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 .
Response to Arguments
Applicant's arguments filed 08/27/2026 have been fully considered but they are not persuasive.
Regarding rejection on claim 16, applicant argued that Aliverti fails to teach “the supplemental measurement determined at least in part using one or more sensors of the computing device,” wherein Aliverti’s image-based scaling and correction technique are directed to positioning, scale, and depth-plane correction for image-based measurement extraction, not identifying a clothing-distorted body for determining a correction factor for clothing distortion. Applicant then argued that Koh concerns tight, normal or loose clothing but not an image-driven identification of a particular clothing-covered body portion that creates distorted body measurements with taking supplemental measurement. Applicant further argued Son fails to teach a supplemental/additional measurement to account for distorted body measurements. Last applicant argued that Nishiyama’s correction is not determined based on a supplemental measurement of a selected clothing-distorted body portion.
However, examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant’s arguments seem to be based on whether each applied prior art in 103 rejection fitting for 102 teaching. As primary reference, Aliverti teach inputting captured image into machine learning model for body measurement (paragraphs 0005, 0008, 0094), wherein there are a variety of sensors (paragraphs 0032-0036, 0058). With respect optical sensor, cited Alivertis clearly teach that additional images being taken for measurement, which would be interpreted as supplemental (paragraphs 0015, 0020-0022, 0025-0026). Aliverti may not teach clothing being a factor of distorted body measurement. But secondary references such as Koh, Son, and Nishiyama do teach how clothing from captured images should be factored into body measurements.
Koh teach extracting body measurements from multiple user images thru machine-learning model (abstract, paragraph 0013, which means additional images contribute to supplemental measurement), wherein clothing information would assist for more accurate measurement results (paragraphs 0077, 0149), and wherein the front and side view photos may include images of the user wearing normally fitted clothing (e.g., not extra loose or extra tight), and additionally, the front and side view photos may include images of the user partially clothed (e.g., shirtless), or having a different type of fit (e.g., tight, loose, etc.) depending on the needs of the AI-based algorithms and associated processes (paragraphs 0078, 0117). So, Koh do teach clothing information from additional images affecting accuracy of body measurement results.
In furthering Koh’s clothing information from images, Son teach deriving user’s body whole size from more than one user images thru machine learning model (paragraphs 0005-0007, 0050, which means additional images are captured for supplemental measurement), wherein user’s body part size is derived based on wrinkles (paragraphs 0006-0007, 0035, 0039, 0044-0045, 0048) in user’s clothing identified in user images (paragraph 0045, wrinkles determines whether the clothing is tight, normal, or loose). So, Koh and Son do teach taking clothing information extracted from images to correct or adjust body measurements.
In furthering how clothing information is applied in body measurement correction, Nishiyama teach acquiring body-shape parameters or measurement values based on captured images (paragraphs 0054-0055, 0114, 0119), wherein body-shape parameter or measurement is corrected by multiplying a correction value (paragraphs 0120-0123, when a subject wearing heavy clothing is imaged, the value of body-shape parameter estimated may differ from the actual body shape of the subject, wherein the body-shape parameter estimated should be corrected, by a correction value). So, Koh, Son and Nishiyama do teach factoring clothing information extracted from captured images as supplemental correction to initial body measurements. 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 Koh, Son and Nishiyama into the method of Alivertis, in order to supplementally include clothing information from additional images in correcting initial body measurement with higher accuracy.
Thus, rejection is proper and maintained.
Regarding rejections on claims 19, 22, 31 and 34, applicant argued that cited combination fail to teach either alternative recited in claim 19 in the context of the supplemental steps of the independent claims, and argued the cited combination fails to teach determining the supplemental measurement from movement of the computing device using device sensors in this manner, nor do they teach using a movement-based supplemental measurement to determine the claimed clothing-associated correction factor.
However, examiner respectfully disagrees. As explained in response to claim 16 above, Aliverti in view of Koh, Son and Nishiyama properly teach determining a supplemental measurement of the selected body portion or determining a clothing-associated correction factor based on that supplemental measurement. Koh, Son and Nishiyama already teach clothing may distort body measurement, wherein measurement correction obviously requires determining at least one of location of a start or end of a body part that is at least partially obscured by the clothing. Koh does teach such determination (e.g., annotation lines) in Figs. 6-8. Since supplemental measurement is based on additional images as explained above, teachings of Aliverti, Koh and Nishiyama in using image frames from video (e.g., movement) to determine body measurement would be interpreted as determining supplemental measurement from a movement of the computing device using the one or more sensors of the computing device. After all, the argued claim does not limit what the one or more sensors are and how the movement is involved in determining the supplemental measurement, which would be broadly opened for different interpretations.
Thus, rejections are proper and maintained.
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
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 October 14, 2026
