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 .
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bellowe, US 2017/0351978 A1, in view of Khoury, US 2017/0255966 A1.
As per Claim 1, Bellowe teaches a computer-implemented method (¶¶ 17-18) comprising:
receiving, by a computing device, geolocation data and auxiliary data associated with driving activities of a user (¶ 97; based on “automobile battery use . . . and/or driving habits”), wherein the auxiliary data comprise a set of driving behaviors for the user that correlate to the geolocation data (¶ 44);
training a machine learning model based upon the geolocation data and the auxiliary data (¶ 103; “the user schedule can be determined based on information received from devices”); and
identifying, by the computing device using the machine learning model, as trained, a plurality of driving routes of the user (¶ 122; as the “information in itinerary can be analyzed for example a row can be mapped and the most efficient route identified”).
Bellowe does not expressly teach: determining, by the computing device, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes; generating, by the computing device, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes; and transmitting, by the computing device, the user map to a user device of the user and to be displayed on a user interface of the user device. Khoury teaches:
determining, by the computing device, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes (¶ 46; “as inferred by the routes typically driven by the driver”);
generating, by the computing device, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes (¶ 47; as shown in Figure 4A); and
transmitting, by the computing device, the user map to a user device of the user and to be displayed on a user interface of the user device (¶¶ 65-67).
At the time of the invention, a person of skill in the art would have thought it obvious to combine the geolocation data from Bellowe with the mapping methods Khoury, in order to create user profiles more readily, and deliver relevant or otherwise useful content more quickly to particular users.
As per Claim 2, Bellowe teaches aggregating, by the computing device, the geolocation data and the auxiliary data before training the machine learning model (¶ 56).
As per Claim 3, Bellowe teaches that training the machine learning model further comprises training the machine learning model based upon the geolocation data and the auxiliary data, as aggregated, to identify patterns in the set of driving behaviors (¶ 102).
As per Claim 4, Bellowe teaches that determining the one or more driving routes traversed by the user more frequently than other driving routes comprises determining the one or more driving routes traversed by the user more frequently than other driving routes based upon a travel frequency threshold derived from the auxiliary data (¶¶ 86-87; based on “a threshold confidence factor”).
As per Claim 5, Bellowe teaches that the one or more driving routes traversed by the user more frequently than other driving routes comprise one of a single driving route or a plurality of sub-routes that comprise the single driving route (¶¶ 46-47).
As per Claim 6, Bellowe teaches that generating the user map comprises utilizing one or more edges of a reference map that match the geolocation data associated with the one or more driving routes traversed by the user more frequently than other driving routes (¶ 59; based on “the coordinates of the geo-fenced area A1 701” of Figure 7).
As per Claim 7, Bellowe teaches that generating the user map further comprises populating the user map with one or more auxiliary data associated with the one or more driving routes traversed by the user more frequently than other driving routes that is pertinent to a driver different from the user (¶ 105; “a user profile is clustered with other user profiles and the data model is built using the information collected from the cluster”).
As per Claim 8, teaches that Bellowe identifying the plurality of driving routes comprises searching an activity table storing the geolocation data and the auxiliary data to identify the plurality of driving routes from the driving activities of the user that have common geolocation data points (¶¶ 86-87).
As per Claim 9, Bellowe teaches a computing system comprising a processor and a memory storing computing instructions (¶ 92; computer system 1100 with processor 1102 and main memory 1104 of Figure 11) that, when executed by the processor, cause the processor to perform operations comprising:
receiving, by the computing system, geolocation data and auxiliary data associated with driving activities of a user (¶ 97; based on “automobile battery use . . . and/or driving habits”), wherein the auxiliary data comprise a set of driving behaviors for the user that correlate to the geolocation data (¶ 44);
training a machine learning model based upon the geolocation data and the auxiliary data (¶ 103; “the user schedule can be determined based on information received from devices”); and
identifying, by the computing system using the machine learning model, as trained, a plurality of driving routes of the user (¶ 122; as the “information in itinerary can be analyzed for example a row can be mapped and the most efficient route identified”).
Bellowe does not expressly teach: determining, by the computing system, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes; generating, by the computing system, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes; and transmitting, by the computing system, the user map to a user device of the user and to be displayed on a user interface of the user device. Khoury teaches:
determining, by the computing system, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes (¶ 46; “as inferred by the routes typically driven by the driver”);
generating, by the computing system, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes (¶ 47; as shown in Figure 4A); and
transmitting, by the computing system, the user map to a user device of the user and to be displayed on a user interface of the user device (¶¶ 65-67).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 10, Bellowe teaches that the operations further comprise aggregating the geolocation data and the auxiliary data before training the machine learning model (¶ 56).
As per Claim 11, Bellowe teaches that training the machine learning model further comprises training the machine learning model based upon the geolocation data and the auxiliary data, as aggregated, to identify patterns in the set of driving behaviors (¶ 102).
As per Claim 12, Bellowe teaches that the operations further comprise identifying the plurality of driving routes based upon the aggregated geolocation data and the auxiliary data, as trained (¶¶ 86-87; based on “a threshold confidence factor”).
As per Claim 13, Bellowe teaches that the one or more driving routes traversed by the user more frequently than other driving routes comprise one of a single driving route or a plurality of sub-routes that comprise the single driving route (¶¶ 46-47).
As per Claim 14, Bellowe teaches that the operations further comprise generating the user map by utilizing one or more edges of a reference map that match the geolocation data associated with the one or more driving routes traversed by the user more frequently than other driving routes (¶ 59; based on “the coordinates of the geo-fenced area A1 701” of Figure 7).
As per Claim 15, Bellowe teaches that the operations further comprise populating the user map with one of the auxiliary data associated with the one or more driving routes traversed by the user more frequently than other driving routes that is pertinent to a driver different from the user (¶ 105; “a user profile is clustered with other user profiles and the data model is built using the information collected from the cluster”).
As per Claim 16, Bellowe teaches that identifying the plurality of driving routes comprises searching an activity table storing the geolocation data and the auxiliary data to identify the plurality of driving routes from the driving activities of the user that have common geolocation data points (¶¶ 86-87).
As per Claim 17, Bellowe teaches a non-transitory computer readable medium storing computing instructions (¶¶ 93-95) that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:
receiving, by the computing device, geolocation data and auxiliary data associated with driving activities of a user (¶ 97; based on “automobile battery use . . . and/or driving habits”), wherein the auxiliary data comprise a set of driving behaviors for the user that correlate to the geolocation data (¶ 44);
training a machine learning model based upon the geolocation data and the auxiliary data (¶ 103; “the user schedule can be determined based on information received from devices”); and
identifying, by the computing device using the machine learning model, as trained, a plurality of driving routes of the user (¶ 122; as the “information in itinerary can be analyzed for example a row can be mapped and the most efficient route identified”).
Bellowe does not expressly teach: determining, by the computing device, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes; generating, by the computing device, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes; and transmitting, by the computing device, the user map to a user device of the user and to be displayed on a user interface of the user device. Khoury teaches:
determining, by the computing device, one or more driving routes of the plurality of driving routes that are traversed by the user more frequently than other driving routes of the plurality of driving routes (¶ 46; “as inferred by the routes typically driven by the driver”);
generating, by the computing device, a user map that comprises the one or more driving routes, wherein the user map is populated with at least information related to one of the auxiliary data associated with the one or more driving routes (¶ 47; as shown in Figure 4A); and
transmitting, by the computing device, the user map to a user device of the user and to be displayed on a user interface of the user device (¶¶ 65-67).
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 18, Bellowe teaches that the operations further comprise aggregating the geolocation data and the auxiliary data before training the machine learning model (¶ 56).
As per Claim 19, Bellowe teaches that training the machine learning model further comprises training the machine learning model based upon the geolocation data and the auxiliary data, as aggregated, to identify patterns in the set of driving behaviors (¶ 102).
As per Claim 20, Bellowe teaches that the operations further comprise identifying the plurality of driving routes based upon the aggregated geolocation data and the auxiliary data, as trained (¶¶ 86-87; based on “a threshold confidence factor”).
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 U.S. Patent No. 10,830,603 (“the ‘603 patent”); over claims 1-27 of U.S. Patent No. 11,668,580 (“the ‘580 patent”); and claims 1-21 of U.S. Patent No. 12,320,662 (“the ‘662 patent”). Although the claims at issue are not identical, they are not patentably distinct from each other because the ‘603 patent, the ‘580 patent and the ‘662 patent each teach; a method for iteratively training a machine learning model based upon geolocation and auxiliary data; and a step of generating a user neighborhood map that includes a plurality of designated driving routes that are transmitted to one or more users.
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661
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