DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice on Prior Art Rejections
2. 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.
Status of Claims
3. This Office Action is in response to the applicant's arguments/remarks filed July 30, 2026. Claims 1-20 are presently pending and are presented for examination.
Response to Arguments/Remarks
4. Nonstatutory Double Patenting. Applicant's arguments/amendments filed July 30, 2026 regarding the Nonstatutory Double Patenting have been fully considered. Applicant's arguments/remarks are not persuasive. Applicant argues that “independent claims 1, 13, and 19 have been amended. In view of the foregoing amendments, Applicant respectfully submits that the nonstatutory double patenting rejection is believed to be moot and overcome”. However, the examiner respectfully disagrees. Although the conflicting claims are not identical, they are not patentably distinct from each other because the differences between the conflicting claims are minor and they are not distinguishing the overall appearance of one over the other. Accordingly, the Nonstatutory Double Patenting rejection is maintained.
5. 35 USC § 101 rejection. Applicant's arguments/amendments filed July 30, 2026 regarding the 35 USC § 101 rejection have been fully considered. Applicant's arguments/remarks are persuasive. Accordingly, the 35 USC § 101 rejection is withdrawn.
6. 35 U.S.C. 112(b) rejection. Applicant's arguments/amendments filed July 30, 2026 regarding the 35 U.S.C. 112(b) rejection have been fully considered. Applicant's arguments/remarks are not persuasive. Applicant argues that “Applicant respectfully submits that a person of ordinary skill in the art would understand that a "trained machine learning model" is used in generating the sustainability impact scores, specifically based on "historical transportation data" that is used to train the machine learning model in order to establish "at least one correlation between a sustainability metric and routes" or "transportation modality options." As such, Applicant respectfully submits that a person of ordinary skill in the art would readily appreciate that the features of the independent claims are clear and/or definite”. However, the examiner respectfully disagrees. The precise boundary of the claim cannot be determined berceuse it is unclear how the claims generate “the sustainability impact score indicating an estimated climate impact of using at least one transportation modality”. For example, the claim is silent about how the transportation modalities impact the climate and how that impact score is determined. Accordingly, the 35 U.S.C. 112(b) rejection is maintained.
7. 35 USC § 103 Rejection. Applicant's arguments/amendments filed July 30, 2026 regarding the 35 USC § 103 rejection have been fully considered. Applicant's arguments/remarks are not persuasive. Accordingly, the 35 USC § 103 rejection is maintained.
The applicant argues that “Rakshit's system (e.g., "pollution analyzer") that generates a pollution score associated with each navigation route based on pollution data associated with travel "segments" that make up the route does not disclose, teach, or suggest "generating, using [a] trained machine learning model, a sustainability impact score for each of the plurality of routes," where the "the trained machine learning model [is] trained using historical transportation data to establish at least one correlation between a sustainability metric and routes," as recited in amended claim 1.”
Pursuant to MPEP 2144 Supporting a Rejection Under 35 U.S.C. 103, I. RATIONALE MAY BE IN A REFERENCE, OR REASONED FROM COMMON KNOWLEDGE IN THE ART, SCIENTIFIC PRINCIPLES, ART-RECOGNIZED EQUIVALENTS, OR LEGAL PRECEDENT, “The rationale to modify or combine the prior art does not have to be expressly stated in the prior art; the rationale may be expressly or impliedly contained in the prior art or it may be reasoned from knowledge generally available to one of ordinary skill in the art, established scientific principles, or legal precedent established by prior case law. In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988); In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992)”
However, the examiner respectfully disagrees. The limitations argued by the applicant are inherently described in a single prior art reference and they are also conventional and known in the art. Rakshit discloses “generating, using the trained machine learning model, a sustainability impact score for each of the plurality of routes, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and routes”. The data analyzer of Rakshit acts as machine learning model to generate a sustainability impact score for each of the plurality of routes (See at least ¶ 20, “pollution analyzer program 130 analyzes pollution information and calculates pollution scores for each of the plurality of navigation routes between the origin and the destination”) using current and historical data (See at least ¶ 27, “In various embodiments pollution data may comprise either real time data or historical data.”). Machine learning models is known and conventionally used in the art, where a machine learning model is defined to be a computer program that learns to find patterns or make decisions from data without being explicitly programmed with step-by-step rules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a navigational system and use a machine learning system to generate a plurality of score routes. Additionally, the secondary reference of Beaurepaire teaches machine learning models for data manipulation of records to classify data characteristics. (See at least fig 1-9, “the point data records can also store ground truth training and evaluation data, machine learning models, annotated observations, and/or any other data. By way of example, the point data records can be associated with one or more of the node data records 203, road segment data records 205, and/or POI data records 207 to support verification, localization or visual odometry based on the features stored therein and the corresponding estimated quality of the features. In this way, the point data records can also be associated with or used to classify the characteristics or metadata of the corresponding records 203, 205, and/or 207.”). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Accordingly, the limitations argued by the applicant are expressly or impliedly contained in the prior art as shown. Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Some documents defining the general state of the art that describe reserve power mode include:
US 9,269,205: AIRCRAFT ENVIRONMENTAL IMPACT MEASUREMENT SYSTEM
US 20240035837: VEHICLE CARBON FOOTPRINT MANAGEMENT
US 20210025721: MULTI-MODE ROUTE SELECTION
US 10036641: Coordinating Travel On A Public Transit System And A Travel Coordination System.
US 20240035836: RANKING CARBON FOOTPRINTS OF VEHICLES TO DETERMINE DRIVING BEHAVIOR MODIFICATIONS.
US 20220107193: USER JOURNEY CARBON FOOTPRINT REDUCTION.
Therefore, for the above reasons, the examiner maintains 35 USC § 103 rejection over claims 1-20.
Continuation Application
8. This application is a continuation application of U.S. Application 18/614,422, filed 03/22/2024, now U.S. Patent # 12,281,904 See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicant(s) desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicant(s) are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Nonstatutory Double Patenting
9. 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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,281,904 Although the claims at issue are not identical, they are not patentably distinct from each other because they disclosed the same subject matter.
Claims 2-12 depend from claim 1 and therefore include the same limitation as claim 1 so they are rejected for the same reason.
Claim 13 and 19 contain similar limitations as claim 1 so they are rejected for similar reasons.
Claims 14-18 and 20 depend from claims 13 and 19 respectively, and therefore include the same limitations as claims 13 and 19, so they are rejected for the same reasons.
19/084,464 (Current Application)
Patent No 12,281,904
A transportation system for assessing a sustainability impact of transportation routes, the system comprising: one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
A transportation system for generating a transportation recommendation, the system comprising: one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
determining a plurality of routes between a first geographic location and a second geographic location;
receiving a transportation request; identifying, using the transportation request, a first geographic location and a second geographic location associated with the transportation request;
generating, using a trained machine learning model, a sustainability impact score for each of the plurality of routes, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and routes, and the sustainability impact score indicating an estimated climate impact of using at least one transportation modality to travel along a route of the plurality of routes;
determining, using the first geographic location and the second geographic location, a plurality of routes between the first geographic location and the second geographic location;
And generating a user interface providing at least one of an indication of the sustainability impact scores for one or more of the plurality of routes or a recommendation generated using the sustainability impact scores.
generating, using a trained machine learning model, a sustainability impact score for each of the plurality of routes, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and routes, and the sustainability impact score indicating an estimated climate impact of using at least one transportation modality to travel along a route of the plurality of routes;
selecting a recommended route from the plurality of routes using the sustainability impact scores of the plurality of routes;
and generating a user interface providing the recommended route.
Claim Rejections - 35 USC § 112
10. 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.
11. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
12. Independent claims 1, 13 and 19 recite “impact score indicating an estimated climate impact of using at least one transportation modality to travel along a route of the plurality of routes” It is unclear how a sustainability impact score is generated by the applicant. The specification dated 03/19/2025 does not define how this feature is determined. As a result of this ambiguity, the precise boundary of the claim cannot be determined. Therefore, the claims are rejected as indefinite under 35 U.S.C. 112(b).
13. Claims 2-12 depend from claim 1 and therefore include the same limitation as claim 1 so they are rejected for the same reason.
14. Independent claim 13 and 19 are similar to claim 1 so they are rejected for the same reasons as claim 1.
15. Claims 14-18 and 20 depend from independent claim 13 and 19 respectively and therefore include the same limitation as claims 13 and 19 so they are rejected for the same reason.
Claim Rejections - 35 USC § 103
16. 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 of this title, 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.
17. Claims 1-8, 10, and 12-20 are rejected under 35 U.S.C 103 as being unpatentable over Rakshit et al, US 2013/0080053, in view of Beaurepaire et al. US 2023/0196246, hereinafter referred to as Rakshit and Beaurepaire, respectively.
Regarding claim 1, Rakshit discloses a transportation system for assessing a sustainability impact of transportation routes, the system comprising: one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
determining a plurality of routes between a first geographic location and a second geographic location (See at least fig 1-5, ¶ 4, 16, 19, 20, 22, 24, 5, “The method comprises a first computer receiving an indication of an origin and a destination. The method further comprises receiving data that describes a plurality of navigation routes between the origin and the destination”), (See at least fig 1-5, ¶ 19, “Map information may include information about the roadway network in the geographic region. In one embodiment, the map database 140 includes node data and segment data. Node data represent physical locations in the geographic region (such as roadway intersections and other positions) and segment data represent portions of routes between the physical locations represented by nodes”), (See at least fig 1-5, ¶ 18, “a route search request that includes information required in a route search, for example, a departure point (origin), a destination, and other search criteria ( for example, the preference for transportation mode).”), (See at least fig 1-5, ¶ 39, “UI 126 sends a request to pollution analyzer program 130 to determine a plurality of navigation routes based on pollution data and user preferences”);
generating, using a trained machine learning model, a sustainability impact score for each of the plurality of routes, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and routes, and the sustainability impact score indicating an estimated climate impact of using at least one transportation modality to travel along a route of the plurality of routes (See at least fig 1-5, ¶ 5, 6, 7, 20, 28, 29, 30, 34, 35, 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route. This can be accomplished in a variety of ways. In one embodiment, the pollution analyzer 130 is configured to process pollution data corresponding to each route. The pollution data can be obtained in a variety of ways. In one embodiment, pollution data corresponding to the various routes is stored in a database”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”); and
generating a user interface providing a recommended route, selected by the one or more processors, using the sustainability impact scores (See at least fig 1-5, ¶ 22, 25, 34, 35, 36, 37, 39, “the UI 126 is configured to display a graphical presentation 416 of a map and a recommended route 414 that has been selected”), (See at least fig 1-5, ¶ 35, “pollution analyzer program 130 transmits a sorted list of candidate navigation routes along with a plurality of corresponding pollution scores and cumulative pollution scores to UI 126”).
Rakshit fails to explicitly disclose a sustainability impact score for each of the plurality of routes.
However, Beaurepaire teaches a sustainability impact score for each of the plurality of routes (See at least fig 1-9, ¶ 3, 4, 6, 24, 25, 26, 27, 30, 31, 32, 5, “The one or more instructions further cause the device to determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area”), (See at least fig 1-9, ¶ 26, “the system 100 is configured determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area. In this embodiment, the system 100 is configured to, based on the determined sustainability score for the third geographic area, provide for display information associated with the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include a sustainability impact score for each of the plurality of routes as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 2, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise selecting a recommended route from the plurality of routes using the sustainability impact scores of the plurality of routes, wherein selecting the recommended route is responsive to determining the recommended route has a sustainability impact score indicating a lowest estimated climate impact of the estimated climate impacts of the plurality of routes (See at least fig 1-5, ¶ 20, 35, 38, 39, 33, “The manner in which pollution analyzer program 130 determines the cumulative score is by assigning a value of 3 to the pollution score corresponding to the highest ranked pollution type ( air pollution), 2 to the next highest ranked pollution type (sound pollution), and 1 to the lowest ranked pollution type”), (See at least fig 1-5, ¶ 25, “the pollution analyzer program 130 of the distributed data processing environment of FIG. 1 for dynamically recommending a route based on pollution data, according to one embodiment of the present invention”).
Regarding claim 3, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving data including an electric power characteristic of the at least one transportation modality; and generating the sustainability impact score for each of the plurality of routes using the electric power characteristic of the at least one transportation modality (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”).
Regarding claim 4, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving user data including historic transportation characteristics of a user, the historic transportation characteristics including at least one of an average transportation speed, an average number of turns during a travel event, or an average travel time; and generating the sustainability impact score for each of the plurality of routes using the historic transportation characteristics of the user (See at least fig 1-5, ¶ 4, “Global Positioning System (GPS) based navigation systems are known that use stored maps to do route planning
based on distance, speed limits and user preference of types of roads.”), (See at least fig 1-5, ¶ 28, “pollution level of each segment (retrieved by pollution analyzer program at step
308), T, represents an estimated amount of time required by the traveler to travel the segment, and D, represents the segment length”).
Regarding claim 5, Rakshit discloses the transportation system of claim 1.
Rakshit fails to explicitly disclose wherein the operations further comprise: receiving geographic data associated with the plurality of routes, the geographic data including at least one of a road density score indicating a number of crossroads along a route, a construction rating indicating one or more construction events along a route, or an efficient travel option indicating availability of a lane available to vehicles traveling with multiple occupants along a route; and generating the sustainability impact score for each of the plurality of routes using the geographic data.
However, Beaurepaire teaches wherein the operations further comprise: receiving geographic data associated with the plurality of routes, the geographic data including at least one of a road density score indicating a number of crossroads along a route, a construction rating indicating one or more construction events along a route, or an efficient travel option indicating availability of a lane available to vehicles traveling with multiple occupants along a route; and generating the sustainability impact score for each of the plurality of routes using the geographic data (See at least fig 1-9, ¶ 40, “detecting the relative distance of the vehicle 105 from a lane or roadway, the presence of other vehicles, pedestrians, traffic lights, lane markings, speed limits, road dividers, potholes, and any other objects, or a combination thereof. Other sensors may also be configured to detect weather data, traffic information, or a combination thereof”), (See at least fig 1-9, ¶ 45, “the system 100 may be configured to analyze the traffic data of the third geographic area to determine the sustainability score of the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include wherein the operations further comprise: receiving geographic data associated with the plurality of routes, the geographic data including at least one of a road density score indicating a number of crossroads along a route, a construction rating indicating one or more construction events along a route, or an efficient travel option indicating availability of a lane available to vehicles traveling with multiple occupants along a route; and generating the sustainability impact score for each of the plurality of routes using the geographic data as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 6, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving historic transportation data associated with each of the plurality of routes, the historic transportation data including at least one of an average transportation speed, an average number of turns during a travel event, an average number of stops during a travel event, or an average travel time during a travel event; and generating the sustainability impact score for each of the plurality of routes using the historic transportation data (See at least fig 1-5, ¶ 4, “Global Positioning System (GPS) based navigation systems are known that use stored maps to do route planning based on distance, speed limits and user preference of types of roads.”), (See at least fig 1-5, ¶ 28, “pollution level of each segment (retrieved by pollution analyzer program at step 308), T, represents an estimated amount of time required by the traveler to travel the segment, and D, represents the segment length”).
Regarding claim 7, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving a time associated with a transportation request for traveling between the first geographic location and the second geographic location; and generating the sustainability impact score for each of the plurality of routes using the time associated with the transportation request (See at least fig 1-5, ¶ 14, “a plurality of users is
equipped with pollution measuring devices and the pollution measuring devices (either mobile 116 or stationary 114) are distributed over various geographic areas, real-time pollution
information of a vast geographic area is obtainable”), (See at least fig 1-5, ¶ 19, “each route segment data record in the map database 140 may contain information related to that particular route segment, for example, but not limited to segment length and amount of time required by the traveler to travel the segment of the route”).
Regarding claim 8, Rakshit discloses the transportation system of claim 1, wherein the sustainability impact score is associated with a fuel efficiency of the at least one transportation modality along the route (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”).
Regarding claim 10, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving audiovisual data associated with at least one travel event of a user; and generating the sustainability impact score for each of the plurality of routes using the audiovisual data (See at least fig 1-5, ¶ 16, “GPS navigation devices, telephones, television
receivers, cell phones, personal digital assistants, netbooks, tablet computers, and/or any type of portable computing devices capable of rendering route guidance data including, navigation route data, and digital roadmap data on the display.”), (See at least fig 1-5, ¶ 39, “UI 126 presents results as a sorted list of navigation routes 418 between the origin 202 and destination 204 along with a plurality of scores 412 corresponding to each navigation route 412”).
Regarding claim 12, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise:
identifying a plurality of transportation modality options for traveling between the first geographic location and the second geographic location, each of the plurality of transportation modality options comprising at least one transportation modality option (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”);
generating a sustainability impact score for each of the plurality of transportation modality options, the sustainability impact score indicating an estimated climate impact of using the at least one transportation modality option to travel between the first geographic location and the second geographic location (See at least fig 1-5, ¶ 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”); and
generating the user interface providing at least one of an indication of the sustainability impact scores for one or more of the transportation modality options or a recommendation generated using the sustainability impact scores of the plurality of transportation modality options (See at least fig 1-5, ¶ 39, “the UI 126 is configured to display a graphical presentation 416 of a map and a recommended route 414 that has been selected”), (See at least fig 1-5, ¶ 35, “pollution analyzer program 130 transmits a sorted list of candidate navigation routes along with a plurality of corresponding pollution scores and cumulative pollution scores to UI 126”).
Rakshit fails to explicitly disclose a sustainability impact score for each of the plurality of transportation modality options.
However, Beaurepaire teaches a sustainability impact score for each of the plurality of transportation modality options (See at least fig 1-9, ¶ 5, “The one or more instructions further cause the device to determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area”), (See at least fig 1-9, ¶ 26, “the system 100 is configured determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area. In this embodiment, the system 100 is configured to, based on the determined sustainability score for the third geographic area, provide for display information associated with the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include a sustainability impact score for each of the plurality of transportation modality options as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 13, Rakshit discloses a computer-implemented method for assessing a sustainability impact of transportation modality options using a trained machine learning model, the computer-implemented method comprising:
identifying, using one or more processors, a plurality of transportation modality options for traveling between a first geographic location and a second geographic location, each of the plurality of transportation modality options comprising at least one transportation modality (See at least fig 1-5, ¶ 4, 16, 19, 20, 22, 24, 5, “The method comprises a first computer receiving an indication of an origin and a destination. The method further comprises receiving data that describes a plurality of navigation routes between the origin and the destination”), (See at least fig 1-5, ¶ 19, “Map information may include information about the roadway network in the geographic region. In one embodiment, the map database 140 includes node data and segment data. Node data represent physical locations in the geographic region (such as roadway intersections and other positions) and segment data represent portions of routes between the physical locations represented by nodes”), (See at least fig 1-5, ¶ 18, “a route search request that includes information required in a route search, for example, a departure point (origin), a destination, and other search criteria ( for example, the preference for transportation mode).”), (See at least fig 1-5, ¶ 39, “UI 126 sends a request to pollution analyzer program 130 to determine a plurality of navigation routes based on pollution data and user preferences”);
generating, using a trained machine learning model and the one or more processors, a sustainability impact score for each of the plurality of transportation modality options, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and transportation modality options, and the sustainability impact score indicating an estimated climate impact of using the at least one transportation modality to travel between the first geographic location and the second geographic location (See at least fig 1-5, ¶ 5, 6, 7, 20, 28, 29, 30, 34, 35, 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route. This can be accomplished in a variety of ways. In one embodiment, the pollution analyzer 130 is configured to process pollution data corresponding to each route. The pollution data can be obtained in a variety of ways. In one embodiment, pollution data corresponding to the various routes is stored in a database”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”); and
generating, using the one or more processors, a user interface providing a recommended transportation modality option, selected by the one or more processors, using the sustainability impact scores (See at least fig 1-5, ¶ 22, 25, 34, 35, 36, 37, 39, “the UI 126 is configured to display a graphical presentation 416 of a map and a recommended route 414 that has been selected”), (See at least fig 1-5, ¶ 35, “pollution analyzer program 130 transmits a sorted list of candidate navigation routes along with a plurality of corresponding pollution scores and cumulative pollution scores to UI 126”).
Rakshit fails to explicitly disclose a sustainability impact score for each of the plurality of routes.
However, Beaurepaire teaches a sustainability impact score for each of the plurality of routes (See at least fig 1-9, ¶ 3, 4, 6, 24, 25, 26, 27, 30, 31, 32, 5, “The one or more instructions further cause the device to determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area”), (See at least fig 1-9, ¶ 26, “the system 100 is configured determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area. In this embodiment, the system 100 is configured to, based on the determined sustainability score for the third geographic area, provide for display information associated with the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rakshit and include a sustainability impact score for each of the plurality of routes as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 14, Rakshit discloses the computer-implemented method of claim 13, further comprising: receiving, using the one or more processors, a user preference of a transportation modality option selected from the plurality of transportation modality options; and selecting, using the one or more processors, a recommended transportation modality option from the plurality of transportation modality options using the user preference (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”).
Regarding claim 15, Rakshit discloses the computer-implemented method of claim 13, wherein the at least one transportation modality includes at least one of a bicycle, a scooter, a bus, vehicle, a shared vehicle, a train, or an airplane (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”).
Regarding claim 16, Rakshit discloses the computer-implemented method of claim 13, further comprising: receiving, using the one or more processors audiovisual data associated with at least one travel event of a user; and selecting, using the one or more processors, a recommended transportation modality option from the plurality of transportation modality options using the audiovisual data (See at least fig 1-5, ¶ 16, “GPS navigation devices, telephones, television receivers, cell phones, personal digital assistants, netbooks, tablet computers, and/or any type of portable computing devices capable of rendering route guidance data including, navigation route data, and digital roadmap data on the display.”), (See at least fig 1-5, ¶ 39, “UI 126 presents results as a sorted list of navigation routes 418 between the origin 202 and destination 204 along with a plurality of scores 412 corresponding to each navigation route 412”).
Regarding claim 17, Rakshit discloses the computer-implemented method of claim 13, further comprising: receiving, using the one or more processors, travel data including geolocation information of a user as the user travels between the first geographic location and the second geographic location; and comparing, using the one or more processors, the travel data with historical travel data associated with a transportation modality option to verify a recommended transportation modality option (See at least fig 1-5, ¶ 4, “Global Positioning System (GPS) based navigation systems are known that use stored maps to do route planning
based on distance, speed limits and user preference of types of roads.”), (See at least fig 1-5, ¶ 28, “pollution level of each segment (retrieved by pollution analyzer program at step
308), T, represents an estimated amount of time required by the traveler to travel the segment, and D, represents the segment length”).
Regarding claim 18, Rakshit discloses the computer-implemented method of claim 13, further comprising:
determining, using the one or more processors and using the first geographic location and the second geographic location, a plurality of routes between the first geographic location and the second geographic location (See at least fig 1-5, ¶ 5, “The method comprises a first computer receiving an indication of an origin and a destination. The method further comprises receiving data that describes a plurality of navigation routes between the origin and the destination”), (See at least fig 1-5, ¶ 19, “Map information may include information about the roadway network in the geographic region. In one embodiment, the map database 140 includes node data and segment data. Node data represent physical locations in the geographic region (such as roadway intersections and other positions) and segment data represent portions of routes between the physical locations represented by nodes”);
generating, using the one or more processors, a sustainability impact score for each of the plurality of routes, the sustainability impact score indicating an estimated climate impact of using at least one transportation modality option to travel along the route (See at least fig 1-5, ¶ 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”);
generating, using the one or more processors, the user interface providing the plurality of routes and the sustainability impact associated with each route, wherein the user interface further includes a travel time and a safety factor associated with each of the plurality of routes (See at least fig 1-5, ¶ 35, “The presentation of multiple routes allows the user to make a route selection considering real-time pollution situation, as discussed further below in conjunction with FIG. 4”), (See at least fig 1-5, ¶ 20, “present information about one or more recommended navigation routes along with corresponding pollution scores to users such that the user may select a particular route”);
receiving, using the one or more processors, a selection of a selected route of the plurality of routes (See at least fig 1-5, ¶ 35, “The presentation of multiple routes allows the user to make a route selection considering real-time pollution situation, as discussed further below in conjunction with FIG. 4”), (See at least fig 1-5, ¶ 20, “present information about one or more recommended navigation routes along with corresponding pollution scores to users such that the user may select a particular route”); and
providing, using the one or more processors, the user interface including the selected route of the plurality of routes (See at least fig 1-5, ¶ 39, “the UI 126 is configured to display a graphical presentation 416 of a map and a recommended route 414 that has been selected”), (See at least fig 1-5, ¶ 35, “pollution analyzer program 130 transmits a sorted list of candidate navigation routes along with a plurality of corresponding pollution scores and cumulative pollution scores to UI 126”).
Rakshit fails to explicitly disclose a sustainability impact score for each of the plurality of routes.
However, Beaurepaire teaches a sustainability impact score for each of the plurality of routes (See at least fig 1-9, ¶ 5, “The one or more instructions further cause the device to determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area”), (See at least fig 1-9, ¶ 26, “the system 100 is configured determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area. In this embodiment, the system 100 is configured to, based on the determined sustainability score for the third geographic area, provide for display information associated with the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include a sustainability impact score for each of the plurality of routes as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 19, Rakshit discloses a non-transitory computer readable medium comprising instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations for assessing a sustainability impact of a plurality of routes and a plurality of transportation modality options using a trained machine learning model, the operations comprising:
determining a plurality of routes between a first geographic location and a second geographic location (See at least fig 1-5, ¶ 35, “The presentation of multiple routes allows the user to make a route selection considering real-time pollution situation, as discussed further below in conjunction with FIG. 4”), (See at least fig 1-5, ¶ 20, “present information about one or more recommended navigation routes along with corresponding pollution scores to users such that the user may select a particular route”);
identifying a plurality of transportation modality options for traveling between the first geographic location and the second geographic location, each of the plurality of transportation modality options comprising at least one transportation modality (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”);
generating, using a trained machine learning model, a sustainability impact score for each combination of the plurality of routes and the plurality of transportation modality options, the trained machine learning model trained using historical transportation data to establish at least one correlation between a sustainability metric and routes or transportation modality options, and the sustainability impact score indicating an estimated climate impact of using the at least one transportation modality along the plurality of routes (See at least fig 1-5, ¶ 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”);
selecting, by the one or more processors, a recommended route from the plurality of routes and a recommended transportation modality option from the plurality of transportation modality options using the sustainability impact scores (See at least fig 1-5, ¶ 35, “The presentation of multiple routes allows the user to make a route selection considering real-time pollution situation, as discussed further below in conjunction with FIG. 4”), (See at least fig 1-5, ¶ 20, “present information about one or more recommended navigation routes along with corresponding pollution scores to users such that the user may select a particular route”); and
generating a user interface providing the recommended route and the recommended transportation modality option using the sustainability impact scores (See at least fig 1-5, ¶ 39, “the UI 126 is configured to display a graphical presentation 416 of a map and a recommended route 414 that has been selected”), (See at least fig 1-5, ¶ 35, “pollution analyzer program 130 transmits a sorted list of candidate navigation routes along with a plurality of corresponding pollution scores and cumulative pollution scores to UI 126”).
Rakshit fails to explicitly disclose a sustainability impact score for each combination of the plurality of routes and the plurality of transportation modality options.
However, Beaurepaire teaches a sustainability impact score for each combination of the plurality of routes and the plurality of transportation modality options (See at least fig 1-9, ¶ 5, “The one or more instructions further cause the device to determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area”), (See at least fig 1-9, ¶ 26, “the system 100 is configured determine a sustainability score for the third geographic area based on an analysis of environmental impact at the third geographic area. In this embodiment, the system 100 is configured to, based on the determined sustainability score for the third geographic area, provide for display information associated with the third geographic area”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include a sustainability impact score for each combination of the plurality of routes and the plurality of transportation modality options as taught by Beaurepaire because it would reduce the environmental impact associated with traveling to a destination as well as the environmental impact associated with various aspects after arrival at a destination (Beaurepaire ¶ 2).
Regarding claim 20, Rakshit discloses the non-transitory computer readable medium of claim 19, wherein the operations further comprise: receiving a user preference indicating a relative preference between at least one of the plurality of transportation modality options or the plurality of routes; and selecting a recommended route and a recommended transportation modality option using the user preference (See at least fig 1-5, ¶ 25, “The user preference information can include user's preferred mode of travel ( e.g., by car, by scooter, walking, bicycling, and the like) and a plurality of pollution criteria, such as, types of pollution to avoid (e.g., air pollution, noise, dust, and the like), among others”), (See at least fig 1-5, ¶ 3, “plurality of air pollution detecting devices may be selectively installed in vehicles, motorbikes, bicycles, or users' portable paraphernalia, such as a helmet worn by a motorbike rider. Air pollution information of various geographic locations over a vast area is obtainable while the vehicles or the users keep moving around”).
18. Claims 9 and 11 are rejected under 35 U.S.C 103 as being unpatentable over Rakshit in view of Beaurepaire further in view of Ramirez et al, US 2023/0306457, hereinafter referred to as Rakshit, Beaurepaire, and Ramirez respectively.
Regarding claim 9, Rakshit discloses the transportation system of claim 1.
Rakshit fails to explicitly disclose wherein the operations further comprise generating at least one insurance policy parameter associated with the recommended route and the at least one transportation modality and providing the at least one insurance policy parameter via the user interface.
However, Ramirez teaches wherein the operations further comprise generating at least one insurance policy parameter associated with the recommended route and the at least one transportation modality and providing the at least one insurance policy parameter via the user interface (See at least fig 1-26, ¶ 32, “Depending on the driver's selection, the vehicle's insurance policy may be adjusted accordingly, for either the current insurance policy or a future insurance policy”), (See at least fig 1-9, ¶ 68, “driver/vehicle may be provided a monetary benefit ( e.g., a credit towards a future insurance policy) for selecting a less risky route”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include wherein the operations further comprise generating at least one insurance policy parameter associated with the recommended route and the at least one transportation modality and providing the at least one insurance policy parameter via the user interface as taught by Ramirez because it would allow insurance company (or its representatives, e.g., agent) to adjust the price quoted/charged for an insurance policy based on risk consumed (Ramirez ¶ 75).
Regarding claim 11, Rakshit discloses the transportation system of claim 1, wherein the operations further comprise: receiving a selection of a selected route from the plurality of routes; and providing the user interface including at least one insurance policy parameter associated with the selected route (See at least fig 1-5, ¶ 27, “Once the plurality of navigation routes has been determined, the pollution analyzer program 130 is configured to process each of the navigation routes to obtain a pollution score corresponding to each route”), (See at least fig 1-5, ¶ 33, “the pollution analyzer 130 determines the cumulative score for each navigation route using a weighted average of all pollution scores corresponding to that navigation route”).
Rakshit fails to explicitly disclose at least one insurance policy parameter.
However, Ramirez teaches at least one insurance policy parameter (See at least fig 1-26, ¶ 32, “Depending on the driver's selection, the vehicle's insurance policy may be adjusted accordingly, for either the current insurance policy or a future insurance policy”), (See at least fig 1-9, ¶ 68, “driver/vehicle may be provided a monetary benefit ( e.g., a credit towards a future insurance policy) for selecting a less risky route”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rakshit and include at least one insurance policy parameter as taught by Ramirez because it would allow insurance company (or its representatives, e.g., agent) to adjust the price quoted/charged for an insurance policy based on risk consumed (Ramirez ¶ 75).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS MARTINEZ whose email is luis.martinezborrero@uspto.gov and telephone number is (571)272-4577. The examiner can normally be reached on Monday-Friday 8:30AM-5:00PM EST.
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/LUIS A MARTINEZ BORRERO/Primary Examiner, Art Unit 3665
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