DETAILED ACTION
Status of the Application
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 .
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
This action is a Final Action on the merits in response to the application filed on 03/30/2026.
Claims 1, 3, 11, 13, and 20 have been amended
Claims 1-20 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 102 rejections of claims 1-20 in the previous office action are withdrawn in light of applicant’s amendments.
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 1-10 are directed towards a method, claims 11-19 are directed towards a system. and claim 20 is directed towards a computer-readable medium, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-20, the independent claims (claims 1, 11 and 20) are directed to managing user data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A method comprising:
retrieving, by the institution computing system, first data from one or more data sources, wherein the first data comprises information relating to other entities having one or more attributes corresponding to attributes of the first entity, geographic data corresponding to the first entity, and metrics associated with an entity category corresponding to the first entity and the other entities;
determining, by the institution computing system, a count of second entities which satisfy a selection criteria associated with the first entity, the selection criteria comprising the entity category of the first entity and a geographical boundary defined by a specific radius of the geographic data corresponding to the first entity;
determining, by a second Al model of the institution computing system receiving an output of the first Al model as an input, a predicted individual throughput for the first entity associated with an individual demand associated with the resource provided by the first entity, according to the count of second entities and the throughput analytics for the time window;
receiving, by the institution computing system, second data corresponding to a current input corresponding to the throughput analytics, and historical inputs.
these steps fall and recite an abstract ideas because they are directed to method of organizing human activity which includes commercial interactions such as business relations (See MPEP 2106.04(a)(2), subsection II). Additionally, Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation.
If a claim limitation, under its broadest reasonable interpretation observation and evaluations, then it falls within the ”mental processes”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface. (Claim 11 recite processor, memory, computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface), ( Claim 20 recite computer-readable medium, circuit, processor, memory, computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface. The claims recite the steps are performed by the computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface.
The limitations of
establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution;
authenticating, by the institution computing system, a user of the first entity accessing the embedded service via the enterprise resource;
forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data;
generating, by the institution computing system, a graphical user interface for rendering via the embedded service within a user interface of the enterprise resource, the graphical user interface comprising a recommendation corresponding to a current throughput based on the predicted individual throughput and the current input.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface. The computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface are recited at a high level of generality. In limitation (a), the AI model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The AI model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites AI model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the computing system, enterprise resource, artificial intelligence model, graphical user interface, user interface. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0021 “A machine learning model 104 may be trained on known input-output pairs such that the machine learning model 104 can learn how to predict known outputs given known inputs. Once the machine learning model 104 has learned how to predict known input-output pairs, the machine learning model 104 can operate on unknown inputs to predict an output.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution;
authenticating, by the institution computing system, a user of the first entity accessing the embedded service via the enterprise resource;
forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data;
generating, by the institution computing system, a graphical user interface for rendering via the embedded service within a user interface of the enterprise resource, the graphical user interface comprising a recommendation corresponding to a current throughput based on the predicted individual throughput and the current input.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-10, 12-19 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 2, 12 recite computing system for enrolling, tagging, assigning; Claim 6, 16, recite AI model for forecasting; claim 7, 8, 17 recite GUI that comprise a range and heat map; claims 9, 18 recite AI model to generate outputs; claims 10, 19 recite receiving input from the enterprise resource.. The dependent claims 2-10, 12-19 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-10, 12-19 recites system, enterprise resource, artificial intelligence model, graphical user interface, user interface, processor, memory, computer-readable medium, circuit which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-10, 12-19 recites system, enterprise resource, artificial intelligence model, graphical user interface, user interface, processor, memory, computer-readable medium, circuit, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-10, 12-19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 11 and 20. Therefore claims 2-7, 9-14, 16-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons for Removing the Prior Art Rejection
The rejections under 35 U.S.C. 103 as to claim 1-20 are removed in light of Applicant's claims and remarks of 03/30/2026, which are deemed persuasive as to independent claim 1. The reasons for withdrawal of the rejections under 35 U.S.C. 103 can be found at the following claim limitations of 03/30/2026 at claim 1 as follows:
Claim 1
establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution;
authenticating, by the institution computing system, a user of the first entity accessing the embedded service via the enterprise resource;
retrieving, by the institution computing system, first data from one or more data sources, wherein the first data comprises information relating to other entities having one or more attributes corresponding to attributes of the first entity, geographic data corresponding to the first entity, and metrics associated with an entity category corresponding to the first entity and the other entities;
forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data;
determining, by the institution computing system, a count of second entities which satisfy a selection criteria associated with the first entity, the selection criteria comprising the entity category of the first entity and a geographical boundary defined by a specific radius of the geographic data corresponding to the first entity;
determining, by a second Al model of the institution computing system receiving an output of the first Al model as an input, a predicted individual throughput for the first entity associated with an individual demand associated with the resource provided by the first entity, according to the count of second entities and the throughput analytics for the time window;
receiving, by the institution computing system, second data corresponding to a current input corresponding to the throughput analytics, and historical inputs;
generating, by the institution computing system, a graphical user interface for rendering via the embedded service within a user interface of the enterprise resource, the graphical user interface comprising a recommendation corresponding to a current throughput based on the predicted individual throughput and the current input.
Applicant’s Remarks of 03/30/2026 at pg. 12-14 as follows:
Pg.12, “The Office action alleges that Siebel discloses "establishing, by an institution computing system, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity," as previously recited in claim 1. (See Office Action, page 10). Siebel describes "an embedded operating system" including a "smart, connected device [which] may also include communication components that allow the device to share data relating to operations of an enterprise with one or more entities, such as the enterprise Internet-of-Things application development platform 3002, a manufacturer of the device, other smart, connected devices, other entities, etc." (Siebel, paragraph [0540]). However, Siebel does not disclose, nevermind teach or suggest, any "embedded operating system" being hosted on a computing system that is a third-party relative to the "enterprise." Therefore, Siebel does not disclose "establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution," as recited in amended claim 1.”
Pg.13, “However, Siebel fails to describe a second machine learning model receiving the output of a separate first machine learning model as input. Therefore, it follows that Siebel does not and cannot disclose, much less teach or suggest, "forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data," and "determining, by a second AI model of the institution computing system receiving an output of the first AI model as an input, a predicted individual throughput for the first entity associated with an individual demand associated with the resource provided by the first entity, according to the count of second entities and the throughput analytics for the time window," as recited in amended claim 1.”
Pg.14, “The information can be used to train and retrain the machine learning model of the machine learning and predictions module 3217." (Siebel, paragraph [0591]). However, none of the cited portions of Siebel, nor Siebel as a whole, disclose "determining, by the institution computing system, a count of second entities which satisfy a selection criteria associated with the first entity, the selection criteria comprising the entity category of the first entity and a geographical boundary defined by a specific radius of the geographic data corresponding to the first entity," as recited in amended claim 1.”
This applies to independent claims 11 and 20 as these claims includes the same feature of claim 1.
Page 9 of 13
Response to Arguments
Applicant’s arguments filed 03/30/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 03/30/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 6-11 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution;
authenticating, by the institution computing system, a user of the first entity accessing the embedded service via the enterprise resource;
retrieving, by the institution computing system, first data from one or more data sources, wherein the first data comprises information relating to other entities having one or more attributes corresponding to attributes of the first entity, geographic data corresponding to the first entity, and metrics associated with an entity category corresponding to the first entity and the other entities;
forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data;
determining, by the institution computing system, a count of second entities which satisfy a selection criteria associated with the first entity, the selection criteria comprising the entity category of the first entity and a geographical boundary defined by a specific radius of the geographic data corresponding to the first entity;
determining, by a second Al model of the institution computing system receiving an output of the first Al model as an input, a predicted individual throughput for the first entity associated with an individual demand associated with the resource provided by the first entity, according to the count of second entities and the throughput analytics for the time window;
receiving, by the institution computing system, second data corresponding to a current input corresponding to the throughput analytics, and historical inputs;
generating, by the institution computing system, a graphical user interface for rendering via the embedded service within a user interface of the enterprise resource, the graphical user interface comprising a recommendation corresponding to a current throughput based on the predicted individual throughput and the current input.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations regarding managing user data, which constitutes methods related to fundamental economic principles or practices, as well as, commercial interactions relating behaviors and business relations, which are still considered an abstract idea under the 2019 PEG. The managing of user data on the equipment is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 9 that Applicant points to as “significantly more” are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
pg. 9-10, “The pending claims (independent claim 1, for example) provide this technical solution by reciting, in part, "establishing, by an institution computing system associated with an institution, a connection with an embedded service of the institution computing system within an enterprise resource of a first entity, the enterprise resource hosted on a computing system maintained by a third-party service provider separate from the institution," "retrieving, by the institution computing system, first data from one or more data sources, wherein the first data comprises information relating to other entities having one or more attributes corresponding to attributes of the first entity, geographic data corresponding to the first entity, and metrics associated with an entity category corresponding to the first entity and the other entities," "forecasting, by a first artificial intelligence (AI) model of the institution computing system, throughput analytics for a regional demand associated with a resource provided by the first entity and the other entities for a time window based on the first data," "determining, by the institution computing system, a count of second entities which satisfy a selection criteria associated with the first entity, the selection criteria comprising the entity category of the first entity and a geographical boundary defined by a specific radius of the geographic data corresponding to the first entity," "determining, by a second AI model of the institution computing system receiving an output of the first AI model as an input, a predicted individual throughput for the first entity associated with an individual demand associated with the resource provided by the first entity, according to the count of second entities and the throughput analytics for the time window," "receiving, by the institution computing system, second data corresponding to a current input corresponding to the throughput analytics, and historical inputs," and "generating, by the institution computing system, a graphical user interface for rendering via the embedded service within a user interface of the enterprise resource, the graphical user interface comprising a recommendation corresponding to a current throughput based on the predicted individual throughput and the current input." Individually, or in combination, based at least on these features, the features of the claims provide a technical improvement that incorporates any alleged abstract idea into a practical application thereof and/or amounts to significantly more than any alleged abstract idea.”
These arguments at pg. 9 and 10 seems to describe a “particular way” of managing user data of the abstract idea. “
Moreover, at pg. 9the Applicant admission that the application is directed to improving the user’s experience and not the computer itself
(at pg. 24, “Additionally, without access to the trends of other entities which operate in a different capacity within the industry (e.g., manufacturers, wholesalers, etc.), business entities are missing critical insight into the overall performance of the industry and how their operation might be affected by such trends."
“Furthermore, the industry trends engine incorporated into the enterprise resource platform provides a precise recommendation to avoid over-ordering and optimizes orders through advanced predictive capabilities based on the retrieved data," according to some example embodiments. Further, paragraph [0018] describes that some implementations of the claimed solution allows users to "obtain a complete view of industry trends and [be] presented with an option to take appropriate action in response to these trends all within their enterprise resource."”)
these arguments the Applicant is admitting that the application is directed to improving the user’s experience and not the computing system or any type of computer or structure.
Moreover, the Examiner would like to point the Applicant to the 2019 PEG, in which ”managing user data”. will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Therefore, the additional elements do not integrate into a practical application
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bjonerud et al., U.S. Pub. 20190102835, (discussing the use of artificial intelligence for determining trends and patterns ).
Cella et al., W.O. Pub. 2020092426A2, (discussing the managing of resources ).
Bapat et al., Revolutionizing Market Analysis Using Machine Intelligence, Trend Prediction, And Large-Scale Data Processing, https://wjarr.com/content/revolutionizing-market-analysis-using-machine-intelligence-trend-prediction-and-large-scale, World Journal of Advanced Research and Reviews, 2023 (discussing the use of artificial intelligence to determine trends).
Verma et al., U.S. Pub. 20220138015, (discussing Implicit geographic/partition context among enterprises in different datacenters, which are geographically distributed.
Kumar et al., U.S. Pub. 20210334282, (discussing prediction of throughput (first model), with recommendation and optimization based on various metrics.
Lin et al., U.S. Patent Number 8370280, (discussing combining outputs of multiple predictive models based on feature vectors and performance indicators.
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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