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 .
Information Disclosure Statement
The information disclosure statements (IDS) were filed on 03/25/2025, 08/27/2025, 12/22/2025, and 05/08/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Status
This action is in response to claims filed on 03/25/2025. Claims 1-20 are considered in this office action. Claims 1-20 are pending examination.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the abstract does not appear to adequately describe the claimed disclosed invention. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1 line 5, claim 10 line 6, and claim 19 line 5, the phrase “…sensor data a global data component…” is unclear. It appears that the language regarding the “feature data component” and the language regarding the “global data component” are meant to be two separate parts of the limitation, however a conjunction is missing from the text. For the purposes of examination, Examiner is interpreting the claim language as “…sensor data component and a global data component…”.
Claims 2-9, claims 11-18, and claim 20 are rejected based on rejected base claim 1, claim 10, and claim 19, respectively, for the same rationale as recited above.
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.
Claims 1, 3, 6-10, 12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mei et al. (US 9,286,524 B1) in view of Han et al. (US 2016/0104438 A1) using the claim interpretations stated above.
Regarding claim 1, Mei teaches “A method (Col. 1 line 61 teaches methods) comprising:
receiving, by one or more processors, sensor data generated by one or more sensors of an autonomous system (Col. 2 lines 43-44 and 48-49 teaches receiving an image of a vehicle environment from cameras (sensors) disposed on a vehicle);
extracting, by the one or more processors from the sensor data, a feature data component associated with a feature represented in the sensor data a global data component (Col. 4 lines 55-60 and Col. 5 lines 27-29 teaches each sample represents a region of interest in the roadway and is used as an initial input feature map that provides up-to-date knowledge of the environment around the vehicle including contours and boundaries of travel lanes (feature component), and Col. 6 lines 53-58 teaches a kernel that preserves relevant information and is designed to be universally applicable to the entire region of interest bounded by the input feature map (global data component));
executing, by the one or more processors, a first set of operations to detect the feature based on the feature data component (Col. 5 line 59 to Col. 6 line 7 teaches inputting one or more input feature maps 502 extracted from the image 300 into network 500 which performs a set of operations to generate a set of output feature maps 506); and
determining, by the one or more processors, a control result based on the feature and the global illumination data, the control result configured to at least in part control a movement of the autonomous system (Col. 4 lines 50-67 teaches using lane marking orientation and position (feature and global data) determined by the neural network 500 to operate vehicle 200 autonomously).”
However, Mei does not explicitly teach the global data component “associated with global illumination data” and “executing, by the one or more processors, a second set of operations to detect global illumination data based on the global data component.”
From the same field of endeavor of image analysis, Han teaches the global data component “associated with global illumination data” and “executing, by the one or more processors, a second set of operations to detect global illumination data based on the global data component (Par. [0396]-[0397] teaches a fourth mapping function MF4 (second set of operations) to divide the low luminance and high luminance parts (global data component associated with global illumination data) of the original image).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of Mei to incorporate the teachings of Han with a reasonable expectation of success to have the global data component taught by Mei be associated with global illumination data and execute a second set of operations to detect global illumination data based on the global data component as taught by Han.
The motivation for doing so would be to enable partitioning of the image based on luminance on which tone mapping can be performed (Han, Par. [0124]).
Regarding claim 10 and claim 19, the limitations of this computer program product claim and this system claim, respectively, are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 1 above as the limitations of computer program product claim 10 and system claim 19 are commensurate in scope to the limitations of rejected method claim 1.
Regarding claim 3, the combination of Mei and Han teaches all the limitations of claim 1 above, and further teaches “wherein the executing the first set of operations comprises: providing, by the one or more processors, the feature data component as an input to a neural network to cause the neural network to generate an output representing an indication of the feature (Mei, Col. 5 line 59 to Col. 6 line 7 teaches inputting one or more input feature maps 502 extracted from the image 300 into neural network 500 which performs a set of operations to generate a set of output feature maps 506).”
Regarding claim 12, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 3 above as the limitations of computer program product claim 12 are commensurate in scope to the limitations of rejected method claim 3.
Regarding claim 6, the combination of Mei and Han teaches all the limitations of claim 1 above, and further teaches “wherein executing the first set of operations comprises:
providing, by the one or more processors, the feature data component as input to a first portion of a neural network to cause the neural network to generate a first output representing an indication of the feature (Mei, Fig. 5 and 6 and Col. 5 line 59 to Col. 6 line 7 teaches inputting one or more input feature maps 502 extracted from the image 300 into a first portion of neural network 500 which performs a set of operations to generate a set of output feature maps 506), and
wherein executing the second set of operations to detect global illumination data based on the global data component comprises:
providing, by the one or more processor, the global data component and at least a portion of the first output of the first portion of the neural network as input to a second portion of the neural network (Mei, Fig. 5 and 6 and Col. 10 lines 42-50 teach the presence layer 524 and the geometry layer 526 (global data component) are applied to each one of the fully connected nodes sets 520 (first output) to determine the orientation and position of the lane marking) to generate the control result (Mei, Col. 4 lines 50-67 teaches using lane marking orientation and position (feature and global data) determined by the neural network 500 to operate vehicle 200 autonomously).”
Regarding claim 15, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 6 above as the limitations of computer program product claim 15 are commensurate in scope to the limitations of rejected method claim 6.
Regarding claim 7, the combination of Mei and Han teaches all the limitations of claim 6 above, and further teaches “wherein the first portion of the neural network and the second portion of the neural network are comprised in a convolutional neural network (Mei, Fig. 5),
wherein providing the feature data component as input to the first portion of the neural network comprises:
providing the feature data component as input to a first layer of the convolutional neural network (Mei, Fig. 5 and 6 and Col. 5 line 59 to Col. 6 line 7 teaches inputting one or more input feature maps 502 extracted from the image 300 into a first portion of neural network 500 which performs a set of operations to generate a set of output feature maps 506), and
wherein providing the global data component and the at least a portion of the first output of the first portion of the neural network to the second portion of the neural network comprises:
providing the global data component and the at least a portion of the first output of the first portion of the neural network to a second layer of the convolutional neural network (Mei, Fig. 5 and 6 and Col. 10 lines 42-50 teach the presence layer 524 and the geometry layer 526 (global data component) are applied to each one of the fully connected nodes sets 520 (first output) to determine the orientation and position of the lane marking).
Regarding claim 16, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 7 above as the limitations of computer program product claim 16 are commensurate in scope to the limitations of rejected method claim 7.
Regarding claim 8, the combination of Mei and Han teaches all the limitations of claim 7 above, and further teaches “wherein the feature data component is associated with a first data size, and the global data component and the at least a portion of the first output of the first portion of the neural network are associated with a second data size that is less than the first data size (Mei, Col. 5 line 66 to Col. 6 line 3 teaches the input feature map 502 is first processed by a convolutional layer 504 to generate a set of output feature maps 506; Col. 7 lines 1-3 and 44-46 teaches the convolutional layer 504 can be followed by a max-pooling layer 508 to downsample the output feature maps 506 which then become the input to the next convolution layer).”
Regarding claim 17, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 8 above as the limitations of computer program product claim 17 are commensurate in scope to the limitations of rejected method claim 8.
Regarding claim 9, the combination of Mei and Han teaches all the limitations of claim 1 above, and further teaches “wherein the control result is associated with one or more movements, the one or more movements comprising: braking, steering, changing lanes, accelerating, or merging into a different lane (Col. 4 lines 47-67 teaches the computing device in autonomous mode issuing commands to various vehicle systems to direct their operation including steering, braking, and accelerating, and using lane marking orientation and position (feature and global data) determined by the neural network 500 to operate vehicle 200 autonomously).”
Regarding claim 18, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 9 above as the limitations of computer program product claim 18 are commensurate in scope to the limitations of rejected method claim 9.
Claims 2, 4-5, 11, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mei et al. (US 9,286,524 B1) in view of Han et al. (US 2016/0104438 A1) and further in view of Zou (US 2019/0187718 A1).
Regarding claim 2, the combination of Mei and Han teaches all the limitations of claim 1 above, however the combination of Mei and Han does not explicitly teach “wherein extracting the feature data component associated with the feature represented in the sensor data comprises: applying, by the one or more processors, a high-pass filter to the sensor data to generate the feature data component.”
From the same field of endeavor of image processing, Zou teaches “wherein extracting the feature data component associated with the feature represented in the sensor data comprises: applying, by the one or more processors, a high-pass filter to the sensor data to generate the feature data component (Par. [0007] teaches the wavelet transformer includes at least one decomposition level with high and low pass filters and the decomposed image data includes at least four sub-bands including LLₖ, HLk, LHk, and HHk, where L represents image data that has passed through a lowpass filter (i.e., global data component) and H represents image data that has passed through a high-pass filter (feature data component)).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Mei and Han to incorporate the teachings of Zou with a reasonable expectation of success to extract the feature data component taught by the combination of Mei and Han by applying a high-pass filter as taught by Zou.
The motivation for doing so would be to have the neural network receive and process a set of sub-bands of filtered and decomposed image data to detect image features based thereon (Zou, Par. [0009]).
Regarding claim 11 and claim 20, the limitations of this computer program product claim and this system claim, respectively, are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 2 above as the limitations of computer program product claim 11 and system claim 20 are commensurate in scope to the limitations of rejected method claim 2.
Regarding claim 4, the combination of Mei and Han teaches all the limitations of claim 1 above, however the combination of Mei and Han does not explicitly teach “applying, by the one or more processors, a transformation to the feature data component to update the feature data component, the transformation associated with a de-noising filter, local contrast enhancement, gain adjustment, thresholding, or noise filtering.”
From the same field of endeavor of image processing, Zou teaches “applying, by the one or more processors, a transformation to the feature data component to update the feature data component, the transformation associated with a de-noising filter, local contrast enhancement, gain adjustment, thresholding, or noise filtering (Par. [0006]-[0007] teaches a wavelet transformer performs a plural two-dimensional discrete wavelet transform function (known in the art as an image denoising algorithm) including sub-band coding to obtain plural levels of sub-bands of decomposed image features, where the wavelet transformer includes at least one decomposition level with high and low pass filters and the decomposed image data (extracted data component) includes at least four sub-bands).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Mei and Han to incorporate the teachings of Zou with a reasonable expectation of success to have the transformation applied to the feature data component taught by the combination of Mei and Han be a denoising wavelet transform filter as taught by Zou.
The motivation for doing so would be to have the neural network receive and process a set of sub-bands of filtered and decomposed image data to detect image features based thereon (Zou, Par. [0009]).
Regarding claim 13, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 4 above as the limitations of computer program product claim 13 are commensurate in scope to the limitations of rejected method claim 4.
Regarding claim 5, the combination of Mei and Han teaches all the limitations of claim 1 above, however the combination of Mei and Han does not explicitly teach “wherein executing the second set of operations to detect the global illumination data based on the global data component comprises: applying, by the one or more processors, a transformation to the sensor data, the transformation associated with a low-pass filter, a tone-mapper, binning, resampling, or downsampling.”
From the same field of endeavor of image processing, Zou teaches “wherein executing the second set of operations to detect the global illumination data based on the global data component comprises: applying, by the one or more processors, a transformation to the sensor data, the transformation associated with a low-pass filter, a tone-mapper, binning, resampling, or downsampling (Par. [0007] teaches the wavelet transformer includes at least one decomposition level with high and low pass filters and the decomposed image data includes at least four sub-bands including LLₖ, HLk, LHk, and HHk, where L represents image data that has passed through a lowpass filter (i.e., global data component) and H represents image data that has passed through a high-pass filter (feature data component)).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Mei and Han to incorporate the teachings of Zou with a reasonable expectation of success to have the transformation applied to the global data component taught by the combination of Mei and Han be a low-pass filter as taught by Zou.
The motivation for doing so would be to have the neural network receive and process a set of sub-bands of filtered and decomposed image data to detect image features based thereon (Zou, Par. [0009]).
Regarding claim 14, the limitations of this computer program product claim are rejected using the combination of cited references Mei and Han based on the exemplary analysis of the method claim 5 above as the limitations of computer program product claim 14 are commensurate in scope to the limitations of rejected method claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE M FITZHARRIS whose telephone number is (469)295-9147. The examiner can normally be reached 7:30 am - 6:00 pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CHRISTIAN CHACE can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.M.F./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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