Quantitative diet tracking and analysis systems and devices

• US 10,565,897 B2
• Filed: 02/20/2018
• Issued: 02/18/2020
• Est. Priority Date: 02/17/2017
• Status: Active Grant

• Current Assignee: Mindful Projects, LLC
• Original Assignee: Mindful Projects, LLC
• Inventors: Crepp, Justin
• Primary Examiner(s): Gebremichael, Bruk A
• Application Number:
  US15/899,711
• Publication Number:
  US 20180240358A1
• Time in Patent Office: 728 Days
• Field of Search: None
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/062 used as a control parameter for the apparatus
• CPC Class Codes: A63B 2230/203 used as a control parameter for the apparatus
• CPC Class Codes: A63B 2230/206 used as a control parameter for the apparatus
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: G09B 19/0092 Nutrition
• CPC Class Codes: G09B 5/02 with visual presentation of the material to be studied, e.g. using film strip
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The present disclosure provides a system that quantitatively tracks an individual'"'"'s diet and exercise using smart devices (phones, watches, and other wearables). Unlike existing programs, which work in energy units (calories), the present system works in mass units (grams) and satisfies the fundamental physics law of conservation of mass. Food ingested is tracked as well as exercise in order to place the user on a quantitative, custom diet that safely and effectively results in weight loss. In addition to rigorously treating the problem of weight loss by addressing the physics that underlies diet and exercise, the system empirically learns about the user over time such that performance may be optimized.

Claims

1. A system for generating a dietary plan including macronutrient mass value units, wherein the system comprises:

a controller;

a heart rate monitor in communication with the controller;

a memory coupled to the controller, wherein the memory is configured to store program instructions executable by the controller;

wherein in response to executing the program instructions, the controller is configured to;

receive a goal weight value from a user;

over a first period of time, receive weight data from a scale in communication with the controller;

over the first period of time, receive dietary data and exercise data, wherein the exercise data is automatically uploaded from the heart rate monitor;

determine a carbon dioxide mass loss over the first period of time based on the exercise data;

determine a consumed insulin load over the first period of time based on the dietary data;

generate at least two insulin load values based on the consumed insulin load over the first period of time, the carbon dioxide mass loss over the first period of time, the weight data over the first period of time, and the goal weight,

wherein a first insulin load value is a maximum amount of a first insulin load the user can consume in order to achieve the goal weight in a first time length, wherein a second insulin load value is a maximum amount of a second insulin load the user can consume in order to achieve the goal weight in a second time length, wherein the first time length is greater than the second time length; and

display the two generated insulin load values on the user interface, wherein the carbon dioxide mass loss (m_CO2) is determined using formula 1,

m_CO2={dot over (v)}_CO2ρ

r<sup>−

1</sup> 

 

(1)

wherein ρ

is density of air, r=29 g mol<sup>−

1</sup>/44 g mol<sup>−

1</sup>,

wherein {dot over (v)} is calculated by formula (2),

{dot over (v)}=a+b ln(HR) 

 

(2)

wherein {dot over (v)} is the volume of air in liters released by an individual in one minute and a and b are coefficients of the empirical fit based on age, gender, and fitness level, wherein HR is heart rate, wherein a and b are determined by monitoring heart rate as a function of time when the user is fasting based on a mass loss rate that is measured using an accurate scale.

2. The system of claim 1, wherein the controller is further configured to receive via a user interface user profile information, wherein the user profile information includes age, race, gender, weight, height, blood pressure, cholesterol, or combinations thereof, wherein the at least two insulin load values are based on user profile information.

3. The system of claim 1, wherein the dietary data is received from the user.

4. The system of claim 1, wherein the controller is further configured to determine at least one of glycemic index, glycemic score, glycemic load, insulin index, or insulin score based on the dietary data.

5. The system of claim 1, wherein the dietary data is received automatically from a food tracking system.

6. The system of claim 1, wherein the controller is further configured to modify the generated at least two insulin values based on the most recent input dietary data, exercise data, and weight data.

7. The system of claim 1, wherein the controller is further configured to generate a macronutrient value, based on the consumed insulin load, the carbon dioxide mass loss, weight data, and the goal weight, wherein the macronutrient value is a carbohydrate value, protein value, fat value, fiber value, sugar value, water value, glycemic index, glycemic score, glycemic load, insulin index, insulin score, or satiety index.

8. A system for generating a dietary plan including macronutrient mass value units, wherein the system comprises:

a controller;

a heart rate monitor in communication with the controller;

a memory coupled to the controller, wherein the memory is configured to store program instructions executable by the controller;

wherein in response to executing the program instructions, the controller is configured to;

receive a goal weight value from a user;

over a first period of time, receive weight data from a scale in communication with the controller;

over the first period of time, receive dietary data and exercise data, wherein the exercise data is automatically uploaded from the heart rate monitor;

determine a carbon dioxide mass loss over the first period of time based on the exercise data;

determine a consumed insulin load over the first period of time based on the dietary data;

generate at least two insulin load values based on the consumed insulin load over the first period of time, the carbon dioxide mass loss over the first period of time, the weight data over the first period of time, and the goal weight,

wherein a first insulin load value is a maximum amount of a first insulin load the user can consume in order to achieve the goal weight in a first time length, wherein a second insulin load value is a maximum amount of a second insulin load the user can consume in order to achieve the goal weight in a second time length, wherein the first time length is greater than the second time length; and

display the two generated insulin load values on the user interface wherein the carbon dioxide mass loss (m_CO2) is determined using formula 1,

m_CO2={dot over (v)}_CO2ρ

r<sup>−

1</sup> 

 

(1)

wherein ρ

is density of air, r=29 g mol<sup>−

1</sup>/44 g mol<sup>−

1</sup>,

wherein {dot over (v)} is calculated by formula (2),

{dot over (v)}=a+b ln(HR) 

 

(2)

wherein {dot over (v)} is the volume of air in liters released by an individual in one minute and a and b are coefficients of the empirical fit based on age, gender, and fitness level, wherein HR is heart rate, wherein a and b are determined by monitoring heart rate as a function of time when the user is fasting based on a mass loss rate that is measured using an accurate scale.