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 .
DETAILED ACTION
This final Office action is in response to applicant’s communication received on June 23, 2026, wherein claims 1-21 are currently pending.
Response to Arguments
Applicant's arguments/remarks filed have been fully considered but they are geared towards the newly amended claims and the newly added limitations to the amended claims. The newly amended claims and the newly added limitations to the amended claims are considered and discussed for the first time in the rejection below.
35 USC §101 discussion:
Applicant’s independent and dependent claims as a whole addresses the need for fleet optimization that balances immediate service needs with long-term operating cost and vehicle availability where the solution discussed by the Applicant is a dispatch framework that combines abstract information/data, simulation (using abstract models – mathematical and using mathematical/numerical information/data), and predictions/forecasting to choose vehicle assignments and charging plans. It predicts service/charging outcomes before committing to them, then updates the assignment strategy as conditions change. The system considers battery state, service urgency, traffic, weather, depot capacity, and historical demand (all abstract information/data) so the fleet can be rebalanced continuously (abstract concept). It aims to optimize both short-term service performance and long-term fleet efficiency. The claims recite collecting/obtaining information/data (where the information itself is abstract in nature – e.g. requests, queues, constraints, costs, rules, performance, states, and the like), data analysis/manipulation (comparing information, predicting, evaluations, moving information around, solving for optimization, etc.,) to determine more data/information, adjusting abstract information based on results, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making. The limitations of the independent claims and dependent claims, under the broadest reasonable interpretation, covers methods of organizing human activity (commercial or legal interactions (i.e. business relations in ride sharing vehicle assignments/dispatching (taxi industry with customer needing vehicle transportation); and fundamental economic activity (fleet management and logistics)); and also managing personal relationships (scheduling rides and also following rules or instructions on how those rides can occur)).
Applicant is mainly using generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13); “automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) in the amended claims. It should be noted that mere automation of a manual process or claiming the improved speed or efficiency inherent with applying the abstract idea on a computer where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to transform an abstract idea into a patent-eligible invention. See MPEP 2106.04(a); MPEP 2106.05(a); MPEP 2106.05(f); FairWarning IP, LLC v. Iatric Sys., 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); Intellectual Ventures I LLC v. Capital One Bank (USA), 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). The generic/general-purpose computers and computing elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above stated abstract idea) in an apply-it fashion using generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13); “automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)). Adding the technical terms/elements which are recited at a high level of generality (as shown above) performing generic/general-purpose computer functions. The additional elements (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13); “automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
Additionally, claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic/general-purpose computer/computing/technical components (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13); “automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claim into eligible subject matter. The additional elements (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13); “automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) or combination of elements in the independent claims (1, 11) and dependent claims (2-7, 9-10, 12-17, 19-22) other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc.(U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, Applicants’ claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claims does not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
See detailed rejection below.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, Claims 1-12 are directed to a method (i.e., process) and claims 13-21 are directed to a system (i.e. machine). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) Applicant’s independent claims (1, 13) addresses the need for fleet optimization that balances immediate service needs with long-term operating cost and vehicle availability where the solution discussed by the Applicant is a dispatch framework that combines abstract information/data, simulation (using abstract models – mathematical and using mathematical/numerical information/data), and predictions/forecasting to choose vehicle assignments and charging plans. It predicts service/charging outcomes before committing to them, then updates the assignment strategy as conditions change. The system considers battery state, service urgency, traffic, weather, depot capacity, and historical demand (all abstract information/data) so the fleet can be rebalanced continuously (abstract concept). It aims to optimize both short-term service performance and long-term fleet efficiency. The independent claims (1, 13) recite collecting/obtaining information/data (where the information itself is abstract in nature – e.g. requests, queues, constraints, costs, rules, performance, states, and the like), data analysis/manipulation (comparing information, predicting, evaluations, moving information around, solving for optimization, etc.,) to determine more data/information, adjusting abstract information based on results, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making. The limitations of the independent claims (1, 13), under the broadest reasonable interpretation, covers methods of organizing human activity (commercial or legal interactions (i.e. business relations in ride sharing vehicle assignments/dispatching (taxi industry with customer needing vehicle transportation); and fundamental economic activity (fleet management and logistics)); and also managing personal relationships (scheduling rides and also following rules or instructions on how those rides can occur)). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as 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 scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04).
Accordingly, since Applicant's claims fall under organizing human activities grouping the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of old/well-known generic/general-purpose computing/technology components/elements/terms (for example, “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13)), in the context of the independent claims (1, 13), the claims encompass the above stated abstract idea (organizing human activity (managing inventory and construction sites in view of abstract information (fundamental economic activity and managing behavior/interactions by following rules or instructions)) and mathematical concepts (using mathematical techniques and using the results of the techniques)).
As shown above, the independent claims (1, 13) recite generic/general-purpose computing/technology components/elements/terms/limitations (“deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13)) which are recited at a high level of generality performing generic/general purpose computer/computing functions. (MPEP 2106.04). The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations ((“deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13))). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements (see list/listing above) do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 13) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements in the independent claims recite using known generic/general-purpose computing/technology components/elements/terms/limitations (“deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13)). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, figure 4 and Pages 24-25 [where Applicant recites general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc., in Applicant’s specification]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claims into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (“deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in independent claim 1); system, “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “simulator…transmit output and parameters,” “simulator…coupled to dispatcher (module),” (in independent claim 13)) or combination of elements in the claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the independent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (14, 25, 26) and dependent claims (15-24) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-12, 14-21) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The dependent claims either individually or in combination are merely an extension of the abstract idea itself. The above rejection discussed for the independent claims fully applies to the dependent claims.
The dependent claims (2-12, 14-21) further state using obtained data/information (where the information itself is abstract in nature – e.g. requests, queues, constraints, costs, rules, performance, states, and the like), data analysis/manipulation (comparing information, predicting, evaluations, moving information around, solving for optimization, etc.,) to determine more data/information, possibly obtaining more abstract information/data, and providing this determined data/information for further analysis and decision-making. These dependent claims also cover methods of organizing human activity (organizing human activity (commercial or legal interactions (i.e. business relations in ride sharing vehicle assignments/dispatching (taxi industry with customer needing vehicle transportation); and fundamental economic activity (fleet management and logistics)); and also managing personal relationships (scheduling rides and also following rules or instructions on how those rides can occur))).
This judicial exception is not integrated into a practical application because the claims and specification recite additional elements as generic/general-purpose computing/technology components/elements/terms/limitations (“automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) performing generic computer/computing/technology functions. (MPEP 2106.04). The dependent claims merely use the same general technological environment and instructions as the independent claims above to implement the abstract idea. The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (“automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)). Hence, the additional elements (“automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
Also, the dependent claims either individually or in combination are merely an extension of the abstract idea itself and the dependent claims (similar to the independent claims) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (“automated,” “deep reinforcement learning (machine-learning algorithm with no technical specifics provided and the element is only stated as being used)” (in claim 1’s dependent claims 2-12); “databases (storing various abstract information),” “learning-based (machine-learning algorithm with no technical specifics provided and the element is only stated as being used),” “module…coupled to simulator,” “state machine (just a generically states model run on processor/computer),” “computer-readable media containing instructions,” “processor,” “cloud network,” “data structure,” (in claim 13’s dependent claims 14-21)) or combination of elements in the dependent claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, dependent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (14, 25, 26) and dependent claims (15-24) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
Prior art discussion (not a prior art rejection – but remains rejected under §101 above)
As per the independent claims 1 and 13, the closet prior art are Chase et al., (US 2022/0381568) and Dhansri et al., (US 2019/0378041). However, neither Chase and Dhansri (the closet prior art) specifically disclose, in combination, the limitations of simulating service and charging assignments for the vehicle… analyzing predicted outcomes of the assignment strategy for the simulated service and charging assignments …adjusting the assignment strategy to maximize a cost-to-go predictions from a deep reinforcement learning (DRL) model, the cost-to-go prediction including a short-term energy efficiency key performance indicator (KPI) of a vehicle assigned to a service request and a long-term KPI of the fleet of vehicles based on the vehicle assigned to the service request, wherein adjusting the assignment strategy includes maximizing a sum of the short-term energy efficiency KPI and the long-term KPI of the fleet of vehicles to balance short-term efficiency with long-term strategic decision-making. The specific ordered combination of the claim elements in the independent claims cannot be found in the prior art (including art cited in PTO-892) and can only be found in Applicants’ Specification. The prior art of record (including art cite on PTO-892) does not teach or suggest (the reference individually or in combination) Applicant’s current independent claims as a whole (it is the entire claimed concept described by the limitations collectively coming together that is not rejected under prior art (the core concept is shown in the claim as a whole — limitations organized in the specific form and coming together collectively to form the concept)). Furthermore, any combination of the cited references and/or additional references to teach all of the claim elements would not be obvious and would result in impermissible hindsight reconstruction.
As per the dependent claims, these claims depend on the independent claims above and incorporate the limitations thereof, and are therefore not rejected under prior art for at least the same rationale as applied to the independent claims above, and incorporated herein.
Note that all the claims are still rejected under §101 rejection and are therefore not allowable.
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Abari et al., (US 2019/0197798): Discusses generating a prediction of requests for autonomous vehicles in a fleet of collectively managed autonomous vehicles based on a current condition and a future event, the prediction including a predicted request level and a predicted duration of the request level. The system may receive status information from fleet vehicles and identify a vehicle in need of service. The system may receive status information from service centers and identify a service center to service the vehicle. The system may determine a time at which to service the vehicle at the identified service center based on the generated prediction of requests, schedule the vehicle for service at the identified service center at the determined time, and instruct the vehicle to drive to the service center to be serviced at the determined time. In particular embodiments, the prediction of requests may be generated using machine learning.
de Perett et al., (US 2023/0393573): Illustrates generating and modifying autonomous vehicle pre-matching circuits defined by autonomous vehicle waypoints and transmit autonomous vehicle prepositioning instructions utilizing autonomous vehicle waypoints to improve overall network coverage. In particular, in some embodiments the disclosed systems utilize a computer-implemented prediction model to determine predicted autonomous vehicle requester devices. The disclosed systems utilize the predicted autonomous vehicle requester devices to divide a service area into subregions and select waypoints defining autonomous vehicle pre-matching circuits within the sub-regions. Moreover, in one or more embodiments, the disclosed systems monitor contextual device information to dynamically modify waypoints and autonomous vehicle pre-matching circuits to respond to changing conditions. In addition, in one or more implementations the disclosed systems utilize computer-implemented optimization models to generate changing autonomous vehicle prepositioning assignments to subregions, circuits, and/or waypoints over time based on current autonomous vehicle locations and/or predicted autonomous vehicle requester devices. For example, the disclosed systems direct one or more autonomous vehicles to traverse a circuit within a subregion defined by the waypoints within the circuit. Additionally, the disclosed systems can transmit additional prepositioning instructions to move between subregions, traverse a different circuit, and or modify a current waypoint within a circuit. In this manner, the disclosed systems can improve efficiency, network coverage, and/or flexibility of a transportation matching system, for example, by dynamically adapting the response of the system based on changing vehicle states and predicted transportation requests.
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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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
Prosecution Insights