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 .
This is the Final Office Action in response to the Amendment filed on July 31, 2026, for Application No. 19/194,935 filed on April 30, 2025, title: “Systems And Methods For Generating Mobility Insurance Products Using Ride-Sharing Telematics Data”.
Status of the Claims
Claims 1-20 were pending. By the 07/31/2026’s Response, claims 1, 3-8, 10-13, 15, and 17-20 have been amended, and no claim has been added or cancelled. Accordingly, claims 1-20 are pending in the application and have been examined.
Priority
This application is a CON of US Application No. 18/170,800 filed on 02/17/2023 (Patented No. 12,315,019) which is a CON pf US Application No. 16/780,507 filed on 02/03/2020 (Patented No. 11,599,947) which claims the benefits of US Provisional Applications Nos. 62/934,932 filed on 11/13/2019; 62/934,948 filed on 11/13/2019; 62/892,916 filed on 08/28/2019; 62/892,853 filed on 08/28/2019. For the purpose of examination, the 08/28/2019 is considered to be the effective filing date.
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 nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,315,019 and claims 1-20 of US Patent No. 11,599,947. Although the claims at issue are not identical, they are not patentably distinct from each other because the examined claims are broader than the references claims in the patents and anticipated by the reference claims. The examined claims recite substantially the same limitations as the reference claims in the patents with minor variations that would have been obvious to one of ordinary skill in the art. Also, both the application and patents are directed to the same field of invention for generating mobility insurance products using ride-sharing telematics data, have the same inventors, and are commonly owned.
Therefore, this rejection is deemed necessary.
Application No. 19/194,935
Patent No. 12,315,019
Claim 8, A computer-implemented method for developing an optimal pricing models, each developed for each of a plurality of drivers operating vehicles associated with at least one transportation network company ("TNC") providing transportation services, the method implemented by a computing device having at least one processor in communication with at least one memory, the method comprising:
Claim 8, A computer-implemented method for developing an optimal pricing model for a driver operating a vehicle for a transportation network company (“TNC”) providing transportation services, the method implemented by a computing device having at least one processor in communication with at least one memory, the method comprising:
training, using at least one of machine learning tools or artificial intelligence tools, one or more machine learning programs by inputting, into the one or more machine learning programs, sample data sets including historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with a driver of the plurality of drivers;
training one or more machine learning programs using historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver;
generating, using the one or more machine learning programs, the optimal pricing model for the driver based upon the historical supply and demand data;
receiving, in real-time, current supply and demand data associated with the plurality of TNC vehicles, wherein the current supply and demand data is indicative of current supply and demand for the transportation services;
receiving from the TNC, in real-time, current supply and demand data associated with [[a]] the plurality of TNC vehicles, wherein the current supply and demand data is indicative of current supply and demand for the transportation services;
re-training, in real-time and using at least one of the machine learning tools or the artificial intelligence tools, the one or more machine learning programs by inputting, into the one or more machine learning programs, the current supply and demand data;
re-training, in real-time, the one or more machine learning programs using the current supply and demand data;
updating, in real-time, by applying the one or more re-trained machine learning programs to the optimal pricing model;
outputting, from the one or more re-trained machine learning programs, an optimal pricing model for the driver during the current supply and demand for the transportation services;
executing the optimal pricing model to determine, in real-time, an optimal usage-based insurance ("UBI") product for the driver; and
determining, in real-time, an optimal usage-based insurance ("UBI") product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with the current supply and demand for the transportation services; and
transmitting, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product.
transmitting, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product.
Application No. 19/194,935
Patent No. 11,599,947
Claim 8, A computer-implemented method for developing an optimal pricing models, each developed for each of a plurality of drivers operating vehicles associated with at least one transportation network company ("TNC") providing transportation services, the method implemented by a computing device having at least one processor in communication with at least one memory, the method comprising:
Claim 8, A computer-implemented method for determining an optimal usage-based insurance (“UBI’) product in real-time for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand for transportation services using a personalized insurance (“PI”) computing device, the PI computing device having at least one processor in communication with at least one memory, the method comprising:
training, using at least one of machine learning tools or artificial intelligence tools, one or more machine learning programs by inputting, into the one or more machine learning programs, sample data sets including historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with a driver of the plurality of drivers;
generating, using one or more machine learning programs, an optimal pricing model for the driver based upon historical supply and demand data associated with a plurality of TNC vehicles and historical driver data associated with the driver;
receiving, in real-time, current supply and demand data associated with the plurality of TNC vehicles, wherein the current supply and demand data is indicative of current supply and demand for the transportation services;
receiving from the TNC in real-time supply and demand data associated with a plurality of TNC vehicles, wherein the supply and demand data includes increased demand data for the transportation services, and wherein the increased demand data represents a current increase in demand for the transportation services;
retrieving driver data for the driver, wherein the driver data includes at least driver history associated with the driver;
re-training, in real-time and using at least one of the machine learning tools or the artificial intelligence tools, the one or more machine learning programs by inputting, into the one or more machine learning programs, the current supply and demand data;
updating, in real-time, by applying one or more machine learning programs to the increased demand data and the driver data, the optimal pricing model fer the driver,
outputting, from the one or more re-trained machine learning programs, an optimal pricing model for the driver during the current supply and demand for the transportation services;
executing the optimal pricing model to determine, in real-time, an optimal usage-based insurance ("UBI") product for the driver; and
determining, in real-time, the optimal UBI product by executing the updated optimal pricing model, wherein the optimal UBI product is personalized for the driver and includes current driving characteristics reflecting at least one risk factor associated with the increased demand for the transportation services and a risk profile associated with the driver; and
transmitting, in-real time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product.
transmitting, in real-time, a message to a user computing device associated with the driver, the message including the determined optimal UBI product and an offer to provide the transportation services with the determined optimal UBI product and at an increased payment rate based upon the increased demand data.
Claim Rejections - 35 USC § 101
Applicant’s arguments and amendments are persuasive. Hence, the rejection is withdrawn.
Claim Rejections - 35 USC § 102/103
An updated prior art search did not identify any art that teaches each and every elements and limitations of the claims at this time.
Conclusion
Claims 1-20 are rejected.
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 concerning this communication or earlier communications from the examiner should be directed to HAI TRAN whose telephone number is (571)272-7364. The examiner can normally be reached Monday-Friday, 9-5.
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HAI TRAN
Primary Examiner
Art Unit 3695
/HAI TRAN/Primary Examiner, Art Unit 3695
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