural language form.;
see also [0072] In such examples, the chat and video conference provider 210 may allow a user to create one or more chat channels where the user may exchange messages with other users (e.g., members) that have access to the chat channel(s). The messages may include text, image files, video files, or other files.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply voice output for SQL queries as taught by Kamble, to the system of Mathew/ Chandrasekarasatry, since it was known in the art that search/query systems provide an LLM
that can be used to respond to a query where the query response can be in chat application as a dialog response to query where the user experience may include elements indicative of a "conversation" with the underlying data querying subsystem and where a chat-like interface or GUI implementations may involve a voice command interface (Kamble [0112-0113]).
As to claim 4, Kamble as modified discloses the method of claim 1, further comprising:
converting the response from text to audio prior to transmitting the response to the
client device
(Kamble teaches output natural language response (from an NL SQL query) can be voice, i.e. “converting the response from text to audio prior to transmitting the response” see [0131] At block 780, the computing system outputs the response to the client device. For example, the response may be returned to the client device via the web-based API to cause the response to appear in the chat-like interface of example GUI 600. The chat-like interface may invoke a user experience including elements indicative of a dialog with the LLM. Other means for displaying the output response can be used including voice, email, notifications, and so on.;).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mathew et al., US Pub. No. 2026/0072902, in view of Chandrasekarasatry et al., US pub. No. 2026/0072909 A1, in view of Kshirsagar et al., US Pub. No. 2025/0272305 A1.
As to claim 9, Mathew/ Chandrasekarasatry do not disclose:
generating the prompt comprises tailoring the prompt based on a location associated with the client device;
However, Kshirsagar discloses the method of claim 1, wherein generating the prompt comprises tailoring the prompt based on a location associated with the client device
(Kshirsagar teaches using context data including natural language outputs/situational data such as a user location, and output generated in response by a conversational chat assistant may be identified for inclusion in the prompt, i.e. “generating the prompt comprises tailoring the prompt based on a location associated with the client device”
see [0109-0110] [0109] A context for an interaction that includes the natural language user input is determined at 604. In some embodiments, the context may include any or all of a variety
of information. For example, the context may include one or more identifiers for a user account, an organization account, or any other account within the computing services environment
150. As another example, the context may include one or more previous natural language inputs or other inputs provided by the user. As another example, the context may include one or more natural language outputs or other operations performed by the computing services environment 150 in the course of the interaction. As yet another example, the context may include metadata characterizing the end user, the organization with which the user is interacting, and/or other suitable characteristics. As still another example, the context may include situational data such as a user location, a database record being accessed, a date and
time, the weather in a particular location, or any other type of information potentially relevant to the interaction.; [0110] In some embodiments, information included and/or determined based on the context determined at 604 may be used to guide the determination of the plan.;
see also Fig. 6 item 600 “Generative Language Model Plan Determination Method” and 620 “identify plan for execution”;
see also [0125] A determination is made at 616 as to whether to select additional input. In some embodiments, upon determining that the plan identification prompt completion
includes information sufficient for identifying a plan for execution, the plan may be identified at 620. For example, the plan identification prompt completion may include a plan for execution. For instance, the plan identification prompt completion may include a set of identifiers corresponding to a selected one or more actions of the subset of actions determined at 610.;
see also [0138] some or all of natural language input provided by an end user and/or natural language output generated in response by a conversational chat assistant may be identified for inclusion in the prompt. In this way, the generative language model may be provided with the natural language context associated with the request to generate novel natural language.;
see also [0151] As yet another example, a conversational chat assistant prompt template
may be configured and executed in the context of a messaging interaction.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply situational data such as a user location to prompt generation as taught by Kshirsagar, to the system of Mathew/ Chandrasekarasatry, since it was known in the art that search/query systems provide that the user input may be provided via natural language where in such a situation, the user's intent may be less clear and may be determined based on one
or more interactions with a generative language model where natural language text included in the input may be used to determine an intent identification input prompt and the intent identification input prompt may include the input text, a natural language request executable by a generative language model, and/or other types of information where for instance, the intent identification input prompt may include a description of actions capable of being performed via the conversational chat assistant where the generative language model may then generate novel text that includes one or more identifiers corresponding with the actions to be performed
based an analysis of the intent in the input text by the generative language model. (Kshirsagar [0098]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yakovlev et al. , US Pub. No.: US 2025/0284688 A1, teaches a database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to
highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from
ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify
relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description;
El Hattami et al., US Patent No.: 12,254,014 B1, teaches a method includes obtaining a topic of a document and an information source associated with the document. The method also includes generating, using a generative machine learning (ML) model, the document based on the topic and the information source. The method additionally includes identifying a query that is associated with the topic and determining, using a validation model, that the query is not
addressed by the document. The method yet additionally includes, based on determining that the query is not addressed by the document, generating an updated document using the generative ML model based on the topic, the information source, and the query. The method further includes determining, using the validation model, that the query is addressed by the updated document, and outputting the updated document.
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/Evan Aspinwall/Primary Examiner, Art Unit 2156
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