DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 19 February 2026 has been entered.
Status of the Claims
The amendment filed on 19 February 2026 has been acknowledged and entered.
Claims 1, 8, and 15 has been amended. No new claims have been added.
Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 19 February 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner. However, the Patent(s) and/or publication(s) cited in the office actions submitted in the information disclosure statements have not been separately considered. A copy of the PTO-1449 is attached hereto.
Response to Amendments and Arguments
Applicant's arguments filed 19 February 2026 with respect to the rejection of claims 1-201 under 5 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 14-16 of 18) that the Office Action (at 7) alleges that the claims are directed towards a method of organizing human activity. Applicant respectfully disagrees. Nevertheless, to further advance prosecution, Applicant hereby amends the claims to include additional statutory subject matter. Applicant submits that the currently amended claims are directed to patent-eligible subject matter for at least three reasons. Firstly, the claims are eligible in light of Example 39 of the Patent Eligibility Guidelines (PEG). Secondly, the claims are eligible in light of and the “practical application” analysis set forth in § 2106.04 of the MPEP. Finally, the claims are eligible according to the guidance of provided by Director Squires in October of 2025 Guidance (hereinafter “Squires Memo”)… Example 39 provides a framework for establishing eligibility even for claims that involve abstract ideas. Indeed, the claim of Example 39 is directed to a method for training a neural network for facial detection. Specifically, the claim of Example 39 recites "training a neural network in a first stage using the first training set" and later "training the neural network in a second stage using the second training set." See Example 39. The Office's analysis of Example 39 explains that the claim does not recite a mental process because the steps are not practically performed in the human mind. See id. and MPEP § 2106.04(a)(2)(III)(A). In a more recent analysis of Example 39, the Office has stated that the claim limitation "training a neural network in a first stage using the first training set" does not recite a judicial exception. See August 4, 2025 memorandum titled "Reminders on Evaluating subject matter eligibility of claims under 35U.S.C.101"at3.
Similar to Example 39, and contrary to the assertions of the Office Action, the currently amended independent claims above also do not recite a judicial exception. Specifically, similar to the claim of Example 39, the claims have been amended to include the following limitations:
"training a machine learning model to predict future time windows by: accessing, from a database connected to the server, training data comprising region characteristics for the region of the computer network and further comprising timing data associated with the region characteristics; generating predicted timing data for the region of the computer network using the machine learning model to process the region characteristics; and modifying parameters of the machine learning model based on comparing the predicted timing data for the region of the computer network with the timing data accessed from the database."
Applicant respectfully submits that, in line with Example 39 and the Office's current approach to § 101 analysis, the claims are patent eligible. Indeed, the recited steps of "training a machine learning model" based on a particular database storing specific training data (e.g., region characteristics and timing data) is analogous to the claim of Example 39 which recites similar training steps on specific training data (sets of facial images). Along these lines, and per the analysis of Example 39, training a machine learning model is not a process that can be performed in the human mind. Additionally, there is no requirement that claims recite the same type of training data (facial images) as the claim in Example 39 for the same rationale to still apply-such a requirement would undermine the purpose of the Examples and the PEG as a whole. Accordingly, Applicant respectfully submits that the claims are eligible at least according to the rationale of Example 39 of the PEG.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the Examiner has reviewed the specification and determined that added limitations are described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept. For instance, paragraphs [0079]-[0080] of the Specification-as-originally-filed disrobes training that can be performed by a mental process (e.g. comparing predicted window start times and predicted window end times with training provider device). If a claim recites a limitation that can practically be performed in the human mind, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Further, the use of a physical aid (i.e., the pen and paper) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of this limitation. Therefore, the Examiner maintains the claims are patent ineligible. Secondly, Applicant’s claims appear to have a different fact pattern than Example 39; and unlike Example 39, which uses “digital facial images” as data used train the model to which transformations are applied including “mirroring, rotating, smoothing, or contrast reduction to create a modified set of facial images,” Applicants training step appears to be related to stored calculations of starting and ending times which can be performed in the human mind. The training steps do not appear to provide practical application which provides a technical improvement to a technical problem by increasing the overall performance of the system, instead the claims appear to provide a business solution to a business problem by use of generic components (see [0030-[0331] in the instant specification). Lastly, Unlike the subject matter of the Squires Memo, Applicant’s claims are not directed to training a machine learning model in a way that provides a technical solution to a technical problem in how computers function. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, pages 16-17 of 18) that, additionally, the MPEP indicates that a claim that integrates an abstract idea into a practical application is not directed to the abstract idea. MPEP §2106.04(d)… Applicant submits that the amended claims satisfy at least Step 2A Prong Two because the claims are directed to specific technical steps to solve problems in a technical environment that are performed by a transportation matching system made up of an interconnected server-device framework. For example, by training a machine learning model to determine a future time window in response to a dynamically determined imbalance of requestor devices and provider devices in the system (based on region characteristic data, historical data, and dynamically determined current balance data as) and by determining an efficiency metric based on a dynamic probability of unavailable provider devices becoming available within the future time window, as more particularly recited above, the claimed features improve the transportation matching system by increasing efficiency, reducing redundant requests, and thereby saving computational resources (e.g., resources that would otherwise be expended in prior, less accurate systems that process more redundant requests due to their inaccuracies), among other technological benefits. See, e.g., Specification at [0029], [0032]. By providing improvements to a technology or technical field, the claims are integrated into practical application and are thus patent eligible. Finally, the claims are patent eligible according to the guidance from Director Squires in the Squires Memo. Indeed, in the Squires Memo, Director Squires states,
"The proper statutory tools for limiting the scope of patents are §§ 102 (novelty), 103 (obviousness), and 112 (written description and enablement). Section 101 should not be misused as a blunt instrument to exclude entire technological fields. To do so risks disqualifying exactly the kinds of advances America needs most - advances in artificial intelligence, biotechnology, and data science." Emphasis added.
Applicant respectfully submits that the machine learning model recited in the claims solves problems in the technological field of balancing transportation matching network resources, and the claim is therefore eligible in eyes of the Director and the Office as a whole.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the claims do not provide a technical improvement to a technical problem by increasing the overall performance of the processor(s). Instead, the claims use generic computer components as tools to implement the abstract idea of matching a provider with a requestor for a future time period. Generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into practical application – see MPEP 2106.05(h). Further, the courts determined that "[p]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101" (Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18)); and the courts also determined that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12." Therefore, the Examiner maintains the claims are patent ineligible and do not integrate the judicial exception into practical application as Applicant has not shown that increasing efficiency, reducing redundant requests, and thereby saving computational resources (e.g., resources that would otherwise be expended in prior, less accurate systems that process more redundant requests due to their inaccuracies), actually is a technical improvement to a technical problem.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more.
Step 1
Claims 1-7 are directed to a method (i.e., a process). Claims 8-14 are directed to a system (i.e., a machine). Claims 15-20 are directed to a computer readable medium (i.e., a manufacture). Therefore, claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A Prong 1
Independent claims 1, 8, and 15 substantially recite:
monitoring/monitor/monitor [ ] communications among provider and requestor within a region of the network to generate a network balance for the region;
generating/generate/generate a region characteristic score for the region based on monitoring [ ] historical behavior for historical matching requests within the region;
training/train/train a machine learning model to predict future time windows by:
accessing/access/access [ ] training data comprising region characteristics for the region [ ] and further comprising timing data associated with the region characteristics;
generating/generate/generate predicted timing data for the region [ ] using the machine learning model to process the region characteristics; and
modifying/modify/modify parameters of the machine learning model based on comparing the predicted timing data for the region [ ] with the timing data;
generating/generate/generate utilizing the machine learning model trained to predict future time windows, a future time window for the region based on the network balance for the region and the region characteristic score for the region;
providing/provide/provide for display [ ] and in response to receiving a matching request from the requestor, a selectable element depicting the future time window; and
receiving/receive/receive, from the requestor [ ], an indication of user interaction with the selectable element to initiate selection of a provider [ ] for the matching request within the future time window. The limitations as a whole recite a method of organizing human activity. The aforementioned limitations as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitations by at least “Managing Personal Behavior or Relationships or Interactions Between People” which includes social activities, teaching, and following rules or instructions. That is, nothing in the claim elements preclude the steps from practically being performed by, managing personal behavior or relationships or interactions between people (monitoring/monitor/monitor; generating/generate/generate; training/train/train; accessing/access/access; generating/generate/generate; modifying/modify/modify; generating/generate/generate; providing/provide/provide; and receiving/receive/receive).
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements (e.g. “a server,” “a computer network,” “device communications,” “provider devices” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device”); claim 8 recites the additional elements (e.g. “a system,” “at least one processor,” “a non-transitory computer readable medium,” “instructions,” “a computer network,” “device communications,” “provider devices” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device”); and claim 15 recites the additional elements (e.g. “a non-transitory computer readable medium,” “instructions,” “at least one processor,” “a computing device,” “a computer network,” “device communications,” “provider devices” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device”) – using the server/at least one processor) to perform the “monitoring/monitor/monitor; generating/generate/generate; training/train/train; accessing/access/access; generating/generate/generate; modifying/modify/modify; generating/generate/generate; providing/provide/provide; and receiving/receive/receive steps. The “server/at least one processor” in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “monitoring/monitor/monitor; generating/generate/generate; training/train/train; accessing/access/access; generating/generate/generate; modifying/modify/modify; generating/generate/generate; providing/provide/provide; and receiving/receive/receive) such that it amounts no more than mere instructions to “apply” the exception using a generic computer component. That is, the aforementioned limitations merely invoke the generic components as a tool to perform the abstract idea, e.g. see MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Independent claims 1, 8, and 15, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the “a server,” “a computer network,” “device communications,” “provider devices,” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device” in claim 1; “a system,” “at least one processor,” “a non-transitory computer readable medium,” “instructions,” “a computer network,” “device communications,” “provider devices” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device” in claim 8; and “a non-transitory computer readable medium,” “instructions,” “at least one processor,” “a computing device,” “a computer network,” “device communications,” “provider devices” “requestor mobile devices,” “a database,” “a requestor mobile device,” “a graphical user interface,” and “a provider device” in claim 15 to perform the monitoring/monitor/monitor; generating/generate/generate; training/train/train; accessing/access/access; generating/generate/generate; modifying/modify/modify; generating/generate/generate; providing/provide/provide; and receiving/receive/receive steps in claims 1, 8, and 15 amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e. inventive concept) to the abstract idea. The claims are ineligible.
As per dependent claims 2, 9, and 16, the recitation of “generating/generate/generate the network balance by monitoring, for the region, a balance between online requestor and online provider…” is further directed to a method of organizing human activity as described in claims 1, 8, and 15, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 3, 10, and 17, the recitation of “determining a point density of pickup locations and destination locations for the historical matching requests within the region” is further directed to a method of organizing human activity as described in claims 1, 8, and 15, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claims 4, 11, and 18, the recitations of “determining a floor area ratio for the region…” and “generating the future time window based on the floor area ratio for the region” are further directed to a method of organizing human activity as described in claims 1, 8, and 15, respectively, and/or a mental process. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 5, 12, and 19 the recitations of “generating a time cycle for the matching request by dividing the future time window…”; “determining, for the time cycle, provider efficiency metrics for the provider…”; and “selecting, for the time cycle, the provider from among the providers to service the matching request…” are further directed to a method of organizing human activity as described in claims 1, 8, and 15, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
As per dependent claim 6, 13, and 20, the recitations of “generating a transition probability matrix for the time cycle by generating probabilities…” ; “selecting the provider to service the matching request according to the transition probability matrix” are further directed to a method of organizing human activity as described in claims 1, 8, and 15, respectively. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Further, the recitation, “at least one provider device” is another computer component recited at a high-level of generality and are merely invoked as a tool to perform the abstract idea. Similar to claim 1, the recitation does not provide a practical application of the abstract idea, or significantly more than the abstract idea.
As per dependent claims 7 and 14, the recitations of “generating, utilizing the machine learning model, a predicted future time window comprising a predicted window start time and a predicted window end time”; “comparing the predicted window start time and the predicted window end time with training provider pickup times to determine a measure of loss of the predicted future time window”; and “modifying, utilizing a loss function parameters of the machine learning model according to the measure of loss” are further directed to a method of organizing human activity as described in claim 1. Therefore, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Dependent Claims 2-7, 9-14, and 16-20 have been given the full two part analysis including analyzing the additional limitations both individually and in combination. Dependent claims 2-7, 9-14, and 16-20, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea of the independent claims. The dependent claims recite no additional elements that would integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Simply implementing the abstract idea on generic computer components is not a practical application of the judicial exception and does not amount to significantly more than the judicial exception. Accordingly, dependent claims 2-7, 9-14, and 16-20 are rejected as being ineligible for patenting under 35 U.S.C. 101 based upon the same analysis
Prior Art Discussion
As per independent Claims 1, 8, and 15, the best prior art:
1) Gorjestani et al. (US PG Pub. 2015/0356501 A1) discloses delivery to mobile devices using route optimization to help position delivery workers at locations to service future orders that have not yet been received; and heat maps can be generated to indicate where new orders are likely to be received over a future period of time (e.g., next 5 minutes, next 30 minutes, next hour) and can be provided to delivery workers on their mobile computing devices.
2) Shoval et al. (US Patent No. 11,574,263 B2) discloses a system and method for providing multiple transportation proposals to a user
3) Spielman et al. (US PG Pub. 2021/0192584 A1) discloses systems and methods for communicating concrete offerings for specific plans for a transportation mode to a transportation requestor device
4) Zhang (US PG Pub. 2021/0027235 A1) discloses systems and methods for vehicle scheduling
5) Schirano (US PG Pub. 2020/0300645 A1) discloses unified booking and status for multi-modal transport
6) Vora et al. (US PG Pub. 2020/0175632 A1) discloses systems and methods for dynamically selecting transportation options based on transportation network conditions.
7) Nimesh et al. (US PG Pub. 2020/0265348 A1) discloses resource allocation using weighted metrics
However, the recited prior art does not disclose or fairly teach:
monitoring, by a server of a computer network, device communications among provider devices and requestor mobile devices within a region of the computer network to generate a network balance for the region;
generating a region characteristic score for the region based on monitoring, by the server, historical device behavior for historical matching requests within the region;
training a machine learning model to predict future time windows by:
accessing, from a database connected to the server, training data comprising region characteristics for the region of the computer network and further comprising timing data associated with the region characteristics;
generating predicted timing data for the region of the computer network using the machine learning model to process the region characteristics; and
modifying parameters of the machine learning model based on comparing the predicted timing data for the region of the computer network with the timing data accessed from the database;
generating, utilizing the machine learning model trained to predict future time windows, a future time window for the region based on the network balance for the region and the region characteristic score for the region
As per independent Claims 1, 8, and 15, the best Foreign prior art,
1) Balva (WO 2019/083528 A1) discloses proactive vehicle positioning determinations
However, Balva does not disclose or fairly teach:
monitoring, by a server of a computer network, device communications among provider devices and requestor mobile devices within a region of the computer network to generate a network balance for the region;
generating a region characteristic score for the region based on monitoring, by the server, historical device behavior for historical matching requests within the region;
training a machine learning model to predict future time windows by:
accessing, from a database connected to the server, training data comprising region characteristics for the region of the computer network and further comprising timing data associated with the region characteristics;
generating predicted timing data for the region of the computer network using the machine learning model to process the region characteristics; and
modifying parameters of the machine learning model based on comparing the predicted timing data for the region of the computer network with the timing data accessed from the database;
generating, utilizing the machine learning model trained to predict future time windows, a future time window for the region based on the network balance for the region and the region characteristic score for the region
As per independent Claims 1, 8, and 15, the best NPL prior art,
1) Wang, Jun and Wang, Xiaolei, “Predicting the matching probability and the expected ride/shared distance for each dynamic ride-pooling order: A mathematical modeling approach”, December 2021Transportation Research Part B Methodological 154(2):125-146 discloses that assuming that every ride-pooling passenger shares vehicle space with at most one another during the entire trip, and ride-pooling orders in each (origin-destination) OD pair appear following a Poisson process with a given rate, we propose a mathematical modeling approach to predict the matching probability, the expected ride distance, and the expected shared distance of each order under a first-come-first-serve strategy in dynamic ride-pooling service.
However, Wang, Jun and Wang, Xiaolei, does not disclose or fairly teach:
monitoring, by a server of a computer network, device communications among provider devices and requestor mobile devices within a region of the computer network to generate a network balance for the region;
generating a region characteristic score for the region based on monitoring, by the server, historical device behavior for historical matching requests within the region;
training a machine learning model to predict future time windows by:
accessing, from a database connected to the server, training data comprising region characteristics for the region of the computer network and further comprising timing data associated with the region characteristics;
generating predicted timing data for the region of the computer network using the machine learning model to process the region characteristics; and
modifying parameters of the machine learning model based on comparing the predicted timing data for the region of the computer network with the timing data accessed from the database;
generating, utilizing the machine learning model trained to predict future time windows, a future time window for the region based on the network balance for the region and the region characteristic score for the region
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Azagirre Lekuona et al. (US PG Pub. 20220044570 A1) discloses dispatching provider devices utilizing multi-outcome transportation-value metrics and dynamic provider device modes
2) Erhun Özkan, Amy R. Ward, “Dynamic Matching for Real-Time Ride Sharing”, STOCHASTIC SYSTEMS, Vol. 10, No. 1, March 2020, pp. 29–70.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/ Supervisory Patent Examiner, Art Unit 3628