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 .
Status of Claims
Pending
1-20
35 U.S.C. 103
1-20
Response to Amendment
This office action is in response to applicant’s arguments and amendments filed 06/24/2026, which are in response to USPTO Office Action mailed 02/24/2026. Applicant’s arguments and amendments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
Duplicate Claim Warning
Applicant is advised that should claim 15 be found allowable, claim 16 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Herman et al. (US 2022/0121950 A1, “Herman”) and further in view of Sane et al. (US 2025/0206331 A1, “Sane”).
Regarding claim 1: Herman teaches: A method of operating an autonomous vehicle, the method comprising: ([0035], [0038])
accessing sensor data captured by at least one sensor corresponding to the autonomous vehicle associated with operation of the autonomous vehicle in an environment, the environment characterized by one or more environmental conditions; ([0065]-[0068])
generating, based on the sensor data and with a machine-learned model, an output that indicates a sensor support level in the environment, ([0018]-[0023])
wherein the machine-learned model is trained using training data, the training data comprising a plurality of instances of logged sensor data depicting examples of a reference object, ([0028], [0047]-[0048], [0052], [0063], [0096]-[0098])
each instance of the plurality of instances of logged sensor data being associated with a label indicating a range at which the reference object was detected in the instances of logged sensor data, and ([0047]-[0055])
wherein the reference object is not [. . .] depicted in the sensor data captured by the at least one sensor; and ([0045]-[0046])
controlling the autonomous vehicle based at least in part on sensor support level ([0065]-[0068]).
However, Herman does not explicitly teach: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor].
Sane teaches: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor] ([0074] visibility system to determine levels of confidence based on: faults or errors are associated with individual sensor corresponding to the sensor data; occlusions in the sensor data; visibility distance (range or distance from sensor that sensor may be relied upon in normal operating conditions) of sensor is compromised or reduced. [0082] visibility system may determine whether or where occlusions may be indicated in the sensor data. static or dynamic obstacles may be obscuring other obstacles, objects, or areas such that the individual sensor may be unable to view or detect other obstacles or objects in the field of view. determine whether occlusions may be indicated in the sensor data, map data corresponding to map of environment used. map data include data corresponding to static obstacles or objects present in field of view or outside the field of view of the individual sensor. historic sensor data used to determine whether objects in sensor data corresponding to previous time stamps casting shadows or otherwise obscuring sensor data corresponding to current time stamps. [0151] map data may include data corresponding to static obstacles or objects that may be present in the field of view or outside the field of view of the individual sensor).
Herman and Sane are analogous art to the claimed invention since they are from the similar field of vehicle controls and sensors. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Herman with the aspects of Sane to create, with a reasonable expectation for success, a system or method of operating an autonomous vehicle wherein the reference object is not present in a field of view of the at least one sensor and not depicted in the sensor data captured by the at least one sensor. The motivation for modification would have been to improve the effectiveness in making informed control determinations in view of potential sensor blockages, visibility distance degradations, occlusions, and/or the like (Sane, [0008]).
Regarding claim 2: Herman-Sane further teach: The method of claim 1, further comprising: selecting a distance from a plurality of distances having a corresponding sensor support quantity that meets a detectability threshold, wherein the output of the machine-learned model comprises the plurality of sensor support quantities over a plurality of distances, a first sensor support quantity corresponding to the first distance of the plurality of distances and a second sensor support quantity corresponding to a second distance of the plurality of distances (Herman: [0054], [0056], [0060]-[0061]).
Regarding claim 3: Herman-Sane further teach: The method of claim 2, the sensor support quantity indicating at least one of a number of lidar points per unit surface area of the reference object that would be returned, a number of radar points per unit surface area of the reference object that would be returned, a result of applying an object detection mask to a portion of the sensor data depicting the reference object, or a result of a second machine-learned model that is trained to identify the reference object in at least a portion of the sensor data (Herman: [0043], [0047], [0054]).
Regarding claim 4: Herman-Sane further teach: The method of claim 2, the detectability threshold indicating a threshold number of returned points per unit surface area of the reference object (Herman: [0043], [0051]).
Regarding claim 5: Herman-Sane further teach: The method of claim 2, the sensor data comprising image data captured by a camera, the detectability threshold indicating a threshold result of applying an object detection mask to a portion of the image data (Herman: [0054], [0043], [0048], [0050]).
Regarding claim 6: Herman-Sane further teach: The method of claim 2, the sensor data comprising image data captured by a camera, the detectability threshold indicating an output of a second machine-learned model trained to identify the reference object in the image data (Herman: [0054], [0022]-[0023], [0052]).
Regarding claim 7: Herman-Sane further teach: The method of claim 1, wherein the sensor support level comprises an indication of a visibility classification in the one or more environmental conditions (Herman: [0068], [0065]-[0066], [0022]).
Regarding claim 8: Herman-Sane further teach: The method of claim 7, wherein the visibility classification is one of nominal, degraded, or severely degraded (Herman: [0065], [0044], [0051], [0068]).
Regarding claim 9: Herman-Sane further teach: The method of claim 1, further comprising: executing the machine-learned model using at least a portion of the training data as input to generate a training output of the machine-learned model; comparing the training output of the machine-learned model to the label data; and modifying the machine-learned model based at least in part on the comparing of the training output of the machine-learned model to the label data (Herman: [0019]-[0023], [0028], [0047]-[0050], [0098]).
Regarding claim 10: Herman-Sane further teach: The method of claim 8, each instance of the logged sensor data also being associated with label data indicating a visibility classification for the respective instance of the logged sensor data (Herman: [0068], [0065]-[0066], [0022]).
Regarding claim 11: Herman teaches: An autonomous vehicle comprising: at least one processor programmed to perform operations comprising ([0035], [0038], [0010])
accessing sensor data captured by at least one sensor corresponding to the autonomous vehicle associated with operation of the autonomous vehicle in an environment, the environment characterized by one or more environmental conditions; ([0065]-[0068])
generating, based on the sensor data and a machine-learned model, an output that indicates a sensor support level in the environment, ([0018]-[0023])
wherein the machine-learned model is trained using training data, the training data comprising a plurality of instances of logged sensor data depicting examples of a reference object, ([0028], [0047]-[0048], [0052], [0063], [0096]-[0098])
each instance of the plurality of instances of logged sensor data being associated with a label indicating a range at which the reference object was detected in the instances of logged sensor data, and ([0047]-[0055])
wherein the reference object is not [. . .] depicted in the sensor data captured by the at least one sensor; and ([0045]-[0046])
controlling the autonomous vehicle based at least in part on sensor support level ([0065]-[0068]).
However, Herman does not explicitly teach: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor].
Sane teaches: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor] ([0074], [0082], [0151]).
Herman and Sane are analogous art to the claimed invention since they are from the similar field of vehicle controls and sensors. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Herman with the aspects of Sane to create, with a reasonable expectation for success, a system or method of operating an autonomous vehicle wherein the reference object is not present in a field of view of the at least one sensor and not depicted in the sensor data captured by the at least one sensor. The motivation for modification would have been to improve the effectiveness in making informed control determinations in view of potential sensor blockages, visibility distance degradations, occlusions, and/or the like (Sane, [0008]).
Regarding claim 12: Herman-Sane further teach: The autonomous vehicle of claim 11, the operations further comprising selecting a distance from a plurality of distances having a corresponding sensor support quantity that meets a detectability threshold, wherein the output of the machine-learned model comprises the plurality of sensor support quantities over a plurality of distances, a first sensor support quantity corresponding to a first distance of the plurality of distances and a second sensor support quantity corresponding to the second distance of the plurality of distances (Herman: [0054], [0056], [0060], [0061]).
Regarding claim 13: Herman-Sane further teach: The autonomous vehicle of claim 12, the sensor support quantity indicating at least one of a number of lidar points per unit surface area of the reference object that would be returned, a number of radar points per unit surface area of the reference object that would be returned, a result of applying an object detection mask to a portion of the sensor data depicting the reference object, or a result of a second machine-learned model that is trained to identify the reference object in at least a portion of the sensor data (Herman: [0043], [0047], [0054]).
Regarding claim 14: Herman-Sane further teach: The autonomous vehicle of claim 12, the detectability threshold indicating a threshold number of returned points per unit surface area of the reference object (Herman: [0043], [0051]).
Regarding claim 15: Herman-Sane further teach: The autonomous vehicle of claim 12, the sensor data comprising image data captured by a camera, the detectability threshold indicating an output of a second machine-learned model trained to identify the reference object in the image data (Herman: [0054], [0043], [0048], [0050]).
Regarding claim 16: Herman-Sane further teach: The autonomous vehicle of claim 12, the sensor data comprising image data captured by a camera, the detectability threshold indicating an output of a second machine-learned model trained to identify the reference object in the image data (Herman: [0054], [0043], [0048], [0050]).
Regarding claim 17: Herman-Sane further teach: The autonomous vehicle of claim 11, wherein the sensor support level comprises an indication of a visibility condition in the environment (Herman: [0068], [0065]-[0066], [0022]).
Regarding claim 18: Herman-Sane further teach: The autonomous vehicle of claim 11, the operations further comprising: executing the machine-learned model using at least a portion of the training data as input to generate a training output of the machine-learned model; comparing the training output of the machine-learned model to the label data; and modifying the machine-learned model based at least in part on the comparing of the training output of the machine-learned model to the label data (Herman: [0019]-[0023], [0028], [0047]-[0050], [0098]).
Regarding claim 19: Herman-Sane further teach: The autonomous vehicle of claim 17, each instance of the logged sensor data also being associated with label data indicating a visibility classification for the respective instance of the logged sensor data (Herman: [0068], [0065]-[0066], [0022]).
Regarding claim 20: Herman teaches: At least one non-transitory computer-readable storage media comprising instructions thereon that, when executed by at least one processor, because the at least one processor to perform operations comprising: ([0035], [0038], [0010], [0101])
accessing sensor data captured by at least one sensor corresponding to an autonomous vehicle associated with operation of the autonomous vehicle in an environment, the environment characterized by one or more environmental conditions; ([0065]-[0068])
generating, based on the sensor data and a machine-learned model, an output that indicates a distance at which a reference object would meet a detectability threshold in the environment, ([0018]-[0023])
wherein the reference object is not [. . .] depicted in the sensor data captured by the at least one sensor; and ([0045]-[0046])
controlling the autonomous vehicle based at least in part on the distance or a visibility classification derived from the distance ([0065]-[0068]).
However, Herman does not explicitly teach: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor].
Sane teaches: [wherein the reference object is not] present in a field of view of the at least one sensor and not [depicted in the sensor data captured by the at least one sensor] ([0074], [0082], [0151]).
Herman and Sane are analogous art to the claimed invention since they are from the similar field of vehicle controls and sensors. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Herman with the aspects of Sane to create, with a reasonable expectation for success, a system or method of operating an autonomous vehicle wherein the reference object is not present in a field of view of the at least one sensor and not depicted in the sensor data captured by the at least one sensor. The motivation for modification would have been to improve the effectiveness in making informed control determinations in view of potential sensor blockages, visibility distance degradations, occlusions, and/or the like (Sane, [0008]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant's arguments further amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. The Applicant’s arguments also do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MADISON B EMMETT whose telephone number is (303)297-4231. The examiner can normally be reached Monday - Friday 9:00 - 5:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tommy Worden can be reached at (571)272-4876. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MADISON B EMMETT/Examiner, Art Unit 3658
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