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: claims 1-11 are examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/18/2023, 3/12/2024, 4/8/2025, 4/22/2025, 7/8/2025, 9/3/2025, 11/17/2025, and 1/6/2026 was filed and considered. The submission is 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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 5-11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by HANEDA et al (US 2019/0197359).
Claim 1, similarly claims 6 and 9:
HANEDA et al (US 2019/0197359) anticipated the following subject matter:
An inclination estimation system for estimating an inclination of an imaging target captured in an image, comprising circuitry configured to:
acquire an image in which an imaging target is captured and acquire estimation target images, which are a plurality of partial images, from the image (figure 1A and 0019 teaches camera for imaging as well as image retrieval of target images, where target image of interest such as themes, object, scenery, people, food, pets, clothing…etc (plurality of partial images) as disclosed in 0036-0037);
output a feature quantity of each of a plurality of estimation target images from each of the acquired plurality of estimation target images by using a feature quantity output model, to which information based on an image is input and which outputs a feature quantity of the image (figure 5B and 0120 teaches output of image feature extraction 131b, where 0178 detail also extraction of images based on high degree or matching (similar) for optimal model), and
estimate a focal position when in focus corresponding to each of the plurality of estimation target images from the output feature quantity (paragraph 0170 detail creation of image with feature with further control values such as focal length, focal position, photography direction (angle and inclination of imaging of target)); and
estimate the inclination of the imaging target captured in the image from the estimated focal position when in focus corresponding to each of the plurality of estimation target images (0105 teaches control system for inclination determination section 113, where 0106-0108, specifically 0108 determine inclination by analyzing images of photograph object (target) estimating shooting direction (whether it is sideways, a front view, shooting upwards, etc.), focal length at the time of shooting, exposure control values…etc)
wherein the feature quantity output model is generated by machine learning from a plurality of learning images associated with focal position information related to a focal position at the time of imaging (0170 detail consideration of time of shooting and viewing of images, where deep learning creates guidance (output) features and focal length, focal position), and
feature quantities of two different learning images are compared with each other according to focal position information associated with the two different learning images, and machine learning is performed based on a result of the comparison (0170 teaches differences between images that have a given evaluation, performed learning so as to detect differences between images that have been evaluated highly, deep learning create guidance (advice) that includes up to features that are barely noticed by a person. As advice, various advice may be included, such as exposure control values (aperture value, shutter speed value ISO sensitivity), focal length, focal position).
Regarding method of claim 6, figures 3-4 show workflow (method).
Regarding non-transitory computer-readable storage medium of claim 9, paragraph 0196 teaches non-transitory computer-readable storage medium.
Claim 2, similarly claims 7 and 10:
The inclination estimation system according to claim 1,
wherein the circuitry focal position estimation means estimates a focal position when in focus corresponding to each of the plurality of estimation target images by using a focal position estimation model to which the feature quantity output from the feature quantity output model is input and which estimates a focal position when in focus corresponding to an image related to the feature quantity (0162 detail feature image classification there are focal length, focal position with classification and division (quantity division) such as scenery, people, food…etc), and
the focal position estimation model is generated by machine learning from in-focus position information related to a focal position when in focus corresponding to each of the learning images (0170 detail leaning model output advice focal length, focal position from images, where 0056 also detail focus and blurred view as part of advice).
Claim 3, similarly claims 8 and 11:
The inclination estimation system according to claim 1, wherein the circuitry controls an inclination of the imaging target when imaging based on the estimated inclination of the imaging target (0105-0108 teaches control for inclination determination section 113 analyze image data (imaging target); 0178 detail performs inclination determination in step S143. This preference determination detects a like model that has a high degree of match to image feature input, feature extraction, and feature learning).
Claim 5:
A biological observation system, comprising: the inclination estimation system (above teaches inclination determination section 113) according to claim 1:
a mounting unit on which a biological sample is mounted as an imaging target related to the inclination estimation system (0199 detail microscope that would include mounting unit with optical with an inclination); and an observation unit for observing the biological sample (0177 detail inspection such as medical such as tumor, determination of tumor).
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over HANEDA et al (US 2019/0197359) in view of Uchida (US 2019/0333204).
Claim 4:
HANEDA et al do not teach the following subject matter:
A semiconductor inspection system, comprising: the inclination estimation system according to claim 1:
a mounting unit on which a semiconductor device is mounted as an imaging target related to the inclination estimation system; and an inspection unit for inspecting the semiconductor device.
Uchida (US 2019/0333204) teaches the following subject matter:
a mounting unit on which a semiconductor device is mounted as an imaging target related to the inclination estimation system; and an inspection unit for inspecting the semiconductor device (0005 detail image inspection for defect in circuit pattern on a substrate (semiconductor), where figure 2E and 0036 detail inspection target area such as circuit with image inspection inclination as well as mount position).
HANEDA et al and Uchida are both in the field of image analysis, especially for inspection of image with regard to inclination alongside machine learning (Uchida detail use of machine learning in figure 4 and 0046) such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify HANEDA et al by Uchida using for circuit on pattern inspection to reduce rework when defect occurs as disclosed by Uchida in 0071, as well as smooth load on system and operation as disclosed in 0080.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WATANABE (US 2019/0005338) teaches IMAGE PROCESSING APPARATUS, IMAGING APPARATUS, MOBILE DEVICE CONTROL SYSTEM, AND RECORDING MEDIUM – 0083 detail calculates the inclination of the object, based on the actual distances from the reference vehicle to the points surrounding the object (the pixels representing the feature of the shape of the object) detected by the short distance body detecting unit 143, and determines the type of the object based on the inclination.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656