DETAILED ACTION
The following FINAL Office Action is in response to Applicant’s Response filed on 02/04/2026.
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.
Status of Claims
Claims 1-20 were previously pending and subject to a non-final Office Action mailed 11/04/2025. Claims 1, 3, 12-14, and 20 were amended. Claims 1-20 are currently pending and are subject to the final Office Action below.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 11-13, filed 02/04/2026, with respect to the 35 U.S.C. 101 rejection of Claims 1-20 have been fully considered and are not persuasive.
Applicant argues that the storing feature allows the dynamic transportation matching system to have more streamlined access to the models for real-time predictions and thus, the claims recite an improved data structure. Examiner respectfully disagrees.
The claims in Enfish were directed to a specific improvement to the way computers operate, embodied in the self-referential table. In contrast, Applicant’s claims merely allow for the storage of the models and then, access to the stored models which amounts to extra-solution activity of mere data storage.
It is also unclear how Applicant’s claims recite an improved data structure by storing the models in their own repository. The dynamic transportation matching system has access to the models for real-time predictions regardless of whether the models are stored in a model repository or a data repository.
Applicant further argues that the claims provide a non-abstract improvement to machine learning based data modeling and the storage of the models within a separate model repository is an improvement to data structure and data access. Examiner respectfully disagrees.
MPEP 2106.05(a) states “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” “Training dual models with machine learning techniques to produce a prediction” is directed to the abstract idea and thus, cannot provide the improvement.
Regarding the improvement to data structure/access, Examiner notes that Enfish recited a self-referential table that functioned differently than conventional database structures and the table provided an improvement to the ways computers operate. In contrast, Applicant’s claimed model repository is merely another storage location for data.
Regarding the improvement to machine learning based data modelling, Examiner notes that Desjardins provided an improvement to the computer component or system performance itself. In contrast, Applicant’s claims provided an improvement to the prediction of cancellation as the accuracy or timeliness of the real-time prediction is improved. However, such improvements are improvements to the abstract idea itself. See MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Accordingly, the 35 U.S.C. 101 rejection of Claims 1-20 is maintained.
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 is directed to an abstract idea without significantly more.
Step 1
Claims 1-11 are directed to a method (i.e., a process) and Claims 12-20 are directed to a system/non-transitory computer readable medium (i.e., a machine). Therefore, the claims all fall within the one of the four statutory categories of invention.
Step 2A - Prong 1:
Independent Claim 1, Claim 12, and Claim 20 recite:
receiving, …a request for transportation specifying a pickup location for the requestor;
matching, … the requestor with a provider for completion of the request;
sending, … the request for transportation;
calculating, … an estimated target for arrival of the provider at the pickup location based on an initial location of the provider;
monitoring, … a progress of the provider towards the pickup location, the monitoring including identifying a subsequent target for arrival of the provider at the pickup location based on a subsequent location of the provider;
determining in real time, … generating data-driven models…, that the matching of the requestor with the provider is eligible for cancellation, wherein the…generates and trains a first model to predict cancellation based on external factors and generates and trains a second model to predict cancellation based on the progress of the provider;
automatically cancelling, … the matching of the requestor with the provider based on determining in real time that the matching of the requestor with the provider is eligible for cancellation;
…notifies the provider of the cancellation…; and
updates an estimated time of arrival…
Certain Methods of Organizing Human Activity
The limitations stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (managing personal behavior or relationships or interactions between people or commercial or legal interactions). Specifically, commercial interactions between a requestor who provides a request for transportation and a service provider who provides the transportation service in light of paragraph 16 of Applicant’s specification “The present disclosure is generally directed to identifying, in real time, a transportation arrangement between a requestor and a provider that could benefit from a re-matching of the requestor with another provider. A dynamic transportation matching system may match a transportation provider with a transportation requestor to complete the transportation request. The dynamic transportation matching system may monitor a progress of the transportation provider to a pickup location as specified in the transportation request. Based on the monitored progress, the dynamic transportation matching system may determine if the transportation provider is making sufficient progress towards the pickup location. In some examples, the dynamic transportation matching system may use a data-driven model (e.g., based on machine learning) to determine if the matching of the transportation provider with a transportation requestor is eligible for cancellation by determining if the transportation provider is not making sufficient progress towards the pickup location. If the dynamic transportation matching system cancels the matching, the dynamic transportation matching system may then match another transportation provider with the transportation requestor to continue to make progress towards completing the transportation request.”
Such processes are specifically managing commercial interactions between a requestor and a service provider. Accordingly, the claims recite an abstract idea.
Mental Processes
Additionally, the broadest reasonable interpretation of “matching”, “calculating”, and “monitoring” fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2), subsection III.
Specifically, “matching” encompasses a user choosing a provider to complete the requestor’s request for transportation. “Calculating” encompasses a user (given an initial location of the provider) determining an estimated target for arrival of 20 minutes. “Monitoring” encompasses a user (given a subsequent location of the provider) determining a subsequent target for arrival of 30 minutes. Accordingly, the claims recite an abstract idea.
Mathematical Concepts
Further, the broadest reasonable interpretation of “calculating” encompasses mathematical concepts that can be performed mentally. “Determining” requires specific mathematical calculations (calculation of a probability that the transportation request will be cancelled) to perform the training of the first and second model and therefore encompasses mathematical concepts. Accordingly, the claims recite an abstract idea.
See Applicant’s specification paragraph 43 “may use the data-driven model(s) to calculate a cancel probability score at regular intervals during the travel time of a transportation request” and paragraph 54 “the cancellation module 410 may use the data included in the one or more data-driven models and the received progress information to calculate a probability that, at a particular point in time between the start of travel of the transportation provider 454 to the pickup location to the arrival of the transportation provider 454 at the pickup location, the transportation request 422 will be cancelled. The cancellation module 410 may compare the calculated probability to a threshold (e.g., a cancellation probability threshold value) to determine if the transportation request 422 is eligible for cancellation.”
Step 2A - Prong 2:
This judicial exception is not integrated into a practical application. The independent claims recite the additional elements of a dynamic transportation matching system, a computing device of the requestor, a computing device of the provider, machine learning, one or more physical processors, one or more memories, a non-transitory computer readable medium, at least one processor of a computing device, and the computing device which are recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP 2106.05(f).
Examiner additionally noting the computing device of the requestor and the computing device of the provider are merely receiving and transmitting data which is insignificant extra solution activity of necessary data gathering and data outputting as the system collects the request from the requestor and sends the request to the provider and notifies the provider of the cancellation and updates an estimated time of arrival for the requestor.
Additionally, the data-driven models generated using machine learning may be considered as generally linking the use of a judicial exception to a particular technological environment or field of use as the limitation merely confines the use of the abstract idea to a particular technological environment (data-driven models which are trained to predict cancellation) and thus fails to add an inventive concept to the claims.
The independent claims also recite the limitation of “wherein the first model and the second model are stored in a model repository separate from a data repository” which is extra-solution activity of mere data storage.
Thus, the claim as a whole, looking at additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components, adding insignificant extra-solution activity to the judicial exception, or field of use which does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claim does 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 elements of a dynamic transportation matching system, a computing device of the requestor, a computing device of the provider, machine learning, one or more physical processors, one or more memories, a non-transitory computer readable medium, at least one processor of a computing device, and the computing device to perform the steps/functions recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
Additionally, the computing device of the requestor and the computing device of the provider are performing functions similar to “receiving or transmitting data over a network” (Symantec) which the courts have recognized as well-understood, routine, and conventional activity. See MPEP 2106.05(d)(II).
Again, the data-driven models generated using machine learning may be considered as generally linking the use of a judicial exception to a particular technological environment or field of use as the limitation merely confines the use of the abstract idea to a particular technological environment (data-driven models which are trained to predict cancellation) and thus fails to add an inventive concept to the claims.
The limitation of “wherein the first model and the second model are stored in a model repository separate from a data repository” is similar to “storing and retrieving information in memory” which is a computer function the courts have recognized as well-understood, routine, and conventional when claimed as extra-solution activity. See MPEP 2106.05(d)(II).
None of the steps of Claim 1, Claim 12, and Claim 20 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to the abstract idea, amount to no more than mere instructions to apply the exception using a generic computer, extra solution activity, or field of use, thus, the analysis does not change when considered as an ordered combination. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1, Claim 12, and Claim 20 are ineligible.
Dependent Claim 2 specifies further what monitoring the progress of the provider comprises. The limitations of Claim 2 are further directed towards mathematical concepts, mental processes, and organizing human activity.
Dependent Claim 3, Claim 13, and Claim 14 specify that the first and second model are stored in the model repository which (as noted above) is extra-solution activity of mere data storage which is similar to the computer function of storing and retrieving information in memory that the courts have identified as well-understood, routine, and conventional activity.
Dependent Claim 4-5 and 13-14 specifies that the system uses machine learning to train the first and second model which is part of the abstract idea of mathematical concepts as identified above. Further, as identified above, the dynamic transportation matching system and machine learning amount to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
Dependent Claim 6 and Claim 15 specifies further what determining that the matching of the requestor with the provider is eligible for cancellation comprises. Claim 7 specifies what the actual travel progression is based on, Claim 8 and Claim 16 specifies what the predetermined travel progression is based on, and Claim 9 specifies what comparing the actual and predetermined travel progression comprises. Thus, the limitations are further directed towards mathematical concepts, mental processes, and organizing human activity.
Dependent Claim 10 (and similarly Claim 18) specifies using the first and second model to determine that the eligibility for cancellation is due to the progress of the provider and matching the requestor with a different provider without penalty to the requestor and Claim 11 (and similarly Claim 19) specifies using the first and second model to determine that the eligibility for cancellation is due to external factors and matching the requestor with a different provider without penalty to the provider. Such limitations are part of the abstract idea of mental processes, mathematical calculations, and organizing human activity.
Dependent Claim 17 specifies flagging the matching as eligible for cancellation and matching the requestor with a different provider which is further narrowing the abstract idea of organizing human activity.
Thus, nothing in dependent claims 2-11 and 13-19 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 are ineligible.
Closest Prior Art
The following is a statement of reasons for the indication of closest prior art:
Examiner noting that there is a 101 rejection on claims 1-20.
Available prior art fails to teach or suggest all of the limitations within the independent claims. Specifically, the limitation of “determining in real time, by the dynamic transportation matching system generating data-driven models using machine learning, that the matching of the requestor with the provider is eligible for cancellation, wherein the dynamic transportation matching system generates and trains a first model to predict cancellation based on external factors and generates and trains a second model to predict cancellation based on the progress of the provider”.
The following are closest prior art:
Quitoriano et al. (US2019/0164432) teaches the limitations of “receiving”, “matching”, “sending”, “calculating”, and “monitoring”.
Rakah et al. (US2018/0209803) teaches the limitation of “cancelling”.
Cirit et al. (US2023/0229966) teaches determining an ETA for a vehicle, using a machine learning model to determine a refined ETA, and based on the refined ETA, matching a driver to the trip.
Con et al. (US2023/0004931) teaches a plurality of regression models used to predict ETA and selecting the most accurate model to determine the ETA of a vehicle at a destination.
Di Lorenzo et al. (US2022/0164913) teaches processing received vehicle location data with a first model and a second model to determine ETA data, using the ETA data to reassigning an appointment from a first driver to a second driver, and modifying one or more models based on the ETA data.
Donnelly et al. (US2018/0342157) teaches a vehicle service cancellation threshold based on a machine learning model trained on obtained information.
Zhang et al. (US2018/0286003) teaches a machine learning model used to estimate waiting time and canceling a transportation service request when estimated waiting time is greater than a threshold time.
Moses et al. (US2015/0242819) teaches training data comprising past appointments and an indication of whether the past appointment was kept, generating a predictive model, and using the model to determine a future appointment will not be kept and to allow a second appointment to be scheduled as the same time as the future appointment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628