METHODS AND SYSTEMS FOR WEIGHT CONTROL BY UTILIZING VISUAL TRACKING OF LIVING FACTOR(S)

• US 20120315609A1
• Filed: 06/21/2012
• Published: 12/13/2012
• Est. Priority Date: 06/10/2011
• Status: Abandoned Application

• Current Assignee: Weight Watchers International Incorporated (Artal Group SA)
• Original Assignee: Weight Watchers International Incorporated (Artal Group SA)
• Inventors: Miller-Kovach, Karen, Gerwig, Ute
• Application Number:
  US13/529,275
• Publication Number:
  US 20120315609A1
• Time in Patent Office: Days
• US Class Current: 434/127
• CPC Class Codes: G09B 19/0092 Nutrition
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets

Abstract

A non-therapeutic method for assisting a person to control weight of the person that includes receiving, by a programmed computer system, input data, calculating, in real-time, by the programmed computer system, at least one actual RCV(t) value over a period of time based, at least in part, on the food data of the input data and stored food data; calculating, in real-time, by the programmed computer system, at least one potential RCV(t) value over a period of time; displaying, in real-time, by the programmed computer system, at least one first graphical indicator representative of the at least one actual RCV(t) value over the period of time; and displaying, in real-time, by the programmed computer system, at least one second graphical indicator representative of the at least one potential RCV(t) value over the period of time.

Claims

1. A non-therapeutic method for assisting a person to control weight of the person, comprising:

receiving, by a programmed computer system, input data,

wherein the input data comprises at least one of the following categories of data;

i) food data representative of at least one first food consumed by the person, and

ii) what-if food data representative of at least one second food that the person considers to consume;

calculating, in real-time, by the programmed computer system, at least one actual RCV(t) value over a period of time based, at least in part, on the food data of the input data and stored food data, wherein the stored food data is data about one or more food consumed by the person over the period of time prior to the receipt of the input data;

calculating, in real-time, by the programmed computer system, at least one potential RCV(t) value over a period of time based, at least in part, on the what-if food data of the input data and the stored food data;

displaying, in real-time, by the programmed computer system, at least one first graphical indicator representative of the at least one actual RCV(t) value over the period of time,

wherein the displaying of at least one first graphical indicator is indicative of;

i) whether the at least one actual RCV(t) value over the period of time deviates from a visual representation of a pre-determined optimum value or a pre-determined optimum range of values, and

ii) an actual deviation if the at least one actual RCV(t) value over the period of time actually deviates from a visual representation of the pre-determined optimum value or the pre-determined optimum range of values, and

wherein the displaying of at least one first graphical indicator provides information that assists the person to control the weight of the person; and

displaying, in real-time, by the programmed computer system, at least one second graphical indicator representative of the at least one potential RCV(t) value over the period of time,

wherein the displaying of at least one second graphical indicator is indicative of;

i) whether the at least one potential RCV(t) value over the period of time deviates from the visual representation of the pre-determined optimum value or the pre-determined optimum range of values and

ii) a potential deviation if the at least one potential RCV(t) value over the period of time actually deviates from the visual representation of the pre-determined optimum value or the pre-determined optimum range of values, and

wherein the displaying of at least one second graphical indicator provides the information that assists the person to control the weight of the person.

2. The non-therapeutic method of claim 1, wherein the displaying of the at least one first graphical indicator comprises:

positioning the at least one first graphical indicator at a first position along a scale, wherein the first position corresponds to the calculated at least one actual RCV(t) value over the period of time;

wherein the displaying of the at least one second graphical indicator comprises;

positioning the at least one second graphical indicator at a second position along the scale, wherein the second position corresponds to the calculated at least one potential RCV(t) value over the period of time; and

wherein the visual representation of the pre-determined optimum value or the pre-determined optimum range of values is positioned at a third position along the scale.

3. The non-therapeutic method of claim 2, wherein the at least one actual RCV(t) value is at least one actual RCAV(t) value and wherein the at least one potential RCV(t) value is at least one potential RCAV(t) value.

4. The non-therapeutic method of claim 3, wherein the at least one actual RCAV(t) value is calculated based at least in part on energy density of:

(i) the food data of the input data and (ii) the stored food data,

wherein the at least one potential RCAV(t) value over the period of time is calculated based at least in part on energy density of;

(i) the what-if data of the input data and (ii) the stored food data,

wherein the pre-determined optimum value or the pre-determined optimum range of values are determined from an energy density range of 0.5-1.6 kcal/gram.

5. The non-therapeutic method of claim 4, wherein the at least one actual RCAV(t) value over the period of time is equal to:

(((amount of [kcal]of the at least one first food/100 gram)×

weight of the at least one first food)+((amount of [kcal]of Food(2)of the stored food data/100 gram)×

weight of consumed Food(2)of the stored food data)+ . . . +((amount of [kcal]of Food(n)of the stored food data/100 gram)×

weight of consumed Food(n)of the stored food data))/(weight of the at least one first food+weight of consumed Food (2)of the stored food data+ . . . +weight of consumed Food(n)of the stored food data),

wherein “

n”

is the total number of Foods of the stored food data;

wherein the at least one first food excludes non-dairy beverages;

wherein the at least one potential RCAV(t) value is equal to;

(((amount of [kcal]of the at least one second food/100 gram)×

weight of the at least one second food)+((amount of [kcal]of Food(2)of the stored food data/100 gram)×

weight of consumed Food(2)of the stored food data)+ . . . +((amount of [kcal]of Food(n)of the stored food data/100 gram)×

weight of consumed Food(n)of the stored food data))/(weight of the at least one second food+weight of consumed Food(2)of the stored food data+ . . . +weight of consumed Food(n)of the stored food data); and

wherein the at least one second food excludes non-dairy beverages.

6. The non-therapeutic method of claim 5, wherein the energy density range is 0.8-1.2 kcal/gram.

7. The non-therapeutic method of claim 5, wherein the energy density range is 1-1.25 kcal/gram

8. The non-therapeutic method of claim 2, wherein the method further comprises:

receiving, by the programmed computer system, weight data of the person, and

displaying, by the programmed computer system, at least one second graphical indicator based at least in part on;

determining, by the programmed computer system, that the person maintains the weight or the person loses weight.

9. The non-therapeutic method of claim 2, wherein a first part of the input data is received from the person and a second part of the input data received from a source other than the person.

10. The non-therapeutic method of claim 9, wherein the source is a remote database.

11. The non-therapeutic method of claim 2, wherein the at least one actual RCV(t) value over the period of time is calculated by:

obtaining weight of protein, PRO(m), for the food data of the input data;

obtaining weight of fat, FAT(m), for the food data of the input data;

obtaining weight of non-dietary fiber carbohydrates, CHO(m), for the food data of the input data;

obtaining weight of dietary fiber, DF(m), for the food data of the input data;

determining a whole number value for the food data of the input data by;

1) determining food energy data for the food data of the input data, FED value, based at least in part on one of;

i) W(PRO)×

Cp×

PRO(m), wherein W(PRO) is a metabolic efficiency factor of protein and wherein Cp is a energy conversion factor of protein,

ii) W(FAT)×

Cf×

FAT(m), wherein W(FAT) is a metabolic efficiency factor of fat and wherein Cf is a energy conversion factor of fat,

iii) W(CHO)×

Cc×

CHO(m), wherein W(CHO) is a metabolic efficiency factor of carbohydrate and wherein Cc is a energy conversion factor of carbohydrate, and

iv) W(DF)×

Cdf×

DF(m), wherein W(DF) is a metabolic efficiency factor of dietary fiber and wherein Cdf is a energy conversion factor of dietary fiber;

2) dividing the determined FED value by a factor data obtained from a storage device

and saving the result as whole number value for the food data of the input data;

determining a daily whole number benchmark data for the person;

determining the food data of the input data'"'"'s whole number value;

summing, over the period of time, whole number values of the food data of the input data and the stored food data.

12. The non-therapeutic method of claim 11, wherein W (PRO) is selected from a range 0.7<

=W(PRO)<

=0.9, W(CHO) is selected from a range 0.9<

=W(CHO)<

=0.99, W(FAT) is selected from a range 0.9<

=W(FAT)<

=1.0 and W(DF) is selected from a range 0<

=W(DF)<

=0.5.

13. The non-therapeutic method of claim 11, wherein W (PRO) is selected from a range 0.75<

=W(PRO)<

=0.88, W(CHO) is selected from a range 0.92<

=W(CHO)<

=0.97, W (FAT) is selected from a range 0.95<

=W(FAT)<

=1.0 and W(DF) is selected from a range 0<

=W(DF)<

=0.25, wherein PRO(m), CHO(m), FAT(m) and DF(m) are expressed in grams, and wherein Cp is selected as 4 kilocalories/gram, Cc is selected as 4 kilocalories/gram, Cf is selected as 9 kilocalories/gram and Cdf is selected as 4 kilocalories/gram.

14. The non-therapeutic method of claim 11, wherein the factor data is a whole number selected from a range between 20 and 100.

15. The non-therapeutic method of claim 2, wherein the at least one actual RCV(t) value over the period of time is based on:

calculating p value for the food data of the input data by the following equation;

16. The non-therapeutic method of claim 2, wherein the at least one first graphical indicator, the at least one second graphical indicator, the visual representation of the pre-determined optimum value or the pre-determined optimum range of values, and the scale are displayed on a portable computing device of the person.

17. A programmed computing device, comprising:

a non-transient memory having at least one region for storing computer executable program code; and

at least one processor for executing the program code stored in the non-transient memory, wherein the program code comprises;

code to receive input data,

wherein the input data comprises at least one of the following categories of data;

i) food data representative of at least one first food consumed by the person, and

ii) what-if food data representative of at least one second food that the person considers to consume;

code to calculate, in real-time, at least one actual RCV(t) value over a period of time based, at least in part, on the food data of the input data and stored food data, wherein the stored food data is data about one or more food consumed by the person over the period of time prior to the receipt of the input data;

code to calculate, in real-time, at least one potential RCV(t) value over a period of time based, at least in part, on the what-if food data of the input data and the stored food data;

code to display, in real-time, at least one first graphical indicator representative of the at least one actual RCV(t) value over the period of time,

wherein the displaying of at least one first graphical indicator is indicative of;

i) whether the at least one actual RCV(t) value over the period of time deviates from a visual representation of a pre-determined optimum value or a pre-determined optimum range of values, and

ii) an actual deviation if the at least one actual RCV(t) value over the period of time actually deviates from a visual representation of the pre-determined optimum value or the pre-determined optimum range of values, and

wherein the displaying of at least one first graphical indicator provides information that assists the person to control the weight of the person; and

code to display, in real-time, at least one second graphical indicator representative of the at least one potential RCV(t) value over the period of time,

wherein the displaying of at least one second graphical indicator is indicative of;

i) whether the at least one potential RCV(t) value over the period of time deviates from the visual representation of the pre-determined optimum value or the pre-determined optimum range of values and

ii) a potential deviation if the at least one potential RCV(t) value over the period of time actually deviates from the visual representation of the pre-determined optimum value or the pre-determined optimum range of values, and

wherein the displaying of at least one second graphical indicator provides the information that assists the person to control the weight of the person.

18. The programmed computing device of claim 17, wherein the code to display the at least one first graphical indicator comprises:

code to position the at least one first graphical indicator at a first position along a scale, wherein the first position corresponds to the calculated at least one actual RCV(t) value over the period of time;

wherein the code to display the at least one second graphical indicator comprises;

code to position the at least one second graphical indicator at a second position along the scale, wherein the second position corresponds to the calculated at least one potential RCV(t) value over the period of time; and

wherein the visual representation of the pre-determined optimum value or the pre-determined optimum range of values is positioned at a third position along the scale.

19. The programmed computing device of claim 18, wherein the at least one actual RCV(t) value is at least one actual RCAV(t) value and wherein the at least one potential RCV(t) value is at least one potential RCAV(t) value.

20. The programmed computing device of claim 19, wherein the at least one actual RCAV(t) value is calculated based at least in part on energy density of:

(i) the food data of the input data and (ii) the stored food data,

wherein the at least one potential RCAV(t) value over the period of time is calculated based at least in part on energy density of;

(i) the what-if data of the input data and (ii) the stored food data,

wherein the pre-determined optimum value or the pre-determined optimum range of values are determined from an energy density range of 0.5-1.6 kcal/gram.

21. The programmed computing device of claim 20, wherein the at least one actual RCAV(t) value over the period of time is equal to:

(((amount of [kcal]of the at least one first food/100 gram)×

weight of the at least one first food)+((amount of [kcal]of Food(2)of the stored food data/100 gram)×

weight of consumed Food(2)of the stored food data)+ . . . +((amount of [kcal]of Food(n)of the stored food data/100 gram)×

weight of consumed Food(n)of the stored food data))/(weight of the at least one first food+weight of consumed Food(2)of the stored food data+ . . . +weight of consumed Food(n)of the stored food data),

wherein “

n”

is the total number of Foods of the stored food data;

wherein the at least one first food excludes non-dairy beverages;

wherein the at least one potential RCAV(t) value is equal to;

(((amount of [kcal]of the at least one second food/100 gram)×

weight of the at least one second food)+((amount of [kcal]of Food(2)of the stored food data/100 gram)×

weight of consumed Food(2)of the stored food data)+ . . . +((amount of [kcal]of Food(n)of the stored food data/100 gram)×

weight of consumed Food(n)of the stored food data))/(weight of the at least one second food+weight of consumed Food(2)of the stored food data+ . . . +weight of consumed Food(n)of the stored food data); and

wherein the at least one second food excludes non-dairy beverages.

22. The programmed computing device of claim 21, wherein the energy density range is 0.8-1.2 kcal/gram.

23. The programmed computing device of claim 21, wherein the energy density range is 1-1.25 kcal/gram

24. The programmed computing device of claim 18, wherein the program code further comprises:

code to receive weight data of the person, and

code to display at least one second graphical indicator based at least in part on;

a determination that the person maintains the weight or the person loses weight.

25. The programmed computing device of claim 18, wherein a first part of the input data is received from the person and a second part of the input data received from a source other than the person.

26. The programmed computing device of claim 25, wherein the source is a remote database.

27. The programmed computing device of claim 18, wherein the code to calculate the at least one actual RCV(t) value over the period of time further comprises:

code to obtain weight of protein, PRO(m), for the food data of the input data;

code to obtain weight of fat, FAT(m), for the food data of the input data;

code to obtain weight of non-dietary fiber carbohydrates, CHO(m), for the food data of the input data;

code to obtain weight of dietary fiber, DF(m), for the food data of the input data;

code to determine a whole number value for the food data of the input data, wherein the whole number value for the food data of the input data is determined by;

1) determining food energy data for the food data of the input data, FED value, based at least in part on one of;

i) W(PRO)×

Cp×

PRO(m), wherein W(PRO) is a metabolic efficiency factor of protein and wherein Cp is a energy conversion factor of protein,

ii) W(FAT)×

Cf×

FAT(m), wherein W(FAT) is a metabolic efficiency factor of fat and wherein Cf is a energy conversion factor of fat,

iii) W(CHO)×

Cc×

CHO(m), wherein W(CHO) is a metabolic efficiency factor of carbohydrate and wherein Cc is a energy conversion factor of carbohydrate, and

iv) W(DF)×

Cdf×

DF(m), wherein W(DF) is a metabolic efficiency factor of dietary fiber and wherein Cdf is a energy conversion factor of dietary fiber;

2) dividing the determined FED value by a factor data obtained from a storage device

and saving the result as whole number value for the food data of the input data;

code to determine a daily whole number benchmark data for the person;

code to determine the food data of the input data'"'"'s whole number value;

code to sum, over the period of time, whole number values of the food data of the input data and the stored food data.

28. The programmed computing device of claim 27, wherein W (PRO) is selected from a range 0.7<

=W(PRO)<

=0.9, W(CHO) is selected from a range 0.9<

=W(CHO)<

=0.99, W(FAT) is selected from a range 0.9<

=W(FAT)<

=1.0 and W(DF) is selected from a range 0<

=W(DF)<

=0.5.

29. The programmed computing device of claim 27, wherein W (PRO) is selected from a range 0.75<

=W(PRO)<

=0.88, W(CHO) is selected from a range 0.92<

=W(CHO)<

=0.97, W (FAT) is selected from a range 0.95<

=W(FAT)<

=1.0 and W(DF) is selected from a range 0<

=W(DF)<

=0.25, wherein PRO(m), CHO(m), FAT(m) and DF(m) are expressed in grams, and wherein Cp is selected as 4 kilocalories/gram, Cc is selected as 4 kilocalories/gram, Cf is selected as 9 kilocalories/gram and Cdf is selected as 4 kilocalories/gram.

30. The programmed computing device of claim 18, wherein the at least one actual RCV(t) value over the period of time is based on:

calculating p value for the food data of the input data by the following equation;