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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/8/2025 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,781,874. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of U.S. Patent No. 11,781,874 anticipate claims 1-20 of the instant 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “identifying an initial route prediction for a training transportation request, wherein the initial route prediction is generated utilizing a route predictive model; and training a route correction predictive model to correct errors of the route predictive model by: generating, utilizing the route correction predictive model, a corrected route prediction for the training transportation request from the initial route prediction generated by the route predictive model; receiving actual route information corresponding to fulfillment of the training transportation request; determining one or more errors between the corrected route prediction and the actual route information; and updating the route correction predictive model based on the one or more errors.”
The recited limitations above are a process that, under the broadest reasonable interpretation, covers performance of the limitation done by a human using generic computer components, under mental processes (evaluation and observation) and certain methods of organizing human activities (business relations). That is, other than reciting “processor”, nothing in the claim element precludes the steps from practically being performed in the mind or by a human using generic computer components. For example, “identifying”, “training”, “generating”, “receiving”, “determining” and “updating” in the context of this claim encompasses the user to mentally and manually receive a transportation request, generate a first route, compare the first route to previously completed routes and identifying and generating at least one error in the first route prediction. In addition, the step of transmitting the route prediction can practically be performed by a human using a generic computer component (mobile device).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements- “non-transitory computer element” and “processor” to perform the above recited steps. The computer elements recited at a high-level of generality (generic computer elements performing a generic computer function of receiving a request, identifying an error and sending the route prediction error to be displayed on a mobile device) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements recited do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the computer elements to perform the steps of claims 1, 8 and 15 amount to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
The limitations of the dependent claims 2-7, 9-14 and 16-20, further describe the identified abstract idea. In addition, the limitations of claims 5-7, 12-14 and 19-20 define how the route for the transportation request determined which further describes the abstract idea. The generic computer components of claims 2-4, 9-10 and 16-17 (machine learning model, neural network) merely serve as the generic computer component and the functions performed by the generic computer components essentially amount to the abstract idea identified above. None of the dependent claims when taken separately in combination with each dependent claims parent claim overcome the above analysis and are therefore similarly rejected as being ineligible.
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fu et al. referred herein as Fu (U.S. Patent No. 10,127,496).
As to claims 1, 8 and 15, Fu teaches a method, a non-transitory computer readable medium and system, comprising:
identifying, by at least one processor, an initial route prediction for a training transportation request, wherein the initial route prediction is generated utilizing a route predictive model; (col 8 lines 1-25 and col 14 lines 52-64, the system determines transportation information to be inputted into a machine leaning model to obtain route predictions)
training a route correction predictive model to correct errors of the route predictive model by: generating, utilizing the route correction predictive model, a corrected route prediction for the training transportation request from the initial route prediction generated by the route predictive model; (col 9 lines 4-26)
receiving actual route information corresponding to fulfillment of the training transportation request; (col 11 lines 45-64)
determining one or more errors between the corrected route prediction and the actual route information; (col 11 lines 45-64, the system determines the error between the estimated route information and the actual route information in order to improve the machine learning)
updating the route correction predictive model based on the one or more errors. (col 11 lines 45-64)
As to claims 2, 9 and 16, Fu teaches the method, the non-transitory computer readable medium and the system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein the initial route prediction is generated utilizing the route predictive model comprising a first machine learning model. (col 5 lines 20-30)
As to claims 3, 10 and 17, Fu teaches the method, the non-transitory computer readable medium and the system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein training the route correction predictive model comprises training a second machine learning model. (col 5 lines 20-51)
As to claims 4, 11 and 18, Fu teaches the method, the non-transitory computer readable medium and the system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein training the route correction predictive model comprises training at least one of a neural network or a decision tree model to correct errors of the route predictive model. (col 5 lines 20-51)
As to claims 5 and 12, Fu teaches the method, the non-transitory computer readable medium and the system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein generating the initial route prediction comprises at least one of: generating a predicted route between a starting location and an ending location; generating a location prediction; generating a route distance prediction; or generating a travel time prediction. (col 4-5 lines 59-10, the system predicts the estimated time of arrival (i.e. travel time))
As to claims 6 and 13, Fu teaches the method, the non-transitory computer readable medium and the system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein generating the corrected route prediction comprises at least one of: generating a corrected route between a starting location and an ending location; generating a corrected location prediction; generating a corrected route distance prediction; or generating a corrected travel time prediction. (col 5 lines 7-10)
As to claim 7, 14 and 20 Fu teaches the method, the non-transitory computer readable medium and system of claims 1, 8 and 15 as discussed above.
Fu further teaches:
wherein generating the corrected route prediction for the training transportation request from the initial route prediction generated by the route predictive model comprises: determining contextual information corresponding to the training transportation request; (col. 9 lines 4-26 and col 11 lines 45-64)
generating, utilizing the route correction predictive model, the corrected route prediction for the training transportation request from the initial route prediction and the contextual information corresponding to the training transportation request. (col. 11 lines 45-64, determining errors between the estimated route and the actual route information and updating the route based on the determination).
As to claim 19, Fu teaches the system of claim 15 as discussed above.
Fu further teaches:
generate the initial route prediction by generating at least one of: a predicted route between a starting location and an ending location; a location prediction; a route distance prediction; or generating a travel time prediction; (col 4-5 lines 59-10, the system predicts the estimated time of arrival (i.e. travel time))
generate the corrected route prediction by generating at least one of: a corrected route relative to the predicted route; a corrected location prediction; a corrected route distance prediction; or a corrected travel time prediction. (col 5 lines 7-10)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEINA ELCHANTI whose telephone number is (313)446-6561. The examiner can normally be reached M-F 8:00 AM-5:00 PM EST.
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/ZEINA ELCHANTI/ Primary Examiner, Art Unit 3628