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 a Non-Final Action on the Merits. Claims 1-20 are currently pending and are addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 11th, 2026 has been entered.
Response to Amendments
The amendment filed on May 11th, 2026 has been considered and entered. Accordingly, claims 1, 5-6, 11, 13-14, and 18 have been amended.
Response to Arguments
The applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of the newly formulated grounds of rejection necessitated by the applicant’s amendments.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 7, 11, 13, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”).
With respect to claim 1, Neumann teaches a method comprising:
receiving, from a vehicle, a request for a map of a geographic location, a driving application associated with the request for which the map is to be used, a position of the vehicle; determining information to be included in the map based on the driving application; generating the map that includes the information (See at least Neumann FIGS. 1-3 and Paragraphs 54-63 “A three-dimensional map 100 is sketched in FIG. 2 , which is displayed for a user of the motor vehicle 10 on the display device 36 of the motor vehicle 10. The three-dimensional map 100 is initially generated by the navigation system 34 of the motor vehicle 10, wherein the generated map 100 displays the surroundings 33 of the motor vehicle 10 in a three-dimensional representation. This takes place in S1. Reference characters S1 to S6 are shown in FIG. 1 , whereas details relating to the three-dimensional map 100 are sketched in FIG. 2 . In S2, a route-related navigation representation 102 is generated in the generated map 100 by evaluating navigation system data of the navigation system 34 if route guidance by the navigation system 34 is activated. The route-related navigation representation 102 includes a route representation 104 of at least a subsection of a route of the motor vehicle 10 … In S3, it is checked whether the at least one driver assistance system 40, 41, 42 of the motor vehicle 10 is activated. If the at least one driver assistance system 40, 41, 42 is activated, in S4, the sensor unit 30, 31 is activated, so that this acquires predetermined sensor data describing the surroundings 33 of the motor vehicle 10, and does so according to a data acquisition rule of the at least one activated driver assistance system 40, 41, 42. In this case, three driver assistance systems 40, 41, 42 are activated. Due to the activity of the lane keeping assistant as the driver assistance system 40, a lane marking 18 of the road 12 is acquired as sensor data and additionally shown in the generated map 100 as a corresponding lane boundary representation 124. In S5, at least one driver assistance system-related additional information representation 120 is generated in the generated map 100 by selecting the acquired sensor data … In S6, the generated map 100 is provided with the generated route-related navigation representation 102 and the generated at least one driver assistance system-related additional information representation 120 in the motor vehicle 10 by the display device 36 of the motor vehicle 10.”);
Neumann fails to explicitly disclose receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation1 associated with the entity that the occupant of the vehicle is looking at; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Bonanni teaches receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation2 associated with the entity that the occupant of the vehicle is looking at (See at least Bonanni FIG. 16 and Paragraphs 169-172 “Process 1600 begins by receiving, from one or more sensors of an autonomous vehicle (AV) operating in an environment, sensor data (1601), and generating passenger attention data from the sensor data for a passenger of the AV (1602). For example, one or more 3D image sensors in the passenger compartment of an AV can generate images that are analyzed by a processing circuit to detect eye gaze direction and/or head/torso pose. The eye gaze direction and/or head/torso pose can be used to generate a passenger LOS vector in a local reference coordinate system fixed to the AV. Process 1600 continues by determining, using a processing circuit, a physical feature of the environment based on the passenger attention data in the environment (1603). For example, the passenger LOS vector is transformed into a reference local-level coordinate system of a 2D/3D digital map or 3D building model, and a PFOV is generated around the passenger LOS vector. Using the 2D/3D digital map or 3D building model, one or more physical features (e.g., landmarks, buildings, monuments, bridges, museums, scene overlooks, natural formations) are determined to fall with the PFOV. In an embodiment the physical features are filtered based on passenger preferences, seat position in the AV and an attention span, as described in reference to FIGS. 13 and 14. Process 1600 continues by obtaining, using the processing circuit, physical feature data (1604). For example, physical feature data can be collected from a database (e.g., a map/POI database 1313, content database 1315) for each physical feature in the PFOV. Process 1600 continues by initiating, using the processing circuit of the AV, an action on the AV based at least in part on the physical feature data (1605). For example, passengers in the AV can each be asked to select from a list of physical features from which the passenger desires to receive content. The content can be aggregated and filtered based on the individual passenger preferences and seat assignment. In embodiment, personalized content (e.g., a virtual tour media, slideshow, audio tour) is created for each passenger in the AV from the physical feature data associated with their individual PFOV. For example, each passenger can be looking out a different window in the AV and in a different direction. Thus, each passenger PFOV can include different physical features. In an embodiment, an action is augmenting a digital map or model with the physical feature data or content (e.g., for a new POI).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann to include receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation associated with the entity that the occupant of the vehicle is looking at, as taught by Bonanni as disclosed above, in order to increase driver safety (Bonanni Paragraph 4 “Techniques are disclosed for utilizing passenger attention data captured in vehicles for vehicle localization and location-based services”).
Neumann in view of Bonanni fail to explicitly disclose transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Urmson teaches generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Neumann in view of Bonanni to include generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map, as taught by Urmson as disclosed above, such that the recommendations are based on the area of interest, in order to ensure accurate recommendations (Urmson “In one aspect, a system and method is provided wherein an autonomous vehicle makes recommendations to a user based on the conditions detected by the car's sensors”).
With respect to claim 5, and similarly claim 13, Neumann in view of Bonanni in view of Urmson teach that the entity that the occupant of the vehicle is looking at comprises a physical structure (See at least Bonanni FIG. 16 and Paragraphs 169-172).
With respect to claim 7, and similarly claim 15, Neumann in view of Bonanni in view of Urmson teach receiving, from the vehicle, feedback about past recommendations; and generating the recommendation based at least in part on the feedback (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
With respect to claim 11, Yanase teaches a computing device comprising one or more processors configured to:
receiving, from a vehicle, a request for a map of a geographic location, a driving application associated with the request for which the map is to be used, a position of the vehicle; determining information to be included in the map based on the driving application; generating the map that includes the information (See at least Neumann FIGS. 1-3 and Paragraphs 54-63 “A three-dimensional map 100 is sketched in FIG. 2 , which is displayed for a user of the motor vehicle 10 on the display device 36 of the motor vehicle 10. The three-dimensional map 100 is initially generated by the navigation system 34 of the motor vehicle 10, wherein the generated map 100 displays the surroundings 33 of the motor vehicle 10 in a three-dimensional representation. This takes place in S1. Reference characters S1 to S6 are shown in FIG. 1 , whereas details relating to the three-dimensional map 100 are sketched in FIG. 2 . In S2, a route-related navigation representation 102 is generated in the generated map 100 by evaluating navigation system data of the navigation system 34 if route guidance by the navigation system 34 is activated. The route-related navigation representation 102 includes a route representation 104 of at least a subsection of a route of the motor vehicle 10 … In S3, it is checked whether the at least one driver assistance system 40, 41, 42 of the motor vehicle 10 is activated. If the at least one driver assistance system 40, 41, 42 is activated, in S4, the sensor unit 30, 31 is activated, so that this acquires predetermined sensor data describing the surroundings 33 of the motor vehicle 10, and does so according to a data acquisition rule of the at least one activated driver assistance system 40, 41, 42. In this case, three driver assistance systems 40, 41, 42 are activated. Due to the activity of the lane keeping assistant as the driver assistance system 40, a lane marking 18 of the road 12 is acquired as sensor data and additionally shown in the generated map 100 as a corresponding lane boundary representation 124. In S5, at least one driver assistance system-related additional information representation 120 is generated in the generated map 100 by selecting the acquired sensor data … In S6, the generated map 100 is provided with the generated route-related navigation representation 102 and the generated at least one driver assistance system-related additional information representation 120 in the motor vehicle 10 by the display device 36 of the motor vehicle 10.”);
Neumann fails to explicitly disclose receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation3 associated with the entity that the occupant of the vehicle is looking at; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Bonanni teaches receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation4 associated with the entity that the occupant of the vehicle is looking at (See at least Bonanni FIG. 16 and Paragraphs 169-172 “Process 1600 begins by receiving, from one or more sensors of an autonomous vehicle (AV) operating in an environment, sensor data (1601), and generating passenger attention data from the sensor data for a passenger of the AV (1602). For example, one or more 3D image sensors in the passenger compartment of an AV can generate images that are analyzed by a processing circuit to detect eye gaze direction and/or head/torso pose. The eye gaze direction and/or head/torso pose can be used to generate a passenger LOS vector in a local reference coordinate system fixed to the AV. Process 1600 continues by determining, using a processing circuit, a physical feature of the environment based on the passenger attention data in the environment (1603). For example, the passenger LOS vector is transformed into a reference local-level coordinate system of a 2D/3D digital map or 3D building model, and a PFOV is generated around the passenger LOS vector. Using the 2D/3D digital map or 3D building model, one or more physical features (e.g., landmarks, buildings, monuments, bridges, museums, scene overlooks, natural formations) are determined to fall with the PFOV. In an embodiment the physical features are filtered based on passenger preferences, seat position in the AV and an attention span, as described in reference to FIGS. 13 and 14. Process 1600 continues by obtaining, using the processing circuit, physical feature data (1604). For example, physical feature data can be collected from a database (e.g., a map/POI database 1313, content database 1315) for each physical feature in the PFOV. Process 1600 continues by initiating, using the processing circuit of the AV, an action on the AV based at least in part on the physical feature data (1605). For example, passengers in the AV can each be asked to select from a list of physical features from which the passenger desires to receive content. The content can be aggregated and filtered based on the individual passenger preferences and seat assignment. In embodiment, personalized content (e.g., a virtual tour media, slideshow, audio tour) is created for each passenger in the AV from the physical feature data associated with their individual PFOV. For example, each passenger can be looking out a different window in the AV and in a different direction. Thus, each passenger PFOV can include different physical features. In an embodiment, an action is augmenting a digital map or model with the physical feature data or content (e.g., for a new POI).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann to include receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation associated with the entity that the occupant of the vehicle is looking at, as taught by Bonanni as disclosed above, in order to increase driver safety (Bonanni Paragraph 4 “Techniques are disclosed for utilizing passenger attention data captured in vehicles for vehicle localization and location-based services”).
Neumann in view of Bonanni fail to explicitly disclose transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Urmson teaches generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Neumann in view of Bonanni to include generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map, as taught by Urmson as disclosed above, such that the recommendations are based on the area of interest, in order to ensure accurate recommendations (Urmson “In one aspect, a system and method is provided wherein an autonomous vehicle makes recommendations to a user based on the conditions detected by the car's sensors”).
With respect to claim 18, Yanase teaches a system comprising a vehicle and an edge server, wherein:
receiving, from a vehicle, a request for a map of a geographic location, a driving application associated with the request for which the map is to be used, a position of the vehicle; determining information to be included in the map based on the driving application; generating the map that includes the information (See at least Neumann FIGS. 1-3 and Paragraphs 54-63 “A three-dimensional map 100 is sketched in FIG. 2 , which is displayed for a user of the motor vehicle 10 on the display device 36 of the motor vehicle 10. The three-dimensional map 100 is initially generated by the navigation system 34 of the motor vehicle 10, wherein the generated map 100 displays the surroundings 33 of the motor vehicle 10 in a three-dimensional representation. This takes place in S1. Reference characters S1 to S6 are shown in FIG. 1 , whereas details relating to the three-dimensional map 100 are sketched in FIG. 2 . In S2, a route-related navigation representation 102 is generated in the generated map 100 by evaluating navigation system data of the navigation system 34 if route guidance by the navigation system 34 is activated. The route-related navigation representation 102 includes a route representation 104 of at least a subsection of a route of the motor vehicle 10 … In S3, it is checked whether the at least one driver assistance system 40, 41, 42 of the motor vehicle 10 is activated. If the at least one driver assistance system 40, 41, 42 is activated, in S4, the sensor unit 30, 31 is activated, so that this acquires predetermined sensor data describing the surroundings 33 of the motor vehicle 10, and does so according to a data acquisition rule of the at least one activated driver assistance system 40, 41, 42. In this case, three driver assistance systems 40, 41, 42 are activated. Due to the activity of the lane keeping assistant as the driver assistance system 40, a lane marking 18 of the road 12 is acquired as sensor data and additionally shown in the generated map 100 as a corresponding lane boundary representation 124. In S5, at least one driver assistance system-related additional information representation 120 is generated in the generated map 100 by selecting the acquired sensor data … In S6, the generated map 100 is provided with the generated route-related navigation representation 102 and the generated at least one driver assistance system-related additional information representation 120 in the motor vehicle 10 by the display device 36 of the motor vehicle 10.”);
Neumann fails to explicitly disclose receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation5 associated with the entity that the occupant of the vehicle is looking at; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Bonanni teaches receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation6 associated with the entity that the occupant of the vehicle is looking at (See at least Bonanni FIG. 16 and Paragraphs 169-172 “Process 1600 begins by receiving, from one or more sensors of an autonomous vehicle (AV) operating in an environment, sensor data (1601), and generating passenger attention data from the sensor data for a passenger of the AV (1602). For example, one or more 3D image sensors in the passenger compartment of an AV can generate images that are analyzed by a processing circuit to detect eye gaze direction and/or head/torso pose. The eye gaze direction and/or head/torso pose can be used to generate a passenger LOS vector in a local reference coordinate system fixed to the AV. Process 1600 continues by determining, using a processing circuit, a physical feature of the environment based on the passenger attention data in the environment (1603). For example, the passenger LOS vector is transformed into a reference local-level coordinate system of a 2D/3D digital map or 3D building model, and a PFOV is generated around the passenger LOS vector. Using the 2D/3D digital map or 3D building model, one or more physical features (e.g., landmarks, buildings, monuments, bridges, museums, scene overlooks, natural formations) are determined to fall with the PFOV. In an embodiment the physical features are filtered based on passenger preferences, seat position in the AV and an attention span, as described in reference to FIGS. 13 and 14. Process 1600 continues by obtaining, using the processing circuit, physical feature data (1604). For example, physical feature data can be collected from a database (e.g., a map/POI database 1313, content database 1315) for each physical feature in the PFOV. Process 1600 continues by initiating, using the processing circuit of the AV, an action on the AV based at least in part on the physical feature data (1605). For example, passengers in the AV can each be asked to select from a list of physical features from which the passenger desires to receive content. The content can be aggregated and filtered based on the individual passenger preferences and seat assignment. In embodiment, personalized content (e.g., a virtual tour media, slideshow, audio tour) is created for each passenger in the AV from the physical feature data associated with their individual PFOV. For example, each passenger can be looking out a different window in the AV and in a different direction. Thus, each passenger PFOV can include different physical features. In an embodiment, an action is augmenting a digital map or model with the physical feature data or content (e.g., for a new POI).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann to include receiving a direction that an occupant of the vehicle is looking; determining an entity that the occupant of the vehicle is looking at based on the position of the vehicle and the direction that the occupant of the vehicle is looking; generating a recommendation associated with the entity that the occupant of the vehicle is looking at, as taught by Bonanni as disclosed above, in order to increase driver safety (Bonanni Paragraph 4 “Techniques are disclosed for utilizing passenger attention data captured in vehicles for vehicle localization and location-based services”).
Neumann in view of Bonanni fail to explicitly disclose transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map.
Urmson teaches generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Neumann in view of Bonanni to include generating a recommendation based on the area of interest; and transmitting the generated map and the recommendation to the vehicle; and causing the vehicle to perform autonomous driving operations based on the generated map, as taught by Urmson as disclosed above, such that the recommendations are based on the area of interest, in order to ensure accurate recommendations (Urmson “In one aspect, a system and method is provided wherein an autonomous vehicle makes recommendations to a user based on the conditions detected by the car's sensors”).
With respect to claim 19, Neumann in view of Bonanni in view of Urmson teach receive the map from the edge server; and autonomously perform one or more driving operations based on the map (See at least Neumann Paragraph 38 “In addition, one embodiment provides that surroundings data describing the surroundings of the motor vehicle are received by a communication connection of an external server unit, another motor vehicle, and/or an infrastructure unit in the motor vehicle. The surroundings data can include, for example, items of information about objects located in the surroundings, for example, traffic signs, other motor vehicles, and/or infrastructure units such as a traffic signal and/or an intersection. However, the surroundings data can alternatively or additionally thereto relate to items of information about a traffic flow, so that, for example, items of information about congestion to be expected can be provided as surroundings data. Such data can be received, for example, from the external unit, such as the external server unit, which provides, for example, items of information on a current congestion volume, a weather condition, and/or an arrangement of traffic signals in the surroundings of the motor vehicle. In the case of a communication connection of the motor vehicle to another motor vehicle, that is to say in the case of a vehicle-to-vehicle communication connection, for example, accurate position data of the other motor vehicle can be transmitted, which can be of interest, for example, for the adaptive cruise control, since the other motor vehicle can possibly determine its own position more accurately than is possible by the sensor unit of the motor vehicle”| Paragraph 66 “The surroundings data, which are transmitted by the external server unit 46, the front vehicle 22, and/or the infrastructure unit 17 to the motor vehicle 10, are evaluated to generate the at least one driver assistance system-related additional information representation 120 in the generated map 100.”) (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
With respect to claim 20, Neumann in view of Bonanni in view of Urmson receive the recommendation from the edge server; display the recommendation to the occupant of the vehicle; receive feedback about the recommendation; and transmit the feedback to the edge server (See at least Neumann Paragraph 38 “In addition, one embodiment provides that surroundings data describing the surroundings of the motor vehicle are received by a communication connection of an external server unit, another motor vehicle, and/or an infrastructure unit in the motor vehicle. The surroundings data can include, for example, items of information about objects located in the surroundings, for example, traffic signs, other motor vehicles, and/or infrastructure units such as a traffic signal and/or an intersection. However, the surroundings data can alternatively or additionally thereto relate to items of information about a traffic flow, so that, for example, items of information about congestion to be expected can be provided as surroundings data. Such data can be received, for example, from the external unit, such as the external server unit, which provides, for example, items of information on a current congestion volume, a weather condition, and/or an arrangement of traffic signals in the surroundings of the motor vehicle. In the case of a communication connection of the motor vehicle to another motor vehicle, that is to say in the case of a vehicle-to-vehicle communication connection, for example, accurate position data of the other motor vehicle can be transmitted, which can be of interest, for example, for the adaptive cruise control, since the other motor vehicle can possibly determine its own position more accurately than is possible by the sensor unit of the motor vehicle”| Paragraph 66 “The surroundings data, which are transmitted by the external server unit 46, the front vehicle 22, and/or the infrastructure unit 17 to the motor vehicle 10, are evaluated to generate the at least one driver assistance system-related additional information representation 120 in the generated map 100.”) (See at least Bonanni Paragraphs 196-172) (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21 “Based on data collected locally and from other vehicles, the computer system on vehicle 101 may process the data and make various kinds of recommendations to the user and recommendations to third parties. Recommendations may be made based on data collected solely from and processed by the user's vehicle. In one aspect, the system and method makes recommendations based both on data that was previously collected by the sensors and, at the time of the recommendation, is currently being collected by the sensors. POI based on what has been visited in the past For example, computer system 131 may provide the user with a recommended point of interest (POI) to visit. At the user's request, processor 131 may select the destination by consulting the data it received from geographic location component 210 the last time the vehicle was in the current geographic area. Processor 131 may then compare its current location with how long vehicle 101 has stayed at a particular POI, such as a restaurant. Depending on the average amount of time that the user stayed at a particular place, and based further on the vehicle's current position, processor 131 may then recommend a specific restaurant or other POI. By way of example, if the vehicle is currently near a POI that the user has previously stayed at for 30 minutes or more, the vehicle may identify the recommended destination and ask the user if they would like to be brought to the POI. The recommended action may be performed automatically. Continuing the foregoing example, vehicle 101 may automatically bring the user to the recommended destination if the user indicates to the vehicle that he or she accepts the recommendation.”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”) further in view of Urmson II (US 10372129 B1) (“Urmson II”).
With respect to claim 2, Neumann in view of Bonanni in view of Urmson fail to explicitly disclose generating the recommendation based at least in part on a time of day.
Urmson II teaches generating the recommendation based at least in part on a time of day (See at least Urmson II Col. 7 lines 27-35 “In addition to recommending points of interest depending on information received from the sensors, the vehicle may also recommend locations to avoid. For instance, the data collected by vehicle 302 may indicate a pattern of avoiding certain commercial districts during a specific time of day. As such, the computer system may recommend that the user avoid certain routes or areas during that time based on the starting and destination address entered by the user in a navigation request”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann in view of Bonanni in view of Urmson to include generating the recommendation based at least in part on a time of day, as taught by Urmson II as disclosed above, in order to ensure accurate recommendations are presented at various times (Urmson II “In one aspect, a system and method is provided wherein an autonomous vehicle makes recommendations to a user based on the conditions detected by the car's sensors.”).
Claims 3-4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”) further in view of Kong (US 20190084571 B1) (“Kong”).
With respect to claim 3, and similarly claim 12, Neumann in view of Bonanni in view of Urmson teach generating recommendations (See at least Urmson FIG. 5 and Col. 6 line 40 – Col. 7 line 21)
Neumann in view of Bonanni in view of Urmson fail to explicitly disclose receiving driving statistics associated with the vehicle; and generating the recommendation based at least in part on the driving statistics.
Kong teaches receiving driving statistics associated with the vehicle; and generating the recommendation based at least in part on the driving statistics (See at least Kong Paragraph 8 “In a further aspect of the disclosure, the computer-implemented method for path planning of autonomous driving vehicles comprises: collecting driving statistics of a plurality of vehicles driving on a plurality of roads with different road configurations; performing an analysis on the driving statistics to identify a list of driving scenarios matching a set of predefined driving scenarios at different locations at different points in time; for each of the driving scenarios, identifying a list of locations at which at least some of the vehicles operated under the same driving scenario, for each of the locations associated with the driving scenario, determining a preferred path segments based on driving statistics associated with the location, and generating a driving scenario to path (scenario/path) data structure for the driving scenario to map specific locations to preferred path segments, wherein the scenario/path data structure is utilized subsequent to plan a path segment of a path at a particular location under a particular driving scenario using a corresponding preferred path segment without having to dynamically calculating the path segment.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann in view of Bonanni in view of Urmson to include receiving driving statistics associated with the vehicle; and generating the recommendation based at least in part on the driving statistics, as taught by Kong as disclosed above, in order to ensure accurate recommendations (Kong Paragraph 1 “More particularly, embodiments of the disclosure relate to driving scenario based lane guideline for path planning of autonomous driving vehicles.”).
With respect to claim 4, Neumann in view of Bonanni in view of Urmson in view of Kong teach that the driving statistics include how long the vehicle has been driven (See at least Urmson Col. 8 lines 3-18 “The vehicle may generate a recommendation based on information provided by both the other vehicle and the current vehicle. For example, processor of vehicle 401 may consider its previously sensed data (e.g., the average times that vehicle 401 has spent at different restaurants), its currently sensed data (e.g., its current location) and the currently sensed data from another vehicle (e.g., the amount of snow at the location of vehicle 402) when generating a recommendation (e.g., going to a different restaurant than the one requested by the user if the weather conditions are bad at the location near the requested restaurant). In that regard, the recommended action may be determined based on the sensors (e.g., GPS receivers) that identify the current and past location of the car, as well as sensors that do not identify the location of the car (e.g., road conditions).”).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”) further in view of Kim (US 20150328985 A1) (“Kim”).
With respect to claim 6, and similarly claim 14, Neumann in view of Bonanni in view of Urmson teaches receiving a direction at which the occupant is looking at a plurality of timesteps (See at least Bonanni FIG. 16 and Paragraphs 169-172).
Neumann in view of Bonanni in view of Urmson fails to explicitly disclose determining the entity that the occupant of the vehicle is looking at when the occupant looks at a particular location for longer than a predetermined period of time.
Kim teaches determining the entity that the occupant of the vehicle is looking at when the occupant looks at a particular location for longer than a predetermined period of time (See at least Kim Paragraph 444 “In addition, in order to obtain an image of a region of interest (ROI) according to coordinates of a point at which the user gazes, the multiple image obtainment apparatuses 900 may, in some implementations, be installed toward multiple regions inside of and outside of the vehicle in such a manner as to obtain image information on the multiple regions inside of and outside of the vehicle.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann in view of Bonanni in view of Urmson, to include determining the entity that the occupant of the vehicle is looking at when the occupant looks at a particular location for longer than a predetermined period of time as taught by Kim as disclosed above, in order to ensure accurate recommendations (Kim Paragraph 2 “This application relates to a driver monitoring system and particularly, to a mobile terminal and a vehicle control apparatus that determine whether a driver of a vehicle is driving in a dangerous driving state.”).
Claims 8-9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”) further in view of Yusuke (JP 2019185306 A) (“Yusuke”) (Translation Attached).
With respect to claim 8, Neumann in view of Bonanni in view of Urmson fail to explicitly disclose determining one or more layers of information to be included in the map.
Yusuke teaches determining one or more layers of information to be included in the map (See at least Yusuke Paragraph 32 “(3) Further, as described in the explanation of FIG. 2, the statistical speed and the statistical acceleration are obtained in lane units. Therefore, when performing automatic travel using statistical speed or statistical acceleration, it is possible to control the travel speed in lane units. It becomes possible to do. (4) In addition, statistically comprehensible road congestion such as rush hours and weekend tourist spots can be included in the map data as static data, so that it does not rely on dynamic information in time series. Understand changing road conditions. (5) In addition, it is possible to provide advanced automatic driving and safe driving support even in places where dynamic data is difficult to obtain such as communication facilities are not available.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann in view of Bonanni in view of Urmson to include determining one or more layers of information to be included in the map, as taught by Yusuke as disclosed above, in order to ensure accurate information is included in the map (Yusuke Paragraph 8 “An object of the present invention is to enable vehicle travel using three-dimensional map data having contents suitable for actual road conditions”).
With respect to claim 9, Neumann in view of Bonanni in view of Urmson in view of Yusuke teaches that the one or more layers of information include static information, dynamic information, and transient information (See at least Yusuke Paragraph 12 “Embodiment 1 FIG. FIG. 1 shows the data structure of the dynamic map 100. The dynamic map 100 includes static information, quasi-static information, quasi-dynamic information, and dynamic information. (1) The static information 104 includes basic map data 104a and statistical speed information 104b. The basic map data 104a is highly accurate three-dimensional map data. The basic map data 104a includes road surface information, lane information, a three-dimensional structure, and the like, and is composed of three-dimensional position coordinates indicating a feature and linear vector data. The statistical speed information 104b will be described later. (2) The quasi-static information, the quasi-dynamic information, and the dynamic information are dynamic data that changes every moment, and are data that is superimposed on the static information based on the position information. (3) The quasi-static information 103 includes traffic regulation information, road construction information, wide area weather information, and the like. (4) The quasi-dynamic information 102 includes accident information, traffic jam information, narrow-area weather information, and the like. (5) The dynamic information 101 includes ITS information (peripheral vehicle information, pedestrian information, signal information, etc.).”).
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20230021643 A1) (“Neumann”) in view of Bonanni (US 20200223444 A1) (“Bonanni”) in view of Urmson (US 11287817 B1) (“Urmson”) further in view of Josefdi (KR 20030017559 A) (“Josefdi”) (Translation Attached).
With respect to claim 10, and similarly claim 17, Neumann in view of Bonanni in view of Urmson fail to explicitly disclose determining whether the information is stored locally; and upon determination that the information is not stored locally, transmitting a request for the information to a remote computing device.
Josefdi teaches determining whether the information is stored locally; and upon determination that the information is not stored locally, transmitting a request for the information to a remote computing device (See at least Josefdi Paragraph 180 “The storage component 145 is a key component that enables a storage mechanism referred to herein as a & quot; mega-store. & Quot; In the mega-store, the server association 120 of FIG. 1 acts as an integrated storage mechanism from the end-user viewpoint. The mega-store is illustrated by the following scenarios. The user logs on to the PC. This logon authenticates the user with the Internet Authentication Service provided by the security component 165. After the user is authenticated, the directory component 150 may be used to determine where the user's information is stored. The storage component 145 then retrieves the data and provides it to the user interface component 140. Thus, personalized information does not require additional user intervention and appears in a user interface, such as a history list, favorite list, inbox, and the like. Thus, when a user moves from a device to a device, they follow their data and applications”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Neumann in view of Bonanni in view of Urmson to include determining whether the information is stored locally; and upon determination that the information is not stored locally, transmitting a request for the information to a remote computing device, as taught by Josefdi as disclosed above, in order to ensure an accurate retrieval of information needed (Josefdi Paragraph 1 “More particularly, the present invention describes a distributed computing services platform for facilitating advanced communication and coordination in an entire computer network”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM ABDOALATIF ALSOMAIRY whose telephone number is (571)272-5653. The examiner can normally be reached M-F 7:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached at 313-446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IBRAHIM ABDOALATIF ALSOMAIRY/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667
1 There is no limiting definition as to what constitutes a recommendation
2 There is no limiting definition as to what constitutes a recommendation
3 There is no limiting definition as to what constitutes a recommendation
4 There is no limiting definition as to what constitutes a recommendation
5 There is no limiting definition as to what constitutes a recommendation
6 There is no limiting definition as to what constitutes a recommendation
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