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 .
Notice to Applicant
This is the first Non-Final Office Action in response to Application Serial Number: 18/870,479, filed on November 29, 2024. Claims 1-8 are pending in this application and have been rejected below.
Priority
The Examiner has noted the Applicant is claiming priority from PCT Application No. PCT/JP2022/023634 filed June 13, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on November 29, 2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-6 are directed towards an apparatus, claim 7 is directed towards a method and claim 8 is directed towards a non-transitory computer-readable storage medium, which are all among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining resource options and prices to present to a user in a market.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, define a problem by formulating optimization of resource options and prices of the options to be presented to each user in a market in which a plurality of types of limited resources are handled; and determine the resource options and the prices of the options to be presented to each user by solving the problem constitutes methods based on commercial or legal interactions and managing personal behavior, or relationships or interactions between people, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of an information apparatus comprising circuitry does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claims 7 and 8 recite certain method of organizing human activity for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites an information processing apparatus comprising circuitry at a high-level of generality such that it amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method performed using an information processing apparatus recited in claim 7 and non-transitory computer-readable storage medium storing an information processing program executable by an information processing apparatus in claim 8 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claims 7 and 8 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including the information processing apparatus comprising circuitry and non-transitory computer-readable storage medium storing an information processing program executable by an information processing apparatus amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 4 and recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-6 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 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.
Claims 1-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tulabandhula et al., U.S. Publication No. 2018/0174089 [hereinafter Tulabandhula].
Referring to Claim 1, Tulabandhula teaches:
An information processing apparatus [0101], comprising:
circuitry configured to [0061], “The model generating processor 206 may include suitable logic, circuitry, code, and/or interfaces that may be configured to execute one or more instructions stored in the memory 210. The model generating processor 206 may execute one or more sets of instructions/programs/code/scripts stored in the memory 210 to perform one or more operations. For example, the model generating processor 206 may be configured to generate a random utility choice model, such as the MNL model, that may capture the historical commuting characteristics of the one or more commuters”; [0027], “A “predictive model” may refer to a random utility choice model, which may enable prediction of a schedule of one or more transportation services at one or more time instances so as to optimize one or more KPI parameters of a transportation agency. In an embodiment, the predictive model may be generated based on historical commuting characteristics of one or more commuters”; [0080]; [0087]; and
determine the resource options and the prices of the options to be presented to each user by solving the problem [0041], “a display screen that may be configured to display one or more travel options in response to the travel request of the commuter, at a graphical user interface (GUI) rendered by the application server 108 over the communication network 110. For example, the application server 108 may render a GUI displaying the one or more types of one or more available transportation services, a service cost for using each of the one or more available transportation services, expected time duration for reaching the destination location, and/or the like”; [0071], “the service schedule of the one or more transportation services for the second defined time duration may be generated, by use of generated predictive model. The service schedule of the one or more transportation services may correspond to a time table of deploying the one or more transportation services of different service types from their origin location for transit along the one or more routes at the one or more time stamps of the second defined time duration…the service schedule of the one or more transportation services for the second defined time duration may be generated based on the service cost for travel between each pair of locations by each of the one or more transportation services.”; [0051], “the application server 108 may be configured to generate the service schedule of the one or more transportation services for a second defined time duration, by use of the generated predictive model, for example, the MNL model trained on the historical commuting characteristics. The generation of the service schedule of the one or more transportation services may be further based on criteria of the transportation system defined by the service provider. The defined criteria may comprise one or more parameters based on at least one of a count, a type, and a capacity of the one or more transportation services”; [0072].
Referring to Claim 2, Tulabandhula teaches the information processing apparatus according to claim 1. Tulabandhula further teaches:
wherein the problem is to maximize a reward for a resource provider [0079], “use of the MNL model in the schedule generation, the service schedule may be optimized such that the one or more KPI parameters, for example, net revenue or net profit, for the service provider may be maximized”; [0100], “The disclosed method maximizes the revenue and other key performance indicators for the service provider of the transportation systems by taking commuter choice behavior in to account and also by learning the changing commuter choice behaviors over a period of time”; [0077], “the extracted historical commuting characteristics of the one or more commuters are taken as an input to generate the predictive model, such as the MNL model, that may be further utilized to generate the service schedule that maximizes the one or more KPI parameters for the service provider”; [0025]; [0070].
Referring to Claim 3, Tulabandhula teaches the information processing apparatus according to claim 1. Tulabandhula further teaches:
wherein the problem maximizes a total reward by repeatedly performing a process of observing users who appear from a set of a plurality users on the basis of a probability distribution, presenting a plurality of options included in a plurality of resources and the prices of the plurality of options to the appearing user, obtaining rewards for resource providers when one option is selected from the plurality of options depending on the probability distribution, reducing a remaining amount of resources of one selected option by 1, and changing the remaining amount of resources for each option depending on a value which follows the probability distribution a plurality of times [0094]-[0095], “The sensitivity analysis for the assortment optimization problem with constraints having unimodular constraint structures, such as capacity constraints, precedence constraints, constraints on quality consistent pricing, may be formulated as a set of unimodular constraints. Such a problem may be formulated as a linear program. Based on the update in one of the choice parameters, an entire column of the constraint matrix of the linear program may change. The processor 202 may be configured to execute the sensitivity analysis under such changes and derive a range of parameters under which the optimal assortment may remain unchanged. The following linear program may be utilized to find the optimal assortment with the unimodular constraints… In an event the update is made in the commuter preference vector v, multiple entries of the constraint matrix of the above linear program may change. Note that if in the t.sup.th round, the k.sub.t.sup.th component of v.sup.t is updated, then only entries of the k.sub.t.sup.th column in the constraint matrix may change. The permissible intervals for any component of v may depend on whether the component is a basic variable or not in the basic feasible solution of the linear program. To simplify notation, it may be assumed that the first e variables are the basic variables. Let the reduced costs of the variables at the end of (t−1).sup.th… Let's consider a case when the k.sub.t-1.sup.th variable may be a non-basic variable. For each element a.sub.il of the constraint matrix (where x.sub.l is a non-basic variable), the processor 202 may determine a range of values such that the new reduced cost of all the variables still remains non-positive… the optimal revenue and the revenue-maximizing assortment remains the same. Thus, for a given change in the commuter preference vector v, the changes in the constraint matrix may be computed. If the change in the constraint matrix satisfies the above conditions, the optimal assortment remains unchanged”; [0079].
Referring to Claim 4, Tulabandhula teaches the information processing apparatus according to claim 1. Tulabandhula further teaches:
wherein the determining further comprises [0027], “The predictive model may employ one or more techniques such as, but not limited to, one or more statistical techniques, one or more natural language processing techniques, one or more neural network techniques, and/or one or more machine learning techniques to predict the schedule of the one or more transportation services. For example, the predictive model may correspond to a multinomial logit (MNL) model that may be configured to capture the historical commuting characteristics of the one or more commuters. The MNL model may quickly solve an assortment optimization problem, for example, five times faster than integer programming algorithms”; [0069].
Referring to Claim 5, Tulabandhula teaches the information processing apparatus according to claim 3. Tulabandhula further teaches:
the circuitry further configured toresource, and a vector representing the current number of repetitions [0037], “A “commuter preference vector” may refer to a set of numerical values, in which each numerical value defines a preference of a commuter for travel along a route by a transportation service. In an embodiment, the commuter preference vector may be generated based on historical commuting characteristics of one or more commuters”; [0072]; and
determine consecutive values of options and prices using mapping from the state, wherein the determining further comprises determining the one option as one action on the basis of the consecutive value [0038], “A “service cost vector” may refer to a set of numerical values, in which each numerical value defines a travel price for traveling between a source location and a destination location by a transportation service. In an embodiment, the service cost vector may be generated based on the travel cost associated with each of the one or more transportation services of a transportation system”; [0072].
Referring to Claim 6, Tulabandhula teaches the information processing apparatus according to claim 5. Tulabandhula
wherein the determining further comprises determining the one action for the consecutive value using mapping from a set of a predetermined number of neighbors in a discrete portion of an action space [0080], “In an embodiment, the optimal assortment for the uncapacitated assortment optimization problem with the MNL model is revenue ordered set. Let the one or more transportation services (of different types, timings, and/or prices) be indexed from “1” to “n” in the decreasing order of prices. Then, the revenue ordered set may be a set, S={1, 2, . . . , j}, for some j. Thus, instead of 2.sup.n assortments in the service schedule, the processor 202 may look for the optimal assortment only in “n” revenue ordered assortments, where n is the number of transportation services. In such cases, the revenue of the revenue ordered assortments may be weakly unimodal. This property may enable the use of faster search algorithms, such as Golden Section Search, and hence, obtain the optimal revenue ordered assortment in time O(log n)”; [0081]; [0094].
Referring to Claim 7, Tulabandhula teaches:
An information processing method performed using an information processing apparatus [0059]-[0060]; [0101], the method comprising:
Claim 7 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Referring to Claim 8, Tulabandhula teaches:
A non-transitory computer readable storage medium storing an information processing program [0007]; [0105]:
Claim 8 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jindal et al. (US 20190339087 A1) – A method for operating a ride-share-enabled vehicle includes determining a target location of the ride-share-enabled vehicle, determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, if any, based on the determined target location of the ride-share-enabled vehicle, determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm, and causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.
Ratti et al., (WO 2018136687 A1) – Real-time, point-to-point, on-demand ride dispatching system. The system provides for optimal operation including a fleet dimensioning module for determining an optimal number of vehicles in a fleet needed to serve a collection of trip requests, and a vehicle dispatching module for dispatching the fleet of vehicles to serve a selected number of trip requests. The fleet dimensioning module employs a shareability network to model trip requests that can be served by a same vehicle to find the minimum number of vehicles in the fleet to serve all trip requests in the collection of trip requests.
Samie et al. (Dynamic Discsrimination Pricing and Freelance Drivers to Rebalance Mixed-Fleet Carsharing Systems) - This paper presents pricing strategies for the relocation problem in mixed carsharing systems with heterogeneous users. On the demand side, a dynamic discriminatory pricing approach is introduced, which can obviate the need for operation-based repositioning, partially or fully. Moreover, it’s assumed that there are earnings-sensitive freelance drivers who may decide to participate in relocation operations, and their decisions depend on the wages are offered by the operation manager. As the operational decisions (trip pricing, relocators hiring, and charging scheduling) are tightly connected, and their interplay can significantly affect the performance of the carsharing system, an integrated model is proposed to optimize trip price and staff wage problems along with vehicle allocation and charge scheduling. To cope with computation difficulties for solving the proposed model, this paper presents a solution framework based on a rolling horizon approach. The results confirm that the proposed pricing strategies outperform common strategies in the literature and can significantly improve the performances of carsharing systems. The sensitivity analysis of several key parameters provides managerial insights into the operations management of carsharing systems.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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