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 .
Status of Claims
This Office Action is in response to the RCE filed 06/12/2026. Claims 1-20 are presently pending and are presented for examination.
Information Disclosure Statement
The Information Disclosure Statement filed on 06/12/2026 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/12/2026 has been entered.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4, 7-12, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Addepalli et. al. (U.S. Patent No. 8,903,593 B1)-IDS in view of Bande Martínez et. al. (U.S. Publication No. 2015/0029014)-IDS in further view of Herrmann et. al. (U.S. Publication No. 2018/0270241).
Regarding claim 9 and similarly with respect to claims 1 and 17
Addepalli discloses “A system for identifying anomalous driving behavior for a vehicle based on machine learning operations, the system comprising: a network interface configured to interface with a processor;” (See Addepalli Fig. 1, Char. 26 disclosing network interfaces coupled to an on-board unit.).
Addepalli discloses “a plurality of sensors affixed to the vehicle and configured to interface with the processor;” (See Addepalli Fig. 1, Chars. 14a-14c disclosing sensors.).
Addepalli discloses “and a memory configured to store non-transitory computer executable instructions and configured to interface with the processor;” (See Addepalli Fig. 1, Char. 24 disclosing a memory element.).
Addepalli discloses “the processor configured to interface with the memory,” (See Addepalli Fig. 1, Char. 24 and 21 disclosing the memory element and processing elements coupled to the on-board unit. The memory is accessible by the OBU, see Col. 17, L.1-6.).
Addepalli discloses “wherein the processor is configured to execute the non-transitory computer executable instructions to cause the processor to: receive a set of time-series driving data,” (See Addepalli Col. 15, L. 17-25 disclosing a trend analyzer receiving data over time from vehicle sensors.).
Addepalli discloses “wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle,” (See Addepalli Col. 15, L. 26-29 disclosing the attribute categories may include vehicle control system information, which may include, speed, tire traction, stability information, RPM, temperatures, ignition advance, air flow rates, oxygen, throttle, coolant temperature, whether or not the fuel system is in closed loop operation corresponding to an <orientation, location, time>. etc.).
Addepalli discloses “wherein the anomalous driver behavior is associated with one or more of: a medical situation, an intoxicated driver, a distracted driver, or previously unidentified road conditions;” (See Addepalli Col. 15, L. 62-67 and Col. 16 L. 1-6 disclosing the trend analyzer may identify abnormal events on the basis of the time-series data exceeding a threshold or normal value, as determined by the output of the machine learning operations, including identifying if a drivers breath alcohol content exceeds a threshold.).
Addepalli discloses “and modify the machine learning operations based on the set of time-series driving data and the identified set of anomalous conditions.” (See [0094] of the instant application disclosing modifying the machine learning operations based on the set of time-series driving data and the identified set of anomalous conditions includes comparing the set of anomalous conditions to a set of threshold values.). (See Addepalli Col. 14, L. 2-10 disclosing performing a comparison of trends and data from machine devices (time-series data) against a policy and set of rules).
Addepalli discloses all the elements of claim 9 except “wherein the set of operating conditions include one or more of: vehicle coordinate data, vehicle movement data, vehicle acceleration data, and vehicle brake system data;”, & “perform machine learning operations on the set of time-series driving data; identify a set of anomalous conditions in the set of time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous driver behavior,” (See Addepalli Col. 16, L. 24-29 & 45-48 disclosing configuring weights using a machine learning component to reflect the learned normal, the weights used to detect abnormal vehicle events, and the model training on the basis of sensor data.).
Herrmann discloses “wherein the set of operating conditions include one or more of: vehicle coordinate data, vehicle movement data, vehicle acceleration data, and vehicle brake system data;” (See Herrmann [0030] disclosing movement sensors configured to detect, generate, and collect movement data when a device is moved, including sensors which may be used to detect movement by collecting and analyzing the sensor data over time. Also see Herrmann [0025] disclosing the mobile computing device may include on-board vehicle systems, and Herrmann [0091] disclosing the data may be used for vehicle or driver insurance or financing (e.g., detecting driving data indicating safe or unsafe driving).).
Addepalli and Herrmann are analogous art, because they are in the same field of endeavor, anomaly detection. It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Addepalli to incorporate the teachings of Herrmann to include detecting vehicle operation time-series data for determining anomalous driver behavior. Doing so provides a known method in the art for anomaly detection enabling driver fingerprinting, incorporated with a reasonable expectation of success as doing so advantageously provides an anomaly technique which may be used in a product safety application, see Herrmann [0002] and [0091].
Bande Martínez discloses “perform machine learning operations on the set of time-series driving data; identify a set of anomalous conditions in the set of time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous driver behavior,” (See Bande Martínez [0080] disclosing performing machine learning operations on time-series data by building multivariate predictive regression models and Bande Martínez [0090] disclosing it is determined that the instantaneous behavior of the driver of the vehicle is abnormal if the result of the comparison between the values of the descriptive functions of “normal driving”, i.e. the reference levels, and those of current driving reaches a specific discrepancy value..).
Addepalli and Bande Martínez are analogous art, because they are in the same field of endeavor, anomaly detection. It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Addepalli to incorporate the teachings of Bande Martínez to include performing machine learning on time-series data. Doing so provides a known method in the art for anomaly detection enabling enhanced monitoring and sustainment of a system incorporated with a reasonable expectation of success as doing so advantageously provides a warning to a driver and/or a remote control center to mitigate abnormal driving behavior, see Bande Martínez [0093].
Regarding claim 2 and similarly with respect to claims 10 and 18
Addepalli discloses “The computer-implemented method of claim 1, wherein the set of time-series driving data is basic safety message data for the vehicle.” (See Addepalli Col. 15, L. 20-51 disclosing attribute categories including vehicle control system information.).
Regarding claim 3 and similarly with respect to claims 11 and 19
Addepalli discloses “The computer-implemented method of claim 2, wherein the basic safety message data comprise a message id, a conditions dataset, a safety data set, and a status dataset.” (See Addepalli Col. 15, L. 20-51 disclosing attribute categories including data related to the attribute category, which would include message IDs and a status dataset, and vehicle control system information, which is a conditions dataset and may be interpreted a safety data.).
Regarding claim 4 and similarly with respect to claims 12 and 20
Addepalli discloses “The computer-implemented method of claim 1, wherein the set of operating conditions further comprise one or more of: time data and vehicle attribute data.” (See Addepalli Col. 15, L. 20-51 & Col. 20 L. 31-35 disclosing attribute categories including whether the fuel system is in closed loop operation corresponding to an <orientation, location, & time, and vehicle control system information including, speed, which corresponds to vehicle attribute data.).
Regarding claim 7 and similarly with respect to claim 15
Addepalli discloses “The computer-implemented method of claim 1, wherein the anomalous driver behavior comprises data indicative of a medical situation, a distracted driver, unidentified road conditions, or combinations thereof.” (See Addepalli Col. 19, L. 65-67 disclosing a system that detects and alerts on car anomalies may include data corresponding to life threating situations.).
Regarding claim 8 and similarly with respect to claim 16
Addepalli discloses “The computer-implemented method of claim 1, wherein modifying the machine learning operations based on the set of time-series driving data and the identified set of anomalous conditions further comprises: comparing, at the one or more processors, the set of anomalous conditions to a set of threshold values.” (See Addepalli Col. 16, L. 24-26 disclosing abnormal vehicle events can be detected by analyzing the conformity scores using various threshold weights.).
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Addepalli et. al. (U.S. Patent No. 8,903,593 B1)-IDS in view of Bande Martínez et. al. (U.S. Publication No. 2015/0029014)-IDS in further view of Herrmann et. al. (U.S. Publication No. 2018/0270241) in even further view Takahashi et. al. (U.S. Publication No. 2019/0173902)-IDS.
Regarding claim 5 and similarly with respect to claim 13
Addepalli modified discloses all the elements of claim 1 and further discloses all the elements of the claimed invention except “The computer-implemented method of claim 1, wherein performing machine learning operations further comprises: generating, at the one or more processors, an isolation forest using the set of time-series driving data.”
Takahashi discloses “wherein performing machine learning operations further comprises: generating, at the one or more processors, an isolation forest using the set of time-series driving data.” (See Takahashi Abstract (Corresponding to Provisional- 62/430,570 overview) disclosing generating isolation forest learning model data for anomaly detection by using vehicle data.).
Addepalli, Bande Martínez, Herrmann, and Takahashi are analogous art, because they are in the same field of endeavor, anomaly detection. It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention to have further modified Addepalli to incorporate the teachings of Takahashi to include generating an isolation forest for identifying outliers in data. Doing so provides a known method in the art for facilitating in vehicle systems with a reasonable expectation of success as doing so provides improved detection efficiency, see Takahashi [0140] (Corresponding to Provisional- 62/430,570 overview).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Addepalli et. al. (U.S. Patent No. 8,903,593 B1)-IDS in view of Bande Martínez et. al. (U.S. Publication No. 2015/0029014)-IDS in further view of Herrmann et. al. (U.S. Publication No. 2018/0270241) in even further view Maeda et. al. (U.S. Publication No. 2012/0041575).
Regarding claim 6 and similarly with respect to claim 14
Addepalli modified discloses all the elements of claim 1 and further discloses all the elements of the claimed invention except “The computer-implemented method of claim 1, wherein identifying the set of anomalous conditions in the time-series driving data further comprises: identifying, at the one or more processors, unusual frequencies for the set of time-series driving data.”
Maeda discloses “wherein identifying the set of anomalous conditions in the time-series driving data further comprises: identifying, at the one or more processors, unusual frequencies for the set of time-series driving data.” (See Maeda [0137] & [0147] disclosing identifying abnormal frequencies in time-series data.).
Addepalli, Bande Martínez, Herrmann, and Maeda are analogous art, because they are in the same field of endeavor, anomaly detection. It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention to have further modified Addepalli to incorporate the teachings of Maeda to include identifying abnormal frequencies in time-series data. Doing so provides a known method in the art for facilitating in vehicle systems with a reasonable expectation of success as doing so provides early and accurate anomaly detection in vehicle data, see Maeda [0017].
Conclusion
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/JERROD IRVIN DAVIS/Examiner, Art Unit 3656
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