Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to Amendment filed on April 13, 2026 and Supplemental Amendment filed on April 15, 2026. Claims 1, 4-7, 9, 13-15, 17-23, and 25-32 are pending. Claims 1, 6, 7, 9, 14, 15, 17-20, and 22 are amended. Claims 2, 3, 8, 10, 11, 12, 16, and 24 are cancelled. Claims 25-32 are newly added.
Response to Arguments
Referring to the 35 USC 101 rejection of the pending claims, as amended, Applicant’s arguments have been considered but are not found persuasive.
Applicant argues that the claims recite improvements in the functionality of generative AI models through the use of insight archives as providing for “seamless integration of insights from multiple generative AI model sessions which may facilitate a comprehensive understanding of evolving topics” and “significantly enhance the speed of generating responses during user sessions”. However Examiner respectfully disagrees. The recitation of the storing of insight information within insight archives is considered insignificant extra solution activity that is merely storing and retrieving data. Furthermore, the claims do not recite any details that would show how the storage of integrated insights achieves the seamlessness and comprehensive understanding as well as enhancing the speed of responses as stated by Applicant, other than by accessing and retrieving stored indexed data. As such, Examiner maintains that the claims as amended, do not recite an improvement to the technology and do not integrate the judicial; exception into a practical application.
Applicant furthermore argues with respect to claim 20, that the converting of the set of information into the first format limitation is a practical application ensuring that the insight data is interpretable and can be accessed to review the data and documents used by the generative AI model to generate an insight. However this argument is not found to be persuasive. The conversion of data into a specific format is a mental step that can be archived by the human mind or using pen and paper. For example, one can change the format of data to a specific format using pen and paper, for example to a specific font or by merely inscribing data seen on a computer to pen and paper. As such, this is a mental step and is not patent eligible.
For at least the reasons stated above the 35 USC 101 rejection of the pending claims is maintained and further in view of the new grounds of rejection addressed below.
Applicant’s arguments with respect to the pending claims, as amended, have been considered but are moot in view of the new grounds of rejection.
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, 4-7, 9, 13-15, 17-23, and 25-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Referring to claim 1 recites:
receiving a prompt by a generative artificial intelligence (AI) model during a session associated with the generative AI model;
obtaining, by the generative AI model in response to receiving the prompt, a first set of information from a first insight archive, the first insight archive including data and a document associated with a first insight, the first set of information from a prior session associated with the generative AI model and stored in the first insight archive;
generating, by the generative AI model, a second insight from at least the first set of information obtained from the first insight archive;
providing as an output, by the generative AI model, the second insight as a response to the prompt;
extracting, by the generative AI model, a second set of information used by the generative AI model to generate the second insight; and
storing, by the generative AI model, the second set of information in a second insight archive, the second set of information including cross-reference information to the first insight archive.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 1 recites limitations that are abstract ideas.
The limitation “generating a second insight from at least the first set of information obtained from the first insight archive” is a mental step. One can mentally determine an insight or analysis given a set of data. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “extracting a second set of information to generate the second insight” is also a mental step. A user can mentally select data from a set of data to generate another insight or analysis of the data.
Step 2A prong 2:
Claim 1 recites the limitations “receiving a prompt during a session associated with the generative AI model” and “obtaining in response to receiving the prompt, a first set of information from a first insight archive, the first insight archive including data and a document associated with a first insight, the first set of information from a prior session associated with the generative AI model and stored in the first insight archive”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 1 recites “providing as an output the second insight as a response to the prompt”. This is an additional element and is mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for output as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 1 recites “storing the second set of information in a second insight archive, the second set of information including cross-reference information to the first insight archive”. This is also an additional element and is using of a computer or other machinery in its ordinary capacity for tasks such as storing or simply adding computer components after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore, Claim 1 recites the following additional elements “generative AI model”, note that these recited additional elements are a high-level recitation of generic computer software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “receiving”, "obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Furthermore, the “storing” limitation identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole do not change this conclusion and the claim is ineligible.
Claim 9 recites:
receiving a first prompt by a generative artificial intelligence (AI) model during a first session associated with the generative AI model;
extracting, by the generative AI model in response to receiving the first prompt, a first set of information used by the generative AI model to generate a first insight;
storing, by the generative AI model, the first set of information in a first insight archive associated with the first insight;
receiving a second prompt by the generative AI model during a second session associated with the generative AI model;
obtaining, by the generative AI model in response to receiving the second prompt, the first set of information from the first insight archive;
generating, by the generative AI model, a second insight from at least in part the first set of information obtained from the first insight archive;
providing as an output, by the generative AI model, a response to the second prompt;
extracting, by the generative AI model, a second set of information used by the generative AI model to generate the second insight; and
storing, by the generative AI model, the second set of information in a second insight archive, the second set of information including cross-reference information to the first insight archive.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 9 recites limitations that are abstract ideas.
The limitations “extracting, in response to receiving the first prompt, a first set of information to generate a first insight”, “generating, a second insight from at least in part the first set of information obtained from the first insight archive” and “extracting a second set of information to generate the second insight” are mental steps. One can mentally select data from a set of data to determine an insight or analysis given the set of data. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2:
Claim 9 recites the limitations
“receiving a first prompt during a first session associated with the generative AI model”, “receiving a second prompt during a second session associated with the generative AI model”, “obtaining, in response to receiving the second prompt, the first set of information from the first insight archive”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 9 recites “providing as an output a response to the second prompt”. This is an additional element and is mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for output as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 9 recites “storing the second set of information in a second insight archive, the second set of information including cross-reference information to the first insight archive”. This is also an additional element and is using of a computer or other machinery in its ordinary capacity for tasks such as storing or simply adding computer components after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore, Claim 9 recites the following additional elements “generative AI model”, note that these recited additional elements are a high-level recitation of generic computer software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “receiving”, "obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Furthermore, the “storing” limitation identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole do not change this conclusion and the claim is ineligible.
Claim 14 recites:
receive during a session a prompt by a generative Al model;
obtain, by the generative AI model, a first set of information from a plurality of insight archives in response to the prompt, each of the plurality of insight archives including an insight and data used by the generative AI model to generate the insight;
generate, by the generative AI model, a first insight from at least the first set of information obtained from the plurality of insight archives, wherein the first insight is not included in the plurality of insight archives;
provide the first insight as an output, by the generative AI model;
extract, by the generative AI model, a second set of information used by the generative AI model to generate the first insight, the second set of information including the first insight and cross-reference information to the plurality of insight archives;
create, by the generative AI model, a first insight archive; and
store, by the generative AI model, the second set of information in the first insight archive.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 14 recites limitations that are abstract ideas.
The limitations “generate a first insight from at least the first set of information obtained from the plurality of insight archives, wherein the first insight is not included in the plurality of insight archives” and “extract a second set of information to generate the first insight, the second set of information including the first insight and cross-reference information to the plurality of insight archives” are mental steps. One can mentally select data from a set of data to determine an insight or analysis given the set of data. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2:
Claim 14 recites the limitations “receive during a session a prompt” and “obtain a first set of information from a plurality of insight archives in response to the prompt, each of the plurality of insight archives including an insight and data to generate the insight”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 14 recites “provide the first insight as an output”. This is an additional element and is mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for output as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 14 recites “create a first insight archive” and “store the second set of information in the first insight archive”. This is also an additional element and is using of a computer or other machinery in its ordinary capacity for tasks such as storing or simply adding computer components after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore, Claim 14 recites the following additional elements “computer system”, “a processor”, “memory including instructions”, and “generative AI model”, note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “receiving”, "obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Furthermore, the “creating” and “storing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole do not change this conclusion and the claim is ineligible.
Claim 20 recites:
receiving, by a computer, a request to provide a first set of information in a first format;
obtaining, by the computer in response to receiving the request, the first set of information from a first insight archive, the first set of information having been extracted from a prior session with a generative artificial intelligence (AI) model and stored in the first insight archive, the first set of information including cross-reference information to a second set of information stored within a second insight archive that was used by the generative AI model to generate an insight stored in the second insight archive;
converting, by the generative AI model, the first set of information into the first format; and
providing as an output, by the generative AI model, the first set of information in the first format.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 20 recites limitations that are abstract ideas.
The limitation “converting the first set of information into the first format” is a mental step. One can change the format of data to a specific format using pen and paper, for example to a specific font. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2:
Claim 20 recites the limitations
“receiving a request to provide a first set of information in a first format” and “obtaining in response to receiving the request, the first set of information from a first insight archive, the first set of information having been extracted from a prior session with a generative artificial intelligence (AI) model and stored in the first insight archive, the first set of information including cross-reference information to a second set of information stored within a second insight archive that was used by the generative AI model to generate an insight stored in the second insight archive”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 20 recites “providing as an output the first set of information in the first format”. This is an additional element and is mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for display as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claim 20 recites the following additional elements “computer”, and “generative AI model”, note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “receiving”, "obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole do not change this conclusion and the claim is ineligible.
Claims 4-7, 15, 17-19, 21-23 and 25-32 depend from claims 1, 9, 14 and 20 and thus include all the limitations of claims 1, 9, 14 and 20, therefore claims 4-7, 15, 17-19, 21-23 and 25-32 recite the same abstract ideas of "mental processes".
Claims 4-7, 15, 17-19, 21-23 and 25-32 furthermore recite:
(claim 4) wherein the second set of information includes the second insight and information associated therewith;
(claims 5,13) wherein the response to the prompt includes the second insight;
(claim 6) wherein the prompt is one of a plurality of prompts received by the generative AI model during the session and the response is one of a plurality of responses output by the generative AI model during the session, and wherein the second set of information includes the plurality of prompts and the plurality of responses;
(claim 7) storing data associated with the second set of information in a cache; obtaining the data from the cache in response to a second prompt; and providing as an output a second response to the second prompt the second response including the data obtained from the cache;
(claim 15) determining that the plurality of insights does not include the first insight;
(claim 17) wherein the second set of information includes the first insight and information associated therewith;
(claim 18) wherein the plurality of insight archives include documents and data associated with the plurality of insights;
(claim 19) wherein the documents and data are associated with prior sessions associated with the generative AI model;
(claim 21) wherein the request includes an indication of the first format;
(claim 22) wherein the first format of the response associated with one or more of a presentation slide, a document, or a spreadsheet;
(claim 23) wherein the request is received in a second session associated with the generative AI model;
(claim 25) wherein storing the second set of information in the second insight archive includes embedding the second insight in metadata of the second insight archive.
(claims 26,32) wherein the first insight archive includes a plurality of revisions of the first insight, wherein each revision of the first insight includes one or more of a revision ID, a timestamp indicating when the revision was made, or revision information associated with the revision.
(claim 27) wherein storing the second set of information in the second insight archive includes storing a signature file.
(claim 28) wherein the signature file includes one or more of a hash value, an identifier of an algorithm used to generate the signature file, and/or a timestamp associated with the signature file.
(claim 29) wherein storing the first set of information in a first insight archive associated with the first insight includes embedding the first insight in metadata of the first
insight archive, wherein obtaining, by the computer in response to receiving the second prompt, the first set of information from the first insight archive includes accessing, by the computer, the metadata of the first insight archive and determining whether the first insight archive is responsive to the second prompt based on metadata.
(claim 30) wherein the first insight is embedded in metadata of the first insight archive.
(claim 31) wherein providing as the output the first set of information in the first format includes outputting the metadata.
Step 1: Claims 4-7, 15, 17-19, 21-23 and 25-32 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 4-7, 15, 17-19, 21-23 and 25-32 recite limitations that are abstract ideas because they depend on claims 1, 9, 14 and 20 which recite mental steps.
Claims 4, 6 and 17 recite: (claim 4) wherein the second set of information includes the second insight and information associated therewith; (claim 6) wherein the second set of information includes the plurality of prompts and the plurality of responses; (claim 17) wherein the second set of information includes the first insight and information associated therewith. These limitations further define the second set of information in the extracting step in claims 1 and 14 which is a mental step and therefore these limitations are also mental steps.
Claim 15 recites “determining that the plurality of insights does not include the first insight”. This is a mental step that can be performed in the human mind. A user can look at a set of data to determine that it does not include a specific item.
Claim 29 recites “determining whether the first insight archive is responsive to the second prompt based on metadata”. This is a mental step. A person can look at a file to determine if it contains relevant to what the user is seeking.
Step 2A prong 2:
Claims 5, 6, 13, and 31 recite: (claims 5,13) wherein the response to the prompt includes the second insight; (claim 6) wherein the prompt is one of a plurality of prompts received by the generative AI model during the session and the response is one of a plurality of responses output by the generative AI model during the session; (claim 31) wherein providing as the output the first set of information in the first format includes outputting the metadata. These limitations further define the receiving of the prompt and providing as output steps in claims 1, 14 and 20 which are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for output as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 21 and 23 recite: (claim 21) wherein the request includes an indication of the first format; (claim 23) wherein the request is received in a second session associated with the generative AI model. These limitations further define the receiving of the request step in claim 20 which are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 18, 19, 26, 30 and 32 recite: (claim 18) wherein the plurality of insight archives include documents and data associated with the plurality of insights; (claim 19) wherein the documents and data are associated with prior sessions associated with the generative AI model; (claims 26,32) wherein the first insight archive includes a plurality of revisions of the first insight, wherein each revision of the first insight includes one or more of a revision ID, a timestamp indicating when the revision was made, or revision information associated with the revision; (claim 30) wherein the first insight is embedded in metadata of the first insight archive.
These limitations further define the obtaining steps in claims 1, 14 and 20 which are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 22 recites: (claim 22) wherein the first format of the response associated with one or more of a presentation slide, a document, or a spreadsheet. These limitations further define the receiving step in claim 20 which are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 29 recites: “wherein obtaining, by the computer in response to receiving the second prompt, the first set of information from the first insight archive includes accessing, by the computer, the metadata of the first insight archive”. These limitations further define the obtaining step in claim 9 which are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 7, 25 recite: (claim 7) storing data associated with the second set of information in a cache; obtaining the data from the cache in response to a second prompt; and providing as an output a second response to the second prompt the second response including the data obtained from the cache; (claim 25) wherein storing the second set of information in the second insight archive includes embedding the second insight in metadata of the second insight archive; (claim 27) wherein storing the second set of information in the second insight archive includes storing a signature file; (claim 28) wherein the signature file includes one or more of a hash value, an identifier of an algorithm used to generate the signature file, and/or a timestamp associated with the signature file; (claim 29) wherein storing the first set of information in a first insight archive associated with the first insight includes embedding the first insight in metadata of the first insight archive.
The obtaining and providing limitations are insignificant extra solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for output as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, the storing and embedding steps are using of a computer or other machinery in its ordinary capacity for tasks such as storing or simply adding computer components after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Step 2B:
With respect to the "obtaining” and “providing” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Furthermore, the “receiving”, “storing” and “embedding” limitations identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-7, 9, 13, 20, 21, 23, 25, and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0148477 by Mehrotra et al (hereafter Mehrotra), and further in view of US 2024/0378208 by Zhou et al (hereafter Zhou).
Referring to claim 1, Mehrotra discloses a method [para 7] comprising:
receiving a prompt by a generative artificial intelligence (AI) model during a session associated with the generative AI model [user submits initial natural language prompt (query) 306 for assistance with an alarm condition or performance issue which is used by the generative AI component 208, para 45-46; para 68, Fig 8a, element 806];
obtaining, by the generative AI model in response to receiving the prompt, a first set of information from a first insight archive, the first insight archive including data associated with a first insight [archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- including prompts submitted by a user during a support session, support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts, para 50; chat histories include alarm resolution notes, para 53, 67, 71], and a document associated with a first insight [industrial documentation data stored in document repository is used in conjunction with the archived chat history data as contextual data 316, para 51, 66, Fig 8a, element 802], the first set of information from a prior session associated with the generative AI model and stored in the first insight archive [archived chat history data stored in a chat history repository that is relevant to the queried alarm condition or performance issue is retrieved, para 70, Fig 8b, element 816; archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts, para 50; chat histories include alarm resolution notes, para 53, 67, 71];
generating, by the generative AI model, a second insight from at least the first set of information obtained from the first insight archive [wherein the initial prompt 306 is enhanced with relevant contextual data 316 including globally accessible documentation 314 and customer-specific documentation associated with the customer entity and chat history data 308, para 49, 54; Fig 8b, elements 814,818; industrial documentation data stored in document repository, para 66, Fig 8a, element 802];
providing as an output, by the generative AI model, the second insight as a response to the prompt [technical support response to the prompt is generated based on the user’s prompts, the subset of document data retrieved from document repository, and the subset of chat history data, para 70, Fig 8b, elements 814,818; technical support response to prompt is rendered, para 70, Fig 8b, element 820, para 54];
extracting, by the generative AI model, a second set of information used by the generative AI model to generate the second insight [user feedback provided, indicating a degree to which the model’s response 302 addressed the user’s issue, is stored by the generative AI component in association with the prompt 306 and corresponding responses 302, para 56, Fig 6; user feedback is used by generative AI model when formulating responses to similar prompts 306, para 50]; and
storing, by the generative AI model, the second set of information in a second insight archive [the prompt and technical support response is stored in the chat history repository, para 70; Fig 8b, element 822; archived chat histories 312 comprise the content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts; each chat history 312 also records user feedback or ratings to the system support response in association with the prompt 306 and corresponding responses 302, para 50, 53, 56].
Referring to claim 1, while Mehrotra discloses all of the above claimed subject matter and also discloses storing the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) and that the archived chat histories contain resolution notes [para 50, 53, 56, 70], it remains silent as to the second set of information including cross-reference information to the first insight archive.
Zhou teaches that an EQB 804 is a data source that relies upon the insight repository as a data source [para 33]- Examiner submits that the EQB and insight repository function as a combined data source. The EQB further comprises a query cache that links the insights (produced by data exploration process when data mining using the EQB and stored in the insight repository) to related queries using the “related query link” e.g. “query numbers or link” (e.g., “Query number-correlation index”) in the query cache [para 102, see query cache entries in Fig 9]. Examiner submits that the linking of the insights to related query entries in the query cache through the “related query link” e.g. “query numbers or link”/“Query number-correlation index” is the claimed cross-reference information to the first insight archive because it connects the insights in the repository with the related queries in the query cache.
Mehrotra and Zhou are analogous art because they are directed to the same field of endeavor- generation of insights from extracted data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage of the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) in Mehrotra to include the query link information that is connected to the insights in insight repository in Zhou because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the linking of correlated queries to execution plans of the query through the query cache in Zhou refines the archived chat histories of Mehrotra by providing an infrastructure to run multiple correlated queries in the dialogue-based user query system of Mehrotra [see Zhou, para 33; Mehrotra, dialogue based queries, para 46, 60, 70].
Referring to claim 9, Mehrotra discloses a method [para 7], comprising:
receiving a first prompt by a generative artificial intelligence (AI) model during a first session associated with the generative AI model and
extracting, by the generative AI model in response to receiving the first prompt, a first set of information used by the generative AI model to generate a first insight [when a prompt 306 is received from a user associated with a customer entity, the content of the prompt is analyzed and retrieves a subset of stored relevant documentation 314 and archived chat histories 312 related to the issue described by the prompt 306, para 51 and 53, see Fig 4; Examiner notes that the prompt here includes the prior prompts stored in the archived chat histories from prior sessions];
storing, by the generative AI model, the first set of information in a first insight archive associated with the first insight [chat histories 312 include prior technical support resolutions, para 44; each chat history of archived chat histories 312 pertains to (prior) prompts submitted by a user during (prior) support sessions, as well as the support guidance, information or resolution recommendations generated by the generative AI model 226 in response to these prompts 306, para 50];
receiving a second prompt by the generative AI model during a second session associated with the generative AI model [user submits natural language prompt (query) 306 for assistance with an alarm condition or performance issue which is used by the generative AI component 208, para 45-46; para 68, Fig 8a, element 806];
obtaining, by the generative AI model in response to receiving the second prompt, the first set of information from the first insight archive [archived chat history data stored in a chat history repository that is relevant to the queried alarm condition or performance issue is retrieved, para 70, Fig 8b, element 816; archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts, para 50; industrial documentation data stored in document repository is used in conjunction with the archived chat history data as contextual data 316, para 51, 66, Fig 8a, element 802; chat histories include alarm resolution notes, para 53, 67, 71];
generating, by the generative AI model, a second insight from at least in part the first set of information obtained from the first insight archive [wherein the initial prompt 306 is enhanced with relevant contextual data 316 including globally accessible documentation 314 and customer-specific documentation associated with the customer entity and chat history data 308, para 49, 54; Fig 8b, elements 814,818; industrial documentation data stored in document repository, para 66, Fig 8a, element 802];
providing as an output, by the generative AI model, a response to the second prompt [technical support response to the prompt is generated based on the user’s prompts, the subset of document data retrieved from document repository, and the subset of chat history data, para 70, Fig 8b, elements 814,818; technical support response to prompt is rendered, para 70, Fig 8b, element 814, 818; technical support response to prompt is rendered, para 70, Fig 8b, element 820, para 54];
extracting, by the generative AI model, a second set of information used by the generative AI model to generate the second insight [user feedback provided, indicating a degree to which the model’s response 302 addressed the user’s issue, is stored by the generative AI component in association with the prompt 306 and corresponding responses 302, para 56, Fig 6; user feedback is used by generative AI model when formulating responses to similar prompts 306, para 50]; and
storing, by the generative AI model, the second set of information in a second insight archive [the prompt and technical support response is stored in the chat history repository, para 70; Fig 8b, element 822; archived chat histories 312 comprise the content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts; each chat history 312 also records user feedback or ratings to the system support response in association with the prompt 306 and corresponding responses 302, para 50, 53, 56].
Referring to claim 9, while Mehrotra discloses all of the above claimed subject matter and also discloses storing the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) and that the archived chat histories contain resolution notes [para 50, 53, 56, 70], it remains silent as to the second set of information including cross-reference information to the first insight archive.
Zhou teaches that an EQB 804 is a data source that relies upon the insight repository as a data source [para 33]- Examiner submits that the EQB and insight repository function as a combined data source. The EQB further comprises a query cache that links the insights (produced by data exploration process when data mining using the EQB and stored in the insight repository) to related queries using the “related query link” e.g. “query numbers or link” (e.g., “Query number-correlation index”) in the query cache [para 102, see query cache entries in Fig 9]. Examiner submits that the linking of the insights to related query entries in the query cache through the “related query link” e.g. “query numbers or link”/“Query number-correlation index” is the claimed cross-reference information to the first insight archive because it connects the insights in the repository with the related queries in the query cache.
Mehrotra and Zhou are analogous art because they are directed to the same field of endeavor- generation of insights from extracted data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage of the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) in Mehrotra to include the query link information that is connected to the insights in insight repository in Zhou because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the linking of correlated queries to execution plans of the query through the query cache in Zhou refines the archived chat histories of Mehrotra by providing an infrastructure to run multiple correlated queries in the dialogue-based user query system of Mehrotra [see Zhou, para 33; Mehrotra, dialogue based queries, para 46, 60, 70].
Referring to claim 20, Mehrotra discloses a method [para 7], comprising:
receiving, by a computer, a request to provide a first set of information in a first format [user submits natural language prompt (query) 306 for assistance with an alarm condition or performance issue with information describing the type of desired support assistance/guidance e.g. information about a device or observed system behavior, or recommended preventative actions for mitigating future problems, para 45-46; para 68, Fig 8a, element 806];
obtaining, by the computer in response to receiving the request, the first set of information from a first insight archive, the first set of information having been extracted from a prior session with a generative artificial intelligence (AI) model and stored in the first insight archive [archived chat history data stored in a chat history repository that is relevant to the queried alarm condition or performance issue is retrieved, para 70, Fig 8b, element 816; archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model 208 in response to the prompts, para 50; chat histories include alarm resolution notes, para 53, 67, 71], the first set of information corresponding to a second set of information stored within a second insight archive that was used by the generative AI model to generate an insight stored in the second insight archive [wherein the prompt 306 is enhanced with relevant contextual data 316 including globally accessible documentation 314 and customer-specific documentation associated with the customer entity and chat history data 308, para 49, 54; Fig 8b, elements 814,818; user feedback provided (reads on: second set of information), indicating a degree to which the model’s response 302 addressed the user’s issue, is stored by the generative AI component in association with the prompt 306 and corresponding responses 302, para 56, Fig 6; user feedback is used by generative AI model when formulating responses to similar prompts 306, para 50];
converting, by the generative AI model, the first set of information into the first format; and providing as an output, by the generative AI model, the first set of information in the first format [wherein a natural language technical support response to the prompt is generated by the generative AI model based on the user’s prompts and the subsets of documentation data and chat history data, para 70, Fig 8b, elements 814,818; technical support response to prompt is rendered, para 70, Fig 8b, element 820, para 54; the format of the response is generated in any suitable format based on the nature of the prompt, e.g. natural language explanations of alarm conditions together with suggested actions or steps to correct the condition, para 70].
Referring to claim 20, while Mehrotra discloses all of the above claimed subject matter and also discloses storing the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) and that the archived chat histories contain resolution notes [para 50, 53, 56, 70], it remains silent as to the first and second sets of information being cross-referenced information.
Zhou teaches that an EQB 804 is a data source that relies upon the insight repository as a data source [para 33]- Examiner submits that the EQB and insight repository function as a combined data source. The EQB further comprises a query cache that links the insights (produced by data exploration process when data mining using the EQB and stored in the insight repository) to related queries using the “related query link” e.g. “query numbers or link” (e.g., “Query number-correlation index”) in the query cache [para 102, see query cache entries in Fig 9]. Examiner submits that the linking of the insights to related query entries in the query cache through the “related query link” e.g. “query numbers or link”/“Query number-correlation index” is the claimed cross-reference information to the first insight archive because it connects the insights in the repository with the related queries in the query cache.
Mehrotra and Zhou are analogous art because they are directed to the same field of endeavor- generation of insights from extracted data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage of the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) in Mehrotra to include the query link information that is connected to the insights in insight repository in Zhou because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the linking of correlated queries to execution plans of the query through the query cache in Zhou refines the archived chat histories of Mehrotra by providing an infrastructure to run multiple correlated queries in the dialogue-based user query system of Mehrotra [see Zhou, para 33; Mehrotra, dialogue based queries, para 46, 60, 70].
Referring to claim 4, Mehrotra/Zhou discloses wherein the second set of information includes the second insight and information associated therewith [Mehrotra, the user feedback corresponding to the technical support response, along with the technical support response and the prompt 306 are stored in the chat history database, para 56, Fig 6].
Referring to claims 5 and 13, Mehrotra/Zhou discloses wherein the response to the prompt includes the second insight [Mehrotra, technical support response to the prompt is based on the subset of chat history data, Fig 9b, element 818].
Referring to claim 6, Mehrotra/Zhou discloses wherein the prompt is one of a plurality of prompts received by the generative AI model during the session and the response is one of a plurality of responses output by the generative AI model during the session, and wherein the second set of information includes the plurality of prompts and the plurality of responses [Mehrotra, plurality of prompts and responses to prompts as part of a chat-based interaction, para 39 and 44; technical support responses are formulated based on analysis of the prompts, para 44; the prompt and the technical support response generated in response to the prompt are stored in association with one another in the chat history repository for use by the generative AI model in generating responses to subsequent similar prompts, para 70, Fig , element 822].
Referring to claim 7, Mehrotra/Zhou discloses storing data associated with the second set of information in a cache; obtaining the data from the cache in response to a second prompt; and providing as an output a second response to the second prompt the second response including the data obtained from the cache [Mehrotra, data associated with the technical support response including the user prompts and the user feedback is stored in the chat history database 312, Fig 8b, element 822 and corresponding portions of the specification; Zhou, one or more insights generated may be stored in relevant cache to be retrieved therefrom, para 4].
Referring to claim 21, Mehrotra/Zhou discloses wherein the request includes an indication of the first format [Mehrotra, prompt specifies type of desired support assistance/guidance e.g. information about a device or observed system behavior, or recommended preventative actions for mitigating future problems, para 45-46; para 68; the format of the response is generated in any suitable format based on the nature of the prompt, e.g. natural language explanations of alarm conditions together with suggested actions or steps to correct the condition, para 70].
Referring to claim 23, Mehrotra/Zhou discloses wherein the request is received in a second session associated with the generative AI model [Mehrotra, multiple chat sessions can be stored in the archived chat history data along with multiple prompts, para 67].
Referring to claim 25, Mehrotra/Zhou discloses that storing the second set of information in the second insight archive includes embedding the second insight in metadata of the second insight archive [Zhou, enterprise query base may be further configured to associate the generated one or more insights to the corresponding query instance received and cache the query instance received, with the one or more insights associated therewith, in at least one data source of the multiple data sources, to be retrieved therefrom, wherein the cached query instance is embedded with a link to a determined query context, para 7, 102, see Fig 9].
Referring to claim 29, Mehrotra/Zhou discloses that storing the first set of information in a first insight archive associated with the first insight includes embedding the first insight in metadata of the first insight archive [Zhou, enterprise query base may be further configured to associate the generated one or more insights to the corresponding query instance received and cache the query instance received, with the one or more insights associated therewith, in at least one data source of the multiple data sources, to be retrieved therefrom, wherein the cached query instance is embedded with a link to a determined query context, para 7, 102, see Fig 9], wherein obtaining, by the computer in response to receiving the second prompt, the first set of information from the first insight archive includes accessing, by the computer, the metadata of the first insight archive and determining whether the first insight archive is responsive to the second prompt based on metadata [Zhou, hash value generated for query instance is used to find a query cache that matches the hash value in order to generate a query plan and generate a response based on the query instance, para 110].
Referring to claim 30, Mehrotra/Zhou discloses that the first insight is embedded in metadata of the first insight archive [Zhou, enterprise query base may be further configured to associate the generated one or more insights to the corresponding query instance received and cache the query instance received, with the one or more insights associated therewith, in at least one data source of the multiple data sources, to be retrieved therefrom, wherein the cached query instance is embedded with a link to a determined query context, para 7, 102, see Fig 9].
Referring to claim 31, Mehrotra/Zhou discloses providing as the output the first set of information in the first format includes outputting the metadata [Zhou, response is generated based on the determined query cache linked to the query instance determined, para 110].
Claims 14, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0148477 by Mehrotra et al (hereafter Mehrotra), and further in view of US 2025/0209307 by Su et al (hereafter Su).
Referring to claim 14, Mehrotra discloses a computer system [industrial technical support system, Abstract; Fig 2, para 37-38] comprising:
a processor [processor(s) 218, Fig 2];
a memory including a generative artificial intelligence (AI) model and instructions causing the processor to perform the instructions [memory 220 comprising software instructions, para 38, Fig 2], wherein the instructions include:
receive during a session a prompt by a generative Al model [user submits initial natural language prompt (query) 306 for assistance with an alarm condition or performance issue which is used by the generative AI component 208, para 45-46; para 68, Fig 8a, element 806];
obtain, by the generative AI model, a first set of information from a plurality of insight archives in response to the prompt, each of the plurality of insight archives including an insight and data used by the generative AI model to generate the insight [archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- including prompts submitted by a user during a support session, support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts, para 50; chat histories include alarm resolution notes, para 53, 67, 71; industrial documentation data stored in document repository is used in conjunction with the archived chat history data as contextual data 316, para 51, 66, Fig 8a, element 802];
generate, by the generative AI model, a first insight from at least the first set of information obtained from the plurality of insight archives [wherein the initial prompt 306 is enhanced with relevant contextual data 316 including globally accessible documentation 314 and customer-specific documentation associated with the customer entity and chat history data 308, para 49, 54; Fig 8b, elements 814,818; industrial documentation data stored in document repository, para 66, Fig 8a, element 802], wherein the first insight is not included in the plurality of insight archives [Examiner submits that the combination of the insight interpolation resulting from the prompt enhanced with relevant contextual data 316 and chat history data 308 does not yet exist in the archived chat histories since the prompt is newly received in a current session and has not yet been processed by the generative AI model to render a technical support response to the prompt];
provide the first insight as an output, by the generative AI model [technical support response to the prompt is generated based on the user’s prompts, the subset of document data retrieved from document repository, and the subset of chat history data, para 70, Fig 8b, elements 814,818; technical support response to prompt is rendered, para 70, Fig 8b, element 820, para 54];
extract, by the generative AI model, a second set of information used by the generative AI model to generate the first insight [user feedback provided, indicating a degree to which the model’s response 302 addressed the user’s issue, is stored by the generative AI component in association with the prompt 306 and corresponding responses 302, para 56, Fig 6; user feedback is used by generative AI model when formulating responses to similar prompts 306, para 50]; and
store, by the generative AI model, the second set of information in a first insight archive [the prompt and technical support response is stored in the chat history repository, para 70; Fig 8b, element 822; archived chat histories 312 comprise the content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts; each chat history 312 also records user feedback or ratings to the system support response in association with the prompt 306 and corresponding responses 302, para 50, 53, 56].
Referring to claim 14, while Mehrotra discloses all of the above claimed subject matter and also discloses storing the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive), that the archived chat histories contain resolution notes [para 50, 53, 56, 70], that the archived chat histories are comprised of chat histories that each include the content of chat sessions between the system and various users across different customer entities and the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts [para 50], and that the first insight archive is not included in the plurality of insight archives [Examiner submits that the content of the chat history data 308 including user prompts and the technical support response associated with the current session is not yet included in the archived chat histories 312 before it has not yet been stored in the chat history data 308, see Fig 8b, element 822 and corresponding portions of specification].
However, it remains silent as to the second set of information including cross-reference information to the first insight archive; and specifically creating an insight archive (chat histories).
Zhou teaches that an EQB 804 is a data source that relies upon the insight repository as a data source [para 33]- Examiner submits that the EQB and insight repository function as a combined data source. The EQB further comprises a query cache that links the insights (produced by data exploration process when data mining using the EQB and stored in the insight repository) to related queries using the “related query link” e.g. “query numbers or link” (e.g., “Query number-correlation index”) in the query cache [para 102, see query cache entries in Fig 9]. Examiner submits that the linking of the insights to related query entries in the query cache through the “related query link” e.g. “query numbers or link”/“Query number-correlation index” is the claimed cross-reference information to the first insight archive because it connects the insights in the repository with the related queries in the query cache.
Mehrotra and Zhou are analogous art because they are directed to the same field of endeavor- generation of insights from extracted data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the storage of the user feedback (i.e. the second set of information) along with the system support responses in each chat history of the archived chat histories 312 (i.e. first insight archive) in Mehrotra to include the query link information that is connected to the insights in insight repository in Zhou because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the linking of correlated queries to execution plans of the query through the query cache in Zhou refines the archived chat histories of Mehrotra by providing an infrastructure to run multiple correlated queries in the dialogue-based user query system of Mehrotra [see Zhou, para 33; Mehrotra, dialogue based queries, para 46, 60, 70].
Still referring to claim 14, while Mehrotra/Zhou disclose all of the above claimed subject matter, it remains silent as to specifically creating an insight archive (chat histories).
Su teaches that after an interactive session 816 has ended, a user could select an archive chat input to store the contents of the interactive session [para 94, Fig 8b, element 818].
Mehrotra, Zhou and Su are analogous art because they are directed the same field of endeavor- conducting dialogue-based sessions using generative AI. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the stored insight (chat) archives of Mehrotra to include specifically creating a new insight (chat) archive because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the provision of the ‘archive chat’ feature of Su further enables the user6 the option of controlling the generation of the archived chat histories of Mehrotra.
Referring to claim 15, Mehrotra/Zhou/Su discloses determining that the plurality of insights does not include the first insight [Mehrotra, wherein more information is determined to be needed to infer sufficient information from the prompt to allow the generative AI model to generate guidance having a high likelihood of addressing a concern conveyed in the prompt and whether more information is needed from the user in order to generate accurate technical support guidance, para 69, Fig 8a, element 808].
Referring to claim 17, Mehrotra/Zhou/Su discloses wherein the second set of information includes the first insight and information associated therewith [Mehrotra, the user feedback corresponding to the technical support response, along with the technical support response and the prompt 306 are stored in the chat history database, para 56, Fig 6].
Referring to claim 18, Mehrotra/Zhou/Su discloses wherein the plurality of insight archives include documents and data associated with the plurality of insights [Mehrotra, archived chat histories 312 include content of chat sessions between the system and various users across different customer entities- each chat history includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts, para 50; chat histories include alarm resolution notes, para 67, 71].
Referring to claim 19, Mehrotra/Zhou/Su discloses wherein the documents and data are associated with prior sessions associated with the generative AI model [Mehrotra, archived chat histories for prompts are used for formulating responses to subsequent similar prompt 306, para 50].
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Mehrotra, in view of Zhou, as applied to claim 20 above, and further in view of US 2024/0249081 by UzZaman et al (hereafter UzZaman).
Referring to claim 22, Mehrotra/Zhou discloses all of the above claimed subject matter and also discloses that the format of the technical support response is generated in any suitable format based on the nature of the prompt, for example in the form of a natural language explanation of conditions together with suggested actions or steps that can be performed to correct the alarm condition, answers to questions about an industrial an industrial asset or device, or other such responses [Mehrotra, para 70]. However it remains silent as to the format of the response associated with one or more of a presentation slide, a document, or a spreadsheet.
UzZaman discloses that customizable content generated based on extracted insights can be in a content format including a slide deck and spreadsheets [para 3, 33].
Mehrotra, Zhou and UzZaman are analogous art because they are directed the same field of endeavor- generating content based on insights. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the format of the technical support response of Mehrotra to be in a presentation slide or spreadsheet format as taught by UzZaman because it would require mere substitution of output formatting.
The ordinary skilled artisan would have been motivated to make this modification because the spreadsheet and slides format of the insight-based customized content of UzZaman further refines the format of the technical support response of Mehrotra.
Claims 27 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Mehrotra, in view of Zhou, as applied to claim 1, and further in view of US 2023/0058013 by Basak et al (hereafter Basak).
Referring to claim 27, while Mehrotra/Zhou discloses all of the above claimed subject matter, and also discloses storing the second set of information in the second insight archive [Mehrotra, the prompt and technical support response is stored in the chat history repository, para 70; Fig 8b, element 822; archived chat histories 312 includes the prompts submitted by a user during a support session as well as the support guidance, information or resolution recommendations generated by the generative AI model in response to the prompts and records user feedback or ratings to the system support response in association with the prompt 306 and corresponding responses 302, para 50, 53, 56], it remains silent as to specifically storing the second set of information including storing a signature file.
Basak discloses that an insights data file 120 is stored that contains a digital signature of the insights provider 130 as the authoritative issuer of the insights data file [para 18, 46, Fig 1].
Mehrotra, Zhou and Basak are analogous art because they are directed the same field of endeavor- accessing content based on stored insights. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the archived chat histories of Mehrotra to include the digital signature of the insights provider 130, as taught by Basak, because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the digital signature of Basak further refines the types of data stored within the archived chat histories of Mehrotra.
Referring to claim 28, Mehrotra/Zhou/Basak discloses that the signature file includes one or more of a hash value, an identifier of an algorithm used to generate the signature file, and/or a timestamp associated with the signature file [Basak, creation time timestamp, para 18].
Claims 26 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Mehrotra, in view of Zhou, as applied to claims 1 and 20 above, and further in view of US 2024/0248898 by Yarlagadda et al (hereafter Yarlagadda).
Referring to claims 26 and 32, while Mehrotra/Zhou discloses all of the above claimed subject matter, and also discloses storing technical support response data (insights) within archived chat histories 312 [Mehrotra, para 50, 53, 56, 70], it remains silent as to the first insight archive including a plurality of revisions of the first insight, wherein each revision of the first insight includes one or more of a revision ID, a timestamp indicating when the revision was made, or revision information associated with the revision.
Yarlagadda discloses storing insight values and metadata related to insights within a uniform data model including a last-updated field that stores a timestamp that indicates the last time that insight source data was polled, modified, and/or created (e.g., using insight retrieval logic) to determine whether new insights have been added to the insight data source [para 34, 57].
Mehrotra, Zhou and Yarlagadda are analogous art because they are directed the same field of endeavor- accessing content based on stored insights. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the archived chat histories of Mehrotra to include the insight timestamp metadata of Yarlagadda, because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the insight timestamp metadata of Yarlagadda further refines the data stored within the archived chat histories of Mehrotra.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chang et al (US 20240395159) directed to: extracting and storing insights within a knowledge graph [Abstract; para 52-53, 78];
Neckermann et al (US 11182748) directed to: augmented data insight generation and provision relevant to a user communication [see whole document, including Fig 2, 3B-D and related portions of specification];
McColgin et al (US 20250335751) directed to: using a generative AI model to generate insight based content including storing metadata associated with insights [para 67, Fig 3B and related portions of specification].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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