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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/05/2025 and 03/05/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are 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)(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) 1-2, 5-10 and 13-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by us patent application publication no. 2022/0319665 to Wang.
For claims 1, 9 and 17, Wang as applied discloses a method for predicting a volume of an object, the method performed by one or more processors of a computing device (see, e.g., FIGS. 1-3A and 6-8), the method comprising:
obtaining a video including an object and obtaining a plurality of frames included in the video (see, e.g., pars. 66-73 and 104-108 and FIG. 1, which teach continuously shooting a plurality of images including the food to be estimated and the stereo reference object by shooting a video);
obtaining object segmentation information based on the plurality of frames (see, (see, e.g., pars. 111-113 and 115-128 and FIGS. 1 and 3A, which teach extracting the stereo reference object region and the to-be-estimated food region);
obtaining multi-dimensional data of the object based on the obtained object segmentation information (see, e.g., pars. 56, 74-85 and 112-130 and FIGS. 1 and 3A, which teach obtaining the to-be-estimated food region size information based on the extracted to-be-estimated food region); and
calculating a volume of the object based on the multi-dimensional data of the object (see, e.g., pars. 74-76 and 139-144 and FIGS. 1 and 3A, which teach calculating the volume of the food based on the to-be-estimated food size information).
For claims 2 and 10, Wang as applied discloses that the obtaining of the video including the object and obtaining of the plurality of frames included in the video includes:
filtering the plurality of frames included in the video, and obtaining the plurality of filtered frames (see, e.g., pars. 72-73, which teach extracting a plurality of key images from the plurality of images to obtain the plurality of reference images and the plurality of food images, for example, each of the plurality of key images comprises a reference image and a food image that are corresponding to each other).
For claims 5 and 13, Wang as applied discloses that the obtaining of the video including the object and obtaining of the plurality of frames included in the video includes:
obtaining a video including a reference object and a target object, and obtaining the plurality of frames included in the video (see, e.g., pars. 66-73 and 104-108, which teach continuously shooting a plurality of images including the food to be estimated and the stereo reference object by shooting a video).
For claims 6 and 14, Wang as applied discloses that the obtaining of the object segmentation information based on the plurality of frames includes:
obtaining reference object segmentation information and target object segmentation information based on the plurality of frames (see, e.g., pars. 111-113 and 115-128 and FIGS. 1 and 3A, which teach extracting the stereo reference object region and the to-be-estimated food region), and
wherein the obtaining of the multi-dimensional data of the object based on the obtained object segmentation information includes:
obtaining reference object multi-dimensional data based on the reference object segmentation information (see, e.g., pars. 56, 74-85 and 112-114, which teach calculating the reference region size information based on the extracted stereo reference object region); and
obtaining target object multi-dimensional data based on the target object segmentation information (see, e.g., pars. 56, 74-85 and 115-130, which teach calculating the to-be-estimated food region size information based on the extracted to be estimated food region).
For claims 7 and 15, Wang as applied discloses that the calculating of the volume of the object based on the multi-dimensional data of the object includes:
obtaining reference length information for the multi-dimensional data of the object based on the reference object (see, e.g., pars. 55-57, 60-65, 74-76, and 139-144 and FIGS. 1 and 3A, which teach acquiring the actual size information and the stereo reference region size information); and
calculating a volume of the target object based on the reference length information (see, e.g., pars. 57, 74-76 and 139-144 and FIGS. 1 and 3A, which teach acquiring volume information of the food according to the actual size information of the reference object, the reference region size information, and the food region size information).
For claims 8 and 16, Wang as applied discloses that the obtaining of the reference length information for the multi-dimensional data of the object based on the reference object includes:
obtaining a proportional parameter for the reference object and the multi- dimensional data of the object (see, e.g., pars. 63 and 91-97, which teach obtaining a first mapping ratio according to the reference region size information of the stereo reference object and the actual size information of the stereo reference object); and
obtaining the reference length information for the multi-dimensional data of the object based on the proportional parameter (see, e.g., pars. 63 and 98-103, which teach obtaining the actual size information based on the first mapping ratio).
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(s) 3, 4, 11, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of us patent application publication no. 2013/0058573 to Suzuki.
For claims 3 and 11, while Wang as applied does not explicitly teach, Suzuki in the analogous art teaches that the filtering of the plurality of frames included in the video, and obtaining of the plurality of filtered frames includes:
detecting blurs included in the plurality of frames included in the video (see, e.g., pars. 28-30 and 68-40 of Suzuki, which teach detecting the amount of blur in the frames of the video); and
filtering the plurality of frames included in the video based on a quantity of the detected blurs (see, e.g., pars. 33-34, 42-44, 48, and 68 of Suzuki, which teach selecting those frames with the smallest amount of blur for a predetermined time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to process the blur as taught by Suzuki because doing so would allow obtaining a still image of high image quality (see pars. 49 and 69 of Suzuki).
For claims 4 and 12, while Wang as applied does not explicitly teach, Suzuki in the analogous art teaches that the filtering of the plurality of frames included in the video based on the quantity of the detected blurs includes:
filtering the plurality of frames included in the video as large as a predetermined number of frames in an order in which the quantity of the detected blurs is smaller. (see, e.g., pars. 33-34, 42-44, 48, and 68 of Suzuki, which teach selecting those frames with the smallest amount of blur for a predetermined time period; the examiner interprets the predetermined time period to suggest the predetermined number of frames because the time period effectively sets a maximum number of frames that can be selected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to process the blur as taught by Suzuki because doing so would allow obtaining a still image of high image quality (see par. 49 and 69 of Suzuki).
Additional Citations
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Kim (us pat. pub. 2022/0222844)
Describes a method for measuring food. In one embodiment, the method calculates an amount of food based on information on a size of a plurality of spaces, a depth of the plurality of spaces, and a capacity of the plurality of spaces formed in a tableware containing the food and an image photographed by photographing.
Metzler et al. (us pat. pub. 2015/0022640)
Describes a volume determining method for an object on a construction site. In one embodiment, the method may include moving a mobile camera along a path around the object while orienting the camera repeatedly onto the object. The method may include capturing a series of images of the object from different points on the path and with different orientations with the camera, the series being represented by an image data set; performing a structure from motion evaluation with a defined algorithm using the series of images and generating a spatial representation; scaling the spatial representation with help of given information about a known absolute reference regarding scale; defining a ground surface for the object and applying it onto the spatial representation; and calculating and outputting the absolute volume of the object based on the scaled spatial representation and the defined ground surface.
Table 1
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Table 1 and form 892.
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/WOO C RHIM/Examiner, Art Unit 2676
