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 .
Specification
Title of the invention
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested:
IMAGE PROCESSING APPARATUS, METHOD AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR GENERATING A COMMENT AND A LEVEL OF DEFECT AS AN OUTPUT RESULT OF A TRAINED MODEL
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 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 12 is directed to, a method performed by an image processing apparatus, the method comprising the steps of obtaining, generating and displaying which are nothing more than software instructions. Software instructions are non-statutory under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-11 and 13 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 1 (claim 13 is similar in scope), the prior art doesn’t teach:
obtain defect information related to a defect detected from a captured image and instruction information for instructing content to include in a comment on the defect;
generate the comment and a level of the defect as an output result of a trained model that is based on the defect information and the instruction information; and
display text including the comment and the level of the defect.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
IKEDA (US2023/0394648A1) discloses an image display device, a method, and a program which can improve the accuracy of determination by comprehensively considering interpretation information by a plurality of determiners. A first inspection target image in which an inspection object is imaged is displayed on a display, a marker in at least one region with which the interpretation information of the first inspection target image is associated is displayed, a second inspection target image subjected to at least one of enlargement processing or highlight processing for a region including at least a part of a region of the selected marker is displayed on the display, at least one of first information, which is document information of the interpretation information associated with the region of the marker selected by a user, or second information, which is statistical information obtained by statistically processing non-document information of the interpretation information, is displayed on the display; TOTH (US2020/0394784A1) discloses an artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations; ZHOU (US2023/0401390A1) discloses an automatic concrete dam defect image description generation method based on graph attention network, including: 1) extract the local grid features and whole image features of the defect image and conduct image coding by using multi-layer convolutional neural network; 2) construct the grid feature interaction graph, and fuse and encode the grid visual features and global image features of the defect image; 3) update and optimize the global and local features through the graph attention network, and fully utilize the improved visual features for defect description. The invention constructs the grid feature interaction graph, updates the node information by using the graph attention network, and realizes the feature extraction task as the graph node classification task. The invention can capture the global image information of the defect image and the potential interaction of local grid features, and the generated description text can accurately and coherently describe the defect information; WU (US2021/0073972A1) discloses receiving image data representative of images of items within a physical environment and depicting defects in at least one item, providing one or more of a set of augmented images using image augmentation based on the image data and a set of synthetic images using ML-based image synthesis based on the image data, processing one of the set of augmented images and the set of synthetic images using an ML model to provide a set of defect characteristics representative of defects in the at least one item, providing one or more root causes of each of the one or more defects by processing the set of defect characteristics and ancillary data, the ancillary data representative of the physical environment, and generating one or more alerts based on the one or more root causes for remediation of at least one root cause of the one or more defects; ZHENG (CN110197176A) discloses image identification technology based on inspection data intelligent analysis system, comprising a data management module, used for inspecting the data into the transmission line, matching the inspection image information, identifying features and defects, defect management module. for managing defect and screening, adding the defect set of reports, a report set module for receiving report set, the defect checking report set, generating the inspection report, a background database for storing inspection data, defect data, normal sample information, sample information and defect inspection report; The invention claims a based on image recognition technology of inspection data intelligent analysis system and analysis method, capable of automatically identifying and optionally of automatically diagnosing the defect to the characteristic inspection image in the inspection image to remark is 4, convenient for the storage of the database; the defect for manual confirmation, may be manually adjusted defect, performing classification to the defect, the severity of the defect reflects to the maintenance personnel; adding the defect report set generating the inspection report; BUFI (WO2021/062536A1) discloses a system and method for visual inspection. The system may be used in a quality assurance station at a manufacturing facility or site. The system may evaluate and determine the quality of manufactured or fabricated articles. The system may include a mechanical subsystem for capturing images of the article. The system may include a sensor such as a camera for capturing data, such as images. The system may include an artificial intelligence system to determine if the article suffers from an impermissible defect.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Fri: 2pm-10pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said A. Broome can be reached at 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612
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