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
This action is in response to the application filed on ----4/12/2024 for application 18/634,115. Claim 1 – 20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/17/2024, 10/29/2024, 5/28/2025, 1/14/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 3, 4, 9, 10, 11, 16, 17 and 18 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.
Claim 3, 10 and 17 recite the limitation "the single data input". There is insufficient antecedent basis for this limitation in the claim. For examination purpose, the term is interpreted as “input data”.
Claim 4, 11, 18 recite the limitation "accurate and timely” and are not clear. The terms “accurate" and “timely” are both relative term which renders the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purpose, these terms are interpreted as intended result and thus bear no patentable weight.
Claim 9 and 16 recite the limitation "the apparatus". There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 – 20 should have been rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because A machine-readable medium having a computer program stored theron, in a broadest reasonable interpretation, can include signals per se.
Regarding the closest paragraph, 00103 states “Embodiments may be provided, for example, as a computer program product which may include one or more transitory or non-transitory machine-readable storage media having stored thereon machine-executable instructions”, "A machine-readable medium may include, but is not limited to …”.
Thus, the claims should have been rejected since they do not fall within at least one of the four categories of patent eligible subject matter.
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 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, 5, 6, 8, 12, 13, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun, US20190149813 in view of Dong et al., (hereinafter Dong), “Camera Anomaly Detection based on Morphological Analysis and Deep Learning”.
Claim 1. Sun discloses: An apparatus comprising: processor circuitry coupled to a memory (Fig. 1 – 2, an vehicle (apparatus) that has onboard computer having processing unit 104 and memory 108), the processor circuitry to:
facilitate one or more sensors (fig. 2, camera(s) 50) to capture one or more images of a scene, wherein an image of the one or more images is determined to be unclear, wherein the one or more sensors include one or more cameras (0017 – 0023, “camera fault detection, notification and recovery”, “determine in real-time … the information of the surrounding environment (scene), such as street signs lane patterns”; 0034, “a splash of mud (unclear) that entirely obscures the image of a camera 50 may severely affect vehicle performance and safety”); and
to identify, in real- time, a sensor associated with the image (refer to the mapping above, Fig. 5 & 0050 – 0054, “In an operation 406, onboard computer 100 may determine a camera fault based on the analyzed image data”; 0064, “computer 100 may provide to the user instructions that detail the location and type of camera fault, and instructions to correct the fault”; fig. 5 shows a pipe-line of image capturing and processing thus identify in real time).
Sun does not explicitly teach:
facilitate a deep learning model
Dong, in the same field of endeavor, explicitly teach:
facilitate a deep learning model (Dong, sec. II.B, “complicated anomalies, such as strip interference, camera occlusion and image blurring, which are difficult to be detected”, “this paper introduces Convolutional Neural Networks (CNN) (deep learning model) to detect these complicated anomalies”)
Sun and Dong both teach video camera fault/anomaly detection and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the deep learning model for the detection of camera fault taught by Dong in the system of Sun to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification for its “strong learning ability and high efficient feature representation” (Dong, sec. II.B).
Claim 5. Sun and Dong combination teaches all the limitation of Claim 1. The combination further teach: the deep learning model comprises one or more neural networks including one or more convolutional neural networks (refer to the mapping in Claim 1 & Dong, sec II.B, the model is convolutional neural network), wherein the image is unclear due to one or more of a technical defect with the sensor or a physical obstruction of the sensors, wherein the physical obstruction is due to a person, a plant, an animal, or an object obstructing the sensor, or dirt, stains, mud, or debris covering a portion of a lens of the sensor (refer to the mapping in Claim 1 & Dong, sec. II.B, the model is to detect camera occlusion (physical obstruction) and/or image blurring (technical defect, out of focus), Sun, 0034, the occlusion can be a splash of mud).
Claim 6. Sun and Dong combination teaches all the limitation of Claim 1. The combination further teach: the processor circuitry is further to provide one or more of real-time notification of the unclear image, and or real-time auto-correction of the sensor (refer to the mapping in Claim 1 & Sun 0064, the system of Sun notifies/instruct user to perform correction actions).
Claim 8, 12, 13 are the corresponding method claim of Claim 1, 5, 6. These claims are rejected with same reason.
Claim 15, 19 are the corresponding machine-readable medium claim of Claim 1, 5. Sun teaches onboard computer and thus inherently include machine-readable medium. These claims are rejected with same reason.
Claim(s) 2, 9, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun, US20190149813 in view of Dong et al., (hereinafter Dong), “Camera Anomaly Detection based on Morphological Analysis and Deep Learning” as applied to Claim 1 above, and further in view of Luo, EP3156944.
Claim 2. Sun and Dong combination teaches all the limitation of Claim 1. The combination further teach: the apparatus comprises an autonomous machine includes one or more of a self-driving vehicle, a self-flying vehicle, a self-sailing vehicle, and an autonomous household device (sun 0018, “an autonomous vehicle”).
Sun and Dong combination does not explicitly teach:
receive one or more data inputs associated with the one or more images to concatenate the one or more data inputs into a single data input to be processed by the deep learning model
Luo, in the same field of endeavor, explicitly teach:
receive one or more data inputs associated with the one or more images to concatenate the one or more data inputs into a single data input to be processed by the deep learning model (0005, “convolutional neural networks have found applicability in RGB-D feature learning”, 0008, “mapping image data and depth information of said image to a 3D point cloud”, “using a fusion (concatenate) model”, “applying classifier to fused features”; 0019, “an image sensor and depth sensor 202”; i.e., the image data and depth data are combined/matched/concatenated into an input data (single data format) to be processed by deep learning model)
Sun and Dong combination and Luo both teach environmental /image data detection and classification using convolutional neural network and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the depth sensor and fusion taught by Luo in the system of Sun and Dong combination to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to “improve the accuracy and robustness” (Luo, 0002).
Claim 9 and 16 are the corresponding method and machine-readable medium claim of Claim 2. These claims are rejected with same reason.
Claim(s) 3 – 4, 10 – 11, 17 – 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun, US20190149813 in view of Dong et al., (hereinafter Dong), “Camera Anomaly Detection based on Morphological Analysis and Deep Learning” as applied to Claim 1 above, and further in view of Hollemans, “Training on the device”.
Claim 3. Sun and Dong combination teaches all the limitation of Claim 1. The combination further teach: the processor circuitry is further to facilitate the deep learning model to receive the single data input to perform one or more deep learning processes including … an inferencing process to obtain real-time identification of the sensor associated with the unclear image, wherein the sensor includes a camera (refer to the mapping in Claim 1, Sun, fig. 2 & 0034, “Processing unit 104 may be configured to detect a camera fault based on the received image data”, combined with the CNN of Dong, the inference processing is executed on the vehicle as in a pipe line that input camera data and output the determination of whether sensor is failed ).
Sun and Dong combination does not explicitly teach:
perform one or more deep learning processes including a training process
Hollemans, in the same field of endeavor, explicitly teach:
perform one or more deep learning processes including a training process (Hollemans, page 3, “There are advantages to training models on the device”, “just because training large models on the device isn't feasible today, doesn't mean it will be impossible forever. Also, not all models need to be large. And most importantly: one-model-for-everyone may not be the best we can do.”)
Sun and Dong combination and Hollemans both teach applications of machine learning models on mobile platform and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the on device learning implementation taught by Hollemans in the system of Sun and Dong combination to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to tailor the machine learning model to fit each individual’s environment (Hollemans, page 3).
Claim 4. Sun, Dong and Hollemans combination renders obviousness of all the limitation of Claim 3. The combination further teach: the processor circuitry is further to facilitate the deep learning model to receive a plurality of data inputs and run the plurality of data inputs through the training and inferencing processes such that the real-time identification of the sensor is accurate and timely (refer to the mapping in Claim 1 & 3, the combination teaches a system that performs training and inference for real-time identification of sensor failure. Examiner notes that one of ordinary skilled in the art would understand that a neural network input layer contains plurality of nodes/cells and thus a plurality of data inputs. Such understanding can be easily find online for example: Tch, “The mostly complete chart of Neural Networks explained” ).
Claim 10 – 11 and 17 – 18 are the corresponding method and machine-readable medium claim of Claim 3 – 4. These claims are rejected with same reason.
Claim(s) 7, 14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun, US20190149813 and Dong et al., (hereinafter Dong), “Camera Anomaly Detection based on Morphological Analysis and Deep Learning” as applied to Claim 1 above, in view of Intel, “Quick Reference Guide for Intel Core Processor Graphics”.
Claim 7. Sun and Dong combination teaches all the limitation of Claim 1. The combination does not explicitly teach: the processor circuitry comprises apparatus comprises one or more processors having a graphics processor circuitry co-located with an application processor circuitry on a common semiconductor package.
Intel, in the same field of endeavor, explicitly teach:
the processor circuitry comprises apparatus comprises one or more processors having a graphics processor circuitry co-located with an application processor circuitry on a common semiconductor package (Intel, page 1 – 10, the various intel main stream processors have integrated GPU. Examiner notes that one of ordinary skilled in the art would know that most modern processors has integrated GPU in the same package, especially, Intel CPUs with integrated GPUs are commonly used in computers).
Sun and Dong combination and Intel both teach application processors and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the GPU feature integrated as taught by Intel in the system of Sun and Dong combination to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to support full graphics and media required by common operation systems (Intel, page 1).
Claim 14 is/are the corresponding method claim of Claim 2 thus rejected with same reason.
Claim 20 is/are the corresponding machine-readable medium claim that recites limitations of Claim 6 and Claim 7 and thus rejected with same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Realpe et al., “Sensor Fault Detection and Diagnosis for autonomous vehicles” which teaches method and device for detecting sensors failure in a vehicle using learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HITESH PATEL can be reached on (571) 270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIEN MING CHOU/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
7/16/26
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