# US 12,004,871 B1 · Personalized three-dimensional body models and body change journey

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- Number: US 12,004,871 B1
- Document ID: US12004871B1
- Status: Granted
- Kind: B1
- Application number: 16/985,922
- Filed: 2020-08-05
- Granted: 2024-06-11
- Earliest claimed: 2020-08-05
- Assignees: Amazon Technologies, Inc.
- Applicants: Amazon Technologies, Inc.
- Inventors: Cyrus Fazeli; Apoorv Chaudhri; Ambrish Tyagi; Prakash Ramu; Amit Kumar Agrawal
- Web page: https://patentgrep.com/patent/US12004871B1

## CPC

- A61B 5/4872: {Body fat}
- A61B 5/004: {adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062)}
- A61B 5/0064: {Body surface scanning}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- G06N 3/08: Learning methods
- G06N 20/00: Machine learning
- G06T 7/0012: {Biomedical image inspection}
- G06T 15/005: {General purpose rendering architectures}
- G06T 15/80: Shading
- G06T 17/00: Three-dimensional [3D] modelling for computer graphics
- G06T 17/10: Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
- G06T 19/20: Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 20/60: relating to nutrition control, e.g. diets
- G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 2503/10: Athletes
- G06T 2207/20081: Training; Learning
- G06T 2207/30004: Biomedical image processing
- G06T 2207/30196: Human being; Person
- G06T 2219/2021: Shape modification
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Described are systems and methods directed to a body change journey that utilizes collected two-dimensional (“2D”) body images of a body of a user, generation and presentation of a personalized three-dimensional (“3D”) body model of the body of the user based on those 2D body images, the generation and presentation of different predicted personalized 3D body models of the body of the user at different body measurements (e.g., different body fat percentages, different muscle mass amounts, different body weights, etc.), and the generation of a body change journey that guides the user from their current body model to a selected target body model that is represented by a target personalized 3D body model of the body of the user.

## Claims

### Claim 1

A computer-implemented method, comprising:
determining a current body composition of a body, the current body composition indicating at least a current body fat measurement value of the body and personalized three-dimensional (“3D”) body parameters corresponding to the body;
generating, based at least in part on the current body composition of the body, a current personalized 3D body model of the body;
determining a target body composition for the body, the target body composition indicating a least a target body fat measurement value for the body and target personalized 3D body parameters for the body;
generating, based at least in part on the current body composition and the target personalized 3D body parameters, a target personalized 3D body model of the body, the target personalized 3D body model representative of a predicted appearance of the body at the target body fat measurement value and with the target body composition;
generating a body change journey that includes at least a nutrition plan and an exercise plan to be followed to change the body from the current body composition to the target body composition, the body change journey having a time duration;
determining a plurality of check-in points during the time duration of the body change journey;
for each of the plurality of check-in points:
determining a predicted personalized check-in body composition predicted for the body at the check-in point; and
generating, based at least in part on the current personalized 3D body model, the target personalized 3D body model, and the predicted personalized check-in body composition predicted for the body at the check-in point, a predicted personalized check-in 3D body model representative of a predicted appearance of the body at the check-in point and having the predicted personalized check-in body composition; and
presenting, on a display, the current personalized 3D body model, the target personalized 3D body model, and at least one of the predicted personalized check-in 3D body models to visually illustrate the change between the current body composition and the target body composition.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein generating each of the plurality of predicted personalized check-in 3D body models further includes:
integrating at least the current personalized 3D body model and the target personalized 3D body model to produce the predicted personalized check-in 3D body model.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, further comprising:
determining, based at least in part on the current body composition of the body, a cohort for the body; and
wherein generating the body change journey includes providing at least the current body composition and the target body composition to a machine learning algorithm trained for the cohort and receiving as an output from the machine learning algorithm at least a portion of the body change journey.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3,
wherein the output from the machine learning algorithm includes a nutrition model indicating a daily calorie limit and daily macronutrient amounts; and
the method further comprising:
determining personalized food preferences corresponding to the body; and
wherein generating the nutrition plan includes generating a personalized nutrition plan based at least in part on the personalized food preferences corresponding to the body and the nutrition model.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, further comprising:
monitoring, at a check-in point of the plurality of check-in points, a progress of the body as the body progresses through the body change journey, wherein the monitoring includes at least:
receiving at least one 2D body image of the body generated at a point during the time duration corresponding to the check-in point;
processing the at least one 2D body image to generate a personalized check-in 3D body model; and
presenting the personalized check-in 3D body model with a predicted personalized check-in 3D body model corresponding to the check-in point to visually represent an actual progress of the body compared to a predicted progress of the body at the check-in point.

### Claim 6

A computing system, comprising:
one or more processors; and
a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors to at least:
determine at least one target body measurement value for a body measurement of a body;
determine, based at least in part on the target body measurement value, a target personalized body composition corresponding to a predicted appearance of the body with the target body measurement value;
generate, based at least in part on the target personalized body composition, a target personalized 3D body model representative of the predicted appearance of the body with the target body measurement value;
generate, based at least in part on the target body measurement value and a current body measurement value of the body, a body change journey to guide the body through a change from the current body measurement value to the target body measurement value, the body change journey having a time duration; and
present at least a portion of the target personalized 3D body model and at least a portion of the body change journey.

### Claim 7 (depends on 6)

The computing system of claim 6, wherein the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
receive a target body image; and
process the target body image to determine the at least one target body measurement value.

### Claim 8 (depends on 7)

The computing system of claim 7, wherein:
the target body image is an image of a second body, wherein the second body is different than the body; and
wherein the program instructions that, when executed by the one or more processors to determine the at least one target body measurement value, further include instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
process the target body image to determine an identity of the second body; and
determine, based at least in part on the identity of the second body, the at least one target body measurement value.

### Claim 9 (depends on 8)

The computing system of claim 8, wherein the second body is a body of a celebrity.

### Claim 10 (depends on 6)

The computing system of claim 6, wherein the target body measurement value is at least one of a weight of the body, a body fat measurement value of the body, or a lean body mass of the body.

### Claim 11 (depends on 6)

The computing system of claim 6, wherein the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
generate a current personalized 3D body model of the body based at least in part on a current body composition of the body, wherein the current personalized 3D body model is different than the target personalized 3D body model.

### Claim 12 (depends on 6)

The computing system of claim 6, wherein the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
generate a predicted personalized check-in 3D body model, wherein the predicted personalized check-in 3D body model is representative of the body having a second body measurement value between the target body measurement value and the current body measurement value.

### Claim 13 (depends on 6)

The computing system of claim 6, wherein:
the program instructions, that when executed by the one or more processors, further cause the one or more processors to at least assign the body to a cohort, the cohort indicative of a plurality of bodies having similar body traits to the body; and
the program instructions, that when executed by the one or more processors to cause the one or more processors to generate the body change journey, further include instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
generate, based at least in part on the target body measurement value, the current body measurement value, and the cohort, the body change journey.

### Claim 14 (depends on 13)

The computing system of claim 13, wherein the similar body traits include one or more of an age, a demographics, an exercise level, a body composition, a known health issue, or current activity level.

### Claim 15 (depends on 6)

The computing system of claim 6, wherein the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
determine, during the time duration, an actual progress of the body;
determine, based at least in part on a comparison of the actual progress of the body with a predicted progress of the body, that the body is not progressing as predicted; and
in response to determination that the body is not progressing as predicted, change at least one of a portion of the body change journey or the time duration.

### Claim 16

A method, comprising:
determining current body composition of a body, the current body composition indicating at least one current body measurement value of the body;
generating, based at least in part on the current body composition, a current personalized three-dimensional (“3D”) body model representative of a current appearance of the body;
determining a target body measurement value that is different than the at least one current body measurement value;
determining, using one or more neural networks, a target personalized body composition for the body with the target body measurement value;
generating, based at least in part on the target personalized body composition, a target personalized 3D body model representative of a predicted appearance of the body with the target body measurement value;
generating a body change journey to be followed to change the at least one current body measurement value of the body to the target body measurement value, the body change journey having a time duration; and
presenting the current personalized 3D body model and the target personalized 3D body model to visually illustrate the change between the at least one current body measurement value and the target body measurement value.

### Claim 17 (depends on 16)

The method of claim 16, wherein the body change journey indicates at least one of an intensity of the body change journey, a likelihood of success of the user completing the body change journey, a personalized food plan that is personalized based on food preferences associated with the body or a personalized exercise plan that is personalized based on exercise preferences associated with the body.

### Claim 18 (depends on 16)

The method of claim 16, wherein determining the target body measurement value further includes:
receiving an image of a second body; and
processing the image to determine, based at least in part on the second body, the target body measurement value.

### Claim 19 (depends on 18)

The method of claim 18, wherein processing the image includes:
processing the image to determine an identity of the second body;
determining, based at least in part on the identity of the second body, a second body measurement value of the second body; and
generating the target body measurement value based at least in part on the second body measurement value of the second body.

### Claim 20 (depends on 16)

The method of claim 16, further comprising:
periodically monitoring a progress of the body during the time duration.