DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/17/26 has been entered.
Response to Arguments
Applicant's arguments filed 6/17/26 have been fully considered but they are not persuasive.
Regarding amended independent claims 1, 8 and 15, and as a result, claims dependent therefrom, Applicant argues that references Padgett and Poirier do not disclose “inputting a third prompt into a third LLM that is downstream from the first LLM and upstream from a second LLM, wherein the third prompt is generated based on the first output; producing, by the third LLM, a third output; and generating, by a processing device, a second prompt that comprises a subset of the second document group based on the third output” as required by the claims (Arguments, pg. 16, third para. – pg. 19, third para.).
Examiner respectfully disagrees as Padgett discloses inputting its training data set into its generative AI model/transformer 402 (i.e., the claimed first LLM) and obtaining outputs as a result from the model (fig. 4), where the outputs are passed on as input into a similarity assessment Layer/MLM 404 (i.e., the claimed third LLM) that is downstream from the generative AI model/transformer 402, but is upstream from generative AI model/transformer 410 (fig. 4; para. [0029]; para. [0104]), corresponding to limitation “inputting a third prompt into a third LLM that is downstream from the first LLM and upstream from a second LLM, wherein the third prompt is generated based on the first output”. Padgett also discloses the similarity assessment Layer/MLM 404 producing an output i.e., the third output, that includes filtered outputs 408 (fig. 4), corresponding to limitation “producing, by the third LLM, a third output”. Padgett further discloses the filtered output as based on a subset of the training data (para. [0038]), as well as inputting this filtered output into generative AI model/transformer 410 (fig. 4), corresponding to limitation “generating, by a processing device, a second prompt that comprises a subset of the second document group based on the third output”. What Padgett does not explicitly disclose include limitation “wherein the second output comprises one or more executable application programming interface (API) endpoint calls”. Poirier discloses this limitation as provided in the Office action (4/1/26, pg., 10-11), and when combined with the teaching of Padgett, the combination renders the claims obvious and as such, Examiner maintains the rejection of the claims.
Response to Amendment
The 35 U.S.C. 101 rejection of the claims is hereby withdrawn in light of amendments to the claims as well as Applicant’s provided arguments (pg. 12-16)
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.
1. Claims 1-3, 8-10 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Padgett et al US 2024/0160902 A1 (“Padgett”) in view of Poirier US 2024/0202539 A1 (“Poirier”)
Per claim 1, Padgett discloses a method comprising:
producing a first output in response to inputting a first prompt into a first large language model (LLM), wherein the first prompt comprises a first document group comprising first descriptions that are hierarchically related to second descriptions in a second document group (fig. 4; The repository may include all or some of the set of training data…., para. [0038]; the training dataset may be a collection of text documents, referred to as a text corpus (or simply referred to as a corpus)…., para. [0042]; Inputs to an LLM may be referred to as a prompt …, para. [0059]; The repository of pre-existing items may be a private database maintained locally by the system in some cases. In one example, the repository of pre-existing items is the set of training data or a subset of the training data …, para. [0082]; para. [0100]; the repository may include copies of works protected by copyright.…, para. [0101]; The training data set for copyright may include examples of determinations of infringement and non-infringement drawn from case law, text books, arbitrations …, para. [0102]; The system 400 may include a first generative AI model 402 configured using a training data set…., para. [0108]; The first generative AI model 402 is used to generate a plurality of outputs. To generate an output, the first generative AI model 402 takes a prompt.…, para. [0109], training data set as including copyright documents from books, case law and arbitrations (i.e. descriptions), repository containing documents that are a subset of the documents of the training data as implying taxonomical hierarchy between documents/descriptions in training data and documents/descriptions in repository), and
wherein the first LLM is limited by a maximum token limit that is less than a token count of the second document group (para. [0004]; The repository may include all or some of the set of training data …, para. [0038]; para. [0049]-[0050]; the token sequence that is inputted to the transformer 50 may be of any length up to a maximum length defined based on the dimensions of the transformer 50 (e.g., such a limit may be 2048 tokens in some LLMs).…, para. [0053]; para. [0062]; In some cases, the repository 406 contains other items, or other items in addition to the original training data set …, para. [0113], limiting token sequence of training data set/first document group (see fig. 4) inputted into generative AI model 402/LLM to up to a maximum token limit (2048 tokens) where repository/second document group includes training data set plus other items in addition (i.e., up to 2048 tokens + other tokens) as implying limiting LLM’s input to less than token length of repository/second document group (i.e., less than 2048 tokens + other tokens));
inputting a third prompt into a third LLM that is downstream from the first LLM and upstream from a second LLM, wherein the third prompt is generated based on the first output (fig. 4A; para. [0029]; The similarity-assessment layer in such a system may include an infringement-assessment MLM that determines the likelihood that an output from the generative AI model would be considered trademark infringement…., para. [0104], output from AI model 402 as third prompt, similarity assessment layer/MLM as third LLM downstream from AL model 402 and upstream from AI model 410)
producing, by the third LLM, a third output (fig. 4A, output from similarity assessment layer/MLM as third output)
generating, by a processing device, a second prompt that comprises a subset of the second document group based on the third output (fig. 4; The repository may include all or some of the set of training data.…, para. [0038]; the system 400 may be configured in a two-stage process to carry out an initial filtering of a set of generated results based on comparison with pre-existing items in order to create a filtered set of outputs. That filtered set of outputs may then be used as the training data for configuring a second generative AI model that is then used for producing real-time results in reply to input prompts …, para. [0107]; para. [0114]); and
producing a second output based on the subset of the second document group in response to inputting the second prompt into the second LLM (fig. 4; para. [0107]; para. [0114], AI model 410 as second LLM)
Padgett does not explicitly disclose wherein the second output comprises one or more executable application programming interface (API) endpoint calls
However, this feature is taught by Poirier (Application Programming Interface) calls (fig. 9; para. [0029]; retriever models (e.g., retriever models or a retrieval agent) can provide additional retrieved information …, para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; The enterprise generative artificial intelligence system can provide that input to a retrieval module 904 (e.g., corresponding to one or more of the agents 506 and/or tools 508) which can then reach out and “retrieve” information from the embeddings store …, para. [0170]-[0173]; subsequent iterations can include the comprehension module 906 generating a new query, request, or other output that is then passed back to the retrieval module. The retrieval module 904 can process that new query and retrieves additional information …, para. [0174])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Poirier with the method of Padgett in arriving at the missing features of Padgett, because such combination would have resulted in accessing insights and accomplishing given tasks (Poirier, para. [029]; para. [0155]).
Per claim 2, Padgett in view of Poirier discloses the method of claim 1,
Padgett discloses wherein the first prompt comprises a user query (para. [0059]; para. [0063]; para. [0109]), and
Poirier discloses: executing the one more API calls to produce a query result that corresponds to the user query (fig. 9; para. [0029]; para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; para. [0174]); and
providing the query result to a user interface (para. [0171]; para. [0174])
Per claim 3, Padgett in view of Poirier discloses the method of claim 1,
Padgett discloses: wherein the first output comprises one or more identifiers based on a user query, the method further comprising: selecting one or more documents from the second document group based on the one or more identifiers (para. [0059]; para. [0063]-[0064]; para. [0109], items of output as including comparable identifiers); and
including the one or more documents from the second document group into the subset of the second document group (para. [0064; para. [0077]).
Per claim 8, Padgett discloses a system comprising:
a processing device (para. [0131]-[0133]); and
a memory to store instructions that, when executed by the processing device cause the processing device to: produce a first output in response to inputting a first prompt into a first large language model (LLM), wherein the first prompt comprises a first document group comprising first descriptions that are hierarchically related to second descriptions in a second document group (fig. 4; The repository may include all or some of the set of training data…., para. [0038]; the training dataset may be a collection of text documents, referred to as a text corpus (or simply referred to as a corpus)…., para. [0042]; Inputs to an LLM may be referred to as a prompt …, para. [0059]; The repository of pre-existing items may be a private database maintained locally by the system in some cases. In one example, the repository of pre-existing items is the set of training data or a subset of the training data …, para. [0082]; para. [0100]; the repository may include copies of works protected by copyright.…, para. [0101]; The training data set for copyright may include examples of determinations of infringement and non-infringement drawn from case law, text books, arbitrations …, para. [0102]; The system 400 may include a first generative AI model 402 configured using a training data set…., para. [0108]; The first generative AI model 402 is used to generate a plurality of outputs. To generate an output, the first generative AI model 402 takes a prompt.…, para. [0109], training data set as including copyright documents from books, case law and arbitrations (i.e. descriptions), repository containing documents that are a subset of the documents of the training data as implying taxonomical hierarchy between documents/descriptions in training data and documents/descriptions in repository), and
wherein the first LLM is limited by a maximum token limit that is less than a token count of the second document group (para. [0004]; The repository may include all or some of the set of training data …, para. [0038]; para. [0049]-[0050]; the token sequence that is inputted to the transformer 50 may be of any length up to a maximum length defined based on the dimensions of the transformer 50 (e.g., such a limit may be 2048 tokens in some LLMs).…, para. [0053]; para. [0062]; In some cases, the repository 406 contains other items, or other items in addition to the original training data set …, para. [0113], limiting token sequence of training data set/first document group (see fig. 4) inputted into generative AI model 402/LLM to up to a maximum token limit (2048 tokens) where repository/second document group includes training data set plus other items in addition (i.e., up to 2048 tokens + other tokens) as implying limiting LLM’s input to less than token length of repository/second document group (i.e., less than 2048 tokens + other tokens));
input a third prompt into a third LLM that is downstream from the first LLM and upstream from a second LLM, wherein the third prompt is generated based on the first output (fig. 4A; para. [0029]; The similarity-assessment layer in such a system may include an infringement-assessment MLM that determines the likelihood that an output from the generative AI model would be considered trademark infringement…., para. [0104], output from AI model 402 as third prompt, similarity assessment layer/MLM as third LLM downstream from AL model 402 and upstream from AI model 410)
produce, by the third LLM, a third output (fig. 4A, output from similarity assessment layer/MLM as third output)
generate a second prompt that comprises a subset of the second document group based on the third output (fig. 4; The repository may include all or some of the set of training data.…, para. [0038]; the system 400 may be configured in a two-stage process to carry out an initial filtering of a set of generated results based on comparison with pre-existing items in order to create a filtered set of outputs. That filtered set of outputs may then be used as the training data for configuring a second generative AI model that is then used for producing real-time results in reply to input prompts …, para. [0107]; para. [0114]); and
produce a second output based on the subset of the second document group in response to inputting the second prompt into the second LLM (fig. 4; para. [0107]; para. [0114])
Padgett does not explicitly disclose wherein the second output comprises one or more executable application programming interface (API) endpoint calls
However, this feature is taught by Poirier (Application Programming Interface) calls (fig. 9; para. [0029]; retriever models (e.g., retriever models or a retrieval agent) can provide additional retrieved information …, para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; The enterprise generative artificial intelligence system can provide that input to a retrieval module 904 (e.g., corresponding to one or more of the agents 506 and/or tools 508) which can then reach out and “retrieve” information from the embeddings store …, para. [0170]-[0173]; subsequent iterations can include the comprehension module 906 generating a new query, request, or other output that is then passed back to the retrieval module. The retrieval module 904 can process that new query and retrieves additional information …, para. [0174])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Poirier with the system of Padgett in arriving at the missing features of Padgett, because such combination would have resulted in accessing insights and accomplishing given tasks (Poirier, para. [029]; para. [0155]).
Per claim 9, Padgett in view of Poirier discloses the system of claim 8,
Padgett discloses wherein the first prompt comprises a user query (para. [0059]; para. [0063]; para. [0109]), and
Poirier discloses: executing the one more API calls to produce a query result that corresponds to the user query (fig. 9; para. [0029]; para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; para. [0174]); and
providing the query result to a user interface (para. [0171]; para. [0174])
Per claim 10, Padgett in view of Poirier discloses the system of claim 8,
Padgett discloses: wherein the first output comprises one or more identifiers based on a user query, and wherein the processing device, responsive to executing the instructions, further causes the system to:
select one or more documents from the second document group based on the one or more identifiers (para. [0059]; para. [0063]-[0064]; para. [0109], items of output as including comparable identifiers); and
include the one or more documents from the second document group into the subset of the second document group (para. [0064]; para. [0077]).
Per claim 15, Padgett discloses a non-transitory computer readable medium, having instructions stored thereon which, when executed by a processing device, cause the processing device to:
produce a first output in response to inputting a first prompt into a first large language model (LLM), wherein the first prompt comprises a first document group comprising first descriptions that are hierarchically related to second descriptions in a second document group (fig. 4; The repository may include all or some of the set of training data…., para. [0038]; the training dataset may be a collection of text documents, referred to as a text corpus (or simply referred to as a corpus)…., para. [0042]; Inputs to an LLM may be referred to as a prompt …, para. [0059]; The repository of pre-existing items may be a private database maintained locally by the system in some cases. In one example, the repository of pre-existing items is the set of training data or a subset of the training data …, para. [0082]; para. [0100]; the repository may include copies of works protected by copyright.…, para. [0101]; The training data set for copyright may include examples of determinations of infringement and non-infringement drawn from case law, text books, arbitrations …, para. [0102]; The system 400 may include a first generative AI model 402 configured using a training data set…., para. [0108]; The first generative AI model 402 is used to generate a plurality of outputs. To generate an output, the first generative AI model 402 takes a prompt.…, para. [0109], training data set as including copyright documents from books, case law and arbitrations (i.e. descriptions), repository containing documents that are a subset of the documents of the training data as implying taxonomical hierarchy between documents/descriptions in training data and documents/descriptions in repository), and
wherein the first LLM is limited by a maximum token limit that is less than a token count of the second document group (para. [0004]; The repository may include all or some of the set of training data …, para. [0038]; para. [0049]-[0050]; the token sequence that is inputted to the transformer 50 may be of any length up to a maximum length defined based on the dimensions of the transformer 50 (e.g., such a limit may be 2048 tokens in some LLMs).…, para. [0053]; para. [0062]; In some cases, the repository 406 contains other items, or other items in addition to the original training data set …, para. [0113], limiting token sequence of training data set/first document group (see fig. 4) inputted into generative AI model 402/LLM to up to a maximum token limit (2048 tokens) where repository/second document group includes training data set plus other items in addition (i.e., up to 2048 tokens + other tokens) as implying limiting LLM’s input to less than token length of repository/second document group (i.e., less than 2048 tokens + other tokens));
input a third prompt into a third LLM that is downstream from the first LLM and upstream from a second LLM, wherein the third prompt is generated based on the first output (fig. 4A; para. [0029]; The similarity-assessment layer in such a system may include an infringement-assessment MLM that determines the likelihood that an output from the generative AI model would be considered trademark infringement…., para. [0104], output from AI model 402 as third prompt, similarity assessment layer/MLM as third LLM downstream from AL model 402 and upstream from AI model 410)
produce, by the third LLM, a third output (fig. 4A, output from similarity assessment layer/MLM as third output)
generate a second prompt that comprises a subset of the second document group based on the third output (fig. 4; The repository may include all or some of the set of training data.…, para. [0038]; the system 400 may be configured in a two-stage process to carry out an initial filtering of a set of generated results based on comparison with pre-existing items in order to create a filtered set of outputs. That filtered set of outputs may then be used as the training data for configuring a second generative AI model that is then used for producing real-time results in reply to input prompts …, para. [0107]; para. [0114]); and
produce a second output based on the subset of the second document group in response to inputting the second prompt into the second LLM (fig. 4; para. [0107]; para. [0114])
Padgett does not explicitly disclose wherein the second output comprises one or more executable application programming interface (API) endpoint calls
However, this feature is taught by Poirier (Application Programming Interface) calls (fig. 9; para. [0029]; retriever models (e.g., retriever models or a retrieval agent) can provide additional retrieved information …, para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; The enterprise generative artificial intelligence system can provide that input to a retrieval module 904 (e.g., corresponding to one or more of the agents 506 and/or tools 508) which can then reach out and “retrieve” information from the embeddings store …, para. [0170]-[0173]; subsequent iterations can include the comprehension module 906 generating a new query, request, or other output that is then passed back to the retrieval module. The retrieval module 904 can process that new query and retrieves additional information …, para. [0174])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Poirier with the medium of Padgett in arriving at the missing features of Padgett, because such combination would have resulted in accessing insights and accomplishing given tasks (Poirier, para. [029]; para. [0155]).
Per claim 16, Padgett in view of Poirier discloses the non-transitory computer readable medium of claim 15,
Padgett discloses wherein the first prompt comprises a user query (para. [0059]; para. [0063]; para. [0109]), and
Poirier discloses: executing the one more API calls to produce a query result that corresponds to the user query (fig. 9; para. [0029]; para. [0065]; para. [0143]; The query response 626 may identify one or more database tables and/or API calls in step 626, and the database tables and/or types can be retrieved, and the APIs calls (e.g., to an artificial intelligence application) can be executed.…, para. [0155]; para. [0163]; para. [0174]); and
providing the query result to a user interface (para. [0171]; para. [0174])
Per claim 17, Padgett discloses in view of Poirier the non-transitory computer readable medium of claim 15,
Padgett discloses: wherein the first output comprises one or more identifiers based on a user query, and wherein the processing device is to: select one or more documents from the second document group based on the one or more identifiers (para. [0059]; para. [0063]-[0064]; para. [0109], items of output as including comparable identifiers); and
include the one or more documents from the second document group into the subset of the second document group (para. [0064]; para. [0077]).
Allowable Subject Matter
Claims 4-7, 11-14, and 18-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
