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 .
Response to Amendment
The Amendment filed January 15, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed October 16, 2025.
Claim Objections
Claim 15 is objected to because of the following informalities: in claim 15 line 7, “determining one or more expected trajectories” should read “determining, by the processor, one or more expected trajectories”. Appropriate correction is required.
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, 4-7, 9-11, 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 Sahawneh et al. (US 2023/0065339 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 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, Sahawneh discloses the one or more expected trajectories of the second vehicle comply with at least one of regulatory requirements or safety requirements (Sahawneh paragraph 0089: “the lane-level route planning data 608 includes speed constraints 612 specific to a segment of the route 602. For example, if the segment includes pedestrians or unexpected traffic, the speed constraints 612 may limit the vehicle 100 to a travel speed slower than an expected speed, e.g., a speed based on speed limit data for the segment”); and responsive to determining a deviation between the observed trajectory and at least one of the one or more expected trajectories satisfies a condition (Sahawneh paragraph 0106: “The autonomous vehicle post-action explanation system 1000 takes as inputs deviation signal 1011…the deviation signal 1011 is a signal that indicates the vehicle has deviated from a planned path”), generating a record including a video of the second vehicle that corresponds to the observed trajectory (Sahawneh paragraph 0119: “if the compiled message 1032 is a video message, the compiled message 1032 can include an animation showing the deviation of the vehicle from the planned path…the compiled message 1032 includes video captured by at least one video camera 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 Green’s collected driving behavior data to incorporate the teachings of Sahawneh’s video message to accommodate a user’s preference for a visual notification (Sahawneh paragraph 0120).
Regarding claim 4, Green, modified by Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 9, Green, modified by Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 Sahawneh, 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 and Sahawneh in view of Lopez-Hinojosa et al. (US 2019/0232870 A1) .
Regarding claim 2, Green, modified by Sahawneh, 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 that predicts the correct or expected driving behaviors can take into account contextual data (Green paragraph 0028: “It is notable that 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”). Sahawneh further discloses such contextual data can be an object, i.e., the driving behavior can be explained by an object in the environment (Sahawneh paragraph 0027: “An autonomous vehicle (AV) can maneuver to avoid an imminent collision or to maintain a predefined distance from nearby objects”). However, Green and Sahawneh fail 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 autonomous vehicles, Lopez-Hinojosa discloses 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 (Lopez-Hinojosa FIG. 6: if conditions are not anomalous, a record indicating anomalous condition is not transmitted to the computing device, where the combination of Green and Sahawneh teaches a vehicle deviating to avoid collision with an object is normal driving behavior that can be predicted). A person of ordinary skill in the art would have the technological capabilities to incorporate both the vehicle driving behavior processing of Green and Sahawneh and the conditional logic of Lopez-Hinojosa into a combined apparatus. Green, modified by Sahawneh, teaches if anomalous driving behavior is detected, the anomalous driving behavior data is collected and transmitted to a remote server computer (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…The collected driving behavior data may be uploaded to the database in the remote server computer”). Lopez-Hinojosa teaches if conditions are not anomalous, a record is not transmitted (Lopez-Hinojosa FIG. 6). The resulting combined apparatus would yield predictable results with each element in the combined apparatus performing the same function as it did separately. The elements of Green and Sahawneh teach one branch, i.e., what happens when anomalous conditions are detected. The elements of Lopez-Hinojosa teach the other branch, i.e., what happens when anomalous conditions are not detected. 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 and Sahawneh to incorporate the teachings of Lopez-Hinojosa to yield the predictable result of transmitting a record only when anomalous driving behavior is detected.
Regarding claim 3, Green, modified by Sahawneh and Lopez-Hinojosa, discloses the vehicle of claim 2, wherein the processor is configured to detect the object subsequent to determining the second deviation (Sahawneh FIG. 12, paragraph 0116: “The simulation system 1020 determines the responsible object(s) 1022 causing the deviation” where the simulation determines the responsible object subsequent to receiving a deviation signal).
Regarding claim 16, it is the corresponding method executed by the vehicle claimed in claim 2. Therefore, Green, modified by Sahawneh and Lopez-Hinojosa, 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 Sahawneh and Lopez-Hinojosa, discloses the limitations of claim 17 as it does the limitations of claim 3.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Green and Sahawneh in view of Wang (CN111047875A).
Regarding claim 8, Green, modified by Sahawneh, discloses the vehicle of claim 5. However, Green fails to explicitly disclose the second remote processor is a processor of a regulatory agency. In the related art of reporting improper vehicle road behavior, Wang discloses the second remote processor is a processor of a regulatory agency (Wang paragraph 0034: “The identification module uploads the identified reporting information to the traffic police reporting platform through the communication module”). 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 remote server computer to incorporate the teachings of Wang’s traffic police reporting platform to help manage the road traffic environment and improve the level of traffic civilization in the whole society (Wang paragraph 0004).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Green and Sahawneh in view of Bhat et al. (US 2017/0249349 A1).
Regarding claim 12, Green, modified by Sahawneh, 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 have been fully considered but they are not persuasive.
Regarding the argument that “the one or more trajectories [in Green and Sahawneh] are not expected trajectories of the second vehicle under the environment such that the one or more expected trajectories of the second vehicle comply with at least one of regulatory requirements or safety requirements”, Sahawneh teaches the planned path includes speed constraints, such as limiting the vehicle to a speed below the speed limit (Sahawneh paragraph 0089). The speed limit represents both a regulatory requirement and a safety requirement. Therefore, Sahawneh teaches the expected trajectory complying with regulatory and safety requirements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.Z./ Examiner, Art Unit 2677
/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677
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