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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/5/2026 has been entered.
Status
This action is in response to the amendment filed on 2/6/2026. Claims 1, 3-10, 12-19 are pending. Claims 1, 10, 19 are amended. No claims have been added. Claims 2, 11, 20 had been previously cancelled.
Response to Arguments
Applicant's arguments filed 2/6/2026 have been fully considered but they are not persuasive. The applicant has argued the previous 101. Specifically “But this conclusion does not apply to amended claims 1, 10, and 19. These claims do not recite commercial interactions. Rather, the claims recite computer-based applications for generating and modifying APIs to generate insight information used to train an AI model for forecasting future transaction executions. Particularly, the claims are directed to predicting future transactions that are likely to occur. The claims are not directed to merely collecting, analyzing, and reporting results. Thus, the above argument does not apply to amended claims 1, 10, and 19.” The examiner respectfully disagrees. According to applicant’s originally filed disclosure the background of the invention is to help a salesperson at a financial institution predict what a client will want to buy next, so they can pitch it to them. This is at its core a commercial interaction because it is about facilitating a sale between two parties (see originally filed specification ¶ 3). The claims recite retrieving information related to a government filing, generating a knowledge graph, generating an API, generating insight information, retrieving information, analyzing the information to generate a workflow, modifying an API based on a workflow, training an algorithm to analyze trading and transaction patterns, storing the information, leveraging to generate a feedback loop, and forecasting a proposed future transaction. Claim 1 involves gathering data about a client, analyzing their past behavior, predicting their future behavior, and using that prediction to facilitate a commercial truncation which is clearly directed to forecasting market/purchase activity (gather, analyze, predict). The Federal Circuit has held similar concepts to be abstract. For example, the Federal Circuit has held that abstract ideas include the concepts of collecting data, analyzing the data, and reporting the results of the collection and analysis, including when limited to particular content. See, e.g., Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340–41 (Fed. Cir. 2017) (identifying the abstract idea of organizing, displaying, and manipulating data); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (characterizing collecting information, analyzing information by steps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas). Thus, under the first prong, claim 1 recites the patent-ineligible judicial exception of certain methods of organizing human activity commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The receiving data, manipulating data, and forecasting data are all directly related to financial instruments.
The applicant has argued “Additionally, amended claims 1, 10, and 19 do not recite a mental process. Rather, amended claims 1, 10, and 19 recite computer-based applications for generating and modifying APIs to generate insight information used to train an AI model for forecasting future transaction executions. Particularly, the claims recite retrieving and analyzing all derivatives in an EDGAR database, which cannot be practically performed in the human mind. Thus, this computer- dependent process is incapable of being practically performed in the human mind as required by MPEP 2106.04(a)(2)III(A). Thus, amended independent claims 1, 10, and 19 cannot be classified as an abstract idea.” The examiner respectfully disagrees. Claims can recite a mental process even if they are claimed as being performed on a computer. The focus of the claim, in view of the specification, is predicting future trades to facilitated sales which is a judgement that a human skilled in the art could do mentally. The use of technology in the steps of the claims is merely just a mechanism for doing the steps faster. The predicted future transaction is a prediction that an experience analyst makes mentally every day. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”). The applicant is claiming technology but it is at a very high level. The applicant is merely using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes.
The applicant has argued “Additionally, these newly claimed details are inextricably tied to and solve problems in current computer technology. Conventional systems are unable to analyze and find intelligence at scale for the myriad of regulatory filings, as described in paragraphs [0076] and [0079] of the instant specification. In contrast, these claimed computer-based features generate and modify APIs to generate insight information used to train an AI model for forecasting future transaction executions, and allow the system to efficiently find, identify, and analyze data or intelligence within the SEC EDGAR database, as described in paragraphs [0077]-[0083]. In other words, by automatically modifying APIs within the system based on a self-generated workflow (as described in independent claims 1, 10, and 19), the system generates and utilizes internal components that increase the efficiency and accuracy of the underlying system/technology. Thus, these claimed features significantly enhance the ability of a computer-based system for analyzing regulatory filings to forecast future transactions. Moreover, the claimed features significantly improve the computer functionality by leveraging and integrating APIs, workflow automation tools, LLM tools, and AI tools onto a single system or platform. For example, independent claims 1, 10, and 19 recite an internally modified API, as well as an internally trained LLM component that are both leveraged to generate the output/forecast. Particularly, this combination increases speed, efficiency, consistency, and accuracy of computing associated with the forecasting of future executions, as compared to conventional systems that rely on third-party systems and unintegrated software. Thus, these claimed elements significantly improve computing functionality over conventional systems and technology.” The examiner respectfully disagrees. Specifically, as claimed it appears that the invention is merely using as a computer as a tool to access information. Although the claims include the term large language model the claim itself does not include the language of a large amount of data, merely the training of an algorithm with the LLM “to analyze trading and transaction patterns.” In Electric Power Group v. Alstom the Federal Circuit held that claims directed to collecting data from multiple sources including specific, real world power grid data analyzing it, and displaying the results were abstract, even though the data source was specific and the scale was large. The court held that merely selecting a particular source of data does not transform an abstract data analysis method into a practical application. Data gathering is not a technical improvement. Even highly specific financial data sources and computation do not constitute practical application when the improvement is to the financial analysis itself rather than the underlying computer technology. Retrieving data from a publicly accessible internet database (even Edgar) is performing a generic computer function. The claims use Edgar as an input not as a technical component that the invention improves or transforms. Edgar is merely being used as a data source. Edgar arguably at most improves the abstract idea it does not improve how the data is retrieved, how the technology functions, or how the models are trained. Applicant’s arguments are not found persuasive.
The applicant has argued the that the claims “Furthermore, Applicant respectfully submits that the above-described improvements made by the recited features integrate the claims into a practical application and the § 101 rejection should be withdrawn for reasons similar to those set forth by the Appeals Review Panel (ARP) in its decision on request for rehearing in Ex parte Desjardins et al. In that decision the panel held that "claims directed to an improvement in the functioning of a computer, an improvement to other technology or technical field are patent eligible." In that case, the panel held that because the claimed system used less storage capacity and enabled a reduction in system complexity, the claims reflected sufficient improvement so as to be integrated into a practical application. See the ARP's decision on request for rehearing in Ex parte Desjardins et al. (Appeal No. 2024-000567, dated September 26, 2025) at pages 8 and 9. Similarly, the claimed features recite generating features and elements for training AI model, as well as modifying these features and elements to generate a positive feedback loop for further training of the AI model to improve the efficiency and accuracy of the AI model performance.” The examiner respectfully disagrees. In Desjardins the technical improvement was to the computer system itself. The improvements were to how the computer operates. Applicant’s claims are directed to accuracy and efficiency of financial predictions, which would at most be an improvement to the quality of the output in the financial data, not to how the technology itself operates. The system at most produces better financial forecasts. The improvement in Desjardins was to the technology the improvement here appears to be in the financial analysis (abstract idea). Further, applicant’s claimed feedback loop in the claims appears to improve a system ability to predict financial trades. In Desjardins the improvement was to a technical operation. The claims do not explicitly recite what technical result the feedback loop and the API modification produce in the functioning of the computer. Applicant’s originally filed specification supports that the focus of the invention is to improving commercial sales rather than a technical improvement.
The applicant has argued “The Office Action also rejects the claims under its § 101 Step 2B analysis. The Office asserts that the claims fail to recite significantly more than an abstract idea for similar reasons as those used in the step 2A analysis and do not amount to significantly more than an abstract idea. But the claims have been amended to be allowable under § 101 Step 2A analysis, as discussed above. Therefore, the amended claims are not susceptible to this ground of rejection. Additionally, these claims have been amended to recite a particular ordered combination of claimed elements, such that the claimed combination of elements contain an inventive concept. Applicant respectfully submits that each element must be examined as "as an ordered combination" of elements. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 218 n.3 (2014). Particularly, the Federal Circuit has held that "an inventive concept can be found in the non- conventional and non-generic arrangement of known, conventional pieces." See Bascom Global Internet Services v. AT&T Mobility, 827 F. 3d 1341 (2016). Applicant respectfully submits that the ordered combination of claimed elements define a non-conventional arrangement, such that the ordered combination set forth in the claims is not routine or conventional. This conclusion is supported by the fact that no prior art is applied to reject these claimed features. Moreover, the Examiner has not established that the combination of claim features is well-understood, routine, or conventional, as required by MPEP 2106.05(d)(I).” The examiner respectfully disagrees. The applicant has not identified what specific technical results emerges from an ordered combination of claimed elements that could not be achieved by the individual components. The claims recite retrieving EDGAR data, generating a knowledge graph, generating APIS, producing insight information, retrieving historical trading data, generating a workflow, modifying APIs, training an LLM, creating a feedback loop, and forecasting future transactions. The Applicant has not stated what technically distinctive result their specific ordered combination produces beyond what the individual components would produce. It is noted that the Examiner did not find any additional element or combination of elements in the rejected claims that are recognized as well-understood, routine, and conventional activities. However, Berkheimer does not require the Examiner make a factual finding that all claim elements are well-understood, routine or conventional. Rather, a Berkheimer factual is required for additional elements or a combination of additional elements outside of the identified abstract idea. See Berkheimer Memo. P. 2. Therefore the previous 101 is maintained and updated below in view of applicant’s amendments. Even if the invention has no prior art it still must not be an abstract idea or a mere routine/conventional method. The lack of prior art does not make an abstract idea eligible.
The independent claims do not to improve the functioning of the processor or mediums. As is stated above, claim 1 does not affect an improvement in any other technology or technical field. Thus, claim 1 at issue amounts to nothing significantly more than instructions to apply the abstract idea of information access using some unspecified, generic computer. Under our precedents, that is not enough to transform an abstract idea into a patent-eligible invention. The previous 101 rejection is maintained and updated in view of applicant’s amendments.
The applicant has amended the claims to overcome the previous 103 rejections. Although APIs, AI algorithms, and LLMs existed in the prior art the limitations of the specifics of storing the modified at least one API, the first information, and the generated workflow in the LLM, and leveraging, via the LLM, the modified at least one API to generate a positive feedback loop for enabling future trading behavior prediction, wherein the forecasting comprises applying the trained AI algorithm to the first knowledge graph to generate the at least one proposed future transaction as well as the government filing, wherein the first information includes all derivatives in an electronic data gathering, analysis, and retrieval (EDGAR) database over a predetermined period of time are not found in the prior art does not anticipate or render obvious the claimed invention. It is because of the combination of elements that the previous 103 rejection was withdrawn.
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, 3-10, 12-19 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 3-9, are directed to a method, claims 10, 11-18 are directed to an apparatus, and claim 19 is directed to a non-transitory computer readable medium. Therefore, claims 1, 3-10, 12-19 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 10, 19, recite a method for forecasting a future transaction, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Claim 1 recites abstract limitations including “A method for forecasting market activity, the method comprising: retrieving, …first information that relates to at least one form that corresponds to a government filing; generating, … based on the first information, a first knowledge graph that relates to a first entity; generating, … analyze the first information and the first knowledge graph; generating, …, insight information into at least one financial instrument that relates to the first entity; retrieving, …, second information that relates to historical actions performed by at least one person that is associated with the first entity and that is based on the generated insight information; analyzing, …, the first information and the second information to automatically generate a workflow; automatically modifying, …, based on the generated workflow; training, … by using the retrieved second information to analyze trading and transactional patterns, wherein the training …includes: storing the modified …, the first information, and the generated workflow …, and leveraging, …, the modified … to generate a positive feedback loop for enabling future trading behavior prediction; and forecasting, … at least one proposed future transaction to be executed with respect to the at least one financial instrument that relates to the first entity, wherein the forecasting comprises using the trained … model to analyze trading and transactional patters associated with the first knowledge graph.” Claim 10 recites abstract limitations including “…retrieve … first information that relates to at least one form that corresponds to a government filing; generate, based on the first information, a first knowledge graph that relates to a first entity…analyze the first information and the first knowledge graph; generate… insight information into at least one financial instrument that relates to the first entity; retrieve second information that relates to historical actions performed by at least one person that is associated with the first entity and that is based on the generated insight information; analyze the first information and the second information to automatically generate a workflow; automatically modify … based on the generated workflow; train … model … by using the retrieved second information to analyze trading and transactional patterns, wherein the training of the …model includes: storing the modified…, the first information, and the generated workflow .., and leveraging, …, the modified … to generate a positive feedback loop for enabling future trading behavior prediction; and forecast,…, at least one proposed future transaction to be executed with respect to the at least one financial instrument that relates to the first entity, wherein the forecasting comprises using the trained … model to analyze trading and transaction patterns associated with the first knowledge graph. Claim 19 recites abstract limitations including “ retrieve… first information that relates to at least one form that corresponds to a government filing; generate, based on the first information, a first knowledge graph that relates to a first entity; …analyze the first information and the first knowledge graph; generate… insight information into at least one financial instrument that relates to the first entity; retrieve second information that relates to historical actions performed by at least one person that is associated with the first entity and that is based on the generated insight information; analyze the first information and the second information to automatically generate a workflow; automatically modify … based on the generated workflow; train a …model… by using the retrieved second information to analyze trading and transactional patterns, wherein the training of the … model includes : storing the modified …, the first information, and the generated workflow …, and leveraging, …, the modified … to generate a positive feedback loop for enabling future trading behavior prediction; and forecast… at least one proposed future transaction to be executed with respect to the at least one financial instrument that relates to the first entity, wherein the forecasting comprises using the trained … model to analyze trading and transactional patters associated with the first knowledge graph. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “using the at least one processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “the at least one processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “the at least one processor” language, the claim steps in the context of the claim encompass an abstract idea directed to a ”Mental Process” and “Certain Methods of Organizing Human Activity.”
Dependent claims 4-6, 13-15 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 3, 7-9, 12, 16-18, will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 10, 19, do not integrate the judicial exception into a practical application. Claim 1 is “method being implemented by at least one processor…retrieving, by the at least one processor from an internet website…; … wherein the first information includes all derivatives in an electronic data gather, analysis, and retrieval (EDGAR) database over a predetermined period of time; generating, by the at least one processor based on the first information, …; generating, by the at least one processor, at least one application programming interface (API) that is configured to analyze the first information and the first knowledge graph; generating, by the at least one processor via the at least one API…; retrieving, by the at least one processor…; analyzing, by the at least one processor…; modifying, by at least one processor, the at least one API; training, by the at least one processor, an artificial intelligence model that is associated with a predetermined large language model (LLM), storing the modified at least one API, …, and the generated workflow in the LLM, and leveraging, via the LLM, the modified at least one API to generate a positive feedback loop for enabling future trading behavior prediction; and forecasting, by the at least one processor via the Al model, … wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. Claim 10 is a “computing apparatus for forecasting market activity, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to: retrieve, from an internet website via the communication interface … wherein the first information includes all derivatives in an electronic data gathering, analysis, and retrieval (EDGAR) database over a predetermined period of time; generate at least one application programming interface (API) …; generate, via the at least one API…, modify an API; train an artificial intelligence model, a large language model (LLM), and forecast, via the AI model,…, wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. Claim 19 is “A non-transitory computer readable storage medium storing instructions for forecasting market activity, the storage medium comprising a second set of executable code which, when executed by a processor, causes the processor to: retrieve, from an internet website, wherein the first information includes all derivatives in an electronic data gathering, analysis, and retrieval (EDGAR) database over a predetermined period of time; … generate at least one application programming interface (API) that is configured to analyze the first information …; generate, via the at least one API,… automatically modify the at least one API; train an artificial intelligence model, a large language model (LLM)…, and forecast, via the AI model, …, wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 4-6, 13-15, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 3, 12, introduces the additional element of “wherein the generating of the first knowledge graph comprises using a Natural Language Processing (NLP) technique with respect to the at least one form.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 7, 16, introduces the additional element of “wherein the at least one API includes at least one from among a first API that is designed to identify a stock ticker, a second API that is designed to determine a common identification of a first institution that executes transactions with respect to the first entity, a third API that is designed to amalgamate a trading history with respect to the first entity, a fourth API that is designed to determine a future buying behavior of the first institution with respect to a stock associated with the first entity, and {P69515 05894794.DOCX}a fifth API that is designed to identify a maturity date of a coupon and to calculate a number of days until an expiry of the coupon.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 8, 17, introduces the additional element of “wherein the at least one API includes at least one from among a sixth API that is designed to determine a count of a number of tickers in a single basket, a seventh API that is designed to collect 10-Q quarterly reports of the tickers in the single basket, and an eighth API that is designed to collect news items that relate to entities associated with the single basket since a predetermined date.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 9, 18, introduces the additional element of “wherein the forecasting comprises using a result of at least one from among the sixth API, the seventh API, and the eighth API to forecast at least one from among a theme and a future exposure with respect to the single basket.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 10, 19, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is “method being implemented by at least one processor…retrieving, by the at least one processor from an internet website…; … wherein the first information includes all derivatives in an electronic data gather, analysis, and retrieval (EDGAR) database over a predetermined period of time; generating, by the at least one processor based on the first information, …; generating, by the at least one processor, at least one application programming interface (API) that is configured to analyze the first information and the first knowledge graph; generating, by the at least one processor via the at least one API…; retrieving, by the at least one processor…; analyzing, by the at least one processor…; modifying, by at least one processor, the at least one API; training, by the at least one processor, an artificial intelligence model that is associated with a predetermined large language model (LLM), storing the modified at least one API, …, and the generated workflow in the LLM, and leveraging, via the LLM, the modified at least one API to generate a positive feedback loop for enabling future trading behavior prediction; and forecasting, by the at least one processor via the Al model, … wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. Claim 10 is a “computing apparatus for forecasting market activity, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to: retrieve, from an internet website via the communication interface … wherein the first information includes all derivatives in an electronic data gathering, analysis, and retrieval (EDGAR) database over a predetermined period of time; generate at least one application programming interface (API) …; generate, via the at least one API…, modify an API; train an artificial intelligence model, a large language model (LLM), and forecast, via the AI model,…, wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. Claim 19 is “A non-transitory computer readable storage medium storing instructions for forecasting market activity, the storage medium comprising a second set of executable code which, when executed by a processor, causes the processor to: retrieve, from an internet website, wherein the first information includes all derivatives in an electronic data gathering, analysis, and retrieval (EDGAR) database over a predetermined period of time; … generate at least one application programming interface (API) that is configured to analyze the first information …; generate, via the at least one API,… automatically modify the at least one API; train an artificial intelligence model, a large language model (LLM)…, and forecast, via the AI model, …, wherein the forecasting comprises using the trained model to analyze trading and transactional patterns associated with the first knowledge graph. These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 4-6, 13-15, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 3, 12, introduces the additional element of “wherein the generating of the first knowledge graph comprises using a Natural Language Processing (NLP) technique with respect to the at least one form.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 7, 16, introduces the additional element of “wherein the at least one API includes at least one from among a first API that is designed to identify a stock ticker, a second API that is designed to determine a common identification of a first institution that executes transactions with respect to the first entity, a third API that is designed to amalgamate a trading history with respect to the first entity, a fourth API that is designed to determine a future buying behavior of the first institution with respect to a stock associated with the first entity, and {P69515 05894794.DOCX}a fifth API that is designed to identify a maturity date of a coupon and to calculate a number of days until an expiry of the coupon.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 8, 17, introduces the additional element of “wherein the at least one API includes at least one from among a sixth API that is designed to determine a count of a number of tickers in a single basket, a seventh API that is designed to collect 10-Q quarterly reports of the tickers in the single basket, and an eighth API that is designed to collect news items that relate to entities associated with the single basket since a predetermined date.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 9, 18, introduces the additional element of “wherein the forecasting comprises using a result of at least one from among the sixth API, the seventh API, and the eighth API to forecast at least one from among a theme and a future exposure with respect to the single basket.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1, 3-10, 12-19 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "wherein the training of the Al algorithm" in the training step. There is insufficient antecedent basis for this limitation in the claim.
The claims that dependent from a previously rejected claim inherit the rejection of the claim from which they depend.
Pertinent prior art includes Zhu et al. (US 20220269936 A1) discloses structuring a machine learning model to forecast a prediction using the time series data, the machine learning model structured to integrate the knowledge graph structure as an error term in the machine learning model. Coyle et al. (US 20240265456 A1) discloses predicting unknown values for assets based on reported data posted on a central repository. Roy et al. (US 20210385124 A1) discloses the creation and use of APIs. Shatsky et al. (US 20240296350 A1) discloses updating and manipulating knowledge graphs. Leyden et al. (US 20200218406 A1) discloses multiple features of APIs.
Conclusion
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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