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
Applicant's arguments filed 2/12/2026 have been fully considered but they are not persuasive.
The amendments are a rewording of claim limitations which were already found or close to limitations in the previous claims.
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 16-24 and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over ZHENG et al 2018/0348781 in view of Huang et al 2022/0188554.
As for claims 16, 23-24 and 28-29 Zheng teaches a method and computer-implemented method for generating reliability indication data of a computer vision model, comprising the following steps:
applying a sensitivity analysis to a first set of visual parameters of a visual parameter specification in order to select a fixed second set of visual parameters whose presence in an input image is an indicator of an unreliable performance of the computer vision model (paragraph 78 where glare or snow is an indication of unreliable data and therefore the visual parameters are optimized or changed to a set better set), the first set of visual parameters characterizing visual data, the second set of visual parameters being a reduced subset of the first set of visual parameters (paragraph 96 shows a validate perception results which results in valid labels being a subset or less than the total of valid and invalid data also figure 14 shows classification which results in subsets of classifications);
after performing the training of the computer vision reliability model, obtaining visual data including an input image or image sequence representing an observed scene (figures 1, 3, 4A, 5 and 10 shows a loop for updating a model. Each new model is made offline (i.e. the initial model).
analyzing (figure 3 analyzes events and tracks objects), by the computer vision reliability model, the observed scene included in the visual data to identify a presence of the second set of visual parameters (object and events);
generating, by the computer vision reliability model, reliability indication data of the computer vision model using the analysis of the observed scene (figure 5 and paragraphs 78, and figure 10 paragraphs 94+); and
outputting, by the computer vision reliability model, the reliability indication data of the computer vision model (validated objects).
Although ZHENG does not specifically teach that the training phase is “offline” and before training, he does teach that it is a “preprocessing step”. Huang et al., however also teach that the training of the neural networks is done offline as evidenced by paragraph 42 and figure 3. Since both systems are directed toward training for autonomous vehicle navigation, it would have been obvious to one of ordinary skill in the art before the effective filing date to include an offline training into Zheng’s system to set basic object detection before the system is used live. One would be motivated to have an offline training since it will be less dangerous to train offline than to train on the fly. It should also be noted that Huang in paragraph 14 also discloses the importance of using reliable data when training. Claim 24 also require that the set of items includes ground truth data, which is also shown in ZHENG paragraph 57.
Note for claim 28 and 29 Zheng teaches self-driving car which need motion to adjust steering (paragraph 73).
Regarding claim 17, ZHENG discloses the computer-implemented method according to claim 16, further comprising:
processing the visual data using the computer vision model configured to perform a classification or regression on the visual data (classification is used in paragraph 69), to thereby characterize an element of the observed scene; and
generating a prediction of the observed scene, wherein the reliability indication data characterizes the reliability of the prediction of the observed scene (predicted class has a confidence value as noted in paragraph 71).
Regarding claim 18, ZHENG teaches a computer-implemented method according to claim 16, further comprising:
communicating the reliability indication data of the computer vision model to a motion control system of an autonomous system; and issuing one or more motion commands to the autonomous system via the motion control system based on the reliability indication data (autonomous driving is fed information for obstacle avoidance and path planning (paragraph 73) both require motion commands).
Regarding claim 19, ZHENG teaches a computer-implemented method according to claim 16, wherein the analyzing of the observed scene included in the visual data using the computer vision reliability model further comprises:
mapping, using a first trained machine learning model of the computer vision reliability model, the visual data to the second set of visual parameters obtained using the sensitivity analysis of the first set of visual parameters (note figure 10 which uses a cross-modality validation or confidence into the candidate training data 1060).
Regarding claim 21, ZHENG discloses a computer-implemented method according to claim 16, wherein the visual data includes one or more of a video sequence, or a sequence of stand-alone images, or a multi- camera video sequence, or a RADAR image sequence, or a LIDAR image sequence, or a sequence of depth maps, or a sequence of infra-red images. (paragraphs 4 and 55 discuss RADAR and LIDAR).
Regarding claim 22, ZHENG discloses a computer-implemented method according to claim 16, wherein the visual parameters include one or any combination selected from the following list: (examiner notes the claim only requires one of the following limitations)
one or more parameters describing a configuration of an image capture arrangement including an image or video capturing device (54), and/or visual data taken in or synthetically generated for spatial and/or temporal sampling, and/or distortion aberration, and/or color depth, and/or saturation, and/or noise, and/or absorption, and/or reflectivity of surfaces ; and/or one or more light conditions in a scene of an image/video, including light bounces, and/or reflections, and/or light sources, and/or fog, and/or light scattering, and/or overall illumination (58); and/or one or more features of the scene of an image/video including: i) one or more objects (63), and/or ii) their position and/or size and/or rotation and/or geometry and/or materials and/or textures; and/or one or more parameters of an environment of the image/video capturing device or for a simulative capturing device of a synthetic image generator, including environmental characteristics, and/or seeing distance, and/or precipitation characteristics (60), and/or radiation intensity; and/or image characteristics including contrast and/or saturation and/or noise; and/or one or more domain-specific descriptions of a scene of an image/video, including one or more cars or road users, or one or more objects on a crossing (obstacles).
Regarding claims 20 and 24, ZHENG discloses a computer-implemented method for training a computer vision reliability model comprising the following steps:
sampling a set of visual parameters from a visual parameter specification (60-62);
obtaining a set of items of visual data, and providing a set of items of ground truth data corresponding to the set of items of visual data based on the sampled set of visual parameters (57), wherein the set of items of visual data and the set of items of ground truth data form a training data set (paragraph 57 uses ground truth data in the training set); and
iteratively training a first machine learning model to analyze at least one item of visual data from the set of items of visual data, and to output a prediction of a mapping of the at least one item of visual data to a subset of the set of visual parameters used to generate the item of visual data (also see figures 21 and 24 which shows multiple pass training).
Although ZHENG fails to specifically teach iteratively training a second machine learning model to predict data of the prediction of the mapping made by the first machine learning model, wherein the reliability indication data is obtained by comparing the prediction of the mapping from the first machine learning model with a corresponding item of ground truth data from the training data set, Huang does. Note Huang uses two learning systems in 2D and 3D neural networks (figure 3). Since both systems use ground truth to train the autonomous vehicle, it would have been obvious to one of ordinary skill in the art before the effective filing date to use multiple neural networks to have advanced learning for safer travel. One would be motivated to combine teachings since ground truth is the most reliable data.
Regarding claims 25 and 26, training learning systems is only effective if reliable information is used, thus ground truth is a sub set of image data as well as ZHENG discusses using the most reliable data which has consistent output.
As for claim 27, both online and offline systems of Huang are both neural networks (Huang 318 and 322).
Information Disclosure Statement
The IDS filed on 1/8/26 has been considered.
Conclusion
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/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482
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