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 .
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.
Claim(s) 1, 4-11, 13-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Green et al. (US 2021/0070286 A1) in view of Nishi (US 2018/0032079 A1) and in further view of Wengreen et al. (US 2020/0320864 A1).
Regarding claim 1, Green discloses a vehicle (FIG. 1, paragraph 0017: “vehicle system 110”) comprising: one or more sensors (FIG. 1, paragraph 0017: “sensors or sensing systems 112 may include, for example, but are not limited to, cameras (e.g., optical camera, thermal cameras), LiDARs, radars, speed sensors, steering angle sensors, braking pressure sensors, a GPS, inertial measurement units (IMUs), acceleration sensors, etc.”); and a processor coupled with the one or more sensors (FIG. 5A, paragraph 0068: “a processor 518”) and stored inside a housing of the vehicle (paragraph 0068: “the data collection device 560 may be integrated with the vehicle as a built-in device”), the processor configured to: collect data regarding an environment surrounding the vehicle from the one or more sensors as the vehicle is driving (paragraph 0019: “may collect contextual data of the surrounding environment based on one or more sensors”); detect a second vehicle in a lane adjacent to the vehicle or in front of the vehicle (FIG. 1, paragraph 0018: “may use one or more sensing signals 122 of the sensing system 112 to collect data of the nearby vehicle 120”) and an observed trajectory of the second vehicle from the collected data (paragraph 0018: “may collect the vehicle data and driving behavior data related to…vehicle driving trajectories”), the observed trajectory indicating a position or speed of the second vehicle over a time period (paragraph 0018: “may collect the vehicle data and driving behavior data related to…vehicle speeds…locations”); determine one or more expected trajectories of the second vehicle based on requirements under the environment (paragraph 0028: “the driving behavior of a vehicle may vary based on the context of the driving environment…use the driving behavior data and the related contextual data for training the prediction model”); compare the observed trajectory with the one or more expected trajectories of the second vehicle (paragraph 0027: “the driving behaviors of a nearby vehicle may be compared to correct or expected driving behaviors as predicted by a prediction model”); responsive to determining a deviation between the observed trajectory and at least one of the one or more expected trajectories satisfies a condition, generate a record indicating the deviation (paragraph 0027: “When its driving behaviors deviate from the correct or expected driving behaviors, the corresponding driving data may be collected and aggregated into the corresponding vehicle models”); and transmit the record to a remote processor (paragraph 0027: “The collected driving behavior data may be uploaded to the database in the remote server computer”). However, Green fails to explicitly disclose the one or more expected trajectories of the second vehicle comply with at least one of regulatory requirements or safety requirements; and generating a record including a video of the second vehicle that corresponds to the observed trajectory.
In the related art of autonomous vehicles, Nishi discloses the one or more expected trajectories of the second vehicle comply with at least one of regulatory requirements or safety requirements (Nishi paragraph 0005: “This Literature discloses a method for determining the safety nature of the course of the vehicle in question by using a predicted course of another vehicle. However, the course of another vehicle is expected to be determined based on the driver's free will while satisfying constraint of traffic regulations”). Green teaches the predicted driving behaviors may include or be associated with one or more possible driving trajectories of the vehicle (Green paragraph 0044). A person of ordinary skill has good reason to pursue the known options within his or her technical grasp to define the one or more possible driving trajectories of the vehicle. Therefore, 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 Green to incorporate the teachings of Nishi to lead to the anticipated success of determining possible driving trajectories to comply with traffic regulations. However, Green, modified by Nishi, still fails to explicitly disclose generating a record including a video of the second vehicle that corresponds to the observed trajectory.
In the related art of vehicle monitoring, Wengreen discloses generating a record including a video of the second vehicle that corresponds to the observed trajectory (Wengreen paragraph 0019: “the first camera of the first vehicle is configured to take an image (e.g., a still picture or a video) of the second vehicle”). Green teaches the collected driving behavior data may be uploaded to the database in the remote server computer (Green paragraph 0027) and such collected driving behavior data can be vehicle images (Green paragraph 0018). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Green to incorporate the teachings of Wengreen since the substitution of images for a video yields predictable results as a video is simply a sequence of images.
Regarding claim 4, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the processor is configured to: capture an image of the second vehicle from the collected data (Green paragraph 0018: “the vehicle system 110 may collect the vehicle data and driving behavior data related to, for example, but not limited to, vehicle images” of the nearby vehicle 120) in response to determining the deviation satisfies the condition (Green paragraph 0027: “When its driving behaviors deviate from the correct or expected driving behaviors, the corresponding driving data may be collected and aggregated into the corresponding vehicle models”); and insert the image of the second vehicle into the record (Green paragraph 0027: “The collected driving behavior data may be uploaded to the database in the remote server computer”).
Regarding claim 5, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 4, wherein the remote processor: detects an identifier of the second vehicle from the image (Green paragraph 0037: “the feature extracting module 202 may use OCR technology on an image of the nearby vehicle to identify one or more features 215 of the vehicle” such as an anonymous vehicle identifier); and transmits the identifier of the second vehicle (Green paragraph 0030: “The remote server computer 230 may include a database 232…the database 232 may include anonymous driving identifiers of a number of vehicles with corresponding past driving behaviors and trajectories information”) and an indication of the deviation to a second remote processor (Green paragraph 0027: “When its driving behaviors deviate from the correct or expected driving behaviors, the corresponding driving data may be collected...The collected driving behavior data may be uploaded to the database in the remote server computer”).
Regarding claim 6, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 5, wherein the remote processor detects the identifier of the second vehicle from the image using object recognition techniques (Green paragraph 0037: “The feature extracting module 202 may use one or more types of computer vision technologies (e.g., optical character recognition (OCR), pattern recognition, agent classifiers, machine-learning-based feature recognition models, etc.) to extract or determine the features associated with the nearby vehicle”).
Regarding claim 7, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 5, wherein the identifier of the second vehicle is a license plate number (Green paragraph 0018: “the sensing system 112 may be used to identify the nearby vehicle 120, which could be based on an anonymous vehicle identifier based on the license plate number”).
Regarding claim 8, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 5, wherein the second remote processor is a processor of a regulatory agency (Wengreen paragraph 0270: “this entity is a police force, a government entity, and/or a law enforcement entity”).
Regarding claim 9, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the processor is configured to: determine the one or more expected trajectories based on the collected data (Green paragraph 0015: “the vehicle may retrieve, from a database, the past driving behavior data associated with the identified nearby vehicle or one or more anonymous features and feed that past driving behavior data to the trained prediction model to predict the driving behaviors and trajectories of the nearby vehicle”).
Regarding claim 10, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 9, wherein the processor is configured to determine the one or more expected trajectories by: identifying one or more objects in front of or next to the second vehicle (Green paragraph 0018: “the vehicle system 110 may collect the vehicle data and driving behavior data related to…an object in a field of view of the vehicle”); and determine the one or more expected trajectories based on the one or more objects (Green paragraph 0028: “use the driving behavior data and the related contextual data for training the prediction model” where the contextual data may involve nearby objects such as nearby vehicles).
Regarding claim 11, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the at least one of the one or more expected trajectories comprises an expected maximum speed (Green paragraph 0027: “the ego vehicle may use a prediction model to predict a driving speed of a nearby vehicle”), and wherein the processor is configured to determine the deviation satisfies the condition by determining the speed of the second vehicle is greater than the expected maximum speed (Green paragraphs 0027, 0043: “When the actual driving speed of the nearby vehicle deviates from the predicted driving speed for a threshold speed, the vehicle may collect the driving behavior data of the nearby vehicle”; in particular, driving at unusually high speeds can be detected as driving behavior that deviates from normal driving behaviors).
Regarding claim 13, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the processor is configured to determine the deviation satisfies the condition by determining the deviation exceeds a threshold (Green paragraph 0027: “When the actual driving speed of the nearby vehicle deviates from the predicted driving speed for a threshold speed, the vehicle may collect the driving behavior data of the nearby vehicle”).
Regarding claim 14, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the processor is configured to insert a location of the second vehicle (Green paragraph 0018: “the vehicle system 110 may collect the vehicle data and driving behavior data related to…locations”) in the record (Green paragraph 0027: “The collected driving behavior data may be uploaded to the database in the remote server computer”).
Regarding claim 15, it is the corresponding method executed by the vehicle claimed in claim 1. Therefore, Green, modified by Nishi and Wengreen, discloses the limitations of claim 15 as it does the limitations of claim 1.
Regarding claim 18, it is the corresponding method executed by the vehicle claimed in claim 4. Therefore, Green, modified by Nishi and Wengreen, discloses the limitations of claim 18 as it does the limitations of claim 4.
Regarding claim 19, it is the corresponding method executed by the vehicle claimed in claim 5. Therefore, Green, modified by Nishi and Wengreen, discloses the limitations of claim 19 as it does the limitations of claim 5.
Regarding claim 20, it is the corresponding method executed by the vehicle claimed in claim 6. Therefore, Green, modified by Nishi and Wengreen, discloses the limitations of claim 20 as it does the limitations of claim 6.
Claim(s) 2-3 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Green, Nishi and Wengreen in view of Becker (US 2018/0144636 A1).
Regarding claim 2, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1, wherein the processor is further configured to: detect a third vehicle and a second observed trajectory of the third vehicle from the collected data, the second observed trajectory indicating a second position or speed of the third vehicle over a second time period; compare the second observed trajectory with one or more second expected trajectories of the third vehicle; determine a second deviation between the second observed trajectory and at least one of the one or more second expected trajectories satisfies the condition or a second condition (Green FIG. 3C, paragraph 0022: “the remote server computer may categorize the driving behaviors of a large number of vehicles (as observed by the fleet of vehicles) into different driving behavior categories”; thus, the processing claimed in claim 1 is performed for a plurality of vehicles). Green also discloses the prediction model can take into account contextual data and such contextual data can be an object, i.e., the driving behavior of a vehicle can be explained by an object in the environment (Green paragraphs 0018 and 0028). However, Green fails to disclose responsive to detecting an object in the at least one of the one or more second expected trajectories, determine not to generate or transmit any records indicating the second deviation. In the related art of detecting dangerous driving behavior, Becker discloses responsive to detecting an object in the at least one of the one or more second expected trajectories (Becker paragraph 0014: “the vehicle can determine that another vehicle pulling over to allow an emergency vehicle to pass is not an indication of a distracted driver”), determine not to generate or transmit any records indicating the second deviation (Becker paragraphs 0020, 0025: “At 214, a precautionary action can automatically be taken based on the distracted driver classification at 212…the precautionary action can include notifying the driver, the distracted driver, or any designated third party” where “in accordance with a determination that the one or more characteristics about the area surrounding the vehicle is not indicative of the distracted driver, foregoing performing the precautionary action”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Green to incorporate the teachings of Becker to automatically warn the driver and/or avoid an unsafe vehicle when detected for safe vehicle navigation (Becker paragraph 0009).
Regarding claim 3, Green, modified by Nishi, Wengreen and Becker, discloses the vehicle of claim 2, wherein the processor is configured to detect the object subsequent to determining the second deviation (Becker paragraph 0014: “the vehicle can determine whether another vehicle is not staying within the center of its lane…the vehicle can determine that another vehicle pulling over to allow an emergency vehicle to pass is not an indication of a distracted driver”).
Regarding claim 16, it is the corresponding method executed by the vehicle claimed in claim 2. Therefore, Green, modified by Nishi, Wengreen and Becker, discloses the limitations of claim 16 as it does the limitations of claim 2.
Regarding claim 17, it is the corresponding method executed by the vehicle claimed in claim 3. Therefore, Green, modified by Nishi, Wengreen and Becker, discloses the limitations of claim 17 as it does the limitations of claim 3.
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Green, Nishi and Wengreen in view of Bhat et al. (US 2017/0249349 A1).
Regarding claim 12, Green, modified by Nishi and Wengreen, discloses the vehicle of claim 1. However, Green fails to disclose generating the record by inserting a storage identifier into the record, the storage identifier causing the remote processor to store data of the record in memory. In the related art of managing remote data storage, Bhat discloses generating the record by inserting a storage identifier into the record, the storage identifier causing the remote processor to store data of the record in memory (Bhat paragraphs 0073, 0077: “Data indicator 1202 of the mobile storage component 1102 may identify a data set 1204 for storage to data store 106…a request generated by mobile device 108 via the mobile storage application 110 may include a data set 1204 to indicate to the storage management application 104 which data to deduplicate and/or store to the data store 106”). 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 Green’s collected driving behavior data to incorporate the teachings of Bhat’s stateless communication to include all the information necessary to service the request in the request itself (Bhat paragraph 0077).
Response to Arguments
Applicant’s arguments, filed 06/30/2026, with respect to the rejection(s) of claim(s) 1-20 under USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Green, Nishi and Wengreen.
Conclusion
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/C.Z./ Examiner, Art Unit 2677
/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677
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