DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 Arguments
2. Applicant’s contention (see pages 7-8 filed 30 January 2026) with respect to the rejection of the independent claims under 35 U.S.C. 103 has been fully considered and is persuasive in view of the amendments provided. Therefore, the rejection of the independent claims under 35 U.S.C. 103 has been withdrawn.
Subsequently, the prior art rejections of all claims dependent therefrom are withdrawn.
However, upon further consideration, new grounds of rejection are warranted (see below).
Claim Rejections - 35 USC § 103
3. 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari (US 2018/0267558), and further in view of Canady (US 2021/0197859) and Kwon (US 11,170,299).
Regarding claim 1, Tiwari discloses a method (vehicle sensor system and method of use; Tiwari at title) comprising:
Generating an aggregate visibility confidence model corresponding to an aggregate field of view of a plurality of sensors of a plurality of modalities of a machine (sensor fusion via multiple sensor modalities used to provide composite traveling environment data from a surrounding field of view of the vehicle and driver, including vision sensing, and the data used to populate an aggregated confidence model for control of the autonomous vehicle; Tiwari at 0015-0017, 0027, 0034, 0053, 0054, 0056), the aggregate visibility confidence model indicating a level of confidence in sensor data, obtained using the plurality of sensors, corresponding to the aggregate field of view as determined using individual determinations of visibility corresponding to the sensors (poor or unreliable perception data of the vehicle’s surroundings leads to low confidence modeling; Tiwari at 0054, 0056).
Performing or adjusting one or more operations of the machine based at least on the visibility confidence model (automated vehicle control adjusted due to high or low confidence modeling; Tiwari at 0056, 0058, 0081).
While Tiwari does generate an aggregate visibility confidence model based on sensor confidence, it cannot be ascertained how sensor modality certainty/uncertainty is ascertained, i.e. the amended “a plurality of visibility confidence models”. Tiwari is also silent as to determining the confidence in sensor data from individual sub-sections.
Kwon, in a similar invention in the same field of endeavor, teaches wherein ground truth data is taken from each sensor modality for use in training machine learning models for said sensors (Kwon at col 6 lines 26-49, col 7 lines 52), so as to increase its reliability and higher calculated confidence (Kwon at col 10 lines 18-39).
Canady, in a similar invention in the same field of endeavor, teaches wherein a specific sensor in an individual sub section of the vehicle field of view is determined to be low level of confidence, i.e. degraded (Canady at abstract, 0008, 0013, 0014) as well as subsequent vehicle control actions taken based on the lower confidence (vehicle control, cleaning the sensor, scheduling maintenance, etc; Canady at abstract, 0008, 0017).
It would be obvious to one of ordinary skill in the art before the time of the claimed invention to augment the aggregated visibility confidence modeling of Tiwari with the autonomous vehicle control and sensor modality modeling of Kown, as well as the specific sensor subsection in the field of vies as taught by Canady and subsequent vehicle actions thereof. Doing so would provide for a self-adjusting aggregated model as well as remediation of a sensor error due to environmental factors and return the vehicle to a safer autonomous driving mode.
Regarding claims 7, 14, and 20, Tiwari discloses a system for control of an autonomous vehicle and method (Tiwari at title, abstract), comprising:
Generating an aggregate visibility confidence models corresponding to one or more fields of view defining potential spatial coverage of sensor data corresponding to respective sensors associated with a machine, the aggregate visibility confidence model a function of different sensor modalities (sensor fusion via multiple sensor modalities used to provide composite traveling environment data from field of view of the vehicle and driver, including vision sensing, for confidence modeling; Tiwari at 0015-0017, 0027, 0034, 0053, 0054, 0056).
Populating one or more portions of the aggregate visibility confidence model with confidence data indicating respective levels of confidence in sensor data corresponding to the one or more fields of view (populating the confidence model with confidence data indicating sensor reliability of the sensor modalities; Tiwari at 0053, 0054).
Aggregating the respective levels of confidence in sensor data corresponding to the one or more fields of view to generate the aggregate visibility confidence model corresponding to an aggregate field of view of the respective sensors associated with the machine (confidence metrics aggregated to compile sensor confidence model; Tiwari at 0053, 0054).
Performing or adjusting performance of one or more operations of the machine based at least on the aggregate visibility confidence model (automated vehicle control adjusted due to high or low confidence modeling; Tiwari at 0056, 0058, 0081).
While Tiwari does generate an aggregate visibility confidence model based on sensor confidence, it cannot be ascertained how sensor modality certainty/uncertainty is ascertained, i.e. the amended “a plurality of visibility confidence models”. Tiwari is also silent as to determining the confidence in sensor data from individual sub-sections.
Kwon, in a similar invention in the same field of endeavor, teaches wherein ground truth data is taken from each sensor modality for use in training machine learning models (Kwon at col 6 lines 26-49, col 7 lines 52), so as to increase its reliability and higher calculated confidence (Kwon at col 10 lines 18-39).
Canady, in a similar invention in the same field of endeavor, teaches wherein a specific sensor in an individual sub section of the vehicle field of view is determined to be low level of confidence, i.e. degraded (Canady at abstract, 0008, 0013, 0014) as well as subsequent vehicle control actions taken based on the lower confidence (vehicle control, cleaning the sensor, scheduling maintenance, etc; Canady at abstract, 0008, 0017).
It would be obvious to one of ordinary skill in the art before the time of the claimed invention to augment the aggregated visibility confidence modeling of Tiwari with the autonomous vehicle control and sensor modality modeling of Kwon, as well as the specific sensor subsection in the field of vies as taught by Canady and subsequent vehicle actions thereof. Doing so would provide for a self-adjusting aggregated model as well as remediation of a sensor error due to environmental factors and return the vehicle to a safer autonomous driving mode.
Regarding claims 2, 8, and 19, Tiwari discloses wherein the one or more operations are performed or adjusted based at least on different weighing for at least two different sensor modalities that are based at least on corresponding determinations of visibility for the at least two different sensor modalities plurality of sensors includes sensors corresponding to one or more sensor modalities (higher confidence data has greater influence in the aggregated modeling and subsequent vehicle control; Tiwari at 0054, 0034, 0049).
Regarding claim 3, the combination teaches wherein the respective levels of confidence are determined based at least on one or more faults or errors associated with an individual sensor of the plurality of sensors; one or more gross-level degradations or blockages; one or more fine-level degradations corresponding to the sensor data; or one or more occlusions being present in the sensor data corresponding to the individual sub- sections of the aggregate field of view (confidence level as a function different degradation levels, such as a slightly degraded sensor due to needing cleaning of individually blocked sensors; Canady at 0008, 0013, 0017).
Regarding claim 4, the combination teaches wherein the one or more gross-level degradations or blockages are determined based on weather data, temperature, or time of day (weather data, lighting, or time of day; Canady at 0001, 0013).
Regarding claim 5, 11, 15, and 18 the combination teaches wherein the one or more occlusions being present is determined based at least on historical sensor data (stored historical data queried; Canady at 0018) or map data corresponding to a map (Canady at 0040).
Regarding claims 6 and 12, Tiwari discloses prior to the performing the one or more operations: sending, in response to a query, data corresponding to the one or more of the respective levels of confidence based on the query (transmitting confidence levels to remote server; Tiwari at 0026, 0029, 0033, 0086).
Regarding claims 9 and 16, the combination teaches wherein the plurality of visibility confidence models are generated at least on one or more fine level degradations corresponding to the sensor data (distortions associated with sensor FOV; Kwon at col 14 lines 57-67).
Regarding claims 10 and 17, the combination teaches wherein the one or more gross level degradations or blockages are determined based at least on environmental conditions affecting substantially all of the sensor data corresponding to a particular sensor (confidence level as a function different degradation levels, such as a slightly degraded sensor due to needing cleaning of individually blocked sensors; Canady at 0008, 0013, 0017).
Regarding claim 13, Tiwari discloses wherein the query is generated based at least on a determination that perception results corresponding to at least two sensors are in disagreement (confidence in data quality between two sensors; Tiwari at 0054).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M DAGER whose telephone number is (571)270-1332. The examiner can normally be reached on M-F 0830-1730.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Angela Ortiz can be reached on 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN M DAGER/
Primary Examiner, Art Unit 3663
09 April 2026
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