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 .
Status of the Claims
Claims 1-20 were previously pending and subject to a non-final office action mailed 10/01/2025. Claims 1 and 10 were amended; no claim was cancelled, or added in a reply filed 01/272026. Therefore claims 1-20 are currently pending and subject to the final office action below.
Response to Arguments
Applicant's arguments filed 01/27/2026 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant argues “These operations are, as understood by those skilled in the art, necessarily rooted in computer technology as they are directed to the real-time, large-scale coordination and optimization of multiple vehicles, drivers, and riders, using dynamic, location-based and rider- specific data and navigation systems, to efficiently route and dispatch vehicles across a fleet in a manner that is computationally intensive and far exceeds the capabilities of human mental work…A human mind, even with pen and paper, cannot practically or feasibly perform, in real time, the complex calculations and optimizations required to simultaneously analyze and reconcile the constantly changing locations, destinations, route constraints, traffic conditions, rider preferences, and vehicle availability for an entire fleet, nor can a human provide instantaneous, turn-by-turn navigation instructions to drivers based on such optimizations….the present claims are not directed to any such economic, commercial, or interpersonal activities. Rather, the claimed invention is directed to a technical solution to a technical problem: the real-time, large-scale coordination and optimization of multiple vehicles, drivers, and riders using dynamic, location-based data and automated navigation systems. The invention leverages computer systems to gather, analyze, and optimize data from multiple sources in real time, solve complex routing problems, and deliver navigation instructions with a speed and accuracy unattainable by humans unaided. The claims do not recite a method of conducting commerce, entering contracts, or managing interpersonal relationships, nor do they organize or manage conventional business or economic activity. Instead, each of the claimed steps is necessarily performed by computer- implemented systems, relying on GPS data, navigation software, and automated optimization algorithms.” (remarks p. 10-12).
Examiner respectfully disagrees. Under MPEP 2106.04(a)(2)(III)(A), the question is not whether the clamed steps could practically be performed in the human mind, but whether the claim limitations themselves recite a mental process. The threshold inquiry at Prong One is simply whether the claim recites a judicial exception, not whether it is difficult or impossible to perform mentally. MPEP 2106.04(a) instructs that the Examiner identify whether specific claim limitations fall within one of the three enumerated groupings: 1. Mathematical concepts, 2. Certain methods of organizing human activity, or 3. Mental processes. The claimed steps of “identifying rider information”, “generating routes”, “optimizing routes” and “transmitting navigation information” are each, at their core, steps of collecting information, analyzing it, and communicating results, which fall within the mental process grouping or, alternatively, the “certain methods of organizing human activity” grouping, as they coordinate and manage the behavior and movements of human riders and drivers.
Regarding Applicant argument that the claims are not directed to a method of organizing human activity: MPEP 2106.04(a)(2)(II) defines “certain methods of organizing human activity” to include managing interactions between people, managing behavior, and fundamental economic and commercial practices. The coordinates of riders, drivers, and vehicles, assigning riders to stops, sequencing stops, dispatching vehicles, constitutes managing the interactions and movements of human participants in a transportation service, which squarely falls within this grouping. The mere fact that a computer system is required to perform the steps does not, by itself, remove the abstract character of the underlying concept (please see MPEP 2106.05(f)(“a claim that merely instructs one to apply an exception using a generic computer does not amount to a practical application”). The computational complexity of the implementation does not change what the claim itself recites at the abstract level.
Applicant’s reliance on the December 5, 2025 Memo cautioning against “overbroad section 101 rejections” is noted but does not overcome the rejection. The Examiner’s rejection is directed to the specific abstract nature of the core claimed operations, collecting, analyzing, and communicating routing data, and is not an overbroad categorical rejection of all fleet management technology. The rejection is consistent with MPEP 2106.04 and the 2019 revised guidance.
Applicant argues “The present claims integrate any alleged judicial exception into a practical application because the claims improve sequencing technology and apply the alleged judicial exception in a meaningful way such that the claim as a whole is more than a drafting effort to monopolize the exception…The claimed invention utilizes automated data gathering, real-time location tracking, navigation optimization algorithms, and instantaneous transmission of turn-by-turn instructions to achieve this technical solution to route optimization. The technical solution is achieved by analyzing, for each route, historical data associated with each rider of the plurality of riders. In addition, the invention further analyzes historical data (including historical timing data) obtained from one or more driver devices associated with the fleet of vehicles, as well as the number of available vehicles in the fleet. These analyses enable the system to dynamically rearrange stop orders, optimize route assignments, and anticipate inefficiencies. These operations are only achievable by computer systems and materially improve the efficiency, speed, and accuracy of vehicle routing and dispatch. This goes far beyond merely automating conventional human tasks or organizing human activity, and instead reflects an inventive application that leverages computer technology to solve a problem that arises uniquely in the context of managing a fleet of vehicles and optimizing their routes in real time…the claims require specific arrangements of technical components and steps that result in concrete, tangible improvements in the technological field. These features demonstrate that the judicial exception is integrated into a practical application. As such, the claims are "significantly more" than the abstract idea itself, and do not raise the policy concerns underlying the judicial exceptions to patent eligibility. Therefore, even assuming the claims recite a judicial exception, they are directed to patent- eligible subject matter under Step 2A, Prong Two of the Alice/Mayo analysis.”(remarks p. 13-14).
Examiner respectfully disagrees. MPEP 2106.04(d) sets forth the considerations for evaluating whether a judicial exception is integrated into a practical application. Among the relevant considerations is whether the claim “improves the functioning of a computer or other technology” (please see MPEP 2106.05(a)). The improvement must be to the technology itself, not merely to the efficiency or speed of the underlying business or operational outcome achieved through use of technology. MPEP 2106.05(a) instructs that improvements to computer functionality must be reflected in the claim through specific technical details of how the improvement is achieved.
Here, the claims recite that an “artificial intelligence engine” performs route optimization, but do not recite any specific algorithm, model architecture, training methodology, data structure, or technical mechanism by which the AI engine achieves any improvement over conventional routing software. The AI engine is invoked generically and functionally, solely as a tool that produces the desired routing outcome. This is precisely the type of claiming described in MPEP 2106.05(f) as “mere instructions to apply an exception” which does not amount to a practical application. The recitation of generic computer components, a server system, driver devices, GPS navigation, performing their expected, generic functions of collecting, processing, and transmitting data does not integrate the abstract idea into a practical application (MPEP 2106.05(d)).
Applicant relies on Enfish and DDR Holdings in support of the practical application argument. With respect to those case law arguments only, the Examiner notes that unlike the lcaims in Enfish, which recited a specific self referential table data structure constituting an improvement to the database itself and DDR Holdings which recited a specific technical server side configuration for retaining website visitor engagement, the present claims recite only the results of optimization (optimized routes, rearranged stop orders) without specifying any particular technical mechanism that distinguishes the claimed system from conventional fleet routing software.
Furthermore, the August 4, 2025 UPSTO Memo cited by Applicant instructs that Examiners evaluate “whether the claim recites only the idea of a solution or outcome…or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome” The present claims recite the outcome, optimized routes with rearranged stop orders, but do not recite any particular technical solution or specific way of achieving that outcome beyond invoking an AI engine generically. Therefore, the claims do not integrate the abstract idea into a practical application.
Applicant argues “Applicant submits that at least the following combination of admitted additional limitations are not conventional, well understood, or routine: "optimizing, by the server system, using an artificial intelligence engine, the plurality of routes to generate a plurality of optimized routes for service by the fleet of vehicles to provide a particular vehicle and a driver for the particular vehicle to provide a ride to each one of the plurality of riders, wherein optimizing the plurality of routes comprises: analyzing, for each route, historical data associated with each rider of the plurality of riders, the historical data comprising: a number of riders assigned to a stop on a respective route, (ii) policies associated with the stop, and (iii) an address of each rider in relation to the stop"; "analyzing historical data, including historical timing data, obtained from one or more driver devices associated with the fleet of vehicles and a number of available vehicles in the fleet of vehicles"; and "responsive to the analyses, rearranging the stop order for a respective route." This combination of additional limitations is not well-known, conventional, or routine prior to the effective filing date of the present application, as evidenced by the absence of any disclosure of similar methods in any known prior art. If the Examiner maintains the 35 U.S.C. § 101 rejection, Applicant respectfully reminds the Examiner of the need to provide evidence that the combination of additional limitations is routine. Additionally, Applicant submits that claim 1 includes other additional limitations as argued in prong 1 of step 2A. The combination including these other additional limitations is further not routine. Furthermore, Examiner has not provided any basis to show that the additional elements are well-understood, routine, or conventional, as required under Berkheimer. Thus, a prima facie showing has not been made that the claims are directed to patent-ineligible subject matter.” (remarks p. 14-15).
Examiner respectfully disagrees. Applicant’s argument is unpersuasive and is fundamentally misdirected with respect to the basis for the Office Action’s step 2B rejection. The step 2B rejection does not rest on a finding that the additional elements are well-understood, routine, and conventional under MPEP 2106.05(d). Rather, the step 2B rejection rests on the independent and separate ground under MPEP 2106.05(f) that the additional elements constitute nothing more than mere instructions to apply the judicial exception using a generic computer, without any specific technical implementation that would meaningfully limit the exception. Because the rejection is grounded in MPEP 2106.5(f) and not MPEP 2106.05(d), Applicant’s arguments directed to the WURC determination are entirely non-responsive to the stated basis of rejection and do not overcome it.
Furthermore, the evidentiary requirements of Berkheimer are exclusively triggered when the Examiner makes a WURC determination. The Office Action’s step 2B rejection does not make such a finding. The rejection rests solely on the ground under MPEP 2106.05(f) that the additional elements constitute mere instructions to apply the abstract idea using a generic computer. An “apply it” finding under MPEP 2106.05(f) is made from the face of the claim and requires no factual evidentiary showing of the type required by Berkheimer. Accordingly, no Berkheimer compliant evidentiary showing is required to maintain this rejection, and Applicant’s demand for such evidence does not create any deficiency in the Office Action as written.
Applicant's arguments filed 01/27/2026 in regards to 103 rejection have been fully considered but they are not persuasive.
Applicant argues “The Office states Racah does not disclose but Khasis discloses "optimizing...using an artificial intelligence engine...the plurality of routes...wherein the artificial intelligence engine uses machine learning, historical data, including historical timing data...to generate and optimize the plurality of routes." See Office Action, pp. 6-7. Khasis considers "parameters and constraints" when recommending the most efficient route to travel between each location on the route. However, these parameters and constraints in Khasis pertain specifically to "historical and/or predicted future traffic, weather, hazard, and avoidance-zone data on road segments." See Khasis, para. [0009]. Furthermore, while Khasis may optimize routes based on "avoidance- zones," these avoidance-zones are defined at the road or vehicle level; they are geographic areas or specific road segments to be avoided either because of "restrictions posted on road-segments on the route" or because they are "defined by an administrator," and may also be based on vehicle attributes. See Khasis, paras. [0050]-[0053]. Critically, these avoidance-zones do not pertain to individual riders or passengers. Accordingly, the cited art has not been shown to disclose the limitations of "optimizing...wherein optimizing the plurality of routes comprises...analyzing...historical data associated with each rider...responsive to the analyses, rearranging the stop order for a respective route" in independent claim 1 and, similarly, in independent claim 10.” (remarks p. 17-18).
Examiner respectfully disagrees. Applicant’s argument is unpersuasive for the following reasons.
First, Applicant’s argument improperly conflates the role of Khasis in the combination with the role of Okazaki and Racah. It is well established that in an obviousness analysis, the references must be considered as a whole and each reference need not teach every limitation of the claim (please see MPEP 2143.01, “each piece of prior art cited in a rejection need not address each and every limitation of the claimed invention”).
The 103 rejection does not rely on Khasis alone to teach the limitation of “analyzing historical data associated with each rider” Rather, Khasis is relied upon for its constraint based dynamic route re-sequencing engine and its use of historical data to optimize routing, while Okazaki is specifically relied upon to teach stop level rider specific data analysis, including the number of passengers boarding and alighting at each stop and Racah is relied upon to teach the identification of rider specific information including each rider’s location and destination address in relation to the stop.
Specifically, Racah discloses that the server system identifies each rider’s origin location, destination, and assigned virtual stop which directly teaches rider specific data including the address of each rider in relation to the stop.
Okazaki discloses analyzing the number of passengers boarding and alighting at each stop, and generating timetables based on that stop level passenger data which directly teaches the number of riders assigned to a stop as historical data.
Khasis is relied upon solely for the re-sequencing engine, the mechanism by which stop order is dynamically rearranged in response to analyses. A person of ordinary skill in the art would have been motivated to feed the rider specific historical inputs of Racah and Okazaki into Khasis’s re-sequencing framework to drive more accurate and efficient stop order optimization, yielding only predicable results (please see MPEP 2143).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1/10 recite “policies associated with the stop” as a distinct category of historical data input to the AI optimization engine. This limitation is new matter. The closest support Examiner could find are paragraphs 36 and 38 which describe that “If the stop order is not optimal (Step 330 – No), based for example, on picking up a special needs child in a wheelchair or any other reason, server 135 may regenerate a stop order based on a user change or new information provided by a user.” And paragraph 41 states “The route may be assigned an identifier, a route group, a beginning date for a new route, a route end date, a specification for a type of route (general education, special education, or wheelchair, for example) and the days of the week the route is to be driven by a fleet vehicle. “ First paragraph 41 puts into questions whether the wheelchair and special needs categorizations are identifiers associated with the stop or the route. Second, categorizing these examples as “policies associate with the stop” is a broad, open ended functional term that, under its plain and ordinary meaning, could encompass an enormous range of stop level rules, regulations, restrictions, and conditions which is essentially an attempt to capture a scope, through the claim, that is broader than what the specification supports. The specification nowhere uses the word “policies”. It discloses only specific examples (wheelchair, special needs, route type classifications) without ever characterizing these as a broader category called “policies”. The disclosure of specific examples does not provide written description support for the broader genus of “policies associate with the stop” which under its plain and ordinary meaning would encompass any rule, regulation, restriction, or condition governing stop level service, a scope far exceeding what the specification describes.
Dependent claims 2-9 and 11-20 are also rejected under 112a for failing to cure the deficiency above.
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) without significantly more.
Claim 1/10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “identifying rider information and stop location information for a plurality of riders; identifying location information, navigation information, and stop timing information for a particular vehicle in a fleet of vehicles; generating a plurality of routes for service by the fleet of vehicles to provide a ride to each one of the plurality of riders, wherein each route includes a stop order; and optimizing the plurality of routes to generate a plurality of optimized routes for service by the fleet of vehicles to provide a particular vehicle and a driver for the particular vehicle to provide a ride to each one of the plurality of riders, wherein optimizing the plurality of routes comprises: analyzing, for each route, historical data associated with each rider of the plurality of riders, the historical data comprising: a number of riders assigned to a stop on a respective route, (ii) policies associated with the stop, and (iii) an address of each rider in relation to the stop; analyzing historical data, including historical timing data, obtained from one or more driver devices associated with the fleet of vehicles and a number of available vehicles in the fleet of vehicles; responsive to the analyses, rearranging the stop order for a respective route and transmitting a first optimized route among the plurality of optimized routes to a first driver device associated with a first vehicle in the fleet of vehicles, the first optimized route including turn by turn navigation information.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of optimizing routes for vehicles which falls under a method of organizing a human activity and mathematical concepts. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical relationships.
This judicial exception is not integrated into a practical application. In particular, the claim recites “a server system including one or more processors”, “an artificial intelligence engine”, “wherein the artificial intelligence engine uses machine learning, historical data, including historical timing data, obtained from one or more driver devices associated with the fleet of vehicles, and a number of available vehicles in the fleet of vehicles to generate and optimize the plurality of routes for service by the fleet of vehicles” and “a first driver device” (claim 1/10) and memory (claim 10). Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are nothing more than mere instructions to apply the exception on a general computer.
Dependent claims 2-9 and 11-20 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (graphical user interface of claim 5/14/19, one or more user devices associated with a first group of riders (claim 8/17) and one or more user devices associated with a second group of riders (claim 9/18), communication interface and the one or more driver devices configured to communicate with the server system over the communication interface are all recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Novelty and Non-Obviousness
Examiner is unaware of any prior art, alone or in combination, which discloses all the limitations of the independent claims, especially: “optimizing, by the server system, using an artificial intelligence engine, the plurality of routes to generate a plurality of optimized routes for service by the fleet of vehicles to provide a particular vehicle and a driver for the particular vehicle to provide a ride to each one of the plurality of riders, wherein optimizing the plurality of routes comprises: analyzing, for each route, historical data associated with each rider of the plurality of riders, the historical data comprising: a number of riders assigned to a stop on a respective route, (ii) policies associated with the stop, and (iii) an address of each rider in relation to the stop; analyzing historical data, including historical timing data, obtained from one or more driver devices associated with the fleet of vehicles and a number of available vehicles in the fleet of vehicles; responsive to the analyses, rearranging the stop order for a respective route”.
The closest prior art is the prior art of record in addition to Wang (US 20210248704) which disclose that the server store commuter assignment data including stop locations recorded via GPS coordinates at the time of boarding scan events. Wang expressly discloses that stop locations are recorded empirically when a rider’s QR code is scanned at the moment of boarding which means that Wang record where a rider physically boarded, not the rider’s home address or residential address in relation to a stop.
Racah discloses a “first absolute walking distance, being a distance from the passenger requested origin point to at least one candidate virtual pickup bus stop.” However, this calculation is performed in real time in direct response to a live ride request submitted by a passenger. The passenger requested origin point is fresh input data submitted at the moment of the request.
Laranang (US 10685521) discloses student identifier data scanned at stops for authorization purposes. Laranang does not disclose a rider address data, no home address association, and no address-based stop assignment calculation of any kind.
Khasis discloses road segment traversal data, traffic conditions, and vehicle sensor logs as historical records. Khasis discloses no rider address data of any kind.
Accordingly, not one of the four cited reference discloses “an address of each rider in relation to the stop” as historical data.
Furthermore, even setting aside the “address” limitation above, the 103 rejection requires reaching into four references, each from distinct technical context, to piece together the three discrete subelements of the claimed historical data:
Racah is relied upon for the ridsharing fleet architecture and real time rider origin point computation, Khasis is relied upon for the AI basedconstraint driven route re-sequencing engine, Wang is relied upon for the number of riders assigned to a stop as stored historical data, and Laranang is relied upon solely for policies associated with the stop.
No reference in the combination, individually or in combination, discloses analyzing all three historical data sub-elements together as inputs to an AI optimization engine that rearranges stop order responsive to those analyses. Each reference was selected because the claim requires it, not because a person of ordinary skill in the art would have independently arrived at the combination. A fifth reference disclosing the “address” limitation above would only further solidify an impermissible hindsight as rationale for the combination.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR ZEROUAL whose telephone number is (571)272-7255. The examiner can normally be reached Flex schedule.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628
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