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 .
Information Disclosure Statement
1. The information disclosure statement (IDS) submitted on 11/12/2025 and 11/08/2024 have been considered by the examiner.
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
3. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,138,073 (herein referred to as Chhatkuli). Although the claims at issue are not identical, they are not patentably distinct from each other because
Regarding claim 1, Chhatkuli discloses A computer-implemented method for predicting a body composition of a user, the method comprising: receiving one or more user images, and one or more user parameters (claim 1 – col. 27, lines 40-43);
generating one or more key points based on the one or more user images (claim 1 – col. 27, lines 44-45); and
generating a prediction of the body composition of the user based on the one or more key points and the one or more user parameters, using a body composition deep learning network (DLN) (claim 1 – col. 27, lines 46-49),
wherein the body composition is a quantitative measurement of one or more constituent components of the body (claim 1 – col. 27, lines 50-52), and
wherein the body composition DLN was trained to generate predictions of the quantitative measurement of the one or more constituent components of the body (claim 1 – col. 27, lines 53-55).
Regarding claim 2, claim 2 has been analyzed and rejected as per claim 2 of Chhatkuli.
Regarding claim 3, claim 3 has been analyzed and rejected as per claim 1 of Chhatkuli (claim 1 – col. 27, lines 56-64).
Regarding claim 4, claim 4 has been analyzed and rejected as per claim 3 of Chhatkuli.
Regarding claim 5, claim 5 has been analyzed and rejected as per claim 4 of Chhatkuli.
Regarding claim 6, claim 6 has been analyzed and rejected as per claim 5 of Chhatkuli.
Regarding claim 7, claim 7 has been analyzed and rejected as per claim 6 of Chhatkuli.
Regarding claim 8, claim 8 has been analyzed and rejected as per claim 7 of Chhatkuli.
Regarding claim 9, claim 9 has been analyzed and rejected as per claim 8 of Chhatkuli.
Regarding claim 10, claim 10 has been analyzed and rejected as per claim 9 of Chhatkuli.
Regarding claim 11, claim 11 has been analyzed and rejected as per claim 10 of Chhatkuli.
Regarding claim 12, claim 12 has been analyzed and rejected as per claim 11 of Chhatkuli.
Regarding claim 13, claim 13 has been analyzed and rejected as per claim 12 of Chhatkuli.
Regarding claim 14, claim 14 has been analyzed and rejected as per claim 13 of Chhatkuli.
Regarding claim 15, claim 15 has been analyzed and rejected as per claim 14 of Chhatkuli.
Regarding claim 16, claim 16 has been analyzed and rejected as per claim 15 of Chhatkuli.
Regarding claim 17, claim 17 has been analyzed and rejected as per claim 16 of Chhatkuli.
Regarding claim 18, claim 18 has been analyzed and rejected as per claim 17 of Chhatkuli.
Regarding claim 19, claim 19 has been analyzed and rejected as per claim 16 of Chhatkuli.
Regarding claim 20, claim 20 has been analyzed and rejected as per claim 18 of Chhatkuli.
Claim Rejections - 35 USC § 102
4. 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.
5. 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.
6. Claims 1-2, 5-7, 10-11, 17-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by El-Sallam, WIPO Publication No. 2020/132713 A1, Publication Date - July 02, 2020.
Regarding claim 1, El-Sallam discloses a method comprising:
A computer-implemented method for predicting a body composition of a user (para 0138 - The present invention will be described with particular reference to analyzing a human body to provide an estimation of an individual's three-dimensional (3D) body shape and its associated body composition and health and wellness risks using (without limiting the generality) one or more of images and/or features and/or any type of data, representation, or information that is capable of one or more of defining, explaining or describing an ecologically and physically valid model or analogy of the body shape and its molecular compositions), the method comprising:
receiving one or more user images (para 0138 - images, para 0148-0149 - collected images, para 0216 - capturing human 2D or 3D imagery data), and one or more user parameters (para 0138 - data, paras 0196, 0208, 0210, 0214, 0255 - parameters of the users such as gender, age, ethnicity, fitness, medical history, medical physiology, height, weight);
generating one or more key points based on the one or more user images (para 0148-0149, 0151, 0220, 0224, 0237-0238 - Landmarks (key points); and figures 7 and 8); and
generating a prediction of the body composition of the user based on the one or more key points and the one or more user parameters, using a body composition deep learning network (DLN), wherein the body composition is a quantitative measurement of one or more constituent components of the body, and wherein the body composition DLN was trained to generate predictions of the quantitative measurement of the one or more constituent components of the body (para 0138 - estimation of an individual's three-dimensional (3D) body shape and its associated body composition, para 0141 - using machine learning (ML) to estimate body composition; para 0142 - Machine learning techniques may include Convolution Neural Network (CNN) technology - which is deep learning network (DLN); para 0152 - use of additional CV, ML and Al models and approaches (driven by the data and machine learned model trained offline), to facilitate actions including those to process the human imagery and the related human features, participant characteristics, medical physiology and epidemiological data captured and processed online in order to estimate body composition, health and wellness risk and any other parameters in the dataset being collected offline; para 0196 - parameters of the users such as gender, age, ethnicity, fitness, medical history, and medical physiology will improve accuracy when used in ML process; para 0238 - estimation of joint centres and anatomical landmarks is an advantageous feature of this process of the embodiment of the invention, resulting in an improvement in the accuracy of the body composition and body measurement estimates. It should also be noted that the CV/ML/AI models developed can estimate a higher number of joint centres; para 0229 - total body composition (i.e. total, segment and region lean mass and fat mass (which is MBF)); para 0279 - percent body fat (which is PBF); para 0283 - estimated body fat, estimated low muscle lean mass; para 0154 - lean muscle mass; para 0239 - whole body and segment lean mass (which is LBM).
Regarding claim 2, El-Sallam teaches "The computer-implemented method of claim 1, wherein the generating one or more key points comprises using a key point DLN" (paras 0237-0238 - using Machine Learning (ML) models to estimate landmarks).
Regarding claim 5, El-Sallam teaches "The computer-implemented method of claim 1, wherein the body composition is an indicator of muscle, bone, water content, or body fat of the user" (see the citations made in the analysis of claim 1).
Regarding claim 6, El-Sallam teaches "The computer-implemented method of claim 1, wherein the body composition is selected from the group consisting of a Lean Body Mass (LBM), a Mass Body Fat (MBF), a Soft Lean Mass (SLM), and a Percent Body Fat (PBF) (see the citations made in the analysis of claim 1).
Regarding claim 7, El-Sallam teaches "The computer-implemented method of claim 1, wherein the one or more user parameters are selected from the group consisting of a height, a weight, a gender, and an age" (see the citations made in the analysis of claim 1).
Regarding claim 10, El-Sallam teaches "The computer-implemented method of claim 1, wherein the one or more user images comprise at least a front image and a side image." (paras 0108, 0237, 0257).
Regarding claim 11, El-Sallam teaches "The computer-implemented method of claim 1, wherein the one or more user images represent a fully clothed user (para 0217 - standardized outfit; standardized outfit will be considered here as fully clothed; figures 3 and 18 also cite clothed user; para 0255 - clothes size).
Regarding claim 17, claim 17 has been similarly analyzed and rejected as per claim 1 citations (further see para 0031).
Regarding claim 18, claim 18 has been similarly analyzed and rejected as per claim 2 analysis.
Regarding claim 20, claim 20 has been similarly analyzed and rejected as per claim 1 citations (further see para 0031).
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. Claims 8-9, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam, WIPO Publication No. 2020/132713 A1, Publication Date - July 02, 2020, and further in view of De Brouwer et al., U.S. Patent Publication No. 2018/0289334 A1.
Regarding claim 8, claim 8 recites "The computer-implemented method of claim 1, further comprising: extracting a head image of the user from the one or more user images, wherein generating the body composition using the body composition DLN is further based on the head image". El- Sallam as cited in the analysis of claim 1 teaches generating a prediction of the body composition of the user based on the one or more key points and the one or more user parameters, using a body composition deep learning network (DLN), using the one or more images, where height and weight are one or more of the user parameters used. El-Sallam teaches extracting body part segments from the image(s) of the body (para 0060) but does not explicitly teach extracting a head image of the user from the one or more user images, wherein generating the body composition using the body composition DLN is further based on the head image. However, De Brouwer teaches a DNN model which is used to predict height and weight from the captured image, where the captured image could be full body image, upper body image, or facial image (para 0025, figure 6), where facial image is a head image; and further teaches extracting facial landmarks (paras 0007, 0009, 0036). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of extracting the head image and estimating height and weight from the head image as taught by De Brouwer in the invention of El-Sallam. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of extracting the head image and estimating height and weight from the head image as taught by De Brouwer in the invention of El-Sallam, in order to automatically and conveniently obtain accurate height and weight values, to understand user's health condition more conveniently and more reliably (see De Brouwer - paras 0006 and 0009). Further adding, El-Sallam already taught generating the body composition using DLN based on user parameters such as weight and height, De Brouwer makes it possible to calculate/estimate height and weight parameters from the face image itself rather than whole body, making process little faster and accurate.
Regarding claim 9, the combined invention of El-Sallam and De Brouwer teaches "The computer-implemented method of claim 8, wherein the head image is a face image, and wherein the face image is extracted using one of a face extraction computer vision module and a face extraction DLN" (scc De Brouwer - para 0007, 0009, 0036).
Regarding claim 12, claim 12 recites "The computer-implemented method of claim 1, further comprising: calculating a height of the user from the one or more user images; and utilizing the height as one of the user parameters" El-Sallam as cited in the analysis of claim 1 teaches utilizing height as one of the user parameters, but does not explicitly teach calculating a height of the user from the one or more user images. However, De Brouwer teaches a DNN model which used to predict age, height and weight from the captured image, where the captured image could be full body image, upper body image, or facial image (para 0025, figure 6). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of calculating a height of the user from the one or more user images as taught by De Brouwer in the invention of El-Sallam. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of calculating a height of the user from the one or more user images as taught by De Brouwer in the invention of El-Sallam, in order to automatically and conveniently obtain accurate height values, to understand user's health condition more conveniently and more reliably (see De Brouwer - paras 0006 and 0009).
Regarding claim 13, claim 13 recites "The computer-implemented method of claim 1, further comprising: calculating a weight of the user from the one or more user images; and utilizing the weight as one of the user parameters." El-Sallam as cited in the analysis of claim 1 teaches utilizing weight as one of the user parameters, but does not explicitly teach calculating a weight of the user from the one or more user images. However, De Brouwer teaches a DNN model which used to predict age, height and weight from the captured image, where the captured image could be full body image, upper body image, or facial image (para 0025, figure 6). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of calculating a weight of the user from the one or more user images as taught by De Brouwer in the invention of El-Sallam. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of calculating a weight of the user from the one or more user images as taught by De Brouwer in the invention of El-Sallam, in order to automatically and conveniently obtain accurate weight values, to understand user's health condition more conveniently and more reliably (see De Brouwer - paras 0006 and 0009).
10. Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over El-Sallam, WIPO Publication No. 2020/132713 A1, Publication Date - July 02, 2020, and further in view of Ferrantelli et al., U.S. Patent Publication No. 2019/0347817.
Regarding claim 14, claim 14 recites "The computer-implemented method of claim 1, further comprising: generating a set of one or more scaled external body feature dimensions based on the one or more key points, wherein generating the body composition using the body composition DLN is further based on the set of one or more scaled external body feature dimensions". El-Sallam as cited teaches measuring mass, height, waist, hips, chest, thigh, bicep, calf, and inseam (paras 0108 and 0120) but does not explicitly talk in terms of dimensions. However, Ferrantelli discloses generating a set of one or more scaled external body feature dimensions based on the one or more key points, wherein generating the body composition using the body composition DLN is further based on the set of one or more scaled external body feature dimensions (figures 17-19; paras 60-61 - generating of scaled/measured external body feature dimensions based on digitization points of landmarks; see figure 18 - Body Fat %, Lean body mass, waist-to-hip ratio (body composition generated based on scaled external body feature dimensions). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of generating a set of one or more scaled external body feature dimensions based on the one or more key points as taught by Ferrantelli in the invention of El-Sallam. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of generating a set of one or more scaled external body feature dimensions based on the one or more key points as taught by Ferrantelli in the invention of El-Sallam, in order to automatically and conveniently obtain accurate body composition measurements, as dimensional measurements will add to very accurate readings.
Regarding claim 15, claim 15 has been analyzed as per citations made in the analysis of claim 16.
Regarding claim 16, the combined invention of El-Sallam and Ferrantelli teaches "The computer-implemented method of claim 15, wherein the one or more external body feature dimensions are selected from the group consisting of a height, a belly depth, a belly length, a belly waist, a bust girth, a hip girth, a neck girth, a thigh girth, an under bust girth, an upper arm girth, a waist girth, and a wrist girth". (Fl-Sallam as cited teaches measuring mass, height, waist, hips, chest, thigh, bicep, calf, and inseam (paras 0108 and 0120); Ferrantelli - figures 17-18 - neck girth, belly waist, and more)
11. The closest prior arts of record do not teach the subject matter as recited in claims 3-4 and 19 in combination with other limitations of claims they depend on.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
June 16, 2026
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