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 .
Introduction
The following is a final Office action in response to Applicant’s amended submission filed on 12/30/2025. Currently claims 1-20 are pending and claims 1, 9, and 15 are independent. Claims 1, 3-6, 11-13, 15, 17 have been amended from the previous claim set dated 9/26/2025. No claims have been added or cancelled.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-20, following the guidance set forth in MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-8) are directed to a statutory category, namely a method. Claim 9 and its dependent claims (claims 10-14) are directed to a statutory category, namely a system/machine. Claim 15 and its dependent claims (claims 16-20) are directed to a statutory category, namely a method.
Step 2A (Prong 1): Claims 1, 9, and 15, which are substantially similar claims to one another, are directed to the abstract idea of “Certain methods of organizing human activity”, or more particularly, “Concepts relating to commercial or legal interactions (including: advertising, marketing or sales activities or behaviors; business relations) (See MPEP 2106).” In this application that refers to using a computer system to manage and analyze the process of organizing rides. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that a dispatcher might perform for a taxi company. The abstract elements of claims 1, 9, and 15, recite in part “Receive request…Determine coverage features…Generate predicted coverage…Generate match…”. Dependent claims 2-8, 10-14, 16-20 add to the abstract idea the following limitations which recite in part “Receive locations…Determine coverage…Update inputs…Generate score…Generate metric…Determine response time…Generate response score…Generate metric…Combine weighted average…Generate filter…Exclude provider devices…Generate predicted time…Generate coverage metrics…Generate efficiency metric…generate match…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, and 15.
Step 2A (Prong 2): Independent claims 1, 9, and 15, which are substantially similar claims to one another, do not contain additional elements that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Processors…Server…Non-transitory computer readable medium…ML model…matching model…Markov model…Requestor device…Provider device…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-8, 10-14, 16-20 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 9, and 15, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Processors…Server…Non-transitory computer readable medium…ML model…matching model…Markov model…Requestor device…Provider device…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (manage and analyze the process of organizing rides) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0162] – “Embodiments of the present disclosure may comprise or utilize a special-purpose or general-purpose computer…”.
Additionally, dependent claims 2-8, 10-14, 16-20 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 3, 6, 9, 11, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 20200012974 A1) in view of Broyles et al. (US 20180259351 A1) further in view of Vora et al. (US 20200175632 A1)
Regarding claims 1 and 9, Dutta discloses a method comprising: receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors); determining, by the one or more servers, regional network coverage features corresponding to transportation provider devices and transportation requester devices of the region (Dutta Fig. 5); generating, by the one or more servers utilizing a machine-learning model (Dutta ¶66 - In some embodiments, ride services module 1008 may use rule-based algorithms and/or machine-learning models for matching requestors and providers), a predicted improvement metric resulting from not assigning a transportation provider device to the transportation request (Dutta ¶26 - The matching operations performed by the systems and methods described herein may account for simulated future scenarios (e.g., including simulated future requests) in order to determine whether to opportunistically make a transportation match (as in FIG. 1) or to decline immediately making the transportation match (as in FIG. 2)); and generating, by the one or more servers utilizing a transportation matching model, a transportation match for the transportation request from the predicted network coverage improvement metric (Dutta ¶52 - At step 950 the method may include matching, by the dynamic transportation matching system).
Dutta lacks a predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted outcomes resulting from not assigning respective transportation provider devices of the region to the transportation request according to the regional network coverage features, the one or more predicted outcomes comprising one or more of: (i) a predicted utilization of the transportation provider devices of the region, (ii) predicted response times or distances of the transportation provider devices of the region, (iii) predicted transportation times for transportation requests of the region, or (iv) predicted idle times of transportation provider devices or the region.
Broyles, from the same field of endeavor, teaches a predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted outcomes resulting from not assigning respective transportation provider devices of the region to the transportation request according to the regional network coverage features, the one or more predicted outcomes comprising one or more of: (i) a predicted utilization of the transportation provider devices of the region, (ii) predicted response times or distances of the transportation provider devices of the region, (iii) predicted transportation times for transportation requests of the region, or (iv) predicted idle times of transportation provider devices or the region (Broyles ¶55 - At step 618, the ride matching system may determine whether matching the request with one or more eligible providers increases the system efficiency or if the ride matching system should wait for the requestor arrival time to decrease, thus making more eligible provider matches available. The ride matching system may also wait for additional eligible providers to become available due to drop-offs from pre-existing matched rides or movement into the eligibility zone of the dynamic eligibility model. Accordingly, the ride matching system may determine the predicted provider availability for the request based on the requestor arrival time and determine whether an eligible match should be made or if the system should wait for additional available providers to become eligible. If the ride matching system determines that matching a provider would not increase the system efficiency based on the predicated availability, the ride matching system may return to step 604 and repeat the process until a match is made).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation provider eligibility techniques of Broyles because Broyles discloses “request matching systems are improved through the more efficient matching processing and fewer resources are required to process the same amount of requestor demand (Broyles ¶17)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation provider eligibility techniques that Broyles discloses because it would increase the efficiency of the system of Dutta by facilitating more efficient rider/provider matches.
Dutta further lacks generating, by the one or more servers utilizing a machine-learning model to process the regional network coverage features, predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted regional outcomes corresponding to a modeled assignment of respective existing transportation provider devices of the region to existing alternate transportation requests other than the transportation request according to the regional network coverage features.
Vora, from the same field of endeavor, teaches generating, by the one or more servers utilizing a machine-learning model to process the regional network coverage features, predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted regional outcomes corresponding to a modeled assignment of respective existing transportation provider devices of the region to existing alternate transportation requests other than the transportation request according to the regional network coverage features (Vora ¶22 - Based at least in part on transportation network condition data 202 and/or user preferences data 204, matching system 206 may generate tailored transportation option suggestions 210. In some examples, tailored transportation option suggestions 210 may include fewer options than default transportation option suggestions 208, may show options ordered differently than default transportation option suggestions 208, and/or may include different options than default transportation option suggestions 208. In some embodiments, tailored transportation option suggestions 210 may be tailored at least in part based on improving system metrics for the transportation network. For example, tailored transportation option suggestions 210 may include and/or highlight options that, if selected, improve overall estimated arrival time across the network, improve network utilization rates, reduce total distance traveled by transportation providers and/or requestors, reduce travel time for transportation providers and/or requestors, reduce fees paid (e.g., bridge tolls and/or express lane fees) by transportation providers, and/or improve any other relevant system metric).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation selection techniques of Vora because Vora discloses “advantages to dynamic transportation management and/or the field of transportation by improving the efficiency of dynamic transportation networks and/or improving user experience (Vora ¶18)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation selection techniques that Vora discloses because it would improve the efficiency of the system of Dutta by increasing the efficiency of rider/provider matches.
Regarding claim 2, Dutta in view of Broyles further in view of Vora discloses receiving requester location data from a plurality of transportation requester devices associated with a plurality of transportation requests within the region; receiving provider location data and provider availability indications from a plurality of transportation provider devices within the region; and determining the regional network coverage features based on the requester location data, the provider location data, and the provider availability indications (Dutta Fig. 4 – Dutta ¶66 - For example, after identity management services module 1004 has authenticated the identity a ride requestor, ride services module 1008 may attempt to match the requestor with one or more ride providers. In some embodiments, ride services module 1008 may identify an appropriate provider using location data obtained from location services module 1006. Ride services module 1008 may use the location data to identify providers who are geographically close to the requestor (e.g., within a certain threshold distance or travel time) and/or who are otherwise a good match with the requestor. Ride services module 1008 may implement matching algorithms that score providers based on, e.g., preferences of providers and requestors; vehicle features, amenities, condition, and/or status; providers' preferred general travel direction and/or route, range of travel, and/or availability; requestors' origination and destination locations, time constraints, and/or vehicle feature needs; and any other pertinent information for matching requestors with providers).
Regarding claim 3 and 11, Dutta in view of Broyles further in view of Vora discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors).
Vora further teaches updating one or more inputs to the machine-learning model based on observed outcomes of prior transportation matches within the region, the observed outcomes comprising one or more of actual provider utilization, actual response times, or actual idle times (Vora ¶34 - In one embodiment, a user event repository 910 that includes data about past requestor demographics and/or transportation option selections may supply the training and/or testing data) and generating the predicted network coverage improvement metrics based on the one or more updated inputs to the machine-learning model (Vora ¶22 - In some embodiments, tailored transportation option suggestions 210 may be tailored at least in part based on improving system metrics for the transportation network. For example, tailored transportation option suggestions 210 may include and/or highlight options that, if selected, improve overall estimated arrival time across the network, improve network utilization rates, reduce total distance traveled by transportation providers and/or requestors, reduce travel time for transportation providers and/or requestors, reduce fees paid (e.g., bridge tolls and/or express lane fees) by transportation providers, and/or improve any other relevant system metric).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation selection techniques of Vora because Vora discloses “advantages to dynamic transportation management and/or the field of transportation by improving the efficiency of dynamic transportation networks and/or improving user experience (Vora ¶18)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation selection techniques that Vora discloses because it would improve the efficiency of the system of Dutta by increasing the efficiency of rider/provider matches.
Regarding claim 6 and 12, Dutta in view of Broyles further in view of Vora further in view of Bentley discloses excluding the given transportation provider device from consideration for matching with the transportation request based on the predicted network coverage improvement metrics (Dutta ¶56 - In some examples, the method may include declining to commit to a matching in light of an additional simulated future transport request... (v) declining to match, by the dynamic transportation matching system, the third transport request with the fourth transport request based at least in part on the fitness of matching the third transport request with the fourth transport request and based at least in part on the fitness of matching the third transport request with the additional simulated future transport request).
Vora further teaches determining that assigning a given transportation provider device to the transportation request would cause the predicted network coverage improvement metrics to fail to satisfy a threshold condition (Vora ¶28 - At step 630, the dynamic transportation matching system may filter out options with undesirable network impacts. For example, the dynamic transportation matching system may filter out options that would add delay for other transportation requestors, increase provider utilization above a predetermined threshold, decrease provider utilization below a predetermined threshold, cause providers and/or capacity to be geographically distributed in a sub-optimal way (e.g. by moving a high-capacity provider from an area of high demand to an area of low demand), and/or any other undesirable impact).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation selection techniques of Vora because Vora discloses “advantages to dynamic transportation management and/or the field of transportation by improving the efficiency of dynamic transportation networks and/or improving user experience (Vora ¶18)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation selection techniques that Vora discloses because it would improve the efficiency of the system of Dutta by increasing the efficiency of rider/provider matches.
Regarding claim 14, Dutta in view of Broyles further in view of Vora discloses determine a predicted request metric based on an estimated travel distance or an estimated travel time of the transportation request (Dutta ¶40 - For example, the evaluation of the utility of a match may include, without limitation, the estimated time of arrival for each requestor to their respective destinations); and generate the transportation match based on a difference between the predicted request metric and the predicted network coverage improvement metric (Dutta ¶46 - FIG. 8 illustrates an example matching scheme 800 for active transportation requests. In some examples matching scheme 800 may represent a set of matching decisions performed with the use of simulated future scenarios 610(1)-(n). As shown in in FIG. 8, the dynamic transportation matching system may perform matchings 830 (in contrast to matchings 530 in FIG. 5) with the use of simulated future scenarios 610(1)-(n)).
Claims 4, 5, 7, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 20200012974 A1) in view of in view of Broyles et al. (US 20180259351 A1) further in view of Vora et al. (US 20200175632 A1) further in view of Bentley et al. (US-10832294-B1)
Regarding claim 4, Dutta in view of Broyles further in view of Vora discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors).
Dutta in view of Broyles further in view of Vora lacks determining, utilizing a device utilization model, a threshold response time associated with transportation requests in the region from network coverage for the region; generating a response time score based on a predicted response time for the transportation request and the threshold response time; and generating the predicted network coverage improvement metric further based on the response time score
Bentley, from the same field of endeavor, teaches determining, utilizing a device utilization model, a threshold response time associated with transportation requests in the region from network coverage for the region; generating a response time score based on a predicted response time for the transportation request and the threshold response time; and generating the predicted network coverage improvement metric further based on the response time score (Bentley COL 24 ROW 59 - Additionally illustrated in FIG. 16, the provider efficiency control system 102 includes the storage manager 1616 configured to store any suitable type of data. As shown, the storage manager 1616 can store target parameters 1618, such as time thresholds, lapse/cancellation thresholds, ETA thresholds, provider efficiency parameter thresholds, etc. In some embodiments, the storage manager 1616 can store the target parameters 1618 and/or any other parameters. Additionally, the storage manager 1616 can permit adjustment, configuration, and/or optimization of the target parameters 1618 and other parameters as applicable).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the dynamic transportation provider techniques of Bentley because Bentley discloses “the provider efficiency control system can lend to improved experiences for providers (Bentley COL 3 ROW 49)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional dynamic transportation provider techniques that Bentley discloses because it would improve the provider experience.
Regarding claim 5, Dutta in view of Broyles further in view of Vora further in view of Bentley discloses combining a weighted average of the provider device utilization score and the response time score (Dutta ¶40 - For example, optimization module 424 may compute a fitness score using a formula that incorporates one or more of the above factors as weighted terms).
Regarding claim 7, Dutta in view of Broyles further in view of Vora further in view of Bentley discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors).
Bentley further teaches generating a predicted transportation time and a predicted idle time for the transportation provider device in connection with the transportation request; and generating the predicted network coverage improvement metric further based on the predicted transportation time and the predicted idle time (Bentley COL 3 ROW 37 - Additionally, by creating an appropriately sized pool of available providers, the provider efficiency control system of the present disclosure can enable the dynamic transportation matching system to more accurately match transportation requests and providers with improved efficiencies (e.g., less estimated time to arrival or “ETA,” less idle time, etc.). Further, the provider efficiency control system can reduce human decision variables introduced to the dynamic transportation matching system by providing dynamic responses or dynamic recommendations to transportation providers that generate more predictable results).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the dynamic transportation provider techniques of Bentley because Bentley discloses “the provider efficiency control system can lend to improved experiences for providers (Bentley COL 3 ROW 49)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional dynamic transportation provider techniques that Bentley discloses because it would improve the provider experience.
Regarding claim 13, Dutta in view of Broyles further in view of Vora further in view of Bentley discloses generate an additional predicted network coverage improvement metric resulting from not assigning an additional transportation provider device to an additional transportation request based on the provider device utilization score and the additional threshold response time (Dutta ¶26 - The matching operations performed by the systems and methods described herein may account for simulated future scenarios (e.g., including simulated future requests) in order to determine whether to opportunistically make a transportation match (as in FIG. 1) or to decline immediately making the transportation match (as in FIG. 2)).
Bentley further teaches generate an additional response time score based on an additional threshold response time associated with transportation requests in an additional geohash of the region from network coverage for the additional geohash (Bentley COL 6 ROW 3 - In other examples, the geohash conditions 202 can include an assortment of metrics and data combinations. For instance, the geohash conditions 202 can include: a mean driver idle time in which a provider device is in the online mode 210 but not matched with a transportation request; a mean pickup time in which a provider device 105 takes to respond to a transport request; a mean service time in which a provider device 105 is associated with transporting a requestor; or any other suitable metric or derivative thereof. In these or other embodiments, one or more metrics comprising the geohash conditions 202 may be combined across any number of the provider devices 105a-105n and/or across one or more geohashes, and/or otherwise mathematically combined, smoothed, normalized, optimized, compared, predicted, etc).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the dynamic transportation provider techniques of Bentley because Bentley discloses “the provider efficiency control system can lend to improved experiences for providers (Bentley COL 3 ROW 49)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional dynamic transportation provider techniques that Bentley discloses because it would improve the provider experience.
Claims 8, 10, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 20200012974 A1) in view of in view of Broyles et al. (US 20180259351 A1) further in view of Vora et al. (US 20200175632 A1) further in view of Chachra et al. (US-10706487-B1)
Regarding claim 8, Dutta in view of Broyles further in view of Vora discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors).
Dutta in view of Broyles further in view of Vora lacks generating an unmatched requester device efficiency metric for the transportation request by utilizing a Markov decision policy corresponding to a plurality of possible request states; and generating the transportation match based on the predicted network coverage improvement metric and the unmatched requester device efficiency metric.
Chachra, from the same field of endeavor, teaches generating an unmatched requester device efficiency metric for the transportation request by utilizing a Markov decision policy corresponding to a plurality of possible request states; and generating the transportation match based on the predicted network coverage improvement metric and the unmatched requester device efficiency metric (Chachra COL 15 ROW 52 - Having received inputs from the dynamic transportation matching system 102, the first machine learner 202 applies an algorithm to the inputs to generate the efficiency parameters 208. For example, in some embodiments, the first machine learner 202 applies a stochastic gradient descent to the inputs. In some such embodiments, the first machine learner 202 applies a lognormal-maximum-likelihood function to the inputs when performing a stochastic gradient descent. Alternatively, in some embodiments, the first machine learner 202 applies another suitable algorithm to the inputs to generate the efficiency parameters 208. Such algorithms may include Kalman filters; Particle filters, which are sometimes called Sequential Monte Carlo (“SMC”) methods; Monte Carlo simulations, such as Markov chain Monte Carlo (“MCMC”); or Hidden Markov Models (“HMM”)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Regarding claim 10, Dutta in view of Broyles further in view of Vora discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors).
Dutta in view of Broyles further in view of Vora lacks determine a difference between a number of transportation requester devices and a number of transportation provider devices within the region.
Chachra, from the same field of endeavor, teaches determine a difference between a number of transportation requester devices and a number of transportation provider devices within the region (Chachra COL 3 ROW 52 - the dynamic transportation matching system optionally inputs, for certain time periods of a specific geographic neighborhood, a number of transportation requests and a number of available transportation vehicles).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta (US 20200012974 A1) in view of in view of Broyles et al. (US 20180259351 A1) further in view of Chachra et al. (US-10706487-B1)
Regarding claim 15, Dutta discloses receiving, by one or more servers, a transportation request from a transportation requester device in a region (Dutta ¶27 - In one example, dynamic transportation matching system 310 may coordinate transportation matchings within a single region for 50,000 vehicles or more on a given day. In some examples, vehicles 320 may collectively form a dynamic transportation network that may provide transportation supply on an on-demand basis to transportation requestors); determining, by the one or more servers, a plurality of state features corresponding to a plurality of possible request states of the transportation request in connection with one or more transportation provider devices (Dutta ¶26 - The matching operations performed by the systems and methods described herein may account for simulated future scenarios (e.g., including simulated future requests) in order to determine whether to opportunistically make a transportation match (as in FIG. 1) or to decline immediately making the transportation match (as in FIG. 2)); probabilities of transitioning between the plurality of possible request states of the transportation request (Dutta ¶33 - Simulation module 422 may use any of a variety of information to generate simulated future scenarios. In some examples, simulated module 422 may start with a current world state and generate one or more potential future scenarios based on the probabilities of various transitions from the current world state to potential future world states. Additionally or alternatively, simulation module 422 may use historical statistical data to generate probability distributions of future scenarios (and then, e.g., generate one or more specific scenarios from the generated probability distributions)) and generating, by the one or more servers utilizing a transportation matching model, a transportation match for a matched transportation provider device utilizing the unmatched requester device efficiency metric (Dutta ¶52 - At step 950 the method may include matching, by the dynamic transportation matching system).
Dutta lacks a predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted outcomes resulting from not assigning respective transportation provider devices of the region to the transportation request according to the regional network coverage features, the one or more predicted outcomes comprising one or more of: (i) a predicted utilization of the transportation provider devices of the region, (ii) predicted response times or distances of the transportation provider devices of the region, (iii) predicted transportation times for transportation requests of the region, or (iv) predicted idle times of transportation provider devices or the region.
Broyles, from the same field of endeavor, teaches a predicted network coverage improvement metrics indicating respective driver opportunity costs in relation to one or more predicted outcomes resulting from not assigning respective transportation provider devices of the region to the transportation request according to the regional network coverage features, the one or more predicted outcomes comprising one or more of: (i) a predicted utilization of the transportation provider devices of the region, (ii) predicted response times or distances of the transportation provider devices of the region, (iii) predicted transportation times for transportation requests of the region, or (iv) predicted idle times of transportation provider devices or the region (Broyles ¶55 - At step 618, the ride matching system may determine whether matching the request with one or more eligible providers increases the system efficiency or if the ride matching system should wait for the requestor arrival time to decrease, thus making more eligible provider matches available. The ride matching system may also wait for additional eligible providers to become available due to drop-offs from pre-existing matched rides or movement into the eligibility zone of the dynamic eligibility model. Accordingly, the ride matching system may determine the predicted provider availability for the request based on the requestor arrival time and determine whether an eligible match should be made or if the system should wait for additional available providers to become eligible. If the ride matching system determines that matching a provider would not increase the system efficiency based on the predicated availability, the ride matching system may return to step 604 and repeat the process until a match is made).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation provider eligibility techniques of Broyles because Broyles discloses “request matching systems are improved through the more efficient matching processing and fewer resources are required to process the same amount of requestor demand (Broyles ¶17)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation provider eligibility techniques that Broyles discloses because it would increase the efficiency of the system of Dutta by facilitating more efficient rider/provider matches.
Dutta further lacks determining, by the one or more servers, a Markov decision model policy corresponding to the plurality of possible request states based on the plurality of state features; generating, by the one or more servers, an unmatched requester device efficiency metric for the transportation request utilizing the Markov decision model policy corresponding to the plurality of possible request states.
Chachra, from the same field of endeavor, teaches determining, by the one or more servers, a Markov decision model policy corresponding to the plurality of possible request states based on the plurality of state features; generating, by the one or more servers, an unmatched requester device efficiency metric for the transportation request utilizing the Markov decision model policy corresponding to the plurality of possible request states (Chachra COL 15 ROW 52 - Having received inputs from the dynamic transportation matching system 102, the first machine learner 202 applies an algorithm to the inputs to generate the efficiency parameters 208. For example, in some embodiments, the first machine learner 202 applies a stochastic gradient descent to the inputs. In some such embodiments, the first machine learner 202 applies a lognormal-maximum-likelihood function to the inputs when performing a stochastic gradient descent. Alternatively, in some embodiments, the first machine learner 202 applies another suitable algorithm to the inputs to generate the efficiency parameters 208. Such algorithms may include Kalman filters; Particle filters, which are sometimes called Sequential Monte Carlo (“SMC”) methods; Monte Carlo simulations, such as Markov chain Monte Carlo (“MCMC”); or Hidden Markov Models (“HMM”)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Regarding claim 16, Dutta in view of Broyles further in view of Chachra discloses determining provider characteristics of a plurality of transportation provider devices within the region; and determining the plurality of state features and the plurality of possible request states based on the provider characteristics (Dutta ¶33 - Simulation module 422 may use any of a variety of information to generate simulated future scenarios. In some examples, simulated module 422 may start with a current world state and generate one or more potential future scenarios based on the probabilities of various transitions from the current world state to potential future world states).
Regarding claim 17, Dutta in view of Broyles further in view of Chachra discloses generating a transition matrix comprising probabilities of transitioning between the plurality of possible request states based on characteristics of a region or a sub-region associated with the transportation request (Dutta ¶33 - Simulation module 422 may use any of a variety of information to generate simulated future scenarios. In some examples, simulated module 422 may start with a current world state and generate one or more potential future scenarios based on the probabilities of various transitions from the current world state to potential future world states).
Chachra further teaches determining the Markov decision model policy based on the probabilities of transitioning between the plurality of possible request states (Chachra COL 15 ROW 52 - Alternatively, in some embodiments, the first machine learner 202 applies another suitable algorithm to the inputs to generate the efficiency parameters 208. Such algorithms may include Kalman filters; Particle filters, which are sometimes called Sequential Monte Carlo (“SMC”) methods; Monte Carlo simulations, such as Markov chain Monte Carlo (“MCMC”); or Hidden Markov Models (“HMM”)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Regarding claim 18, Dutta in view of Broyles further in view of Chachra discloses determining a remaining time associated with the transportation request (Dutta ¶21 - In some examples, the dynamic transportation matching system may operate under one or more constraints and/or objectives to fulfill submitted requests within a short amount of time following the requests (e.g., within 30 seconds, within 60 seconds, within 90 seconds, etc.)).
Chachra further teaches generating the unmatched requester device efficiency metric for the transportation request utilizing the Markov decision model policy according to the remaining time associated with the transportation request (Chachra COL 15 ROW 52 - Alternatively, in some embodiments, the first machine learner 202 applies another suitable algorithm to the inputs to generate the efficiency parameters 208. Such algorithms may include Kalman filters; Particle filters, which are sometimes called Sequential Monte Carlo (“SMC”) methods; Monte Carlo simulations, such as Markov chain Monte Carlo (“MCMC”); or Hidden Markov Models (“HMM”)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Regarding claim 19, Dutta in view of Broyles further in view of Chachra discloses receiving an additional transportation request from an additional transportation requester device in the region (Dutta ¶31- As an example, request identification module 420 may identify active requests 410 (e.g., requests submitted by transportation requestors that have yet to be matched with a transportation provider and/or other transportation requestors; and/or requests that are available to be matched with additional transportation providers and/or additional transportation requestors)); and generating an additional unmatched requester device efficiency metric for the additional transportation request utilizing the additional Markov decision model policy (Dutta ¶41 - Matching module 426 may then issue matches 440, corresponding to the matches identified in the first stage by optimization module 424. In some examples, this may leave unmatched requests 442, which may populate active requests 410. Dynamic transportation matching system 310 may continue searching for matches for active requests 410 (e.g., unmatched requests 442 and any newly added requests) by repeating the steps described above performed by dynamic transportation matching modules 312).
Chachra further teaches determining an additional Markov decision model policy corresponding to the additional transportation request (Chachra COL 15 ROW 52 - Alternatively, in some embodiments, the first machine learner 202 applies another suitable algorithm to the inputs to generate the efficiency parameters 208. Such algorithms may include Kalman filters; Particle filters, which are sometimes called Sequential Monte Carlo (“SMC”) methods; Monte Carlo simulations, such as Markov chain Monte Carlo (“MCMC”); or Hidden Markov Models (“HMM”)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic transportation methodology/system of Dutta by including the transportation matching techniques of Chachra because Chachra discloses “systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area (Chachra ABS)”. Additionally, Dutta further details that “The present disclosure is generally directed to matching transportation requests (Dutta ¶15)” so it would be obvious to consider including the additional transportation matching techniques that Chachra discloses because it would improve the system of Dutta increasing efficiency of the matching process.
Regarding claim 20, Dutta in view of Broyles further in view of Chachra discloses generating the transportation match for the matched transportation provider device utilizing the unmatched requester device efficiency metric and the additional unmatched requester device efficiency metric (Dutta ¶41 - Matching module 426 may then issue matches 440, corresponding to the matches identified in the first stage by optimization module 424. In some examples, this may leave unmatched requests 442, which may populate active requests 410. Dynamic transportation matching system 310 may continue searching for matches for active requests 410 (e.g., unmatched requests 442 and any newly added requests) by repeating the steps described above performed by dynamic transportation matching modules 312).
Response to Arguments
Applicant's arguments filed 12/30/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to the MPEP guidance for 35 USC § 101 rejections, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application.
Applicant first argues that the claimed invention is patent eligible because the claimed invention is an improvement to a technology. Examiner also does not find this persuasive because the claimed invention is interpreted as an improvement to the business process of matching rides that is facilitated through the use of a computer, and not an improvement to a computer (e.g. inventing a faster processor or more efficient memory).
Applicant also argues that the claims are patent eligible because they are integrated into a practical application. Examiner does not find this persuasive because, as addressed above, the identified additional elements merely limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Examiner will note that the training of the ML model potentially provides a route to overcoming the 101 rejection, however training/retraining of ML models is not sufficient by itself to overcome the 101 rejection. Examiner might suggest reviewing claim 2 of example 47 (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf) or the recent Federal Circuit ruling in Recentive v. Fox (https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf).
Regarding the 35 USC § 103 rejections on the previous Office action, Applicant amended the independent claims to further limit the claims with respect system wide predicted improvements. In light of this amendment, Examiner agrees that the previously cited references did not clearly teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Vora as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Conclusion
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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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624