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
Status of Claims
This action is a Final action on the merits in response to the application filed on 04/13/2026.
Claims 1, 5 and 11 have been amended. Claim 4 has been cancelled. Claims 1 – 3 and 5-11 are currently pending and have been examined in this application.
Examiner acknowledges receipt of Priority Documents dated 02/24/2026.
Examiner acknowledges consideration of IDSs dated 08/08/2024 and 11/15/2024.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “ This process involving machine learning to establish a personalized match for incentives to address vehicle imbalance, is inherently computational and complex. It requires the processing of vast amounts of historical data, real-time user input, and the application of a sophisticated machine learning algorithm, which cannot practically be performed in the human mind.” (pgs. 7-8)
Based on MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer.” Here, generic computer components (e.g. a processor circuit) are performing generic computer functions such as specifying a reference facility, extracting management facilities, specifying an uneven distribution facility, generating and outputting an uneven distribution facility and using a trained model to determine a matching facility. The use of the trained model is performing complex mathematics which could be performed with a pen/paper or a generic computer as demonstrated in the instance claim. Further, the Specification indicates that a general purpose machine learning method is used (see ¶0078).
Applicant argues, “ Furthermore, while the claimed method involves "organizing human activities" (i.e., incentivizing users to move vehicles), it is not merely a high-level statement of a fundamental economic principle or practice. Instead, it provides a specific,
technological solution to a technical problem in the technical field of vehicle fleet management…” (pg. 8)
Examiner respectfully disagrees. The claims encompass Certain Methods of Organizing related to managing behaviors but for the recitation of generic computer components (e.g. a processor circuit). For example, specifying a reference facility to rent a vehicle, extracting other facilities within a determined distance and determining a facility with uneven distribution and a potential benefit to rent from involves managing personal behavior. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity. Further, Applicant has cited in the remarks that the claimed method involves “organizing human activities”, which further confirms the cited abstract idea.
The problem disclosed in the Specification related to utilizing uneven distribution facilities (see Abstract and ¶0044-¶0045) is not a technically driven problem nor is it related to a technical field. The claim appears to be related to an improved business process to match users with uneven distribution facilities which does not amount to an improvement in a technology (a computer) or a technical field.
Applicant argues, “ Even if currently amended Claim 1 were considered to recite a judicial exception under Step 2A Prong 1, the additional elements in the claim integrate any such exception into a practical application by significantly improving vehicle
management technology.” (pgs. 8-9)
The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of a processor circuit of the computer and a non-transitory computer-readable recording medium. These are generic computer components recited at a high level of generality as performing generic computer functions (Spec ¶0032).
For instance, The steps of specifying as a reference facility a plurality of management facilities that manage unrented vehicles; extracting one or more management facilities within a determined range from the reference facility, specifying an uneven distribution facility with an advantage involve data gathering and analysis functionality. The step of generating information that indicates the specified uneven distribution facility and outputting to user is producing a result based on the analysis and generically presenting to a user. The step of using a trained model to determine uneven distribution facility in which the advantage in a case where the user uses the uneven distribution facility matches the advantage emphasized by the user is analyzing data using complex mathematics. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant argues, “ This limitation, achieved through the use of a "trained model" for personalized incentive matching, provides a clear technological improvement to the field of vehicle management.” (pg. 9)
Examiner notes that vehicle management/vehicle fleet management is not related to a technical field. The use of a trained model for personalized incentive management is analyzing data using a machine learning model (e.g. complex mathematics) performed by a generic computer and is encompasses the abstract idea.
As stated above, the problem disclosed in the Specification related to utilizing uneven distribution facilities (see Abstract and ¶0044-¶0045) is not a technically driven problem nor is it related to a technical field. The claim appears to be related to an improved business process to match users with uneven distribution facilities which does not amount to an improvement in a technology (a computer) or a technical field.
Applicant argues, “ Claim 1 contains an inventive concept that amounts to significantly more than any identified abstract idea, rendering it patent eligible under Step 2B.” (pgs. 10-11)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
Further in regards to Applicant’s arguments that the claims create an unconventional and non-generic arrangement of known elements, Examiner respectfully disagrees. In Bascom the court found that although the additional elements were generic computer elements when considered individually, when considered in combination an inventive concept was found in the non-conventional and non-generic arrangement of the additional elements. The claims in Bascom did not merely recite the abstract idea of filtering content along with the requirement to perform it on the Internet, or to perform it on a set of generic computer components. The inventive concept is in the technical feature of network technology in a filtering system by associating individual accounts with their own filtering scheme and elements while locating the filtering.
Unlike Bascom, the instance claims are directed to specifying a reference facility, extracting management facilities, specifying an uneven distribution facility, generating and outputting an uneven distribution facility and using a trained model to determine a matching facility performed by a generic computer (apply it). The independent claims do not amount to significantly more nor do they provide an inventive concept.
Applicant argues, “Therefore, we believe that Audet does not disclose the specific technical features of currently amended Claim 1, which now explicitly includes the use of a trained model for personalizing the selection of an uneven distribution facility based
on the user's emphasized advantages…” (pgs. 12-13)
Examiner respectfully disagrees. Yip discloses using a trained machine learning model to predict one or more entities a user is predicted to visit in a remote location based on an input indicating historical interactions of the user (see ¶0049). The prediction may utilize user preference data (see ¶0016), user historical behavior/interaction data at an entity (see ¶0020-¶0022) (user has previously used an uneven distribution facility) to train the machine learning model to predict one or more entities to visit and/or a predicted level of interest (see ¶0023). Audit disclose evaluating and determining past use of vehicles at a station and renters desired destination or arrival station, then the system proposes alternate destinations associated with advantageous rental costs or reward points (see ¶0069-¶0070). Thus, the combination of the evaluation to determine alternate locations of Audet with the machine learning model to predict one or more entities a user is predicted to visit produces a result of matching uneven distribution facilities based on use historical and preference data.
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 1-3 and 5-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
specifying, [at a processor circuit of the computer,] as a reference facility, any one of a plurality of management facilities that manage unrented vehicles, based on input by a user;
extracting, [at the processor circuit], among the plurality of management facilities stored in a memory of the computer, one or more management facilities that exist within a predetermined range from the reference facility and of which a number of unrented vehicles being managed is to be changed, as one or more uneven distribution facilities;
specifying, [at the processor circuit], among the one or more uneven distribution facilities, an uneven distribution facility in which an advantage in a case where the user uses the uneven distribution facility matches an advantage emphasized by the user; and
generating, [at the processor circuit,] information that indicates the specified uneven distribution facility, and outputting the information to the user so that the information is viewable by the user, wherein
by using a trained model generated based on at least one of information on whether the user has used the uneven distribution facility presented in past, information on the user input by the user, and information on an action history of the user, the uneven distribution facility in which the advantage in a case where the user uses the uneven distribution facility matches the advantage emphasized by the user is specified from among the one or more uneven distribution facilities.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing behaviors but for the recitation of generic computer components (e.g. a processor circuit). For example, specifying a reference facility to rent a vehicle, extracting other facilities within a determined distance and determining a facility with uneven distribution and a potential benefit to rent from involves managing personal behavior. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data.
Independent Claim 11 substantially recites the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to uneven distribution facilities where unrented vehicles need to be decreased (analysis); Claim 3 is directed to uneven distribution facilities where unrented vehicles need to be increased (analysis); Claim 5 is directed to information on at least one uneven distribution facility; Claims 6-7 are directed to the uneven distribution facilities are determined based on unrented vehicles (analysis); Claim 8 is directed to maintenance facilities; Claim 9 is directed to specifying management facilities based on route search and Claim 10 is directed to outputting information.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a processor circuit of the computer. Claim 11 recites the additional elements of a non-transitory computer-readable recording medium, a computer and a processor circuit of the computer. These are generic computer components recited at a high level of generality as performing generic computer functions (Spec ¶0032).
For instance, The steps of specifying as a reference facility a plurality of management facilities that manage unrented vehicles; extracting one or more management facilities within a determined range from the reference facility, specifying an uneven distribution facility with an advantage involve data gathering and analysis functionality. The step of generating information that indicates the specified uneven distribution facility and outputting to user is producing a result based on the analysis and generically presenting to a user. The step of using a trained model to determine uneven distribution facility in which the advantage in a case wher the user uses the uneven distribution facility matches the advantage emphasized by the user is analyzing data using complex mathematics. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-3 and 5-11 are not patent eligible.
Claim Rejections - 35 USC § 103
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 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 (AIA ) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Audet (US 2011/0010300) in view of YIP et al. (US 2024/0249155).
Claim 1:
Audet discloses:
A vehicle management facility information providing method implemented by a computer, the method comprising: (see at least ¶0047, multiple vehicle rental stations; see also Abstract)
specifying, at a processor circuit of the computer, as a reference facility, any one of a plurality of management facilities that manage unrented vehicles, based on input by a user; (see at least ¶0068-¶0069, renter identifies a desirable destination or arrival station; see also ¶0024 mobile computing device)
extracting, at the processor circuit, among the plurality of management facilities stored in a memory of the computer, one or more management facilities that exist within a predetermined range from the reference facility and of which a number of unrented vehicles being managed is to be changed, as one or more uneven distribution facilities; (see at least ¶0067, determine the number of vehicles at each station to determine which stations have too many vehicles vs too less; see also ¶0076-¶0077, distances between stations are determined)
specifying, at the processor circuit, among the one or more uneven distribution facilities, an uneven distribution facility in which an advantage in a case where the user uses the uneven distribution facility matches an advantage emphasized by the user; and (see at least ¶0077, if the renter wants more arrival incentive rewards they are informed to bring the vehicle to a specific arrival station)
generating, at the processor circuit, information that indicates the specified uneven distribution facility, and outputting the information to the user so that the information is viewable by the user, wherein (see at least ¶0075-¶0077, renter receives SMS msg regarding rewards at a station)
While Audet disclose the above limitations, Audet further discloses at least one of information on whether the user has used the uneven distribution facility presented in past, information on the user input by the user, and information on an action history of the user (see at least ¶0063-¶0064, user activity is tracked), Audet does not explicitly disclose the following limitations; however, Yip does disclose:
by using a trained model generated based on at least one of information on whether the user has used the uneven distribution facility presented in past, information on the user input by the user, and information on an action history of the user, [the uneven distribution] facility in which the advantage in a case where the user uses [the uneven distribution] facility matches the advantage emphasized by the user is specified among the one or more [uneven distribution] facilities. (see at least ¶0049, the machine learning system main train a machine learning model to output a prediction of entities that a user is to visit based historical user interaction data; see also ¶0016, the prediction may utilize user preference data; ¶0020-¶0022, user historical behavior/interaction data at an entity (user previously used an uneven distribution facility))
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the determining of number of rental vehicles available at multiple stations, selecting and facilitating vehicle rental of Audet with the machine learning predictive functionality of YIP 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. Further the combination assists with determining the likelihood a user will visit a particular location (see ¶0049).
Claim 11 for a CRM (Audet see ¶0025) substantially recites the subject matter of Claim 1 and is rejected based on the same rationale.
Claim 2:
Audet and Yip disclose claim 1. Audet further discloses:
wherein the one or more uneven distribution facilities include, among the plurality of management facilities, the one or more management facilities that exist within the predetermined range from the reference facility and of which the number of unrented vehicles is to be decreased, and (see at least ¶0068, determine which stations or too crowded vs not enough vehicles; see also ¶0021, determine rental costs or rewards based on vehicle availability and distance between stations; ¶0076-¶0077, if the renter wants more arrival incentive rewards they are informed to bring the vehicle to a specific arrival station and the distance; see also Abstract)
the specifying of the uneven distribution facility includes specifying, among the one or more uneven distribution facilities stored in the memory, an uneven distribution facility in which an advantage in a case where the user rents a vehicle in the uneven distribution facility matches the advantage emphasized by the user. (see at least ¶0068, determine which stations or too crowded vs not enough vehicles; see also ¶0021, determine rental costs or rewards based on vehicle availability and distance between stations; ¶0076-¶0077, if the renter wants more arrival incentive rewards they are informed to bring the vehicle to a specific arrival station and the distance)
Claim 3:
Audet and Yip disclose claim 1. Audet further discloses:
wherein the one or more uneven distribution facilities include, among the plurality of management facilities stored in the memory, the one or more management facilities that exist within the predetermined range from the reference facility and of which the number of unrented vehicles is to be increased, and (see at least ¶0068, determine which stations or too crowded vs not enough vehicles; see also ¶0021, determine rental costs or rewards based on vehicle availability and distance between stations; ¶0076-¶0077, if the renter wants more arrival incentive rewards they are informed to bring the vehicle to a specific arrival station and the distance; see also Abstract)
the specifying of the uneven distribution facility includes specifying, among the one or more uneven distribution facilities stored in the memory, an uneven distribution facility in which an advantage in a case where the user returns a vehicle in the uneven distribution facility matches the advantage emphasized by the user. (see at least ¶0068, determine which stations or too crowded vs not enough vehicles; see also ¶0021, determine rental costs or rewards based on vehicle availability and distance between stations; ¶0076-¶0077, if the renter wants more arrival incentive rewards they are informed to bring the vehicle to a specific arrival station and the distance)
Claim 5:
Audet and YIP disclose claim 1. Audet further discloses:
wherein in a case where the trained model has not been generated, information on at least one of the uneven distribution facilities is presented to the user. (see at least ¶0075-¶0077, providing station recommendations)
Claim 6:
Audet discloses claim 1. Audet further discloses:
wherein the one or more management facilities extracted as the one or more uneven distribution facilities are determined based on the number of unrented vehicles that are managed by each of the plurality of management facilities. (see at least Abstract; see also ¶0068, the number of vehicles at a station are evaluated to determine critical need, too many or too little vehicles)
Claim 7:
Audet and Yip disclose claim 6. Audet further discloses:
wherein the one or more management facilities extracted as the one or more uneven distribution facilities are determined based on a result of predicting a change in the number of unrented vehicles managed in each of the plurality of management facilities. (see at least ¶0068, utilizes The system determines which stations 12 have too little units therein to meet the "departure" demand and which stations 12 have too much vehicles 17 therein to meet the "arrival" )
Claim 10:
Audet and Yip disclose claim 1. Audet further discloses:
wherein the outputting of the information to the user includes outputting an advantage in a case where the uneven distribution facility is used together with the information that indicates the specified uneven distribution facility. (see at least ¶0075, renter takes a vehicle from station and receives a sms message informing them there is additional incentive if they bring vehicle to an empty station; see also ¶0025, sending info to user
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Audet (US 2011/0010300) in view of YIP et al. (US 2024/0249155) further in view of Hirose et al. (US 2017/0098176).
Claim 8:
While Audet and Yip discloses claim 1 and Audet further discloses wherein as the one or more uneven distribution facilities (see Abstract), neither explicitly disclose the following limitation; however Hirose does disclose:
[wherein as the one or more uneven distribution facilities], a maintenance facility that satisfies a predetermined condition is selected among maintenance facilities that perform maintenance of the vehicle. (see at least ¶0010, selecting a facility for maintenance of shared vehicle; see also ¶0061, based on necessity level for maintenance selecting a maintenance facility)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the determining of number of rental vehicles available at multiple stations, selecting and facilitating vehicle rental of Audet and the machine learning predictive functionality of YIP with the determining a need for maintenance and selecting a maintenance facility of Hirose to assist with managing shared vehicles (see ¶0003).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Audet (US 2011/0010300) in view of Beaurepai et al. (US 2020/0149903).
Claim 9:
While Audet discloses claim 1, Audet does not explicitly disclose the following limitations; however, Beaurepai does disclose:
wherein the specifying of the reference facility includes specifying one of the plurality of management facilities as a reference facility based on a route search condition input by the user. (see at least ¶0109, routing platform presents the candidate routes and transport modes based on user preferences regarding navigation routes)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the determining of number of rental vehicles available at multiple stations, selecting and facilitating vehicle rental of Audet with the candidate routes based on user preference of Beaurepaire to help facilitate user selecting an appropriate location.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Hiruta et al. (US 20200097863) discloses a vehicle management system that manages a vehicle that is to be used by a plurality of users generates schedule information based on an action schedule of the next user following the current user, and outputs to the next user a suggestion of changing a return location where the current user terminates the use of the vehicle.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
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