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 communications filed on 07/21/2026.
Claims 1, 6 and 7 have been amended. Claims 8-10 have been added. Therefore, Claims 1 – 10 are currently pending and have been examined in this application.
Response to Amendments
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “Even if the claims are viewed as touching upon mathematical concepts or methods of organization, they are patent-eligible because the additional elements integrate the judicial exception into a practical application.” (pgs. 7-8)
Examiner respectfully disagrees. The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of circuitry and 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. The steps of generating the tour route according to the policy and model parameters by inputting a distance matrix and visit destination information as graph data to graph convolution layer and calculating conditional probability of a next visit destination by the attention mechanism 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). 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 specific combination of architectural components acts to expand the functional capacity of automated vehicle routing systems, allowing them to solve highly complex, competing physical constraints that conventional algorithms cannot resolve. Because the claim imposes meaningful, structural limitations on the abstract concepts by tying them to a specialized network architecture executing a tangible engineering function, the claim is integrated into a practical application and is patent-eligible under 35 U.S.C. § 101.” (pg. 8)
The problem noted by Applicant of prior vehicle routing problem solutions failed to accommodate scenarios where resource demand and supply places co-exist is not a technically driven problem as a generic computer components are performing complex mathematics to generate tour routes. The claims appear to be directed to an improved business process for generating tour routes. There is no support in the Specification or the claims of an improvement in a technology or a technical field.
Applicant’s arguments, see Remarks pgs. 8-10, filed 07/21/2026, with respect to Zhang have been fully considered and are persuasive. The 35 U.S.C. 102 rejection has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 7, 9 and 10 are rejected under 35 U.S.C. 112, (b)/second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 6 recites, “ the attention mechanism” at line 15. There is insufficient antecedent basis for this limitation. Claim 7 is rejected based on the same rationale. Claims 9 and 10 are rejected based on their dependency on Claims 6 and 7.
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 1-10 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 which information on the demand places is generated, and (ii) a second condition under which information on the supply places is generated, wherein the model includes an embedded layer and a graph convolution layer as an encoder;
learn a policy represented by the model through reinforcement learning using the training data; and
generate the tour route according to the policy and the model parameters by inputting a distance matrix and visit destination information as graph data to the graph convolution layer to perform graph convolution.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing interactions (providing instructions) 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 that use generation conditions where the model includes an embedded layer and a graph convolution layer as an encoder, learning a policy by the model through reinforcement learning and generating a tour route according to the policy and the model parameters by inputting a distance matrix and visit destination information as graph data to the graph convolution layer to perform graph convolution involves collecting and analyzing data using complex mathematics to determine tour routes (managing interactions). Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
Claim 6 recites:
generating training data for training a model that generates the tour route by using generation conditions that include (i) a first condition under which information on the demand places is generated, and (ii) a second condition under which information on the supply places is generated, wherein the model includes an embedded layer and a graph convolution layer as an encoder;
learning a policy represented by the model through reinforcement learning using the training data;
generating the tour route according to the policy and the model parameters by inputting a distance matrix and visit destination information as graph data to the graph convolution layer to perform graph convolution and calculating a conditional probability of a next visit destination by the attention mechanism,
wherein the first condition includes a range of a total number of the demand places, a range of maximum capacities of the resources at the demand places, and a range of consumption rates of the resources at the demand places, and wherein the second condition includes a range of a total number of the supply places.
Claim 7 substantially recites the subject matter of Claim 6.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to managing interactions (providing instructions) 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, generating training data for training a model that generates a tour route by using generation conditions and embedded layer and a graph convolution layer as an encoder, learning a policy represented by the model through reinforcement learning and generating a tour route according to the policy and the model parameters by inputting a distance matrix and visit destination information as graph data to the graph convolution layer to perform graph convolution and calculating a conditional probability of a next visit involves collecting and analyzing data using complex mathematics to determine tour routes (managing interactions). Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
The dependent claims encompass the same abstract ideas identified above. 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 using complex math); Claim 5 is directed to generating the tour route according to the learned policy (analyzing data); Claim 8 is directed to a model comprising a decoder as an actor network(analyzing data using complex math);; Claim 9 is directed to decoder comprises an attention mechanism (analyzing data using complex math); and Claim 10 is directed to a decoder comprises a neural network(analyzing data using complex math).
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of 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. The steps of generating the tour route according to the policy and model parameters by inputting a distance matrix and visit destination information as graph data to graph convolution layer and calculating conditional probability of a next visit destination by the attention mechanism 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 1-10 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Qin et al. (US 12601090) discloses Vehicle repositioning and fleet management are related to classical vehicle routing problems, where machine learning methods with deep neural networks may be used as new approaches to the traveling salesman problem (TSP) and the vehicle routing problems (VRP), under an encoding-decoding framework.
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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