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 .
This action is in response to the application filed 2 July 2025. Claims 1-5 are pending and have been examined.
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite estimating the value of actions related to redeploying shared vehicles between ports of a shared transportation service by means of an action value function, and obtaining (i.e., calculating) the action value function so as to identify the action that maximizes a future action value (reward/profit), which is a mathematical concept and a certain method of organizing human activity (commercial interactions).
Statutory Category
Regarding Step 1 of the subject matter eligibility test, claim 1 is directed to an information processing device which, under the BRI in light of paras. [0053]-[0057] and FIG. 15, is a computer apparatus, i.e., a machine. Claims 2-5 depend from claim 1 and are likewise directed to a machine. Therefore, the claims fall within a statutory category of invention (Step 1: YES). See MPEP 2106.03.
STEP 2A Prong One
The claims recite an abstract idea. Specifically, independent claim 1 recites the following limitations (the abstract idea is set forth in bold):
• "an action value function for estimating a value of an action related to redeployment of a vehicle between a plurality of ports in a shared transportation service in which the vehicle is shared"; and
• "perform reinforcement learning with respect to the action value function using the information acquired by the acquisition unit to obtain the action value function for selecting an action maximizing a future action value."
Mathematical Concepts
The identified limitations recite mathematical concepts, namely mathematical relationships and mathematical calculations. See MPEP 2106.04(a)(2), subsection I. The "action value function" is, by its plain meaning to one of ordinary skill in the art and as confirmed by the specification, a mathematical function Q(St, at) that relates a state and an action to an expected discounted cumulative reward (par. [0026]-[0027]; Expressions (1)-(2)). A function relating variables in this manner is a mathematical relationship. Further, "obtaining the action value function for selecting an action maximizing a future action value" sets forth the mathematical operation of determining the function whose output is maximized over the candidate actions (i.e., arg max Q(St, at), para. [0042]), which in light of the specification is accomplished by iteratively calculating the function to minimize an error E between estimated and empirical values (Expression (3), paras. [0028], [0041]). A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula. In re Grams, and n.1 (Fed. Cir. 1989); MPEP 2106.04(a)(2), subsection I.
The claim thus does more than merely involve a mathematical concept. Consistent with the distinction drawn in the Office's August 4, 2025 memorandum between USPTO Example 39 and USPTO Example 47, claim 2, the claim here identifies by name the specific mathematical function being calculated (the action value function) and the mathematical objective of the calculation (maximizing a future action value), just as Example 47, claim 2 was found to recite mathematical calculations by naming the backpropagation and gradient descent algorithms. Even if the phrase "perform reinforcement learning" is not itself considered part of the recited mathematical concept, the action value function and the obtaining of that function to maximize a future action value are, and the "perform reinforcement learning" language is addressed, in the alternative, as an additional element under Step 2A Prong Two and Step 2B below.
Certain Methods of Organizing Human Activity
The identified limitations also fall within the "commercial or legal interactions" sub-grouping (including advertising, marketing or sales activities or behaviors, and business relations) of certain methods of organizing human activity. See MPEP 2106.04(a)(2), subsection II.B. The claims are directed to deciding how a vehicle sharing business should redeploy its rental inventory among its ports so as to maximize the reward of the service. The specification expressly identifies the problem being solved as a business problem: the vehicle sharing service "is a service provided by a private company, and thus is desirably operated to maximize the reward (profit) for the service as a whole" (para. [0004]); the stated object is to "optimize redeployment of vehicles so as to maximize a reward (profit)" (para. [0006]); the "reward" is "a profit obtained" (para. [0023]); and the stated result is that "a reward (profit) in the service can be maximized" (para. [0048]). The actions being valued (deploying bicycles at a port, collecting bicycles from a port, and replacing batteries) are business operations carried out by service staff using a redeployment truck (para. [0017]). Determining how a business should allocate its resources among locations to maximize profit is analogous to the commercial activities identified in MPEP 2106.04(a)(2), subsection II.B, such as using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, and offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc.
Because the claim recites both a mathematical concept and a certain method of organizing human activity, both groupings are identified to make the record clear; the limitations are considered together as a single abstract idea for further analysis. See MPEP 2106.04, subsection II.B. Accordingly, claim 1 recites an abstract idea.
STEP 2A Prong Two:
Identification of Additional Elements
Claim 1 recites the following additional elements beyond the identified abstract idea:
• (A) "an information processing device";
• (B) "an acquisition unit configured to acquire information ";
• (C) "a learning and inference unit configured to" perform the recited operations, and (in the alternative, to the extent not part of the abstract idea) the generic recitation "perform reinforcement learning"; and
• (D) the recitation that the actions relate to "redeployment of a vehicle between a plurality of ports in a shared transportation service in which the vehicle is shared."
Analysis of Additional Elements
The additional elements have been evaluated individually and in combination, and the claim has been considered as a whole, including how the additional elements interact with the recited exception. See MPEP 2106.04(d), subsection III.
Mere Instructions to Apply the Exception (MPEP 2106.05(f))
Additional elements (A)-(C) amount to no more than mere instructions to implement the abstract idea on a computer. Considering the factors of MPEP 2106.05(f): (1) the claim recites only the outcome of obtaining an action value function for selecting an action maximizing a future action value, and does not recite how that outcome is technologically accomplished; (2) the claim invokes a generic computer (the "device" and its "units") and a generic, known machine learning technique ("reinforcement learning," paras. [0007], [0021]) merely as tools to perform the calculation and business determination identified above; and (3) the application of the exception is general rather than particular, as it covers any reinforcement learning technique applied to any information about any shared vehicle and any ports. This is analogous to the recitation "using the trained ANN" in USPTO Example 47, claim 2, which was found to provide nothing more than mere instructions to implement the abstract idea on a generic computer because it placed no limits on how the ANN functions. See also Recentive Analytics, Inc. v. Fox Corp., (claims that do no more than apply established machine learning methods to a new data environment are not patent eligible), cited in the August 4, 2025 memorandum at n.17. As in Alice, such recitations are tantamount to adding the words "apply it" to the judicial exception. Alice Corp. Pty. Ltd. v. CLS Bank Int'l.
Insignificant Extra-Solution Activity (MPEP 2106.05(g))
Additional element (B), acquiring information input to the action value function, is mere data gathering recited at a high level of generality. The claim places no limits on what information is acquired or how, and "acquire" broadly includes obtaining information or computing it with a predetermined formula (para. [0010]). All uses of the recited exception require such data gathering, so this limitation is necessary data gathering that does not impose a meaningful limit on the claim. See MPEP 2106.05(g); USPTO Example 47, claim 2.
Field of Use and Technological Environment (MPEP 2106.05(h))
Additional element (D) merely confines the abstract idea to a particular field of use or technological environment, namely the redeployment of shared vehicles between ports of a shared transportation service. Limiting an abstract idea to a particular environment does not integrate it into a practical application. See MPEP 2106.05(h); Parker v. Flook; Bilski v. Kappos. To the extent "perform reinforcement learning" is treated as an additional element, it likewise merely confines the calculation to the technological environment of machine learning. See USPTO Example 47, claim 2 (recitation of "using a trained ANN" merely confines the abstract idea to a particular technological environment).
Considering the additional elements individually and in combination, and considering the claim as a whole, the claim does not integrate the judicial exception into a practical application. The additional elements do not impose any meaningful limits on practicing the abstract idea. Accordingly, claim 1 is directed to an abstract idea.
STEP 2B
As discussed with respect to Step 2A Prong Two, additional elements (A)-(C) amount to no more than mere instructions to apply the exception using generic computer components and a generic machine learning technique, and additional element (D) merely links the exception to a field of use. The same analysis applies at Step 2B; mere instructions to apply an exception and field-of-use limitations cannot provide an inventive concept. See MPEP 2106.05(f), (h).
Well-Understood, Routine, Conventional Activity Analysis
Additional element (B) was found to be insignificant extra-solution activity at Step 2A Prong Two and has been re-evaluated at Step 2B. See MPEP 2106.05, subsection I.A. It remains insignificant and is well-understood, routine, and conventional, as supported by the following evidence in accordance with MPEP 2106.07(a), subsection III:
• Acquiring information – The courts have recognized receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner. See MPEP 2106.05(d), subsection II, citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016); OIP Techs., 788 F.3d at 1363. The specification likewise describes the acquisition as broadly obtaining or computing information (para. [0010]) using a generic communication apparatus, network card, or communication module (para. [0060]).
• Generic computer implementation – The specification describes the claimed device and units as generic computing hardware and software, including a general processor (e.g., a CPU), memory (e.g., ROM, RAM), storage, and a communication apparatus, with no particular method of realization (paras. [0051]-[0063]; FIG. 15). See MPEP 2106.07(a), subsection III(A) (citation to the specification).
• Reinforcement learning – The specification itself describes reinforcement learning, including the agent/state/action/reward/policy framework and the state and action value functions, as a known type of machine learning (paras. [0007], [0021], [0023]-[0028]). See MPEP 2106.07(a), subsection III(A).
Considering the additional elements individually and in combination, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 is not patent eligible.
Dependent Claims Analysis
The dependent claims have been evaluated individually and in combination with the limitations of the claims from which they depend. They do not add limitations that integrate the judicial exception into a practical application or that provide an inventive concept:
Claim 2 further specifies that the vehicle is reusable by battery replacement; that the actions include deployment of a vehicle to a port, collection of a vehicle from a port, and battery replacement; and that the learning and inference unit computes a deployment evaluation value, a collection evaluation value, and a battery replacement evaluation value for each port "using a predetermined computation formula" as the reward, and performs the reinforcement learning based on those values for all ports. Computing values using a predetermined computation formula recites a further mathematical calculation (see Expressions (5)-(7), paras. [0030]-[0032], which multiply usage counts, usage fees, member ratios, impressions, and unit advertising costs). Valuing deployment, collection, and battery replacement actions of the vehicle sharing business in terms of reward further recites the commercial interaction identified above. The battery-replaceable vehicle is a further field-of-use limitation (MPEP 2106.05(h)). No additional element beyond those addressed for claim 1 is introduced, and the claim does not require actually deploying, collecting, or replacing anything.
Claim 3 further specifies that the vehicle has an advertisement displayed on its body and that the deployment and collection evaluation values are computed using impressions (the number of times an advertisement is viewable) as an element. Computing evaluation values using impression counts is a further mathematical calculation (Expressions (5)-(6)), and valuing vehicle placement according to advertising exposure falls within "advertising, marketing or sales activities or behaviors," a commercial interaction. See MPEP 2106.04(a)(2), subsection II.B; Ultramercial, Inc. v. Hulu, LLC. The advertisement on the vehicle body is not recited as being displayed or controlled by the claimed device and merely further limits the field of use.
Claim 4 further specifies the types of vehicles (rental electric bicycle, rental electric kick scooter, rental motorbike, and rental car restartable by battery replacement and having an advertisement on the body). This merely limits the abstract idea to a particular field of use or technological environment and does not integrate the exception into a practical application or provide significantly more. See MPEP 2106.05(h).
Claim 5 further specifies that, in an action inference stage, the learning and inference unit substitutes current state information acquired by the acquisition unit into the obtained action value function to acquire an action value for each of various actions, and selects the action maximizing the acquired action value as the action to be taken. Substituting values into a function to compute a result is a mathematical calculation, and selecting the maximum of the computed values (arg max, para. [0042]) is a further mathematical calculation that also, under its BRI, encompasses a mental evaluation and judgment of comparing a set of values and choosing the largest, which can practically be performed in the human mind or with pen and paper once the values are available. See MPEP 2106.04(a)(2), subsections I and III. Selecting which redeployment action "to be taken" also further recites the commercial interaction identified above. The acquisition of current state information is additional insignificant data gathering (MPEP 2106.05(g)), which is well-understood, routine, and conventional for the reasons given for claim 1 (MPEP 2106.05(d), subsection II). The claim merely identifies an action; it does not recite performing or controlling any redeployment, truck, vehicle, or battery replacement, and therefore does not apply the exception in a meaningful way beyond generally linking it to the shared transportation environment.
For the foregoing reasons, claims 1-5 are rejected under 35 U.S.C. 101 as being directed to a judicial exception (an abstract idea) without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 5 is/are rejected under pre-AIA 35 U.S.C. 102(a)(1) as being anticipated by Pan et al., 2019, A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems, The Chinese University of Hong Kong, pp 1-9 (hereafter Pan).
Referring to Claim 1, Pan teaches an information processing device, said device comprising:
an acquisition unit configured to acquire information input to an action value function for estimating a value of an action related to redeployment of a vehicle between a plurality of ports in a shared transportation service in which the vehicle is shared (see; Introduction par. 1 of Pan teaches a bike sharing system (i.e. vehicle sharing), par. 3 rebalance sharing, pg. 2, col. 2 Problem Definition – pg. 3, col. 1 acquiring information (i.e. information input) about the sharing process, par. 3, col. 2 The HRP Algorithm par. 1-2 estimating a value to be used to decide how to redistribute bikes (i.e. vehicles) based on the acquired and analyzed information regarding sharing).
a learning and inference unit configured to perform reinforcement learning with respect to the action value function using the information acquired by the acquisition unit to obtain the action value function for selecting an action maximizing a future action value (see; pg. 3, col. 2 MDP Formulation of Pan teaches providing a learning algorithm that takes temporal and special dependencies to address performance, pg. 2, col. 1, par. 2 provides reinforcement learning providing the maximum service level in real time and in the future (i.e. maximum future action value)).
Referring to Claim 5, see discussion of claim 1 above, while Pan teaches the processing device above, Pan further discloses a device having the limitations of:
in an action inference stage, the learning and inference unit substitutes state information at a current time point acquired by the acquisition unit into the action value function obtained by the reinforcement learning to acquire an action value when each of various actions is performed in a state at the current time point, and selects the action maximizing the acquired action value as an action to be taken (see; pg. 2, col. 1 par. 2 – 1st bullet point of Pan teaches rebalancing using region and temporal dependencies (i.e. current time) and possible future state and then maximizing the long term service level (i.e. actions to balance supply and demand)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al., 2019, A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems, The Chinese University of Hong Kong, pp 1-9 (hereafter Pan) in view of Rehm (U.S. Patent 11,615,409 B1).
Referring to Claim 2, see discussion of claim 1 above, while Pan teaches the processing device above, Pan further discloses a device having the limitations of:
the action related to redeployment of the vehicle includes redeployment of a vehicle to a port, collection of a vehicle from a port (see; pg. 1, col. 2 bottom – pg. 2, par. 2 of Pan teaches a rebalancing system that collects and distributes to specific locations).
the learning and inference unit computes a deployment evaluation value related to the redeployment, a collection evaluation value related to the collection (see; pg. 2, col. 1 – par. 1of Pan teaches using deep reinforcement learning and collected information that is utilized to perform rebalancing).
as a reward included in the value of the action, and performs the reinforcement learning based on deployment evaluation values, collection evaluation values (see; pg. 1, col. 2 bottom – pg. 2, col. 1 of Pan teaches understanding real time redistribution from deploying and offering people incentives (i.e. reward) to collect and move vehicles).
Pan teaches the redeployment of vehicles and using rewards for reinforcement but does not explicitly disclose the following limitation.
Rehm teaches the vehicle shared in the shared transportation service is a vehicle reusable by replacement of an installed battery (see; Abstract and col. 1, line (63) – col. 2, line (10) of Rehm teaches ebike recharging (i.e. battery change)), and
replacement of the battery mounted on the vehicle, (see; col. 4, lines (18-49) of Rehm teaches the replacement and recharging (i.e. battery change)), and
a battery replacement evaluation value related to the battery replacement for each port using a predetermined computation formula (see; col. 4, lines (18-49) of Rehm teaches tracking battery life (i.e. evaluation) and triggering an action which is a recharge to be performed (i.e. replace battery charge) col. 5, lines (20-31) and monitoring service that tracks actions), and
battery replacement evaluation values obtained for all ports (see; col. 4, lines (18-49) of Rehm teaches tracking battery life (i.e. evaluation) and triggering an action which is a recharge to be performed (i.e. replace battery charge)).
The Examiner notes that Pan teaches similar to the instant application teaches a deep reinforcement learning framework for rebalancing dockless bike sharing system.
Specifically, Pan discloses the modeling and using deep reinforcement learning to rebalance vehicle locations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Rehm teaches battery management for electric shareable vehicles and as it is comparable in certain respects to Pan which a deep reinforcement learning framework for rebalancing dockless bike sharing system as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pan the vehicle shared in the shared transportation service is a vehicle reusable by replacement of an installed battery, replacement of the battery mounted on the vehicle, a battery replacement evaluation value related to the battery replacement for each port using a predetermined computation formula, and battery replacement evaluation values obtained for all port as taught by Rehm since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pan, and Rehm share the common goal of maintaining electric vehicles that are sharable and optimize the utilization of the electric vehicles as the combination requires only the straightforward application of a known and proven technique to manage electric vehicles, as the combination requires only the straightforward application of a known and proven technique to an analogous management of sharable vehicles and analysis of data in order to maximize the utilization of resource, and they do not contradict or diminish the other alone or when combined.
Claims 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al., 2019, A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems, The Chinese University of Hong Kong, pp 1-9 (hereafter Pan) in view of Rehm (U.S. Patent 11,615,409 B1) in further view of Ziegler (U.S. Patent Publication 2012/0085829 A1).
Referring to Claim 3, see discussion of claim 2 above, while Pan in view of Rehm teaches the processing device above, Pan and Rehm discloses maintaining electric vehicles that are sharable and optimize the utilization of the electric vehicles. However, Pan in view of Rehm does not explicitly disclose a device having the following limitations, however,
Ziegler teaches the vehicle shared in the shared transportation service is a vehicle having an advertisement displayed on a body (see; par. [0024] of Ziegler teaches a product promotion with a log on the product including a QR code for scanning, par. [0058] the products include scoters (i.e. sharable transportation), and
the learning and inference unit computes the deployment evaluation value and the collection evaluation value using impressions (see; par. [0083] of Ziegler teaches the use of a QR code as part of a logo on an product and when scanned provide and evaluation on the deployment of products, par. [0058] the products include scoters (i.e. sharable transportation), and
which are the number of times that an advertisement is viewable, as an element (see; par. [0051] of Ziegler teaches monitoring a number of times the QR code is scanned (i.e. viewed), par. [0064] and is used to provide promotional offers (i.e. advertisements), par. [0051] including monitoring the number of times a consumer scans the code)).
Discussion of Pan similarity to the instant application with Rehm being comparable to Pan is found above. Additionally, Haver teaches the determining of products within a location while adding advertising to promote the product and as it is comparable in certain respects to Pan and Rehm which teaches a deep reinforcement learning framework for rebalancing dockless bike sharing system as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pan and Rehm the vehicle shared in the shared transportation service is a vehicle having an advertisement displayed on a body, the learning and inference unit computes the deployment evaluation value and the collection evaluation value using impressions, and which are the number of times that an advertisement is viewable, as an element as taught by Rehm since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pan, Rehm, and Zeigler share the common goal of maintaining products and optimizing the utilization of the products for potential customers as the combination requires only the straightforward application of a known and proven technique to manage electric vehicles and applying specific logo based advertisement to promote the service, as the combination requires only the straightforward application of a known and proven technique to an analogous management of sharable vehicles and analysis of data in order to maximize the utilization of resource, and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 3 above, while Pan in view of Rehm in further view of Ziegler teaches the processing device above, Pan in view of Rehm in further view of Ziegler discloses maintaining products and optimizing the utilization of the products for potential customers. However, Pan does not explicitly disclose a device having the following limitations, however,
Rehm teaches wherein the vehicle shared in the shared transportation service is each of a rental electric bicycle, a rental electric kick scooter, a rental motorbike, and a rental car allowed to be restarted by and having an advertisement on the body (see; col. 8, lines (32-55) of Rehm teaches shareable vehicles include all manner of battery operated vehicles including a scooters, and motorbikes (i.e. sharable vehicles)), and
battery replacement (see; col. 4, lines (18-49) of Rehm teaches tracking battery life (i.e. evaluation) and triggering an action which is a recharge to be performed (i.e. replace battery charge) col. 5, lines (20-31) and monitoring service that tracks actions).
The Examiner notes that Pan teaches similar to the instant application teaches a deep reinforcement learning framework for rebalancing dockless bike sharing system.
Specifically, Pan discloses the modeling and using deep reinforcement learning to rebalance vehicle locations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Rehm teaches battery management for electric shareable vehicles and as it is comparable in certain respects to Pan which a deep reinforcement learning framework for rebalancing dockless bike sharing system as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Pan the vehicle shared in the shared transportation service is each of a rental electric bicycle, a rental electric kick scooter, a rental motorbike, and a rental car allowed to be restarted by battery replacement and having an advertisement on the body as taught by Rehm since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Pan, and Rehm share the common goal of maintaining electric vehicles that are sharable and optimize the utilization of the electric vehicles as the combination requires only the straightforward application of a known and proven technique to manage electric vehicles, as the combination requires only the straightforward application of a known and proven technique to an analogous management of sharable vehicles and analysis of data in order to maximize the utilization of resource, and they do not contradict or diminish the other alone or when combined.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Homma (U.S. Patent Publication 2022/0398607 A1) discloses a method for inverse reinforcement learning and information processing apparatus.
Xu et al. (U.S. Patent 11,455,582 B2) discloses systems and methods for optimizing an online on-demand service.
QIN et al. (U.S. Patent Publication 2022/0187084 A1) discloses vehicle repositioning on mobility on demand platforms.
Acuna Agost et al. (U.S. Patent Publication 2021/0398061 A1) discloses a reinforcement learning systems and methods for inventory control and optimization.
Haver et al. (U.S. Patent 9,824,387 B2) discloses systems and methods involving proximity, mapping, indexing, mobile advertising and/or other features.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN S SWARTZ whose telephone number is (571)270-7789. The examiner can normally be reached Mon-Fri 9:00 - 6:00.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
