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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 10, 2026 has been entered.
Status of the Claims
Claims 1-16 were previously pending and subject to a final rejection dated November 28, 2025. In the RCE, submitted on February 10, 2026, claims 1-2, 9-10, and 12 were amended. Therefore, claims 1-16 are currently pending and subject to the below non-final rejection.
Response to Arguments
Applicant’s Remarks on Pages 7-14 of the RCE, regarding the previous rejection of the claims under 35 U.S.C. 101 have been fully considered but are not found persuasive.
On Pages 7-8 of the RCE, in comparing Applicant’s claims to Countour v GoPro, Applicant states “independent claim 1 includes computations that are not abstract, but are instead applied in a structured and integrated way that results in an improvement to computer-related technology. In particular, amended independent claim 1 describes a computer- implemented method to adaptively allocate one of a plurality of service requests to direct a service provider to move from one location to another to complete each of the plurality of service requests. A predicted severity level detection module computes a first predicted severity level indicated in a service request of the plurality of service requests based on real-time location-based data during an estimated arrival time window. The real-time location-based data received from a communication device of the service provider indicates dynamic location of the available service providers. The predicted severity level detection module further computes a second predicted severity level based on the real-time location-based data. A priority level comparison module evaluates a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted. A priority level calculation module then adjusts the priority level of the service request based on the evaluated level difference. A service request allocation module then allocates one service request associated with the highest priority level to the service provider based on the adjusted priority level, directing the available service providers to move towards the allocated service request based on an indication on the communication device of the service provider. This improves the functioning of the vehicle dispatch systems during peak load conditions by enabling the processor to effectively compute priorities, associated with an available service provider based on real-time location specific data to fulfil a service request. By efficiently executing the computer-implemented method, via specific modules, the method reduces computational overhead, enhances scalability, and increases responsiveness of the vehicle allocation engine based on the optimized predictions of arrival time windows in real-time. As a result, the vehicle dispatch systems achieve faster decision-making by accurate predictions and greater reliability under high-demand scenarios.”
Examiner respectfully disagrees and notes, nothing in claims or specification discloses “an improvement to computer-related technology” as Applicant alleges. See MPEP 2106.04(d)(1), “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.”) (emphasis added).
Here nothing in the claims or specification discloses an improvement in “the functioning of the vehicle dispatch systems during peak load conditions” or “reduces computational overhead, enhances scalability, and increases responsiveness of the vehicle allocation engine” that would qualify as an improvement in the functioning of a computer, or an improvement to other technology or a technical field. Rather, “when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)” it is not sufficient to show an improvement in computer-functionality. Thus, the bare assertion of an improvement without the necessary details are not found persuasive.
Examiner further notes that “adaptively allocate one of a plurality of service requests to direct a service provider to move from one location to another to complete each of the plurality of service requests…computes a first predicted severity level indicated in a service request of the plurality of service requests based on …location-based data during an estimated arrival time window. The…location-based data received from …the service provider indicates dynamic location of the available service providers….further computes a second predicted severity level based on the…location-based data….evaluates a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted… then adjusts the priority level of the service request based on the evaluated level difference… then allocates one service request associated with the highest priority level to the service provider based on the adjusted priority level, directing the available service providers to move towards the allocated service request based on an indication….of the service provider” are all limitations that recite the abstract idea. It is unclear what technical components of the “vehicle dispatch system” are improved, as alleged.
Similar to Trading Technologies, it appears Applicant is arguing an improvement to a business process of service provider allocation (“effectively compute priorities, associated with an available service provider based on….location specific data to fulfil a service request…the optimized predictions of arrival time windows in real-time. As a result, the vehicle dispatch systems achieve faster decision-making by accurate predictions and greater reliability under high-demand scenario”) rather than an improvement to any underlying technology or computers (e.g., real-time tracking technology, “specific modules”, communication device or the processor). See MPEP 2106.05(a)(II) “in Trading Technologies... the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (emphasis added). Thus, Applicant’s arguments are not found persuasive.
On Pages 8-9 of the RCE, Applicant argues “ When service provider availability is low, the number of real-time service requests that can be fulfilled in a given location is limited by the number of available drivers nearby. This condition is alleviated via the claimed method by adaptively allocating service requests by computing severity level based on real-time location-based data to a service provider. This is facilitated via accurate prediction of estimated time of arrival (ETA) window that helps efficiently allocate service requests to the service providers who would become available in the high-demand location. Further, ETAs are updated in regular time intervals to avoid redundancy and computational overheads, and to ensure accurate predictions of nearby service providers for service request allocation. The computer-implemented complex predictive and adaptive allocation method described in amended independent claim 1 involves computing severity levels of a lack of available service providers for completing available service requests condition at a destination and current location based on real-time location-based data. The location-based data is received from a communication device of the service provider. Furthermore, a service request allocation module allocates the service request with the highest priority to the service provider based on adjusted priority level to direct the available service providers to move from one location to another to complete the each of the plurality of service requests. Thus, this multi-factor, time-sensitive decision-making method requires real-time adaptive prioritization to allocate highest priority service requests to service providers based on predicted ETA windows to facilitate effective re-routing of the service providers. Further, the volume of real-time location specific data computing, the need for precise timing calculation (such as estimated arrival time), and the complexity of evaluating multiple service requests to cause movement of service providers to a region where there is lack of available service providers, make it a process that is beyond mere organising human activity or business process.”
Examiner respectfully disagrees and notes that “adaptively allocating service requests by computing severity level based on….location-based data to a service provider…via accurate prediction of estimated time of arrival (ETA) window that helps efficiently allocate service requests to the service providers who would become available in the high-demand location. Further, ETAs are updated in regular time intervals to avoid redundancy and computational overheads, and to ensure accurate predictions of nearby service providers for service request allocation. The… complex predictive and adaptive allocation method described in amended independent claim 1 involves computing severity levels of a lack of available service providers for completing available service requests condition at a destination and current location based on…location-based data. The location-based data is received from …the service provider. Furthermore…. allocates the service request with the highest priority to the service provider based on adjusted priority level to direct the available service providers to move from one location to another to complete the each of the plurality of service requests. Thus, this multi-factor, time-sensitive decision-making method requires…adaptive prioritization to allocate highest priority service requests to service providers based on predicted ETA windows to facilitate effective re-routing of the service providers. Further, the volume of…location specific data computing, the need for precise timing calculation (such as estimated arrival time), and the complexity of evaluating multiple service requests to cause movement of service providers to a region where there is lack of available service providers” are all limitations that recite the abstract idea.
Additionally, as discussed above, “when the increased speed comes solely from the capabilities of a general-purpose computer” it is not sufficient to show an improvement in computer-functionality (See FairWarning). Thus, processing “the volume of real-time location specific data” and “evaluating multiple service requests” with the use of generic computer components does not take the claim out of the grouping of a certain method of organizing human activity (e.g., commercial interaction).
On Page 9 of the Response, Applicant further argues “This multi-step method operates in a structured, algorithmic manner within a computerized environment, solving a concrete technical problem: how to process real-time location-based data received from a communication device of the service provider to dynamically allocate requests and optimize service provider distribution via accurate ETA prediction. Amended independent claim 1 recites an efficient computer-implemented method for accurately predicting arrival time windows, such as estimated arrival time window of service providers for prioritizing and assigning service requests at both the origin and destination locations. Further, the method is executed via specific modules that are not generic computer components but specially designed technical components. These modules improve the accuracy of dispatch systems by efficient allocation during peak hours and reduce operational overheads, enhancing scalability, and increasing responsiveness of the vehicle dispatch system. See Application …Therefore, the steps described in amended independent claim 1 represent a technological improvement in the domain of real-time service dispatch systems and are not mere steps for organising a human activity.”
Examiner respectfully disagrees and again notes, it is unclear what “technical problem” is being solved (e.g., what technical problem associated with real-time technology). Applicant recites “real-time location-based data received from a communication device” at a high-level of generality such that when viewed as 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)).
On Paged 10-11 of the RCE, in arguing that the claims intergrade the abstract idea into a practical application, Applicant attempts to analogize the claims to claim 1 of Example 40 by stating “Similarly, amended independent claim 1 collects real-time location-based data received from a communication device of the service provider and utilizes the collected real-time location-based data to compute severity levels to re-route service providers from one location to another. This improves the accuracy of vehicle dispatch systems. The service providers are re-routed by prioritising service requests based on an adjusted priority level by computing severity levels based on real-time location-based data. This adaptive allocation process is not mere organizing human activity, as it requires real-time analysis of dynamic location-based data and priority-based decision-making across multiple service requests to predict accurate arrival time windows for service requests allocation. The computer-implemented method thus improves responsiveness of the allocation engine. As a result, the vehicle dispatch systems achieve faster decision-making by accurate predictions and greater reliability under high-demand scenarios and are not directed to merely organizing human activity or business processes.”
Examiner respectfully disagrees and notes the method of Claim 1 of Example 40 limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. Here, nothing in the claims recites an improvement in any claimed network performance. As reiterated above, “collect…location-based data received from…the service provider and utilizes the collected… location-based data to compute severity levels to re-route service providers from one location to another. This improves the accuracy of vehicle dispatch systems. The service providers are re-routed by prioritising service requests based on an adjusted priority level by computing severity levels based on….location-based data…. analysis of dynamic location-based data and priority-based decision-making across multiple service requests to predict accurate arrival time windows for service requests allocation….As a result, the vehicle dispatch systems achieve faster decision-making by accurate predictions and greater reliability under high-demand scenarios” describe the abstract idea. The high-level recitation of “real-time” data, a communication device, and allocation engine amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
On Page 11 of the RCE, Applicant further argues “Amended independent claim 1, thus, reflects ‘an improvement in the functioning of a computer, or an improvement to other technology or technical field’. The claim elements, when considered as a whole and read in light of the specification, reflect a specific and structured computer-implemented technique that facilitates accurate ETA prediction of the service provider and uses the predicted ETAs for efficient allocation of service requests to service providers in real time, reducing computational overhead during supply crunch conditions. Further, the method accurately computes real-time severity levels at both destination and current location of the service provider by considering current time window and few next time windows to keep allocation accurate. The re-routing of service providers by dynamically adjusting service request priorities based on real-time location-based data results in improved dispatching systems thus solving a real- world technical problem of sub-optimal dispatching systems. See Application, paragraphs [0003], [0020], [0045], and [0062].”
Examiner respectfully disagrees and as discussed above, it is unclear what specific technology in the dispatching system is improved. None of the above cited paragraphs in the specification disclose an improvement in technology (e.g., technology in real-time location data). Therefore, as noted above “facilitates accurate ETA prediction of the service provider and uses the predicted ETAs for efficient allocation of service requests to service providers …., reducing computational overhead during supply crunch conditions. Further, the method accurately computes… severity levels at both destination and current location of the service provider by considering current time window and few next time windows to keep allocation accurate. The re-routing of service providers by dynamically adjusting service request priorities based on … location-based data results in improved dispatching systems thus solving a real- world technical problem of sub-optimal dispatching systems” recite the abstract idea.
On Pages 11-13 of the RCE, in arguing that “claim 1 amounts to ‘significantly more’ than an abstract idea” Applicant states “Independent claim 1 amounts to ‘significantly more’ at least because the ‘additional elements’ add ‘a specific limitation other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application.’ The elements claimed in independent claim 1 are not routine or conventional in the field of service request allocation for location-based delivery systems. The features of amended independent claim 1 ‘computing, via a predicted severity level detection module, a first predicted severity level, based on real-time location-based data ... at a destination location ... during an estimated arrival time window ... the real-time location-based data, indicating dynamic location of the available service providers, received from a communication device of the service provider; computing, via the predicted severity level detection module, a second predicted severity level, based on the real-time location-based data,... at a current location of the service provider ...; evaluating, via a priority level comparison module, a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted; adjusting, via a priority level calculation module, the priority level of the service request based on the evaluated level difference...; allocating ... via a service request allocation module, one service request associated with the highest priority level ... to the service provider; directing the service provider towards the allocated service request based on an indication on the communication device of the service provider,’ qualify as ‘significantly more’ not only because they are not generic computer functions, but also because they add specific limitations that are unconventional. As the Federal Circuit elaborated in BASCOM, ‘[t]he inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art... [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.’”
Examiner respectfully disagrees and as discussed above “‘computing, via a predicted severity level detection module, a first predicted severity level, based on…location-based data ... at a destination location ... during an estimated arrival time window ... the....location-based data, indicating dynamic location of the available service providers, received from …the service provider; computing, via the predicted severity level detection module, a second predicted severity level, based on the real-time location-based data,... at a current location of the service provider ...; evaluating, via a priority level comparison module, a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted; adjusting, via a priority level calculation module, the priority level of the service request based on the evaluated level difference...; allocating ... via a service request allocation module, one service request associated with the highest priority level ... to the service provider; directing the service provider towards the allocated service request based on an indication on the communication device of the service provider,’” describe the abstract idea; and the additional elements of “real-time” data and a communication device amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)). Therefore is not persuasive that the use of “real-time” data or a communication device are additional elements that add “a specific limitation other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application” as alleged.
On Page 13 of the RCE, Applicant further argues “Conventionally, service request allocation systems rely on incentive-based repositioning strategies that depend on service provider discretion. Conventional dispatch systems struggle to adapt to dynamic supply-demand imbalances, especially during peak hours or in underserved locations. Traditional approaches do not incorporate predictive detection of future provider shortages, nor do they dynamically adjust service request priority based on predicted detection. As a result, service platforms face challenges in optimizing provider distribution and fulfilling service requests efficiently. See Application paragraphs [0002] and [0003]. In contrast, amended independent claim 1 describes a non-conventional, computer- implemented method that processes real-time location data through specific modules to predict service providers' arrival time windows, such as estimated arrival time (ETA) window at destination location and considers current time windows and next few time windows at current location to compute severity levels for adaptive allocation of service requests by adjusting a priority level of the service request. The method operates within a computing system and evaluates predicted severity levels at both the destination and current locations of the service provider. The method then adjusts the priority of service requests, and effectively allocates, in real-time, the service request with the highest priority to the service provider. These computational steps represent ‘significantly more’ than known practices and are thus not a mere step of organizing human activity or business process. This unconventional arrangement of operations, as emphasized in BASCOM, amounts to ‘significantly more’ than an abstract idea of mental processes. It provides a specific technical solution that improves vehicle dispatch systems by predicting accurate arrival time windows and computing accurate severity levels for efficient allocations of service requests, facilitating improvements for real-time service platforms such as instant delivery or ride-hailing systems.”
Examiner respectfully disagrees and notes similar to Trading Technologies, it appears Applicant is arguing an improvement to a business process of service request allocation (“predict service providers' arrival time windows, such as estimated arrival time (ETA) window at destination location and considers current time windows and next few time windows at current location to compute severity levels for adaptive allocation of service requests by adjusting a priority level of the service requests”, “evaluates predicted severity levels at both the destination and current locations of the service provider. The method then adjusts the priority of service requests, and effectively allocates… the service request with the highest priority to the service provider” and “improves vehicle dispatch systems by predicting accurate arrival time windows and computing accurate severity levels for efficient allocations of service requests, facilitating improvements for …service platforms such as instant delivery or ride-hailing systems”) rather than an improvement to any underlying technology or computers (e.g., real-time tracking technology). See MPEP 2106.05(a)(II) “in Trading Technologies... the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (emphasis added). Thus, Applicant’s arguments are not found persuasive.
Applicant’s Remarks on Pages 14-21 of the RCE, regarding the previous rejection of the claims under 35 U.S.C. 103 have been fully considered and are found persuasive in view of the amended claims.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-8 recite a method (i.e., a process), and claims 9-16 recite a system (i.e., a machine). Therefore the claims all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claims 1 and 9 recite adaptively allocating one of a plurality of service requests to a service provider, each of the plurality of service requests requiring the service provider to move from one location to another to complete the each of the plurality of service requests, by: computing a first predicted severity level, based on location-based data, of a lack of available service providers for completing available service requests condition at a destination location indicated in a service request of the plurality of service requests during an estimated arrival time window within which the service provider is estimated to move to the destination location from a pickup location to complete the service request, the location-based data, indicating dynamic location of the available service providers, received from the service provider; computing a second predicted severity level, based on the location-based data, of the lack of available service providers for completing available service requests condition at a current location of the service provider during one of a current time window, a time window subsequent to the current time window and the estimated arrival time window; evaluating a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted; adjusting the priority level of the service request based on the evaluated level difference between the first predicted severity level and the second predicted severity level; allocating based on the adjusted priority level, one service request associated with a highest priority level from the plurality of service requests to the service provider; and directing the service provider towards the allocated service request based on an indication of the service provider.
The claims as a whole recites a certain method of organizing human activity. The limitations recited above, under broadest reasonable interpretation, recite the abstract idea of a certain method of organizing human activity, e.g., commercial interactions or fundamental economic practices. Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1 and 9 as a whole amounts to: “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The claims recite the additional elements of: (i) a computer-implemented method executed by at least one processor and memory including computer program code (claim 1); (ii) at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with at least one processor, cause the system to perform a method (claim 9); (iii) modules (a predicted severity level detection module, a priority level comparison module, a priority level calculation module, and a service request allocation module) (claims 1 and 9); (iv) real-time data (real-time location-based data) (claims 1 and 9); and (v) a communication device.
The additional elements (i) – (v) are recited at a high-level of generality such that, when viewed as 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)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (See Fig. 6) do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, claims 1 and 9 are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements in claims 1 and 9 amount to no more than reciting the words “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; or generally link the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements discussed above do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea. Thus, claims 1 and 9 are ineligible.
Dependent claims 2-8 and 10-16 further recite details which merely narrow the previously recited abstract idea limitiaitions. For these reasons, as described above with respect to claims 1 and 9 respectively, these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2-8 and 10-16 are also ineligible.
Allowable over Prior Art
Claims 1-16 are allowable over the prior, because the prior art fails to disclose, teach or suggest “computing, via the predicted severity level detection module, a second predicted severity level….of the lack of available service providers for completing available service requests condition at a current location of the service provider during one of a current time window, a time window subsequent to the current time window and the estimated arrival time window; evaluating…a level difference between the first predicted severity level and the second predicted severity level to determine whether a priority level of the service request is to be adjusted; adjusting the priority level of the service request based on the evaluated level difference between the first predicted severity level and the second predicted severity level; allocating based on the adjusted priority level one service request associated with a highest priority level from the plurality of service requests to the service provider” as recited in claim 1 (and similarly in claim 9) in combination with the other limitations in the claim. The closest prior art includes:
--- U.S. Patent Application Publication No. 2002/0019760 to Murakami et al. (hereinafter “Murakami”). Murukami discloses a port that includes a terminal which notifies a host computer of the number of currently available vehicles and passenger demands for vehicles. The terminal of a port P, at which a vehicle user wishes to ride a vehicle, transmits to the host the existing vehicle count at the port P in question and the ride demand. The surplus/shortage computing unit computes a surplus or a shortage of vehicles based on the demands and existing vehicle count sent from the terminal at each port P as well as on predicted starting trips. The computation of the vehicle surplus or shortage takes into account those arriving trips at destination ports which are predicted by the destination information included in the demands. If a vehicle is lacking, the vehicle distribution determining unit computes a waiting time based on a predicted arriving time of a redistributed vehicle designated by the vehicle redistribution determining unit.
U.S. Patent Application Publication No. 2022/0157174 to Izumida et al. (hereinafter “Izumida”). Izumida discloses determining the order of priority for vehicle allocation, and allocating the vehicle to a first user whose order of priority for vehicle allocation is the highest among the plurality of users.
U.S. Patent No. 6,584,488 to Brenner et al. (hereinafter “Brenner”). Brenner discloses a priority value is a function of the resource allocation desired for a particular thread, as indicated by its NICE value. When a user desires to decrease a priority of a particular thread, the NICE value is increased. The NICE value is multiplied by two, although other factors may be utilized based on the level of decrease in priority desired. In this illustration, iNICE (NICE*2), when utilized as a multiplier and then added, serves to further increase the penalty with NICE. The priority calculation for increasing a thread's priority may also be adjusted based on the level of increase desired.
U.S. Patent Application Publication No. 2018/0124207 to Marueli et al. (hereinafter “Marueli”). Marueli discloses a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests from the particular area.
Prior Art
The following is prior art not cited but considered relevant:
U.S. Patent Application Publication No. 2021/0366287 to Lee (hereinafter “Lee”). Lee discloses providing a big data-based AI automatic allocation matching service using taxi demand prediction.
Conclusion
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/RUPANGINI SINGH/Primary Examiner, Art Unit 3628