DETAILED ACTION
This final Office action is responsive to amendments filed March 16th, 2025. Claims 1, 3, 5, 10, 12, 14, and 19 have been amended. Claims 24 and 25 have been added. Claims 21 and 22 have been cancelled. Claims 1, 3-10, 12-19, and 23-25 are presented for examination.
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 regarding claim rejections under 35 USC 101 filed 3/16/26 have been fully considered but they are not persuasive.
On pages 13-19 of the provided remarks, Applicant argues that the amended claims present statutory subject matter. Beginning on page 14 of the provided remarks, Applicant argues “A. The amended independent claims are integrated into a practical application”. Citing the amended limitations of the independent claims, Applicant argues “no reasonable interpretation of amended claims 1, 10, and 19 would characterize these claims as reciting features that represent insignificant, extra-solution activities.” Examiner asserts that the argument by Applicant is moot as the previous OA dated 12/23/35 did not include any of the argued limitations as additional elements. However, as noted in the rejection below, the amended claims recite features that represent insignificant extra-solution activities. Applicant’s arguments are not persuasive.
Continuing on page 15 of the provided remarks, Applicant argues “these newly claimed details are inextricably tied to and solve problems in current computer technology.” Examiner respectfully disagrees and asserts that the use of an AI algorithm, as stated above, is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. While Applicant argues that the claims “improve the speed, accuracy, and performance of the computer-based system”, Examiner cites the following, “[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.”); Alice, 573 U.S. at 223 (“Thus, if a patent’s recitation of a computer amounts to a mere instruction to implement an abstract idea on a computer, that addition cannot impart patent eligibility.”). Applicant’s arguments are not persuasive.
Applicant argues on page 16 of the provided remarks “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.” Examiner respectfully disagrees and asserts that the amended claims are not applicable to the claims of the argued rehearing. While Applicant argued that “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”, the ARP, per MPEP 2106.04(d), “In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems.” Therefore, the specific improvement to the operation of the machine learning model reflected the improvement disclosed in the specification and integration into a practical application. This is not applicable to the argued improvements of the amended claims. Therefore, Applicant’s arguments are not persuasive.
On pages 16-17 of the provided remarks, Applicant argues “B. The amended independent claims recite significantly more than an abstract idea”. Citing the analysis under Step 2A, Applicant argues that “the amended claims are not susceptible to this ground of rejection”. Examiner respectfully disagrees and asserts that when analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Applicant’s arguments are not persuasive.
Applicant argues "C. The amended independent claims are not directed to an abstract idea". Specifically, on page 17 of the provided remarks, Applicant argues "These claims do not recite commercial interactions. Rather, the claims recite computer-based applications, including a workflow automation tool with scalable architecture, for iteratively performing computer-based functions to update a forecast of future transaction executions and direct user actions." Examiner respectfully disagrees and asserts that the claimed limitations are forecasting market activity based on a knowledge graph and historical actions performed by at least one person associated with a first entity, which is a commercial interaction in the form of marketing. While Applicant argues, "no court case of example from the MPEP has been cited", Examiner asserts that this level of support is not required to establish the recitation of the abstract idea. Applicant's arguments are not persuasive.
Applicant continues on page 17 of the provided remarks to argue that the claims do not recite a mental process. Examiner respectfully disagrees and asserts that while Applicant argues a single limitation of the independent claims, the claims further recite, forecasting market activity by generating a knowledge graph and forecasting based on the knowledge graph and historical action performed by at least one person, at least one proposed future transaction to be executed, which are functions of the human mind in the form of observation, judgment, and evaluation. The generated forecast is developed using both the knowledge graph and the historical actions of the person associated with the first entity. Therefore, the amended claims recite a mental process. Applicant’s arguments are not persuasive.
On pages 18-20 of the provided remarks, Applicant argues “D. Various dependent claims have been amended and added to provide an additional ground for patentability under 101”. Beginning with dependent claims 5 and 14, Applicant argues “these features provide a technological improvement by implementing a plurality of computer-based features to improve the efficiency of the system for identifying forms for derivative intelligence, as described in paragraph [0081] of the specification as filed.” Examiner respectfully disagrees and asserts that the claimed “workflow automation tool” is recited at a high level of generality such that the claimed implementation to retrieve forms steps/functions of the claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Applicant’s arguments are not persuasive.
Regarding dependent claim 24, Applicant argues on page 19 of the provided remarks, “this feature provides a technological improvement by continuously updating and improving knowledge graphs, which optimizes the speedy construction of knowledge graphs and allows for the processing and ingesting of vast amounts of data in a short period of time.” Examiner respectfully disagrees and asserts that the generation of the second knowledge graph, contrasting with the first knowledge graph, and swapping out amended data fields are mental processes in the form of observation, judgment, and evaluation. This editing of the generated knowledge graph could be executed using pen and paper as the argued “continuously updating and improving” of the graphs is not present within the claim. Therefore, the claim is directed to the abstract idea. Applicant’s arguments are not persuasive.
Finally, regarding dependent claim 25, Applicant argues “this feature provides a technological improvement by automatically identifying and resolving errors in a vast array of data, thereby improving the speed and accuracy of the computer-based system and technology.” Examiner respectfully disagrees and asserts that the identification of an error is recited with a high-level of generality such that this is a mere observation of the human mind. Additionally, the resolution of the error using “a prediction model in unsupervised learning” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Finally, as stated above, regarding Applicant argument that the claim “improving the speed and accuracy of the computer-based system and technology”, Examiner cites, “[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.”); Alice, 573 U.S. at 223 (“Thus, if a patent’s recitation of a computer amounts to a mere instruction to implement an abstract idea on a computer, that addition cannot impart patent eligibility.”). Therefore, the 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 19-24, filed 03/16/25, with respect to claims 1, 3-10, 12-19, and 23-25 have been fully considered and are persuasive. The 35 USC 103 rejection of 12/13/25 has been withdrawn.
Claim Objections
Claims 1, 10, and 19 are objected to because of the following informalities: the limitation beginning "analyzing" recites "the activity" which lacks antecedent basis and should recite "activity". Appropriate correction is required.
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, and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (method), 10 (computing apparatus), and 19 (non-transitory computer-readable storage medium) and dependent claims 3-9, 12-18, and 23-25 respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process), claim 10 is directed to a computing apparatus (i.e. machine), and claim 19 is directed to a non-transitory computer-readable storage medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite forecasting market activity, the method being implemented by at least one processor, the method comprising: automatically monitoring, by the at least one processor via an automated ecosystem, derivatives associated with regulatory filings in an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC), wherein the monitoring includes leveraging a Natural Language Processing (NLP) technique to extract at least one from among structured and unstructured data from the EDGAR database; analyzing, by the at least one processor via a workflow automation tool associated with the automated ecosystem and that includes an artificial intelligence (AI) algorithm, the derivatives, wherein the workflow automation tool leverages subject matter expertise for the analyzing of the derivatives, wherein the workflow automation tool includes a scalable architecture that systematically updates based on the activity occurring on the EDGAR database, and wherein the workflow automation tool automatically analyzes hierarchy for different data fields; retrieving, by the at least one processor from the EDGAR database and based on a result of the analyzing, first information that relates to at least one form that corresponds to a government filing; generating, by the at least one processor via the workflow automation tool and based on the first information, a first knowledge graph that relates to a first entity; retrieving, by the at least one processor from a memory and based on the result of the analyzing, second information that relates to historical actions performed by at least one person that is associated with the first entity; training, by the at least one processor and based on the second information, the AI algorithm for analyzing trading and transactional patterns; forecasting, by the at least one processor via the trained AI algorithm and based on the first knowledge graph and the second information, at least one proposed future transaction to be executed by the at least one person with respect to the first entity, wherein the forecasting of the at least one proposed future transaction is generated by applying the trained Al algorithm to the first knowledge graph; retrieving, by the at least one processor, at least one amendment to the first information; revising, by the at least one processor via the workflow automation tool, the first knowledge graph, based on each respective amendment of the at least one amendment; iteratively performing, by the at least one processor, each of the retrieving of the second information, the training, and the forecasting, for each amendment of the at least one amendment to generate an updated forecast and automatically directing, by the at least one processor via the workflow automation tool, a user to an action based the updated forecast and a predicted behavior of the user that is learned by the workflow automation tool from historical behavior of the user, wherein the directing of the user to the action includes timing information for performing the action, and wherein the timing information is based on a predicted timing associated with the forecasting of the at least one proposed future transaction to be executed by the at least one person, wherein the workflow automation tool is continuously updated based on behavior of the user (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are forecasting market activity based on a knowledge graph and historical actions performed by at least one person associated with a first entity; updating the forecast based on amendments to the first information; and directing the user to an action including timing information based on the updated forecast and predicted behavior of the user, which is a commercial interaction in the form of marketing. The Applicant’s claimed limitations are forecasting market activity, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are forecasting market activity by monitoring derivatives associated with regulatory filings in an EDGAR database; analyzing activity occurring in an EDGAR database; generating a knowledge graph; forecasting based on the knowledge graph and historical action performed by at least one person, at least one proposed future transaction to be executed; revising the knowledge graph based on an amendment; generating an updated forecast; and directing the user to an action and timing information based on the updated forecast and predicted behavior of the user, which are functions of the human mind in the form of observation, judgment, and evaluation. Additionally, the knowledge graph could be generated utilizing pen & paper. The Applicant’s claimed limitations are forecasting market activity using a generated knowledge graph, which recite the abstract idea of Mental Process.
In addition, dependent claims 7-9, 16-18, and 23-25 further narrow the abstract idea and recite further defining the proposed future transactions; a derivative financial instrument; revising the knowledge graph based on amendments to forms; analyzing derivatives; tracking, constructing, and predicting relationships among documents; revising the knowledge graph by generating a second knowledge graph; and identifying an error in at least one of the EDGAR database and the second information. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as marketing/fundamental economic practices as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas. Dependent claims 3-6 and 12-15 will be discussed in Prong 2 analysis below.
Step 2A Prong 2: In this application, the above “retrieving, by the at least one processor from the EDGAR database and based on the result of the analyzing, first information that relates to at least one form that corresponds to a government filing; retrieving, by the at least one processor from a memory and based on the result of the analyzing, second information; retrieving, by the at least one processor, at least one amendment to the first information; iteratively performing, by the at least one processor, each of the retrieving of the second information” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “at least one processor; the at least one processor via an automated ecosystem; an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC); a workflow automation tool; 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; 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” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 1, 10, and 19 recite the following limitation, “wherein the monitoring includes leveraging a Natural Language Processing (NLP) technique to extract at least one from among structured and unstructured data from the EDGAR database”. The “leveraging a Natural Language Processing (NLP) technique” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 1, 10, and 19 recite the following limitation, “training, by the at least one processor and based on the second information, the AI algorithm for analyzing trading and transactional patterns”; “applying the trained Al algorithm to the first knowledge graph”; and “iteratively performing, by the at least one processor, each of the retrieving of the second information, the training, and the forecasting, for each amendment of the at least one amendment to generate an updated forecast”. The “training, by the at least one processor and based on the second information, the AI algorithm”; and “applying the trained Al algorithm to the first knowledge graph”; and “iteratively performing, by the at least one processor, each of the retrieving of the second information, the training, and the forecasting, for each amendment of the at least one amendment to generate an updated forecast” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Dependent claims 3 and 12 recite the following limitation, “generating of the first knowledge graph comprises using the (NLP) technique with respect to the at least one form”. The “using the (NLP) technique with respect to the at least one form” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Dependent claims 4 and 13 recite the following limitation, “generating of the first knowledge graph comprises applying at least one predetermined tree search algorithm to the first information”. The “applying at least one predetermined tree search algorithm to the first information” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Dependent claim 25 recites the following limitations, “wherein the workflow automation tool resolves the error through a prediction model in unsupervised learning”. The “prediction model in unsupervised learning” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 7-9, 16-18, and 23-25 further narrow the abstract idea and dependent claims 5-6 and 14-15 additionally recite “the at least one form is fileable with the federal government of the United States of America (USA) and is publicly available and includes at least one from among a Form NPORT, a Form NMFP, a Form ADV, and a Form 13F”; “the workflow automation tool implements a combination of a keyword search, an entity recognition technique, and the historical behavior of the user to retrieve the at least one from among the Form NPORT, the Form NMFP, the Form ADV, and the Form 13F”; and “ the Form 13F includes at least one from among a Form 13F notice filing, a Form 13F combination report, and a Form 13F holdings report the Form 13F includes at least one from among a Form 13F notice filing, a Form 13F combination report, and a Form 13F holdings report” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “at least one processor”, “workflow automation tool”, and “computing apparatus” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “at least one processor; the at least one processor via an automated ecosystem; an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC); a workflow automation tool; 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; 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” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1, 3-9, and 23-25; computing apparatus claims 10, 12-18; and non-transitory computer-readable medium claim 19 recite “at least one processor; the at least one processor via an automated ecosystem; an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC); a workflow automation tool; 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; 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”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0039 and 0041-42 and Figures 1-3. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “retrieving, by the at least one processor from the EDGAR database and based on the result of the analyzing, first information that relates to at least one form that corresponds to a government filing; retrieving, by the at least one processor from a memory and based on the result of the analyzing, second information; retrieving, by the at least one processor, at least one amendment to the first information; iteratively performing, by the at least one processor, each of the retrieving of the second information” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “artificial intelligence (AI) algorithm” of independent claims 1, 10, and 19 is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Nelson et al., US 2004/0215546, noting in paragraph [0029] that “use conventional artificial intelligence techniques to implement a predefined strategy.” See also, Schlunk et al. US 2005/0240500, noting in paragraph [0019] that “Any conventional artificial intelligence decision making technology may be used (e.g., an expert system).” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim.
With respect to reliance on “natural language processing (NLP)” to generate the knowledge graph within independent claims 1, 10, and 19 as well as dependent claims 3 and 12, this activity is recognized as well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Morsa, US 2006/0085408 (paragraph 0144: well -known-to-the-arts natural language processing (NLP) (computational linguistics) or some other method as is well known to the arts may be used). See also, Szabo, US Pat. No. 5,966,126 (col. 6, lines 57-62 and col. 28, lines 16-19: e.g., definitions may be produced in known manner, such as by explicit definition, or through use of assistive technologies, such as natural language translators; user defines a search using prior known techniques, such as natural language searching.
With respect to reliance on “at least one predetermined tree search algorithm” to generate the knowledge graph within dependent claims 4 and 13, this activity is recognized as well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Shimada, US 2001/0025362 (paragraph 0057: conventional preferential tree search algorithm). See also, Craighead, US 9,069,792 B1 (paragraph 0067: Such a search, based on the revision number key, can be performed using well understood tree search algorithms.)
With respect to reliance on “prediction model in unsupervised learning” to resolve the error in dependent claim 25, this activity is recognized as well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Ullrich, US 2016/0142497 A1 (paragraph 0041: Module 333 may also base a location prediction on past activity and/or correlations between application use and subsequent activities, using, for example, conventional unsupervised learning or neural-net techniques). See also, Agnihotram, US 2019/0370396 A1 (paragraph 0004: One such conventional method is unsupervised learning technique which involves training a machine (computer) using information which is neither classified nor labeled and by running an algorithm without any guidance.)
In addition, claims 7-9, 16-18, and 23-25 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 5-6 and 14-15 additionally recite “the at least one form is fileable with the federal government of the United States of America (USA) and is publicly available and includes at least one from among a Form NPORT, a Form NMFP, a Form ADV, and a Form 13F”, “wherein the workflow automation tool implements a combination of a keyword search, an entity recognition technique, and the historical behavior of the user to retrieve the at least one from among the Form NPORT, the Form NMFP, the Form ADV, and the Form 13F”, and “the Form 13F includes at least one from among a Form 13F notice filing, a Form 13F combination report, and a Form 13F holdings report” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “at least one processor”, “workflow automation tool”, and “computing apparatus” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 10, and 19 recite a combination of claim limitations that, as drafted, under considerations of the broadest reasonable interpretation of the claimed invention, are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art of record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. The closest prior art of the record discloses:
Guan (U.S 2021/0042767 A1) discloses systems and methods for digital content prioritization to accelerate hyper - targeting using optimized channel assignment using predictive analytics forecasting. However, Guan fails to explicitly disclose, teach or suggest automatically monitoring, by the at least one processor via an automated ecosystem, derivatives associated with regulatory filings in an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC), wherein the monitoring includes leveraging a Natural Language Processing (NLP) technique to extract at least one from among structured and unstructured data from the EDGAR database; analyzing, by the at least one processor via a workflow automation tool associated with the automated ecosystem and that includes an artificial intelligence (Al) algorithm, the derivatives, wherein the workflow automation tool leverages subject matter expertise for the analyzing of the derivatives, wherein the workflow automation tool includes a scalable architecture that systematically updates based on the activity occurring on the EDGAR database, and wherein the workflow automation tool automatically analyzes hierarchy for different data fields; retrieving, by the at least one processor from the EDGAR database and based on a result of the analyzing, first information that relates to at least one form that corresponds to a government filing.
Koch (U.S 2020/0334369 A1) discloses collection of articles from different sources, processes each article to extract information about the article (such as the topics covered in the article), and stores the information in one or more knowledge graphs. However, Koch fails to explicitly disclose, teach or suggest automatically monitoring, by the at least one processor via an automated ecosystem, derivatives associated with regulatory filings in an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC), wherein the monitoring includes leveraging a Natural Language Processing (NLP) technique to extract at least one from among structured and unstructured data from the EDGAR database; analyzing, by the at least one processor via a workflow automation tool associated with the automated ecosystem and that includes an artificial intelligence (Al) algorithm, the derivatives, wherein the workflow automation tool leverages subject matter expertise for the analyzing of the derivatives, wherein the workflow automation tool includes a scalable architecture that systematically updates based on the activity occurring on the EDGAR database, and wherein the workflow automation tool automatically analyzes hierarchy for different data fields; retrieving, by the at least one processor from the EDGAR database and based on a result of the analyzing, first information that relates to at least one form that corresponds to a government filing.
Estes (U.S 9,348,815 B1) discloses building a graph of global enterprise knowledge from data, with integration of a set of knowledge services in the form of a rich Application Programming Interface (API) to access a “Knowledge Graph” abstracted from the data. However, Estes fails to explicitly disclose, teach or suggest automatically monitoring, by the at least one processor via an automated ecosystem, derivatives associated with regulatory filings in an Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database of the United States Securities Exchange Commission (SEC), wherein the monitoring includes leveraging a Natural Language Processing (NLP) technique to extract at least one from among structured and unstructured data from the EDGAR database; analyzing, by the at least one processor via a workflow automation tool associated with the automated ecosystem and that includes an artificial intelligence (Al) algorithm, the derivatives, wherein the workflow automation tool leverages subject matter expertise for the analyzing of the derivatives, wherein the workflow automation tool includes a scalable architecture that systematically updates based on the activity occurring on the EDGAR database, and wherein the workflow automation tool automatically analyzes hierarchy for different data fields; retrieving, by the at least one processor from the EDGAR database and based on a result of the analyzing, first information that relates to at least one form that corresponds to a government filing.
Therefore, the combination of claim limitations, when considered in view of the available field of prior art, are rendered neither obvious nor anticipated.
However, the present claims are not in condition for allowance because the claims are rejected under 35 USC 101, as set forth in the current office action. Therefore, the claims are not in condition for allowance at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cheng, Dawei, et al. "Knowledge graph-based event embedding framework for financial quantitative investments." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2022/0067278 A1
Huang et al.
SYSTEM FOR ENTITY AND EVIDENCE-GUIDED RELATION PREDICTION AND METHOD OF USING THE SAME
US 2019/0005029 A1
Mills et al.
SYSTEMS AND METHODS FOR NATURAL LANGUAGE PROCESSING OF STRUCTURED DOCUMENTS
THIS ACTION IS MADE FINAL. 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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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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