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 first action on the merits in response to the application filed on 04/24/2025.
Claims 1 – 7 are currently pending and have been examined in this application.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
…generate training data for training a model that generates the tour route by using generation conditions that include (i) a first condition under within information on the demand places is generated, and (ii) a second condition under which information on the supply places is generated; and
learn a policy represented by the model through reinforcement learning using the training data
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing interactions but for the recitation of generic computer components (e.g. circuitry and crm). For example, generating a tour route of a moving body(vehicle with driver) visiting demand places, training a model that generates the tour route and learning a policy by the model through reinforcement learning involves managing interactions. 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 Claims 6 and 7 substantially recite 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 first and second conditions; Claim 3 is directed to randomly sample data and generating training data (analyzing data); Claim 4 is directed to the model is a pointer network (analyzing data) and Claim 5 is directed to generating the tour route according to the learned policy (analyzing data).
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of an apparatus and circuitry. Claim 7 recites the additional element of a non-transitory computer readable storage medium. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec Figure 1).
For instance, generating training data for training a model that generates the tour route by using generation conditions and learning a policy represented by the model through reinforcement learning using the training data involves 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. circuitry and crm). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. circuitry and crm). 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 circuitry and a crm 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 1-7 are not patent eligible.
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 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.
(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, 6 and 7 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Zhang et al. (US 2020/0124429).
Claim 1:
Zhang discloses:
A tour route generation apparatus for generating a tour route of a moving body visiting demand places that require resources supplied by the moving body, and supply places that provide the resources to the moving body, the tour route generation apparatus comprising: (see at least ¶00006, determining a routing using reinforcement learning to consider one or more routes to a plurality of locations; see also ¶0010, plurality of locations, demand information , position information along a route and remaining capacity of a vehicle leaving the locations; see also ¶0044, moving goods from one or more pickup locations to one or more delivery locations)
circuitry configured generate training data for training a model that generates the tour route by using generation conditions that include (i) a first condition under which (see at least Figure 3 and associated text; see also ¶0054-¶0055, training multiple reasonably good policies using multiple RLs for the same VRP; see also ¶0072)
learn a policy represented by the model through reinforcement learning using the training data. (see at least ¶0054-¶0055, training multiple reasonably good policies using multiple RLs for the same VRP)
Claim 6 for a method and Claim 7 for a crm (Figure 1) substantially recite the subject matter of Claim 1 for an apparatus (Figure 1) and are rejected based on the same rationale.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Peng et al. (US 2023/0288212) discloses a route planning and/or path planning using machine learning to learn a policy that can generate an optimized demand coverage, service, and/or capacity fulfilment route plan for a transportation service (e.g., an on-demand transportation service).
Zhang et al. (US 2020/0232802) discloses determining routing and, in particular, to systems and methods for determining routing by identifying promising routing solution candidates and selectively optimizing the identified routing solution candidates.
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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