DETAILED ACTION
Response to Arguments
The amendment filed 14 December 2025 has been entered in full. Accordingly, claims 1-19 are pending in the application.
Regarding the rejections under 35 U.S.C. 102(a)(1) and (a)(2), the applicant argues on page 9 of the Remarks that Yano does not disclose the preamble of independent claim 1 since “Yano’s ‘object’ to be recognized is not a ‘landmark with known position in a world coordinate system.’” The examiner respectfully disagrees, since Yano at [0025] states: “The data holding unit 113 holds in a storage device 101 (see FIG. 3) data (data of an image (model image) of a reference object) related to plural kinds of reference objects acquired in advance.” Then see [0032]: “A) to identify the corresponding position in the scene image for each model feature point”.
The applicant also argues on page 9 of the Remarks that “Yano never teaches or suggests that sharpness is used as a probabilistic indicator of feature presence” since “Yano’s ‘definition’ merely categorizes regions of the scene image as high, medium, or low definition to decide which descriptor (e.g., SIFT, LBP, ORB) should later be applied”. The examiner respectfully disagrees. Yano at [0059]-[0060]: “The embodiment will be described on the assumption that a scene image has a region (reflection region) with illumination or a shadow reflected, but has neither a complete halation region nor a black crushing region, and the reflection region is detected as a medium-definition region. There is a possibility that wrong correspondence is included in the matching process in which the feature points having the closest feature quantity are associated with each other.”
The examiner understands this passage as follows: medium definition/sharpness means there is a possibility that the feature point selected in the matching process is not a feature of an object but rather a halation or black crushing region (and results in the possibility of a wrong correspondence). According to Fig. 6A-B, it appears high definition means that no halation or black crushing region is present, and so it is certain that object feature quantities can be detected. According to [0063] (“a complete halation region and a black crushing region (halation and black crushing regions) are present in a scene image, and these regions are detected as low-definition regions. In the halation and black crushing regions, the feature quantities such as SIFT and LBP cannot be detected.”), it appears that low definition means that it is certain that object feature quantities cannot be detected. In this way, the definition/sharpness of Yano indicates the “likelihood of that projection being a feature or not”.
The applicant further argues on page 10 of the Remarks that the “claimed determination of spatial variations, a continuous distribution of sharpness magnitudes, is absent”. The examiner respectfully disagrees. Definition/sharpness, defined as low, medium, and high, are determined (see [0055]) and depicted in Fig. 6A-B as transitioning regions. The presence of medium and high definition regions meets the claimed “spatial variations in image sharpness”.
The applicant further argues on pages 10-11 of the Remarks that the “’definition’ in Yano affects the choice of feature quantity but not the identification of the features.” The examiner respectfully disagrees. Yano is directed to an “object recognition device”. In Fig. 5, Yano calculates definition, selects the type of feature points (as pointed out by the applicant), and then perform a similarity calculation to output a recognition candidate at steps S40 and S60. Accordingly, Yano meets “identifying the at least one candidate feature based on the determined spatial variations in image sharpness.”
The applicant lastly argues on pages 12-13 of the Remarks that the combination of Yano and Ma does not meet “wherein the spatial variations in image sharpness of the received image are determined by applying a deep neural network, in particular embodied as a convolutional neural network, to the received image, wherein said deep neural network is trained in an end-to-end fashion” since Ma “performs perceptual blur classification, not continuous sharpness estimation. The probabilistic blur outputs do not correspond to physically meaningful sharpness magnitudes and cannot provide the continuous, spatially-resolved sharpness information required by the claimed method”.
The examiner respectfully disagrees. The claims do not currently require “continuous, spatially-resolved sharpness information”. Ma on page 5163, right column states: “Since each entry in our blur map indicates the blur degree of the corresponding pixel in the image…”. Blur refers to a lack of clarity/definition and corresponds to a lack of sharpness. A person of ordinary skill in the art, seeing the example of Ma, would appreciate the benefits of and be capable of implementing the sharpness/definition estimation portion of the Yano method in a deep learning framework. The combination would predictably improve sharpness (and/or blur, as they are related) estimation. The applicant argues that a “person skilled in the art of robotic vision would have no reason to incorporate Ma’s aesthetic blur-mapping model into Yano’s descriptor-selection process”. However, the examiner did not present that combination in the previous Office action.
The applicant further argues that integrating the outputs of Yano and Ma would require a complete redesign of the Yano architecture, “not a routine substitution”. However, the examiner did not present the combination as a routine substitution. The examiner asserts that taking an off-the-shelf convolutional neural network (CNN) and adapting it for operations (such as image sharpness/blur estimation) is easily within the skillset of one of ordinary skill in the art. The applicant’s specification itself appears to show that a standard CNN is used to carry out part of the applicant’s method.
For these reasons, the art rejections are respectfully maintained.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1, 8, 16, 17, and 19 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Yano et al. (U.S. Pub. No. 2020/0211221), hereinafter “Yano”.
Regarding claim 1, Yano teaches:
Method for identifying at least one candidate feature in an image of a scene of interest captured by a camera, said at least one candidate feature comprising at least one feature, wherein said scene of interest is in an environment which comprises N landmarks with known positions in a world coordinate system, and wherein the at least one feature corresponds to a projection of at least one landmark of the N landmarks into the image by the camera, the method comprising the following steps (See the Abstract.):
a) receiving said image of the scene of interest, wherein said image comprises spatial variations in image sharpness (See Figs. 6A-B and [0018]: “a scene image that is an image as a recognition processing target is acquired, definition indicating the degree of sharpness in each unit region of the acquired scene image is detected”), wherein a level of image sharpness of a projection appearing in said image is indicative of a likelihood of that projection being a feature or not (See [0059]: “The embodiment will be described on the assumption that a scene image has a region (reflection region) with illumination or a shadow reflected, but has neither a complete halation region nor a black crushing region, and the reflection region is detected as a medium-definition region.”);
b) determining spatial variations in image sharpness of the received image (See [0027]: “The feature selection unit 112 identifies that each region of the scene image is any one of a region where the definition is high, a region where the definition is medium, and a region where the definition is low on the basis of the definition of each unit region calculated by the definition calculation unit 111.”); and
c) identifying the at least one candidate feature based on the determined spatial variations in image sharpness (See [0027]: “The feature quantity used for the region where the definition is high is set as a feature quantity (for example, SIFT feature quantity: feature quantity for high definition) that is high in expressing capability, the feature quantity used for the region where the definition is medium is set as a feature quantity (for example, LBP: feature quantity for medium definition) that is high in the degree of robustness, and the feature quantity used for the region where the definition is low is set as a feature quantity (a three-dimensional coordinate position, a normal line, or the like: feature quantity for low definition) that is not affected by illumination conditions.”).
Regarding claim 8, Yano teaches:
Computer program product comprising instructions which when executed by a computer, cause the computer to carry out a method according to claim 1 (See the storage device in Fig. 3.).
Regarding claim 16, Yano teaches:
Assembly according to claim 15, further comprising a localizing apparatus on which the camera (8) and the light source (7) are arranged, and wherein the localizing apparatus is configured to move during the camera exposure time interval (6) (See [0035]: “A recognition result of the object recognition device 1 is sent to, for example, a robot control device (not shown), and an object recognized as a specific object is taken out by a robot or the like to be placed at a predetermined place.”).
Regarding claim 17, Yano teaches:
Assembly according to claim 16, further comprising an inertial measurement unit arranged on the localizing apparatus, wherein the inertial measurement unit is embodied as an accelerometer and/or as a gyroscope (See [0002]: “In the case where a work object is handled using a robot, means for measuring the position and posture of the work object is necessary. Therefore, a visual sensor is used.”).
Regarding claim 19, Yano teaches:
Assembly, comprising (i) a camera, (ii) a light source, (iii) a plurality of landmarks, and (iv) a controller, wherein the controller is configured to carry out a method according to claim 1 (See Fig. 1. Illumination is disclosed in [0022].).
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.
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yano (U.S. Pub. No. 2020/0211221) in view of Ma et al. (Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks, 12 June 2018, arXiv, Pages 1-12), hereinafter “Ma”.
Claim 2 is met by the combination of Yano and Ma, wherein
Yano teaches:
Method according to claim 1, wherein
Yano does not appear to disclose the following; however, Ma teaches:
the spatial variations in image sharpness of the received image are determined by applying a deep neural network, in particular embodied as convolutional neural network, to the received image, wherein said deep neural network is trained in an end-to-end fashion (See Fig. 3, deep neural network for obtaining a blur map from an input image. Then see page 1, left column: “Given a natural photographic image, the goal of local blur mapping is to label every pixel as either blurry or non-blurry, resulting in a blur map.”).
Yano and Ma together teach the limitations of claim 2. Ma is directed to a similar field of art (sharpness/blur estimation). Therefore, Yano and Ma are combinable. Modifying the system and method of Yano by adding the capability of estimating image sharpness via a deep neural network, as taught by Ma, would yield the expected and predictable result of improved local quality assessment. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Yano and Ma in this way.
Allowable Subject Matter
Claims 3-7 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.
None of the prior art of record, individually or in combination, discloses in
claim 3: “(ii) determining the sharpness image, the sharpness image having same dimensions as the received image, based on the filtered received image, wherein each sharpness image pixel, each sharpness image pixel having a corresponding filtered received image pixel, of the sharpness image comprises a respective value indicating a magnitude of change in pixel intensities of filtered received image pixels in a neighborhood around the corresponding filtered received image pixel.”
claim 4: “wherein the sharpness image is determined based on a combination of a plurality of component sharpness images each having same dimensions as the received image.”
claim 5: “(ii) further determining at least one simple closed curve in the image mask, with pixel elements of the simple closed curve comprising as value '1', and assigning the value '1' to the image mask pixels in the respective interior of the at least one simple closed curve; and (iii) providing, based on the image mask pixels with value '1', the at least one candidate feature.”
claim 6 as a whole
Dependent claim 7 includes the same allowable subject matter as claim 6.
Claims 9-15 and 18 are allowed.
The following is an examiner’s statement of reasons for allowance: the prior art of record, individually or in combination, does not disclose or suggest in claim 9: “with
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Dependent claim 14 is dependent on claim 9 and includes the same allowable subject matter.
Claim 18 combines the limitations of independent claim 1 and dependent claim 3, which was indicated as having allowable subject matter.
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571)270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677