DETAILED ACTION
Notice to Applicant
1. The following is a NON-FINAL Office action upon examination of application number 18/178,132 filed on March 03, 2023, in response to Applicant’s Request for Continued Examination (RCE) filed on October 03, 2025. Claims 1-21 are pending in this application and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 18/178,132 filed 03/03/2023 claims Priority from Provisional Application 63268829, filed 03/03/2022.
Response to Amendment
4. In the response filed October 03, 2025, Applicant amended claims 1, 9, 15, and 17, and did not cancel any claim. New claim 21 was presented for examination.
5. Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
6. Applicant's arguments filed October 03, 2025, have been fully considered.
7. Applicant submits “The Claims Do Not Recite a Judicial Exception.” [Applicant’s Remarks, 10/03/2025, page 12]
The Examiner respectfully disagrees. In response, it is noted that claim 1 recites steps and concepts falling under the “Mathematical Concepts” grouping of abstract ideas set forth in MPEP 2106. The Examiner maintains that when evaluated under Step 2A Prong One, the “receive input data from a plurality of sources; normalize the received input data; analyze the normalized input data, the analyzing comprising generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules; store the output; continuously monitor the output as the output is stored, wherein continuously monitoring comprises: monitoring an output value as the output is stored; determining a threshold value based on the one or more rules; and triggering a restful endpoint when the monitored output value meets or exceeds the determined threshold value: predict, based on the triggering, two or more classifications, each of the two or more classifications comprising a category associated with a raw data of each stored output, wherein the predicting is performed using a model, the model being trained to: embed the raw data of each stored input into a vector representation; recognize patterns or features indicative of particular classes associated with the raw data; generate one or more decision boundaries separating the particular classes; and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries” are part of the abstract idea itself, i.e., are steps within the “mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations” group within the enumerated groupings of abstract ideas set forth in MPEP 2106. These steps require some sort of mathematical analysis. It is clear from Applicant’s claims that the method involves mathematical concepts such as mathematical algorithms, mathematical relationships, and calculations. Therefore, this necessarily suggests that the method includes concepts related to “mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations” group within the enumerated groupings of abstract ideas set forth in MPEP 2106.
Furthermore, the Examiner maintains that the claims set forth or describe steps that can be accomplished mentally such as via human observation and perhaps with the aid of pen and paper, which fall under the “Mental Processes” abstract idea grouping set forth in MPEP 2106. The 101 rejection found the limitations in claim 1 to recite an abstract idea that falls into the “mental processes” based on the limitations receive input data from a plurality of sources; normalize the received input data; analyze the normalized input data, the analyzing comprising generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules; store the output; continuously monitor the output as the output is stored, wherein continuously monitoring comprises: monitoring an output value as the output is stored; determining a threshold value based on the one or more rules; and triggering a restful endpoint when the monitored output value meets or exceeds the determined threshold value: predict, based on the triggering, two or more classifications, each of the two or more classifications comprising a category associated with a raw data of each stored output, generate one or more reports based on the raw data and the predicted two or more classes of data for each entry of the raw data.” These limitations recite an abstract idea that falls into the “Mental processes — concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” group within the enumerated groupings of abstract ideas set forth in MPEP 2106. As claimed, the steps can be practically performed mentally, by a human evaluating information. The above noted limitations can be accomplished mentally such as via human observation/judgement perhaps with the aid of pen and paper. These steps describe data gathering, observation, and decision making. Thus, the steps recite the abstract concept of “mental processes.” Therefore, Applicant’s arguments under Step 2A Prong 1 are not persuasive because the claims have been shown to set forth or describe activities falling under the “Mental Processes” abstract idea grouping set forth in MPEP 2106. For the reasons above, this argument is found unpersuasive. The Examiner has addressed the claim amendments in the updated rejection below, responsive to the limitations introduced by the claim amendments.
For the reasons above, Applicant’s argument is not persuasive.
8. Applicant submits “For example, at least the step of “predicting two or more classifications associated with each stored output using a natural language processing (NLP) model, the NLP model being trained to embed the raw data of each stored input into a vector representation|,] recognize patterns or features indicative of particular classes associated with the raw data[,] generate one or more decision boundaries separating the particular classes[,] and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries,” as recited in claim 1, does not constitute mathematical relationships, formulas, equations, or calculations (e.g., predicting...classes” is not a mathematical relationship, formula, equation, or calculation). Therefore at least this feature of the claims cannot be deemed a mathematical concept. This same feature also cannot be deemed as part of a mental process because it cannot practically be performed in the mind (e.g., “using a...NLP...model” cannot be performed by a human with the aid of pen and paper). Therefore, at least this feature of the claims also cannot be deemed a mental process.” [Applicant’s Remarks, 10/03/2025, pages 12-13]
In response to Applicant’s argument that “at least the step of “predicting two or more classifications associated with each stored output using a natural language processing (NLP) model, the NLP model being trained to embed the raw data of each stored input into a vector representation|,] recognize patterns or features indicative of particular classes associated with the raw data[,] generate one or more decision boundaries separating the particular classes[,] and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries,” as recited in claim 1, does not constitute mathematical relationships, formulas, equations, or calculations (e.g., predicting...classes” is not a mathematical relationship, formula, equation, or calculation). Therefore at least this feature of the claims cannot be deemed a mathematical concept,” it is noted that the sue of a natural language processing model involved mathematical operations such as vector embedding, pattern recognition, and decision boundary generating. These operations are well-understood algorithmic and mathematical processes. The steps recited effectively describe abstract mathematical operations applied to data, satisfying the criteria for a mathematical concept judicial exception under 35 U.S.C. 101. Accordingly, this argument is found unpersuasive.
Second, in response to Applicant’s argument that “using a...NLP...model” cannot be performed by a human with the aid of pen and paper, it is noted that the fact that NLP operations cannot be practically performed mentally by a human, does not exclude the claim from being directed to an abstract idea. The Examiner has addressed the claim amendments in the updated rejection below, responsive to the limitations introduced by the claim amendments.
9. Applicant submits “Even further, “continuously monitor[ing] the output,” “determining a threshold value based on the one or more rules,” and “triggering a restful endpoint when the monitored output value meets or exceeds the determined threshold value,” as recited in the claims, involves complex computer processing that cannot be performed mentally and also do not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols.” [Applicant’s Remarks, 10/03/2025, page 14]
The Examiner respectfully disagrees. While the claim recites “continuously monitoring the output,” “determining a threshold value based on the one or more rules,” and “triggering a restful endpoint,” these steps fundamentally involve numerical values, comparing them to predetermined criteria, and initiating an action once the criteria are met. Such steps can practically be performed by a human using pen and paper or simple mental calculations, for instance tracking a value over tie, checking if it exceeds a threshold, and deciding to send a notification. Therefore, these steps fall within the mental process abstract idea grouping, as they describe concepts that humas routinely perform without the need for specialized technology, The mere use of a computer to automate this process does not confer patent eligibility. Accordingly, this argument is found unpersuasive.
10. Applicant submits “Even if the pending claims were directed toward a judicial exception (which they are not), they would still be patent-eligible under Prong Two of Step 2A because they integrate any alleged exception into a practical application. See MPEP § 2106.04(d). Specifically, the pending claims present additional elements that reflect “an improvement in the functioning of a computer,” and “appl[y] or use|] the judicial exception in [a] meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.” MPEP § 2106.04(d)(I).” [Applicant’s Remarks, 10/03/2025, page 15]
In response to Applicant’s assertion that “the claims integrate any alleged judicial exception into a practical application,” the Examiner respectfully disagrees. Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In this instance, the additional elements recited in exemplary claim 1 are: a memory, at least one data storage medium, at least one processor, logic, a natural language processing model, and a user interface. These elements have been considered individually and in combination, however these computing elements amount to using a generic computer programmed with computer-executable instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment, which is not sufficient to amount to a practical application, as noted in MPEP 2106. See also MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Instead, the memory, at least one data storage medium, at least one processor, logic, a natural language processing (NLP) model, and a user interface (claim 1); amounts to using generic computing devices as tools to implement the abstract idea, which does not amount to a technological improvement or otherwise indicate a practical application. See MPEP 2106.05(f). Notably, Applicant’s Specification acknowledges that the invention may be implement with generic computing devices. See Specification, paragraphs 0076.
Even assuming arguendo that an improvement was achieved, improving the method for providing regulatory insights analysis using only generic computing devices does not improve the computing devices or any technology, but instead any incidental improvement achieved by automating the claim steps would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Accordingly, the generic computing elements do not integrate the judicial exception into a practical application.
Moreover, allowing a user to view one or more generated reports provides a benefit to the end user, not an improvement to the technology.
Lastly, in response to Applicant’s argument that “the pending claims…appl[y] or use[] the judicial exception in [a] meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception,” it is noted that preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the two-step framework. Applicant’s attempt to show that the recited abstract idea is a specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim. See the July 2015 Update: Subject Matter Eligibility that explains that questions of preemption are inherent in the two-part framework from Alice Corp and Mayo and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more…the latter pose no comparable risk of preemption, and therefore remain eligible.” The absence of complete preemption does not guarantee the claim is eligible. Therefore, “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). See also OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015).
For the reasons above, this argument is found unpersuasive.
11. Applicant submits “the claims integrate any alleged abstract idea into a practical application by reciting specific improvements to computer functionality because the claims recite an automated system that uses a model trained to, e.g., “process documents or portions of documents” and “classify them to a broader taxonomy or category” (i.e., “two or more different classes of data”). Even further, and based on the “predicted two or more difference classes of data,” the system may also, e.g., “generate summaries in coherent text” and explain “key drivers or factors that underly a given output metric.” See, e.g., Applicant’s specification at Paragraph [0053].” [Applicant’s Remarks, 10/03/2025, page 18]
The Examiner respectfully disagree. Applicant’s argument that the claims improve computer functionality by automating document processing, classification, and summary generation is not persuasive. These claimed functions involve organizing information and producing outputs that are conventional steps in data handling and analysis. The claims do not specify any technical improvements to computer hardware, or software systems, nor do they describe enhancement to how the computer performed these tasks. Simply implementing these abstract ideas on a generic computer does not satisfy the requirement to integrate the abstract idea into a practical application that improves computer functionality. The claim lacks limitations that would show a technical solution that advances the operation of the computer or a particular technological field. Accordingly, this argument is found unpersuasive.
Further, in response to Applicant’s argument that “the combination of vector embedding, pattern recognition, decision boundary generation, and class predictions works together with the natural language processing (NLP) model to provide a specific technological solution that goes beyond merely linking abstract ideas to a technological environment” [Remarks at page 19], it is noted that these steps describe standard machine learning techniques applied to data processing and do not specify any technical improvement to the functioning of the computer itself. Accordingly, this argument is found unpersuasive.
12. Applicant submits “the specific arrangement…provides meaningful limits that go beyond merely linking abstract ideas to a technological environment. Thus, rather than reciting the steps at a high level of generality, amended claim 1 provides a specific, discrete, and interconnected implementation that leverages machine learning and continuous monitoring to solve the technical problem of proactively identifying regulatory compliance issues before they materialize. For these additional reasons, the claims also amount to “significantly more” than any abstract idea, and the rejection of claim 1 based on an abstract idea under 35 U.S.C. § 101 should be withdrawn additionally for this reason.” [Applicant’s Remarks, 10/03/2025, page 22]
The Examiner respectfully disagrees. Applicant’s assertion that the specific arrangement of machine learning and continuous monitoring provides meaningful limits beyond the abstract ideas is not persuasive. The claimed combination recites routine functions, machine learning for classification and threshold-based monitoring, tata are widely used in various technical and non-technical fields. The claims does not recite any specific, unconventional configuration or improvement to the underlying technology or computer architecture. Merely applying generic machine learning techniques alongside continuous monitoring does not constitute “significantly more” under 35 U.S.C. 101. Without concrete technical improvements, the claim remains directed to an abstract idea. Accordingly, this argument is found unpersuasive.
13. Applicant submits “Soleimani also fails to teach any “predict[ing of classifications], based on the triggering…” Soleimani fails to teach any causal relationship between a triggering event and the prediction of classifications. While Soleimani may generally describe classification functionality, it does not specifically teach “predicting” in response to a “triggering.” Soleimani lacks any disclosure of the temporal or causal relationship where a classification prediction is initiated as a direct result of “triggering a restful endpoint,” as recited in claim 1.” [Applicant’s Remarks, 10/03/2025, pages 24-25]
The Examiner respectfully disagrees. In response to Applicant’s argument it is noted that Soleimani discloses the use of trained machine learning models to analyze data and provide classification outputs (col. 29, lines 18-26), including classifying textual datasets (col. 31, lines 27-50). Soleimani further teaches that such models may be used in various application including natural langue processing and real-time or near-real time processing (cl.. 27, lines 58-57), which reasonably compromises the type of prediction and classification recited in claim 1.
Soleimani clearly describes applying a trained model upon receiving new input data and generating classification in response. Under the broadest reasonable interpretation, the act of receiving input ad initiating the classification process can be understood as a triggering event. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Soleimani teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
14. Applicant submits “Soleimani does not specifically teach training an NLP model to “recognize patterns or features indicative of a particular class” or to “predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries,” as recited in claim 1.” [Applicant’s Remarks, 10/03/2025, page 25]
The Examiner respectfully disagrees. In response to Applicant’s argument that “Soleimani does not specifically teach training an NLP model to recognize patterns or features indicative of a particular class,” it is noted that Soleimani expressly discusses machine learning models that “learn, categorize, and make predictions about data” and that can “identify patterns or trends in input data” (col. 27, lines 26-57). Soleimani further explains that during training, input data is iteratively supplied to the machine learning model “to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data” (col. 28, lines 15-29).
Additionally, Soleimani describes that the model may be trained using supervised or unsupervised learning technique, including transformer and other neural network architectures suitable for natural language processing (col. 31, lines 27-43). Under the broadest reasonable interpretation, identifying patterns during training for classification purposes reasonably encompasses recognizing features indicative of a particular class, as recited in claim 1. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Soleimani teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
Last, in response to the Applicant’s argument “Soleimani does not specifically teach training an NLP model to “predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries,” as recited in claim 1,” the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 10/03/2025, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates new citations to address the amended limitations in claim 1 and supports a conclusion of obviousness of the amended claims.
15. Applicant submits “the applied prior art fails to teach or suggest training the NLP model to “embed the raw data of each stored input into a vector representation; recognize patterns or features indicative of particular classes associated with the raw data; generate one or more decision boundaries separating the particular classes; and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries” as also recited in amended claim 1.” [Applicant’s Remarks, 10/03/2025, pages 25-26]
In response to the Applicant’s argument “the applied prior art fails to teach or suggest training the NLP model to “embed the raw data of each stored input into a vector representation; recognize patterns or features indicative of particular classes associated with the raw data; generate one or more decision boundaries separating the particular classes; and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries” as also recited in amended claim 1,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 10/03/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates new citations to address the amended limitations in claim 1 and supports a conclusion of obviousness of the amended claims.
16. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Examiner’s Note
17. The amendment document filed on October 03, 2025 is considered non-compliant because it has failed to meet the requirements of 37 CFR 1.121 or 1.4. Specifically, the claim amendments are not properly indicated. The status indicator of claim 15 should indicate “Currently Amended” instead of “Previously Presented” since claim 15 was amended. Examiner points out that all claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of “currently amended,” and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text (i.e. the text of the original subject matter should not be underlined). The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived. Only claims having the status of “currently amended,” or “withdrawn” if also being amended, shall include markings. For further amendments and for further explanation of the amendment format required by 37 CFR 1.121, see MPEP § 714.
Claim Rejections - 35 USC § 101
18. 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.
19. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
20. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-8, 21), method (claims 9-16), and non-transitory computer readable medium (claims 17-20), and is directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-21 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite abstract ideas that fall into the “Mathematical Concepts” such as mathematical relationships, formulas and calculations and “Mental Processes” or concepts performed in the human mind such as via observation, evaluation, and judgment, as set forth in the enumerated groupings of abstract ideas set forth in MPEP 2106 the limitations for managing personal behavior or interactions or following rules or instructions for assigning human agents to tasks. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a memory, at least one data storage medium, and at least one processor configured to: receive input data from a plurality of sources; normalize the received input data; analyze the normalized input data, the analyzing comprising using logic for generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules; store the output; continuously monitor the output as the output is stored, wherein continuously monitoring comprises: monitoring an output value as the output is stored; determining a threshold value based on the one or more rules; and triggering a restful endpoint when the monitored output value meets or exceeds the determined threshold value: predict, based on the triggering, two or more classifications, each of the two or more classifications comprising a category associated with a raw data of each stored output, wherein the predicting is performed using a natural language processing (NLP) model, the NLP model being trained to: embed the raw data of each stored input into a vector representation; recognize patterns or features indicative of particular classes associated with the raw data; generate one or more decision boundaries separating the particular classes; and predict two or more different classes of data for each entry of the raw data based on the vector representation and the one or more decision boundaries; generate one or more reports based on the raw data and the predicted two or more classes of data for each entry of the raw data; and receive, from a user and via a user interface: additional input data; a request to view the one or more generated reports; or a request for an additional output. These steps can be performed by a human with the aid of pen and paper, and are therefore a mental step, and also are performable as mathematical relationships, formulas, equations, and/or calculations. Considered together, these steps set forth an abstract idea falling under the “Mathematical Concepts” and “Mental Processes” abstract idea groupings set forth in MPEP 2106.
Because the above-noted limitations recite steps falling within the “Mathematical Concepts” abstract idea grouping and the “Mental Processes” abstract idea grouping, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry. Independent claims 9 and 17 recite similar limitations as those recited in claim 1 and therefore are found to recite the same abstract idea(s) as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claims 1, 9, and 17, the additional elements are: a memory, at least one data storage medium, at least one processor, logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 1); logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 9), instructions, at least one processor, logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 17). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “receiving” steps are evaluated as additional elements, these steps amount at most to insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claims 1, 9, and 17, the additional elements are: a memory, at least one data storage medium, at least one processor, logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 1); logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 9), instructions, at least one processor, logic, a natural language processing (NLP) model, the NLP model being trained, and a user interface (claim 17). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes that generic computer devices that may be used to implement the invention, which cover virtually any computing device under the sun (Specification at paragraph [0076]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the “receiving” steps, even if considered as additional elements, these steps at most amounts to receiving/transmitting data, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
With respect to reliance on “natural language processing (NLP),” 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, Pub. No.: 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, Patent 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). See also, Schutt et al., Pub. No.: US 2023/0208880 (paragraph 0025: a conventional or other natural language processing model may be applied to the unstructured text to extract the identities of the components and/or relationships between components.).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-8, 10-16, and 18-21 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-8 and 21 recite “store the normalized input data prior to analyzing the normalized input data,” “generate the calculation attributes based on the normalized input data from at least one of the plurality of sources; and store the calculation attributes,” “receive the one or more rules as configured by the user; and store the one or more rules,” “using the stored output or the predicted two or more classifications,” “wherein triggering the restful endpoint provides at least one additional function based on the monitored output value,” “distribute the stored output or the predicted two or more classifications,” “provide insights based on the stored output or the predicted two or more classifications using at least one of a model or a pipeline generated,” “wherein the one or more reports include an interactive visualization based on the continuously monitored output and the predicted two or more classes of data for each entry of the raw data,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Mathematical Concepts” and “Mental Processes” abstract idea groupings. The other dependent claims have been evaluated as well, but similar to dependent claims 2-8 and 21, recite details/steps that merely refine the same abstract idea recite in the independent claims. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. Dependent claim 2, 5, 7-8, 10, 13, 15-16, 18-19, and 21 recite additional elements of a transient data storage, build at least one of a data mart or a data lake, multiple client-side devices, a machine learning platform, and the trained NLP model. However, when evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
21. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
22. 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.
23. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
24. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
25. Claims 1, 3, 5-6, 8-9, 11, 13-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hanson et al., Pub. No.: US 2023/0267105 A1, [hereinafter Hanson], in view of Cai et al., Pub. No.: US 2019/0347282 A1, [hereinafter Cai], in further view of Soleimani et al., Patent No.: US 11,501,084 B1, [hereinafter Soleimani].
As per claim 1, Hanson teaches a system for providing regulatory insight analysis (paragraphs 0002, 0033), comprising:
a memory (paragraph 0071: “The techniques described herein may be implemented in one or more computer programs executing on (or executable by) a programmable computer or electronic device having any combination of any number of the following: a processor, a storage medium readable and/or writable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), an input device, an output device, and a display.”; paragraph 0072),
at least one data storage medium (paragraph 0071: “The techniques described herein may be implemented in one or more computer programs executing on (or executable by) a programmable computer or electronic device having any combination of any number of the following: a processor, a storage medium readable and/or writable by the processor (including, for example, volatile and non-volatile memory and/or storage elements), an input device, an output device, and a display; paragraph 0078), and
at least one processor (paragraph 0071: “The techniques described herein may be implemented in one or more computer programs executing on (or executable by) a programmable computer or electronic device having any combination of any number of the following: a processor, a storage medium readable and/or writable by the processor…”; paragraphs 0072, 0074) configured to:
receive input data from a plurality of sources (paragraph 0007: “The present invention is directed to a data aggregation and normalization system for aggregating data from disparate data sources…”; paragraph 0040, discussing a data aggregation and normalization system for collecting, collating or aggregating data, such as for example financial and non-financial data, from a variety of different data sources…As shown, the data aggregation and normalization system can include a plurality of data sources…The data acquired by the data sources can be conveyed through any suitable data connection);
normalize the received input data (paragraph 0034: “a data aggregation and normalization system for aggregating data from disparate data sources, processing the data to clean the data and to normalize or standardize the data using one or more data models”; paragraph 0042, discussing that the common data model can serve to conform, organize, and normalize elements of data…; paragraph 0054, discussing that the data aggregation and normalization system employs the illustrated units and modules to form a complete, efficient, and robust data normalization unit for automatically extracting, cleaning, and normalizing data for subsequent use by a reporting unit);
analyze the normalized input data (paragraph 0034, discussing a data aggregation and normalization system for aggregating data from disparate data sources, processing the data to clean the data and to normalize or standardize the data using one or more data models, and then applying one or more discrete machine learning techniques to the normalized data to provide meaningful data insights and predictions…; paragraph 0054, discussing that the data aggregation and normalization system employs the illustrated units and modules to form a complete, efficient, and robust data normalization unit for automatically extracting, cleaning, and normalizing data for subsequent use by a reporting unit),
the analyzing comprising using logic for generating an output based on a first input including the normalized input data, a second input including calculation attributes, and a third input including one or more rules (paragraph 0034, discussing a data aggregation and normalization system for aggregating data from disparate data sources, processing the data to clean the data and to normalize or standardize the data using one or more data models, and then applying one or more discrete machine learning techniques to the normalized data to provide meaningful data insights and predictions. The normalized data can also be processed by one or more rep