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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/29/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 16-44 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 16-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliverti et al. (US2016/0314576) in view of Koh et al. (US2019/0357615), Son et al. (JP2019128923) and Nishiyama et al. (US2016/0071322).
To claim 16, Aliverti teach a computer-implemented method comprising:
capturing, at a computing device, one or more images of a body in which at least one portion of the body is covered by clothing in at least some of the one or more images (paragraphs 0015, 0065, instructed to take the video in tight fitting);
determining, at the computing device, one or more initial body measurements of the body based on the one or more images (paragraphs 0015-0016);
analyzing the one or more images at the computing device (paragraphs 0090-0093);
determining, at the computing device, at least one supplemental measurement of the selected portion of the body, the supplemental measurement determined at least in part using one or more sensors of the computing device (paragraphs 0020-0022, 0025-0026); and
applying, at the computing device, the correction factor to the one or more initial body measurements of the body to produce a corrected body measurement (Figs. 5A-B, paragraphs 0013-0014, 0088, 0091).
But, Aliverti do not expressly disclose to find a portion of the body covered by the clothing which creates distorted body measurements within at least some of the one or more initial body measurements; based on the analyzing, selecting the portion of the body covered by the clothing which creates the distorted body measurements in at least some of the one or more images to take a supplemental measurement; determining, at the computing device and based on the supplemental measurement, a correction factor associated with the clothing.
However, Alivertis teaches machine learning (paragraph 0094), which obviously shows that accuracy of measurement is improved based on prior body measurement analysis of images.
Koh teach body measurement system utilizing deep learning networks (Figs. 1A-B), comprising: taking body images and analyzing (151-152 of Fig. 1B), based on the analyzing (Figs. 153-154, 156 of Fig. 1B), selecting a portion of the body (body part segmentation) covered by the clothing in at least some of the one or more images to take a supplemental measurement (paragraphs 0077-0078, whether the user is dressed in tight, normal, or loose clothing for more accurate results), and output accurate body measurement extraction (155, 158 of Fig. 1B).
In furthering Koh’s teaching on determining whether the user is dressed in tight, normal, or loose clothing for more accurate results, Son teach having an automated feature of identifying wrinkle shape of the clothes worn by the user included in the user image, which determines whether or not the clothes are in close contact with each part of the user’s body (paragraphs 0044-0045, 0058), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teachings of Koh and Son into the method of Aliverti, in order to provide further accurate body measurement extraction by taking clothing distortion into consideration.
In furthering obviousness of correction factor associating with clothing, Nishiyama teach using a first imaging module to capture images of a first subject wearing clothing (Fig. 1; paragraphs 0054, 0237), estimating the body-shape parameter of the first subject (paragraphs 0068, 0112-0114), using a second imaging module to sequentially or simultaneously to operation of said first imaging module to capture depth images (paragraphs 0055-0056), determining a correction value corresponding to clothing and applying said correction value to obtain a body-shape parameter that indicates a more accurate body shape of the first subject (paragraphs 0090-0092, 0121-0123), which corresponds to nonideal body measurement image capturing scenario in Aliverti.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Nishiyama into the method of Aliverti, Koh and Son, in order to estimate true body size.
To claims 29, Aliverti, Koh, Son and Nishiyama teach a computing device (as explained in response to claim 16 above).
To claims 17 and 30, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the correction factor compensates for body distortions due to the clothing on the body (as explained in response to claim 16 above, e.g., Nishiyama, paragraphs 0090-0092).
To claim 18, Aliverti, Koh, Son and Nishiyama teach claim 17.
Aliverti, Koh, Son and Nishiyama teach wherein the correction factor compensates for body distortions due to at least one of a pleat, crease, or a fold of the clothing (as explained in response to claim 17 above, wherein pleat, crease, or a fold of the clothing would have been obvious, as applicant admitted prior art).
To claims 19 and 31, Aliverti, Koh, Son and Nishiyama teach claims 17 and 30.
Aliverti, Koh, Son and Nishiyama teach wherein determining the at least one supplemental measurement of a portion of the body further comprises: determining at least one of a location of a start or end of a body part that is at least partially obscured by the clothing; or determining a distance the clothing extends perpendicularly away from the surface of the body (as explained in response to claim 16 above, wherein a start or end of a body part is opened for interpretation broadly).
To claims 20 and 32, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the correction factor is based at least in part on a difference between the supplemental measurement and the one or more initial body measurements of the body (Aliverti, paragraphs 0057, 0091).
To claims 21 and 33, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach further comprising sending the corrected body measurement to a server (Aliverti, paragraphs 0041, 0066, 0071-0072, 0076, 0081, 0083-0084, analysis at remote server) and receiving, at the computing device, a size recommendation based on the corrected body measurement (Nishiyama, paragraphs 0081, 0180, 0208, size recommendation would be obvious due to template matching with highest degree of similarity).
To claims 22 and 34, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the supplemental measurement is determined from a movement of the computing device using the one or more sensors of the computing device (Aliverti, paragraphs 0020-0022, 0025-0026).
To claims 23 and 35, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the supplemental measurement is determined from additional images captured by the computing device (Aliverti, paragraph 0054).
To claims 25 and 37, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach obtaining a feedback instruction at the computing device, the feedback instruction providing an indication of proximity of the computing device to the portion of the body (Koh, paragraphs 078-0079, 0118, 0138).
To claims 24 and 36, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach further comprising: receiving, at the computing device, sensor data from a second device, wherein the sensor data from the second device comprises information regarding at least one of a location of the computing device, a position of the computing device, a location of at least a portion of the body, or a position of at least a portion of the body (Aliverti, paragraph 0058, as another parameter, the methods descried herein may be completely automatized methods, which do not require any user intervention and that provides all final measurements in a completely automatic manner).
To claims 26 and 38, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the correction factor is based at least in part on a difference between the supplemental measurement and the one or more initial body measurements of the body and at least one of a style of clothing covering at least a portion of the body and a type of textile covering the body (Aliverti, paragraph 0057).
To claims 27 and 39, Aliverti, Koh, Son and Nishiyama teach claims 16 and 29.
Aliverti, Koh, Son and Nishiyama teach wherein the capturing of the one or more images comprises obtaining one or more frames of a video (paragraph 0065, instructed to take video in tight fitting).
To claims 28 and 40, Aliverti, Koh, Son and Nishiyama teach claims 27 and 39.
Aliverti, Koh, Son and Nishiyama teach wherein the video is captured as part of an augmented reality system (Aliverti, paragraphs 0010, 0053, virtual reality).
To claim 41, Aliverti, Koh, Son and Nishiyama teach claim 16.
Aliverti, Koh, Son and Nishiyama teach wherein the selecting is based on a type of clothing worn on the body (Koh, paragraphs 0077-0078).
To claim 42, Aliverti, Koh, Son and Nishiyama teach claim 41.
Aliverti, Koh, Son and Nishiyama teach wherein the type of clothing is identified by the computing device using features from the one or more images compared with clothing signatures associated with clothing models (obvious, as the computing device does recognize clothing as top or bottom; also in Son, paragraph 0047).
To claim 43, Aliverti, Koh, Son and Nishiyama teach claim 16.
Aliverti, Koh, Son and Nishiyama teach wherein the selecting is based on inconsistencies or errors in the one or more images (Koh, paragraphs 0077-0078, obvious in view of selection being for correction and adjustment, which means there is inconsistency or error in image).
To claim 44, Aliverti, Koh, Son and Nishiyama teach claim 16.
Aliverti, Koh, Son and Nishiyama teach wherein the selecting is based on a desired clothing item identified at the computing device (Koh, paragraphs 0077-0078, obvious in view of selection being for a type of clothing computing device identified as either top or bottom).
Conclusion
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 June 2, 2026
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