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 .
Response to Arguments
Applicant's arguments filed 2/3/26 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 rejection of the claims, Applicant argues that the amended claims include limitation “wherein the second output comprises one or more executable application programming interface (API) endpoint calls” as well as steps involving enforcing a token limit relationship while traversing hierarchically related document groups that could not be performed in a human mind, and as such, argues that the claims are not directed to an abstract idea (Arguments, pg. 12, first - second para.).
Examiner respectfully disagrees. Given that an API call/request is a message sent to a server/computer asking an API to provide a service or information and the limitation involves merely outputting the API call/message without significantly more, it is not inconceivable for a human to manually/mentally provide a message corresponding to an API call in the form of textual code corresponding to the call. Also, producing document groups having descriptions that are hierarchically related while limiting the length of input/tokens to a LLM correspond to data analyzing/evaluation steps, and as such Examiner maintains that the claims are directed to the abstract idea of data analysis.
Applicant further argues that the specification describes the invention being improved by increasing the speed in which queries are processed, while enabling the use of LLM to increase the accuracy of processing queries, and as such, argues that the additional elements of the claims improve computer functionality and another technological field (arguments, g. 12, third para. – pg. 14, ln 2)
Examiner respectfully disagrees as there is no evidence provided to show how the invention increases accuracy or speed or processing beyond tying the steps to a large language model implemented by/stored a/in a generic processor/memory (see Applicant’s figure 5). That a computer can perform the steps of query processing with increased speed or faster than a human would does not provide a practical application - “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” and the “use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept, see OIP Techs., 788 F.3d at 1363 (citing Alice, 573 U.S. at 224). Also, “merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.” see Intellectual Ventures I LLC, 792 F.3d at 1370. Furthermore, reducing errors/increasing accuracy does not provide any improvement to other technology or to a computer implementing the claimed steps, where such reduction corresponds to an improvement to the abstract idea of data gathering, but not to the functioning of the computer or to another technology.
Regarding the 35 U.S.C. 102 rejection of independent claims 1, 8 and 15, and a as a result, claims dependent therefrom with reference Padgett, Applicant argues that Padgett does not disclose distinct first and second document groups in which the first group’s descriptions are in a hierarchical relationship to descriptions in the second document group nor a first prompt including a first group of hierarchically related descriptions as claimed (Arguments, pg. 14, second para. – pg. 16, first para.).
Examiner respectfully disagrees as Padgett discloses its training data set as including a collection of text documents drawn from case law, text books and arbitrations (para. [0042]; para. [0102]) i.e., a group of documents including descriptions (the claimed first document group), and where its repository includes a subset of the training data set that includes documents (para. [0038]; para. [0082]; para. [0101]) i.e., a subset of a group of documents including descriptions (the claimed second document group), corresponding to a group documents including descriptions that are taxonomically/hierarchically related to descriptions of a second group of documents (i.e., generic-specific relationship). Padgett also discloses inputting the training data set into its generative AI model/transformer 402 and obtaining outputs as a result from the model (fig. 4), corresponding to claimed limitation “producing a first output in response to inputting a first prompt into a 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”.
Applicant further argues that Padgett does not disclose a second document group “much less establish that the token count of any such group exceeds the LLM's maximum token limit as the claim expressly requires” and as such, argues that Padgett fails to disclose limitation “wherein the LLM is limited by a maximum token limit that is less than a token count of the second document group” (Arguments, pg. 16, second para. – pg. 17, third para.).
Examiner respectfully disagrees as the instant claims do not expressly require a token count of either group to exceed the LLM’s maximum token limit like Applicant argues. As identified above, Padgett discloses a first group of documents and a second group of documents. Padgett also discloses the token sequence that is inputted to the generative model 404/transformer 50 from the training data set/first document group may be up to a maximum length of 2048 tokens (para. [0053]; para. [0062]), and that in some cases, the repository of documents 406/the second document group could contain other items in addition to the original training data set (para. [0113]), i.e., the repository of documents can include the training data set plus other data. Therefore, since the input from the training data set/first document group provided to the generative model 406 can be of a length that is the maximum of 2048 tokens (see para. [0053]), and the repository/second document group could include more tokens than the training data set/first document group as a result of including other items in addition to the original training data set (see para. [0113]), then the tokens in the repository/second document group would include more tokens than the tokens inputted into the AI model 406/LLM, and as a result, be more than the maximum token limit (i.e., maximum token limit that is less than a token count of the second document group), and as such, Padgett discloses limitation “wherein the LLM is limited by a maximum token limit that is less than a token count of the second document group”.
Regarding the rejection of dependent claims 2, 9 and 16 with additional reference Poirier, Applicant argues that the dependent claims are patentable over Padgett in view of Poirier in light of arguments presented above for the specific limitations argued above for the independent claims. Examiner respectfully disagrees as provided above and as presented in the rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of prompt/data analysis without significantly more. The claims 1, 8 and 15 recite the steps of “producing a first output in response to inputting a first prompt into a 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, and wherein the LLM is limited by a maximum token limit that is less than a token count of the second document group” (i.e., a data analysis step), “generating, by a processing device, a second prompt that comprises a subset of the second document group corresponding to the first output” (i.e., a data analysis/retrieval step) and “producing a second output based on the subset of the second document group in response to inputting the second prompt into the LLM, wherein the second output comprises one or more executable application programming interface (API) endpoint calls” (i.e., a data analysis/post solutional step), corresponding to steps achievable in manually/mentally analyzing input to provide output, and as such corresponds to the mental processes category of abstract ideas. This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements, where the generically recited computer elements (processing device, system, memory, computer readable medium) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because steps “generating, by a processing device, a second prompt that comprises a subset of the second document group corresponding to the first output” and “producing a second output based on the subset of the second document group in response to inputting the second prompt into the LLM” correspond to the well-understood, routine, conventional computer function of providing output in response to input i.e. “gathering and analyzing information using conventional techniques and displaying the result” and “collecting information, analyzing it, and displaying certain results of the collection and analysis”, as recognized by the court decisions listed in MPEP § 2106.05, and as presented in cited references Padgett, Poirier and Hawes (See PTO 892, 11/3/25).
The dependent claims 2-7, 9-14 and 16-20 also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
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 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 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));
generating, by a processing device, a second prompt that comprises a subset of the second document group corresponding to the first 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 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 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 machine learning model (MLM), 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 MLM 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));
generate a second prompt that comprises a subset of the second document group corresponding to the first 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 MLM (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 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 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));
generate, by the processing device, a second prompt that comprises a subset of the second document group corresponding to the first 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 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.
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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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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