DETAILED ACTIONS
This Office Action is in response to the application 18/175255, filed on 03/27/2023.
Claims 1-20 are presently pending.
Definition of terms that may be used for citation purpose:
page = pg., paragraph = p., column = col., line = ln., for example page 5 = pg.5
Response to Arguments
Applicant’s arguments filed 8/18/2025 are not persuasive and Examiner respectfully disagree. Applicant argues: “While Fields recites various examples of mitigating instructions, Fields does not disclose delegating vehicle control to a remote operator. Accordingly, Applicant respectfully submits that Fields does not disclose "delegating vehicle control of the ego vehicle to a remote operator in response to the predicted movements defined in the model of abnormal driving detection" and "the remote operator providing automated control of the ego vehicle". Examiner’s Response: Examiner respectfully disagrees with the applicant. Fields explicitly discloses in col.5 ln.25, “the novel systems, methods, and/or techniques disclosed herein may send commands or instructions to one or more surrounding vehicles to automatically or autonomously perform or implement a mitigating action in response to the detected anomalous behavior of the remote vehicle.” And in col.16 ln.63, “mitigating 515 the effect of the remote vehicle's anomalous behavior(s) may include automatically providing an instruction to one or more components of the subject vehicle to automatically modify an operation of the subject vehicle. For example, based upon the detected anomalous behavior(s) of the remote vehicle, the subject vehicle's speed may be automatically adjusted, the subject vehicle's brakes may be automatically applied”. Therefore mitigation of the remote vehicle is by definition an example of a remote operator controlling the vehicle. The claim nor the specification doesn’t provide detail on any particular “operator”, so the remote server that sends the commands could be interpreted as an operator. Therefore, sending the commands is an example of providing control to the “remote” vehicle. Particularly for an autonomously vehicle as disclosed by Fields. Therefore the claim amendments does not overcome the rejections applied.
Applicant’s arguments filed 8/18/2025 stating that the claims do not recite a “mental process” is not persuasive. Applicant argues: “…none of the concepts that recite a mental process are included in the claims.” Examiner Respectfully disagrees. Amendments and arguments to address Claims 1-20 rejection under 35 U.S.C. 101 as being directed to an abstract idea without significantly more has not overcome the rejection and therefore maintained. The amendments of adding “delegate vehicle control of the ego vehicle to a remote operator…” is a very broad term and there is no detail in the specification explaining what it means to “delegating”. In fact, the term “delegating” is not written or described in the specification. Further, the amendment, “the remote operator providing automated control of the ego vehicle” is also not written nor described in the specification. Therefore, it is unclear what it means to “provide automated control”. Under broadest reasonable interpretation, “delegating” is interpreted as selecting an operator, which can reasonably be performed in the mind. The term of “providing”, which is mentioned at this high level of generalization can be interpreted as post solution activity by outputting the results of the abstract idea, and therefore not significant. As a result, the 35 U.S.C. 101 is maintained.
Applicant’s arguments filed 8/18/2025 stating, “Applicant respectfully submits that Ucar is a patent publication assigned to the Applicant and is thus not prior art under 35 U.S.C. 102(c). Thus, withdrawal of these rejections is respectfully requested.” Is not clear and more explanation is needed. “Ucar” is not part of the rejection for claims 3-5 and 14-16.
Response to Amendment
Applicant’s amendments filed 8/18/2025 have overcome the following objections and rejections.
Amendments to Figures 5 and 6 are accepted and therefore Objections to the Drawings are withdrawn.
Amendments to address Claim 1 have overcome the Claim Objection and therefore withdrawn.
Amendments to address Claim 2 rejection under 35 U.S.C. 112(b) has overcome the rejection and is therefore withdrawn.
Claim Rejections - 35 USC § 112
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant’s claim language has been amended to add the limitations, “delegating vehicle control of the ego vehicle to a remote operator” and “and the remote operator providing automated control of the ego vehicle.”. However, there is no written description in the specification on “delegating” and “providing” the remote operator control. The specification only mentions the “anomaly managing client circuit 210 can receive information from various vehicle sensors to determine whether a remote operator should be ready to operate the vehicle by performing the driving operations, or be ready to assist the driver of a semi-autonomous vehicle with a limited driving situation from afar.” (see paragraph 55) There no mention on how this system both delegates and provides actual control to the remote operator. Therefore these limitations are considered new matter and should not be claimed this way. Applicant is encouraged to use language and terminology that is supported in the written disclosure.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 112(b) because limitation recites "the algorithm" in claims 1 and 12. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 12 analysis is as follows.
A method comprising:
receiving first driving data from a sensor of the ego vehicle, the first driving data comprising a gap in time-series data entries of movements of the second vehicle;
receiving second driving data, the second driving data comprising data that fills in the gap in the time-series data entries of the second vehicle;
aggregating the first driving data with the second driving data to create aggregated driving data and generate metadata based on the aggregated driving data, wherein the aggregated driving data comprising movements of the second vehicle that exceed a threshold value of predicted movements defined in a model of abnormal driving detection, and wherein the metadata is used to select the model of abnormal driving detection; and
delegating vehicle control of the ego vehicle to a remote operator in response to the predicted movements defined in the model of abnormal driving detection;
and the remote operator providing automated control of the ego vehicle.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claims recite the limitation of aggregating the first driving data with the second driving data to create aggregated driving data and generate metadata based on the aggregated driving data, wherein the aggregated driving data comprising movements of the second vehicle that exceed a threshold value of predicted movements defined in a model of abnormal driving detection, and wherein the metadata is used to select the model of abnormal driving detection; and delegating vehicle control of the ego vehicle to a remote operator in response to the predicted movements defined in the model of abnormal driving detection; These limitations, as drafted, are simple processes that under its broadest reasonable interpretation, covers performance of the limitations in the mind. Nothing in these limitations are too complex to be performed in the human mind with the aid of a pen and paper. For example, metadata is defined as data about other data, so to generate data about driving data that is received is merely a process of analyzing information. Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitations recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements of receiving first driving data from a sensor of the ego vehicle, the first driving data comprising a gap in time-series data entries of movements of the second vehicle; receiving second driving data, the second driving data comprising data that fills in the gap in the time-series data entries of the second vehicle; and the remote operator providing automated control of the ego vehicle. The receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “providing” step is also mentioned at a high level of generality and amounts to mere post solution activity which is a form of insignificant extra-solution activity. Claim 1 analysis is similar as claim 12, with the more additional elements being a generic processor and memory that’s mentioned at a high level of generality and used to apply the abstract idea.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere data gathering and post solution activity.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving steps and the providing step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification recites that the metadata is generated by sensors of the ego vehicle (e.g., camera, lidar, radar, etc.), and the specification does not provide any indication that these sensors are operating in any way outside its conventional use. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Dependent Claims
Dependent claims 2-11 and 13-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application [provide concise explanation]. Therefore, dependent claims 2-11 and 13-20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 12.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 7-13, and 18-20 are being rejected under 35 U.S.C. 103 as being unpatentable over Fields et al. (US10540892) in view of Akbarzadeh et al. (US20210063199A1).
Regarding Claim 1 and 12. (Currently Amended) Fields discloses an ego vehicle for programmatically determining abnormal driving performed by a second vehicle, the ego vehicle comprising: memory; and one or more processors configured to execute machine readable instructions stored in the memory to: receive first driving data from a sensor of the ego vehicle (see at least col. ln., one or more processors associated with the first vehicle and based upon a set of sensor data, a set of characteristics that is indicative of one or more behaviors of a remote vehicle operating within the vehicle environment), the first driving data comprising a gap in time-series data entries of movements of the second vehicle (see at least col.6 ln.20); receive second driving data, (see at least col.19 ln.10); aggregate the first driving data with the second driving data to create aggregated driving data and generate metadata based on the aggregated driving data, wherein the aggregated driving data comprising movements of the second vehicle (see at least col.6 ln.15, movement of multiple vehicles) that exceed a threshold value of predicted movements defined in a model of abnormal driving detection (see at least col.13 ln.55), and wherein the metadata is used to select the model of abnormal driving detection (see at least col. 2 ln.45, anomalous vehicle behavior characteristics; determine, based upon the comparison of the one or more behaviors of the remote vehicle and the set of anomalous vehicle behavior characteristics); delegate vehicle control of the ego vehicle to a remote operator in response to the predicted movements defined in the model of abnormal driving detection (see at least col.5 ln.26 “the novel systems, methods, and/or techniques disclosed herein may send commands or instructions to one or more surrounding vehicles to automatically or autonomously perform or implement a mitigating action in response to the detected anomalous behavior of the remote vehicle.”); (see at least col. ln., and the remote operator providing automated control of the ego vehicle. (see at least col.16 ln.63, “mitigating 515 the effect of the remote vehicle's anomalous behavior(s) may include automatically providing an instruction to one or more components of the subject vehicle to automatically modify an operation of the subject vehicle. For example, based upon the detected anomalous behavior(s) of the remote vehicle, the subject vehicle's speed may be automatically adjusted, the subject vehicle's brakes may be automatically applied”.) Fields doesn’t explicitly disclose wherein the second driving data comprising data that fills in the gap in the time-series data entries of the second vehicle.
However, Akbarzadeh discloses a map and localization system for driving applications wherein explicitly includes riving data comprising data that fills in the gap in the time-series data entries of the second vehicle. (see at least p.65, The campaigns may allow for targeted or selective generation of data of certain types and/or at certain locations in order to fill in gaps, provide additional data to improve accuracy, update maps when road changes are detected (e.g., via health checking 112), and/or for other reasons.). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the metadate as collected by Fields et al. to include timeseries gaps as disclosed by Akbarzadeh et al. to improve accuracy in the system.
Regarding Claim 2 and 13. (Currently Amended) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 1. Fields discloses further wherein the algorithm is applied intermittently to detect abnormal driving. (see at least col.15 ln.25, the analysis or analyses of the historical vehicle behavior data 475 may determine different thresholds and/or ranges of the vehicle behavior characteristics (e.g., over time, magnitudes, etc.) that are indicative of respective anomalous vehicle behaviors.) *Examiner interprets thresholds that trigger the detection is an example of intermittent, meaning not always.
Regarding Claim 7 and 18. (Currently Amended) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 1. Fields discloses further, wherein the instructions are further configured to cause at least one of the one or more processors to: receive the model of abnormal driving detection from a cloud-based anomaly managing system (Fields et al., as modified, column 14 line 39 discloses that part of "behavior characteristics” of the vehicle that have been determined to be anomalous can be stored in the server. Same paragraph discloses that the behavioral characteristics can be accessed from a device. Therefore, the processor can be configured to receive such a model from the cloud based anomaly management system. It is disclosed that the processors and devices are in a remote server. However, it is understood by a person of ordinary skill in the art at time of this publication that such devices, capable of reading the type of data discussed above, can be located within the control systems of the ego vehicle itself.) that generates the model of abnormal driving detection using a time series analysis or pattern matching. (Column 14 line 31 of Fields et al., as modified, discloses that a set of anomalous vehicle behavior characteristics may be generated from historical vehicle behavior. Column 14 line 60 discloses that portion of historical vehicle behavior may be time-series data. This is substantially the same as what is disclosed in this segment of Claim 7.).
Regarding Claim 8 and 19. (Original) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 1. Fields discloses further, wherein the first driving data includes a geographic location of the ego vehicle and the second vehicle. (Fields et al., as modified, discloses in column 19 line 22 and column 22 line 6 that driving data can be geo-location data which is substantially the same as geographic location data. It is understood by a person of ordinary skill in the art at time of this publication that the sensors and devices of ego vehicle are capable of recording data from the second vehicle or from ego vehicle, via devices such as the GPS. This is substantially the same as what is disclosed in this segment of above claim.).
Regarding Claim 9 and 20. (Original) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 1. Fields discloses further, wherein the model of abnormal driving detection is determined by identifying reoccurring and repeated abnormal driving behavior that has been previously reported. (Fields et al., as modified, in column 14 line 31 discloses that anomalous behavior characteristics are generated from historical vehicle behavior. Reoccurring and repeated abnormal driving behavior that has been previously reported qualifies as historical vehicle behavior.).
Regarding Claim 10. (Currently Amended) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 1. Fields discloses further, wherein the instructions are further configured to cause at least one of the one or more processors to: transmit the first driving data and the second driving data to a cloud-based anomaly managing system, wherein the cloud-based anomaly managing system uses the first driving data and the second driving data to update driving conditions for other vehicles. (Fields et al., as modified, discloses in column 18 line 43 a cloud computing system in which some of the functionalities discussed in this reference can be implemented. Field et al., as modified, also discloses here that data can be uploaded or downloaded. Column 11 lines 22 to 64 discloses the many devices located on-board the vehicle’s computer systems that can communicate with any device within or outside of the vehicle. Column 11 line 18 discloses specifically that sensor data can be transmitted to server. Therefore, we can conclude that devices onboard the vehicle can transmit data to a cloud-based system and that system can be an anomalous driving detection system. Column 5 line 19 to line 40, and column 11 line 7, disclose that vehicles in the vicinity of the ego vehicle are alerted to the abnormal driving of another vehicle. It is understood that this function can be performed by the cloud- based anomaly management system).
Regarding Claim 11. (Currently Amended) The combination of Fields and Akbarzadeh discloses all the limitations of the ego vehicle of claim 10. Fields discloses further, wherein the instructions are further configured to cause at least one of the one or more processors to: distribute identification of the second vehicle to the other vehicles. (Fields et al., as modified, discloses in column 5 line 19 to line 40, and column 11 line 7, that vehicles in the vicinity of the ego vehicle are alerted to the abnormal driving of another vehicle. It is understood that when vehicles in the vicinity of the vehicle, that is behaving abnormally, are alerted to the existence of such a vehicle a minimum level of information related to that vehicle may be provided as part of this alert. On the other hand, “Identification”, as disclosed in above segment of claim 11, is not defined in the claims or specifications of this application. We equate above mentioned minimum level of information as identification for that vehicle by applying the broadest reasonable interpretation to the word “identification”.)
Claims 3-5 and 14-16 are being rejected under 35 U.S.C. 103 as being unpatentable over Fields et al. (US10540892) in view of Akbarzadeh et al. (US20210063199A1) in view of Ansari (US9711050B2).
Regarding Claim 3 and 14 (Original) The combination of Fields andAkbarzadah discloses all the limitations of the ego vehicle of claim 1. The combination doesn’t explicitly disclose, wherein the second driving data is received by the ego vehicle by generating a temporary network between the ego vehicle and the second vehicle, and the temporary network is a peer-to-peer network between the ego vehicle and the second vehicle.
Ansari discloses receiving data by ego vehicle from other vehicles via a temporary network between, and the temporary network is a peer-to-peer network (Column 26 line 61 discloses vehicles communicating via a peer-to-peer network to perform discovery and crowd-sourced navigation. This requires communication between the vehicles using a peer-to-peer network by establishing a network between the ego vehicle and the second vehicle. And, communication can involve receiving data. It is understood that a peer-to-peer network can be a temporary or permanent network as needed.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Fields et al., as modified, to disclose the ego vehicle of claim 1 receiving second data from another vehicle by generating a temporary peer-to-peer network between the ego vehicle and the second vehicle, as disclosed by Ansari, in order to yield the predictable result of having a more efficient communication system between above mentioned vehicles.
Regarding Claim 4 and 15 (Original) The combination of Fields and Akbarzadah discloses all the limitations of the ego vehicle of claim 1. The combination doesn’t explicitly disclose, wherein the second driving data is received by the ego vehicle by generating a temporary network between the ego vehicle and the second vehicle, and the temporary network is a Vehicle-To-Vehicle (V2V) network.
Ansari discloses receiving data by ego vehicle from other vehicles via a temporary network between, and the temporary network is a vehicle-to-vehicle network (Ansari discloses in claim 15 ,and in column 27 line 4, a vehicle-to-vehicle communication protocol that facilitates identifying peers. This requires communication between the vehicles which can include receiving data. It is understood that a V2V network can be a temporary network.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Fields et al., as modified, to disclose the ego vehicle of claim 1 receiving second data from another vehicle by generating a temporary vehicle-to-vehicle network between the ego vehicle and the second vehicle, as disclosed by Ansari, in order to yield the predictable result of having a more efficient communication system between above mentioned vehicles.
Regarding Claim 5 and 16. (Original) The combination of Fields and Akbarzadah discloses all the limitations of the ego vehicle of claim 1. The combination doesn’t explicitly disclose, wherein the gap in the time-series data entries of movements of the second vehicle is determined using metadata generated by the ego vehicle.
Ansari discloses a system capable of determining that a gap exists in the data received (Column 29 lines 8 to 35 discloses a system capable of determining lack of data in the digital map related to a newly formed pothole or defect in the road. The reference discloses that if a vehicle drives through the lane with a smooth line or curve, but abruptly brakes, the system infers that the road has defects or potholes. Therefore, the system is determining that there is a defect, such as a pothole in the road, and attempts to update the map to reflect this finding. The reference refers to this as lack of data in the digital map. We interpret this lack of data as a gap in the data. Column 29 lines 35 to 43 disclose that the matched segment module and unmatched segment module both provide metadata to the map updating module. The metadata may include obstacle metadata road geometry refinement metadata, road closure and reopening metadata, missing intersection meta data, missing road data and one-way correction metadata. The map updating module updates the digital map in the map database. Ansari, as discussed above, discloses that the system, using data collected by the vehicle, itself determines that the digital map has a gap. Ansari does not explicitly mention that the ego vehicle does this determination. A server in the cloud is making this determination. However, it is obvious to one of ordinary skill in the art at time of this invention that the processors in the ego vehicle can equally make the same determination as discussed above if the same algorithms are implemented in the computer systems of the ego vehicle.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Fields et al., as modified, to disclose the gap in the time-series data entries of movements of the second vehicle being determined by the ego vehicle using metadata generated, similar to how Ansari et al. discloses that the system can determine a gap in data as discussed above, to yield the predictable result of having a more capable abnormal driving detection system.
Claims 6 and 17 are being rejected under 35 U.S.C. 103 as being unpatentable over Fields et al. (US10540892) in view of Akbarzadeh et al. (US20210063199A1) in view of Seyhan et al. (US20210221382A1).
Regarding claim 6 and 17, Fields et al., as modified, does not disclose the system or method, wherein the system or method, respectively, is further configured cause at least one of the one or more processors to: to receive the model of abnormal driving from a cloud based anomaly management system that generates model using a trained machine learning model.
Seyhan et al., discloses a system and method, wherein the instructions are further configured to cause at least one of the one or more processors to receive the model of abnormal driving at a cloud based anomaly management system using machine learning algorithm (Seyhan discloses in paragraph (20) an abnormal driving detection system that can be managed at a cloud based system. Fig. 3 shows such a cloud based system used for all vehicle communications and a server. And, paragraph (30) discloses that in one or more implementations, the vehicle can include a system executing a machine learning algorithm trained to recognize categorical aggressive, distracted or reckless behavior (ADR) types, such as tailgating, frequent/fast lane changes, drifting, etc., and thereby automatically detecting the ADR behavior based on sensor data from the on-board sensors. Therefore, this system determines model of abnormal behavior exhibited by a vehicle. We have previously established in the 35 U.S.C. 103 rejection section related to claim 7 that Fields et al., as modified, discloses the ego vehicle of claim 1 being further configured to receive the model of abnormal driving detection from a cloud-based anomaly managing system.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Fields et al., as modified, based on the teaching of Seyhan et al., to disclose receiving model of abnormal driving behavior from a cloud based anomaly management system that generates the model using a trained machine learning model in order to yield the predictable result of having a more capable anomalous driving detection system.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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JELANI A. SMITH
Supervisory Patent Examiner
Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662