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 .
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 4-6 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (CN 106248394 A On-line Detecting Device For Automobile Air Outlet And Detecting Method Thereof); in view of Bauer et al. (WO 2021043419 A1 METHOD AND DEVICE FOR OPERATING AN AUTOMATION SYSTEM), hereinafter, “Bauer”.
Regarding claim 1 Yang teaches, detect a vehicle contained in a captured image, the vehicle being configured to move in a factory in which a plurality of manufacturing steps is performed to manufacture and ship the vehicle (Please note, claim 5. As indicated an on-line detection method for manufacturing automobile outlet), the vehicle being classified into a plurality of states in accordance with an appearance of the vehicle, the appearance varying among the plurality of manufacturing steps (Please note, claim 5. As indicated manufacturing the product, the product out from the mould by the mechanical arm and transferred to the work detection device; working table after detecting the product. by the automatic clamp clamping the product, and the product through automatic aligning device, scanning the products via the camera and image information and the state information of the detecting device to the computer image processing system), the detecting apparatus comprising a first processor configured to: acquire the captured image; acquire state information indicating one of the states of the vehicle contained in the captured image. (Please note, claim 5. As indicated plan view state information of the computer image processing system receives the state information of the image information and a detection device, the scanned image information and detection device by integrated calculation to generate a by-product, The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.); detect the vehicle contained in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image. (Please note, page 5, paragraphs 2-4. As indicated scanning the product through camera and image information and the state information of the detecting device to the computer image processing system; plan view state information of the computer image processing system receives the state information of the image information and the detection device, the scanned image information and detection device by integrated calculation to generate a product; The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.).
Yang does not expressly teach, utilizing a machine learning model.
Bauer teaches, machine learning model utilization (Please note, page 15, third paragraph. As indicated by means of the third algorithm to propose machine learning.).
Yang & Bauer are combinable because they are from the same field of endeavor.
At the time before the effective filing date, it would have been obvious to a person of ordinary skill in the art to utilize this machine learning model operation of Bauer in Yang’s invention.
The suggestion/motivation for doing so would have been as indicated on page 15, third paragraph, “As a result, the execution unit 23 is set up to carry out the two production steps through the respective proposed production plant 41 for the automatic production of at least part of the product, several parts of the product or several products”.
Therefore, it would have been obvious to combine Bauer with Yang to obtain the invention as specified in claim 1.
Regarding claim 4 Yang teaches, wherein the first processor is configured to acquire the state information by acquiring step information on one of the manufacturing steps, being performed on the vehicle, and identifying the state information associated with the one of the manufacturing steps, identified by the acquired step information by using a step database in which the state information is associated with each of the plurality of manufacturing steps. (Please note, page 11, example 3. As indicated the invention claims an online detection method for manufacturing automobile outlet, wherein it comprises the following steps: moulding the manufactured product, the product out from the mould by the mechanical arm and transferred to workbench of the detecting device; working table after detecting the product, the product and the product by the automatic alignment device 13 by automatic clamp 11; scanning the product through camera and image information and the state information of the detecting device to the computer image processing system (1). the computer image processing system 1 receives the state information of the image information and the detection device, the scanned image information and the detected device state information by integrated calculation to generate a product plan view; The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.).
Regarding claim 5 Bauer teaches, wherein the first processor is configured to, when the captured image is input, acquire the state information by inputting the captured image to a specific model that is a machine learning model trained so as to output the state information. (Please note, page 5, fifth paragraph. As indicated the production plan 26 in Fig. 4 comprises several lines with production step descriptions, several lines with production system descriptions as well as several lines with capability descriptions. Furthermore, the production plan 26 is preferably stored in the database 20 and is made available by the database 20.).
Regarding claim 6 Yang teaches, wherein the first processor is configured to acquire the state information by acquiring image capture information on image capture devices and identifying the state information associated with one of the image capture devices, identified by the acquired image capture information, by using a state database in which the image capture information is associated with the state information. (Please note, claim 5. As indicated scanning the products via the camera and image information and the state information of the detecting device to the computer image processing system (1); plan view state information (4) of the computer image processing system (1) receives the state information of the image information and a detection device, the scanned image information and detection device by integrated calculation to generate a by-product, (5) The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.).
Regarding claims 12 and 14, similar analysis as those presented for claim 1, are applicable.
Regarding claim 13 Bauer teaches, wherein the second detection model is trained in advance to identify the target region by inputting a plurality of training images each containing the vehicle and a state correct label, and the state correct label is associated with each of the plurality of training images and indicates the state of the vehicle contained in the training image. (Please note, page 9, eight paragraph. As indicated when a quantity of unlabelled data, such as parameters of production systems or of production system capabilities, is available in the database, but manual labeling or identification of the data is associated with a high level of human or economic effort. In such a case, the algorithm can actively ask the operator for labels for the respective data. This type of iterative, supervised learning is preferably referred to as active learning.).
Claims 7-10 are rejected under 35 U.S.C. 103(a) as being unpatentable over Yang (CN 106248394 A On-line Detecting Device For Automobile Air Outlet And Detecting Method Thereof); in view of Bauer et al. (WO 2021043419 A1 METHOD AND DEVICE FOR OPERATING AN AUTOMATION SYSTEM), hereinafter, “Bauer” as applied to claim 1 above, and further in view of Wei et al. (CN 102534461 A Process For Remanufacturing Engine Crankshaft By Automatic High-speed Electric Arc Spraying.), hereinafter, “Wei”.
Regarding claim 8 Yang teaches, detect a vehicle contained in a captured image, the vehicle being configured to move in a factory in which a plurality of manufacturing steps is performed to manufacture and ship the vehicle (Please note, claim 5. As indicated an on-line detection method for manufacturing automobile outlet), the vehicle being classified into a plurality of states in accordance with an appearance of the vehicle, the appearance varying among the plurality of manufacturing steps (Please note, claim 5. As indicated manufacturing the product, the product out from the mould by the mechanical arm and transferred to the work detection device; working table after detecting the product. by the automatic clamp clamping the product, and the product through automatic aligning device, scanning the products via the camera and image information and the state information of the detecting device to the computer image processing system), the detecting apparatus comprising a first processor configured to: acquire the captured image; acquire state information indicating one of the states of the vehicle contained in the captured image. (Please note, claim 5. As indicated plan view state information of the computer image processing system receives the state information of the image information and a detection device, the scanned image information and detection device by integrated calculation to generate a by-product, The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.); detect the vehicle contained in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image. (Please note, page 5, paragraphs 2-4. As indicated scanning the product through camera and image information and the state information of the detecting device to the computer image processing system; plan view state information of the computer image processing system receives the state information of the image information and the detection device, the scanned image information and detection device by integrated calculation to generate a product; The plan view and pixel point for producing the product with the corresponding relation of the actual size, calculating the actual size of the product, and generating a measurement data table.).
Bauer teaches, machine learning model utilization (Please note, page 15, third paragraph. As indicated by means of the third algorithm to propose machine learning.)
Yang and Bauer do not expressly recite, correcting a distortion.
Wei recites correcting a distortion (Please note, paragraph 0034. As indicated
distortion detection and correction.).
Yang, Bauer & Wei are combinable because they are from the same field of endeavor.
At the time of the invention, it would have been obvious to a person of ordinary skill in the art to utilize this distortion correction of Wei in Yang & Bauer’s invention.
The suggestion/motivation for doing so would have been as indicated on paragraph 0034, “to perform the surface for alignment.”.
Therefore, it would have been obvious to combine Yang, Bauer with Wei to obtain the invention as specified in claim 8.
Regarding claim 7, Wei recites, the plurality of manufacturing steps includes painting the vehicle, and the plurality of states includes an unpainted state and a painted state, the unpainted state indicates the state of the vehicle before being painted in painting the vehicle, the painted state indicates the state of the vehicle after being painted in painting the vehicle. (Please note, paragraph 0044. As indicated in the spraying process, the crankshaft rotates lower driven by the changeable computer, spray on journal surface to do linear motion, the crankshaft rotates one circle can finish one a spray painting journal.).
Regarding claim 9, Wei recites, rotate the captured image such that a moving direction of the vehicle is oriented in a predetermined direction. (Please note, Abstract of the invention. As indicated process for remanufacturing engine crankshaft by automatic high-speed electric arc spraying. In the spraying process, the crankshaft rotates at speed of less than pi/30-pi/15 r/s under the clamping of position changer, two sides of the crankshaft journal are equally divided with key points on the peripheral direction.).
Regarding claim 10, Wei recites, wherein the plurality of states including a platform state, the platform state being a state of a platform in which the vehicle includes at least a wheel, a chassis, a drive unit configured to accelerate the vehicle, a steering device configured to change a traveling direction of the vehicle, a braking device configured to decelerate the vehicle, a vehicle controller configured to control an operation of the vehicle, and a vehicle communication unit configured to communicate with another apparatus other than the host vehicle. (Please note, paragraph 0004. As indicated the electric arc spraying and manufacturing method for crankshaft of engine efficiency, the crankshaft clamped on the positioner capable of rotating, under the off-line mode: selecting some key points on the crankshaft journal, the gun movement moves on to these key points in order, and adjusting posture, orderly storing each position point of the posture and motion parameters in the controller. after the programmable robot along the key point according to the set program track, lower the working state the robot transfers the stored path and attitude crankshaft automatic spraying operation.).
Allowable Subject Matter
Claims 2-3 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The closest applied Prior Art of record fails to disclose or reasonably suggest wherein each of the plurality of first detection models is trained in advance to identify the target region by inputting a plurality of training images, and the plurality of training images includes M (M is an integer greater than or equal to two) first training images each containing the vehicle that is classified to the one of the states and N (N is an integer greater than or equal to zero and less than M) second training images each containing the vehicle that is classified to another one of the states, different from the one of the states.
Examiner’s Note
The examiner cites particular figures, paragraphs, columns and line numbers in the references as applied to the claims for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages and figures may apply as well.
It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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/AMIR ALAVI/Primary Examiner, Art Unit 2668 Sunday, February 1, 2026