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 12/10/2025.
Claims 3, 4, 10, 11, and 16 have been canceled.
Claims 1, 8, and 15 have been amended.
Claims 1, 2, 5-9, 12-15, and 17-20 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The 35 U.S.C. 103 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments.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, 2, 5-9, 12-14 are directed towards a system, and claims 15, 17-20 are directed towards a method, both 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, including training and updating data models. 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, 2, 5-9, 12-15, and 17-20, the independent claims (claims 1, 8, and 15) are directed to managing the completions and completed forms, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1,
receiving input event signals and storing corresponding input event records associated with the specific user entity, each of the input event records representing a respective quantized input event;
incrementing, respectively for each one of at least some of the input event records, one or more respective quantized resource of the specific user entity by a respective input quantity, the respective input quantity fetched by the first entity from a respective other party;
receiving output event signals and storing corresponding output event records associated with the specific user entity, each of the output event records representing a respective quantized output event;
discriminating, using the trained model, for at least some of the output event records, a respective at least one digital diversion attribute, by correlating between user activities and use or likelihood of use of diversion vehicles;
calculating a contact trajectory based at least in part on each output event record for which a respective at least one digital diversion attribute is discriminated;
forward tracking the contact trajectory to determine a capacity for intersection of the specific user entity with at least one first-entity dispatched diversion vehicle;
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which include commercial interaction such as marketing activities or business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction, then it falls within the “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 device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network. The claims recite the steps are performed by the device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network.
The limitations of
A system for signaling a networked device upon a contact trajectory determined to have capacity for intersection with a diversion vehicle, the system comprising:
a computing system of a first entity including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions;
a network connection operatively connecting agent devices to the computing system, wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each specific user entity of multiple user entities:
decrementing, respectively for each one of at least some of the output event records, a respective output quantity from the one or more quantized resource of the specific user entity, the respective output quantity discharged by the first entity;
iteratively training a computer-implemented machine learning model, wherein the model is an artificial neural network, wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploying the trained model;
upon the determined capacity satisfying at least one threshold criterion, sending a signal via the network connection to at least one agent device for display, by the agent device, information identifying at least one of the specific user entity, an account of the specific user identity, and the one or more quantized resource of the specific user entity.
are mere data gathering and processing 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 device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network. The device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network are recited at a high level of generality. In limitation (a), the machine learning model/artificial neural network is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning/artificial neural network 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 machine learning 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 device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network. 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 processing. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0050 “The artificial neural network may be trained using an iterative training algorithm”]) 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
A system for signaling a networked device upon a contact trajectory determined to have capacity for intersection with a diversion vehicle, the system comprising:
a computing system of a first entity including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions;
a network connection operatively connecting agent devices to the computing system, wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each specific user entity of multiple user entities:
decrementing, respectively for each one of at least some of the output event records, a respective output quantity from the one or more quantized resource of the specific user entity, the respective output quantity discharged by the first entity;
iteratively training a computer-implemented machine learning model, wherein the model is an artificial neural network, wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploying the trained model;
upon the determined capacity satisfying at least one threshold criterion, sending a signal via the network connection to at least one agent device for display, by the agent device, information identifying at least one of the specific user entity, an account of the specific user identity, and the one or more quantized resource of the specific user entity.
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 device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network 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, 5-7, 9, 10-14, and 17-20 do not contain any new additional 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, 9 recite diversion vehicle for running an app; claims 5, 12 recite artificial neural network for training data; claims 7, 14 recite a signals and devices for displaying . The dependent claims 2, 5-7, 9, 10-14, and 17-20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2, 5-7, 9, 10-14, and 17-20 recites device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2, 5-7, 9, 10-14, and 17-20 recites device, memory, storage device, processor, diversion vehicle, network, signal, machine learning, artificial neural network, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2, 5-7, 9, 10-14, and 17-20 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, 8, and 15. Therefore claims 2, 5-7, 9, 10-14, and 17-20 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.
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Reasons for Removing the Prior Art Rejection
The rejections under 35 U.S.C. 103 as to claim 1, 2, 5-9, 12-15, and 17-20 are removed in light of Applicant's claims and remarks of 12/10/2025, 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 12/10/2025 at claim 1 as follows:
Claim 1
A system for signaling a networked device upon a contact trajectory determined to have capacity for intersection with a diversion vehicle, the system comprising:
a computing system of a first entity including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions;
a network connection operatively connecting agent devices to the computing system, wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each specific user entity of multiple user entities:
receiving input event signals and storing corresponding input event records associated with the specific user entity, each of the input event records representing a respective quantized input event;
incrementing, respectively for each one of at least some of the input event records, one or more respective quantized resource of the specific user entity by a respective input quantity, the respective input quantity fetched by the first entity from a respective other party;
receiving output event signals and storing corresponding output event records associated with the specific user entity, each of the output event records representing a respective quantized output event;
decrementing, respectively for each one of at least some of the output event records, a respective output quantity from the one or more quantized resource of the specific user entity, the respective output quantity discharged by the first entity;
iteratively training a computer-implemented machine learning model, wherein the model is an artificial neural network, wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploying the trained model;
discriminating, using the trained model, for at least some of the output event records, a respective at least one digital diversion attribute, by correlating between user activities and use or likelihood of use of diversion vehicles;
calculating a contact trajectory based at least in part on each output event record for which a respective at least one digital diversion attribute is discriminated;
forward tracking the contact trajectory to determine a capacity for intersection of the specific user entity with at least one first-entity dispatched diversion vehicle;
upon the determined capacity satisfying at least one threshold criterion, sending a signal via the network connection to at least one agent device for display, by the agent device, information identifying at least one of the specific user entity, an account of the specific user identity, and the one or more quantized resource of the specific user entity.
Applicant’s Remarks of 12/03/2025 at pg. 4-6 as follows:
Pg. 4 “This rejection is respectfully traversed in view of the present amendments.
Independent claim 1 has been amended to distinguish the applied prior art. It recites, among other limitations, executable code that, when executed, causes the processor to:
iteratively training a computer-implemented machine learning model, wherein the model is an artificial neural network, wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploying the trained model;
discriminating, using the trained model, for at least some of the output event records, a respective at least one digital diversion attribute, by correlating between user activities and use or likelihood of use of diversion vehicles;"
Pg. 6 “Katz does not disclose, teach, or suggest this step. The independent claims have been amended to more clearly point out this distinction, reciting a step of "discriminating, using the trained model, for at least some of the output event records, a respective at least one digital diversion attribute, by correlating between user activities and use or likelihood of use of diversion vehicles."”
This applies to independent claims 8 and 15 as these claims includes the same feature of claim 1.
Response to Arguments
Applicant’s arguments filed 12/10/2025 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 12/10/2025.
Regarding the 35 U.S.C. 101 rejection, at pg. 1-4 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. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. 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:
A system for signaling a networked device upon a contact trajectory determined to have capacity for intersection with a diversion vehicle, the system comprising:
a computing system of a first entity including one or more processor and at least one of a memory device and a non-transitory storage device, wherein said one or more processor executes computer-readable instructions;
a network connection operatively connecting agent devices to the computing system, wherein, upon execution of the computer-readable instructions, the computing system performs steps comprising, for each specific user entity of multiple user entities:
receiving input event signals and storing corresponding input event records associated with the specific user entity, each of the input event records representing a respective quantized input event;
incrementing, respectively for each one of at least some of the input event records, one or more respective quantized resource of the specific user entity by a respective input quantity, the respective input quantity fetched by the first entity from a respective other party;
receiving output event signals and storing corresponding output event records associated with the specific user entity, each of the output event records representing a respective quantized output event;
decrementing, respectively for each one of at least some of the output event records, a respective output quantity from the one or more quantized resource of the specific user entity, the respective output quantity discharged by the first entity;
iteratively training a computer-implemented machine learning model, wherein the model is an artificial neural network, wherein the model is trained by:
inserting training data into an iterative training and testing loop to predict a target variable;
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level;
deploying the trained model;
discriminating, using the trained model, for at least some of the output event records, a respective at least one digital diversion attribute, by correlating between user activities and use or likelihood of use of diversion vehicles;
calculating a contact trajectory based at least in part on each output event record for which a respective at least one digital diversion attribute is discriminated;
forward tracking the contact trajectory to determine a capacity for intersection of the specific user entity with at least one first-entity dispatched diversion vehicle;
upon the determined capacity satisfying at least one threshold criterion, sending a signal via the network connection to at least one agent device for display, by the agent device, information identifying at least one of the specific user entity, an account of the specific user identity, and the one or more quantized resource of the specific user entity.
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 the managing user actions, which constitutes methods related to commercial interaction such as marketing activities or business relations which are still considered an abstract idea under the 2019 PEG. The managing user actions 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.
Next, regarding
“The amended claim recites limitations in the form of automated processes that the human mind is not capable of practically performing including, for example, "inserting training data into an iterative training and testing loop to predict a target variable;" and "repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, until the error is less than a predetermined, acceptable level."”.
The Examiner would like to direct the Applicant to the MPEP 2106.05(i) explicitly states that:
“Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
iv. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)”, as the Applicant case or claimed technical improvement is not akin to said Westlake case. Additionally, all improvements recited in the claim (managing the completions of form) are directed towards an existing business process that does not integrate the judicial exception into a practical application because the claim recites additional elements at a high-level of generality used to execute mere instructions in order to implement the abstract idea on a general purpose computer.
Regarding, Example 39 improved the computer technology for identifying human faces in digital images, which is not similar to the present invention. Specifically, unlike Example 39 that was found to improve the training process of a neural network, it appears the present inventions uses previous received information or additional data to predict problems identified in a loop process for analyzing users in a gaming environment. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea).
Lastly, the general use of machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. The claims discloses the defining of machine learning models at a high-level of generality, without improving the machine learning models as the limitations are just teaching well-known obvious steps of what a machine learn model does. Therefore, currently, the machine learning recited in the claims is solely used a tool to perform the instructions of the abstract idea.
The Examiner would like to point the Applicant to the 2019 PEG, in which implementing services on behalf of a provider 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).
Additionally, the Examiner did consider “the Kim Memo” and all examination falls in line with the memo. Therefore, these arguments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mannix et al., U.S. Pub. 20200043040, (discussing the monitoring of users business which includes analyzes signals from online sources, producing reports, analytics, etc.).
Samuel et al., W.O. Pub. 2018071433, (discussing the users access and interaction in an e-commerce environment).
Mir et al., Effects Of Pre-Purchase Search Motivation On User Attitudes Toward Online Social Network Advertising: A Case Of University Students., https://www.cjournal.cz/files/170.pdf, Journal of Competitiveness, 2014 (discussing the monitoring of users interaction online).
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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/UCHE BYRD/Examiner, Art Unit 3624
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