Personalized Food Identification and Nutrition Guidance System

• US 20110318717A1
• Filed: 11/28/2010
• Published: 12/29/2011
• Est. Priority Date: 06/23/2010
• Status: Abandoned Application

• Current Assignee: Laurent Adamowicz
• Original Assignee: Laurent Adamowicz
• Inventors: Adamowicz, Laurent
• Application Number:
  US12/954,881
• Publication Number:
  US 20110318717A1
• Time in Patent Office: Days
• US Class Current: 434/127
• CPC Class Codes: G09B 19/0092 Nutrition
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets

Abstract

A user presents a food item to a device. In response, the device provides the user with advice about whether or not to eat the food item. The advice is also based on personalized food preferences or restrictions and medical conditions of the user. The advice may also be based on food-related data obtained from other users, such as personalized food preferences, restrictions, medical conditions, and food intake histories of such users. The user may accept or reject the advice provided to the user by the system. If the user rejects the advice, the device may identify one or more alternative food items within the vicinity of the device or any other location requested by the user and provide the user with advice about whether or not to eat the alternative food items. The user may accept or reject this alternative advice.

Claims

1. A computer-implemented method for use with a device being used by a user, the method comprising:

(1) receiving input from a user representing a presentation from the user of an initial food item within the vicinity of a particular location;

(2) using the device to;

(a) sense the initial food item; and

(b) develop food identification data descriptive of the initial food item; and

(3) developing initial personalized nutrition advice for the user related to the initial food item, based on at least one of;

(a) the food identification data; and

(b) personalized food data associated with the user.

2. The method of claim 1, further comprising:

(4) providing the initial personalized nutrition advice to the user.

3. The method of claim 2, wherein (4) comprises providing the initial personalized nutrition advice to the user using at least one of text, voice, photo, video, light, vibration, and ring tone.

4. The method of claim 2, further comprising:

(5) receiving, from the user, an input indicating whether the user accepts the initial personalized nutrition advice.

5. The method of claim 4, further comprising:

(6) recording the user'"'"'s input indicating whether the user accepts the initial personalized nutrition advice in a food intake history of the user.

6. The method of claim 4, wherein the user'"'"'s input indicating whether the user accepts the initial personalized nutrition advice indicates that the user rejects the initial food item, and wherein the method further comprises:

(6) identifying alternative food identification data descriptive of at least one alternative food item within the vicinity of the particular location;

(7) developing alternative personalized nutrition advice for the user related to the at least one alternative food item, based on at least one of;

(a) the alternative food identification data descriptive of the at least one alternative food item; and

(b) the personalized food data associated with the user;

(8) providing the alternative personalized nutrition advice for the at least one alternative food item to the user.

7. The method of claim 6, wherein (6) comprises identifying the at least one alternative food item using data from an external source.

8. The method of claim 7, wherein (6) comprises identifying the at least one alternative food item by:

(6)(a) identifying the current geographic location of the device; and

(6)(b) identifying at least one alternative food item within the vicinity of the current geographic location of the device using an external data source of geo-referenced food data.

9. The method of claim 1, wherein the user presents the initial food item to the device by taking at least one of a picture and a video of the initial food item.

10. The method of claim 1, wherein the user presents the initial food item to the device by reading a bar code associated with the initial food item.

11. The method of claim 1, wherein the user presents the initial food item to the device by reading an RFID tag associated with the initial food item.

12. The method of claim 1, wherein the user presents the initial food item to the device by providing a description of the initial food item to the device.

13. The method of claim 1, wherein (2)(a) comprises sensing the initial food item to obtain food sensed data, and wherein (2)(b) comprises identifying the food identification data based on the food sensed data.

14. The method of claim 1, wherein (2)(a) comprises sensing the initial food item using at least one of the following technologies:

Gas chromatography (GC), GC-mass spectrometry (GCMS), mass spectrometry in non-vacuum environment, Atmospheric Pressure Chemical Ionization (APCI), Micro Electro-Mechanical Systems (MEMS), ion mobility spectroscopy, dielectrophoresis, infrared spectroscopy, near-infrared spectroscopy, chemical and conductometric sensors, electronic nose sensors, synthetic olfaction sensors, solid state sensors, Raman sensors, photo analysis, 3D photo modeling, video analysis, biosensors, bio-mimetic systems, photometric sensors, bar code scanning, reading of Radio Frequency Identification (RFID) tags, micro-cantilevers, nano-cantilevers, and any miniaturized equipment developed to smell gas molecules such as volatile organic compounds and peptides.

15. The method of claim 1, wherein (2)(a) comprises sensing the initial food item using at least one of the above technologies in multivariate analysis.

16. The method of claim 1, wherein the initial personalized nutrition advice comprises advice to eat the initial food item.

17. The method of claim 1, wherein the initial personalized nutrition advice comprises advice not to eat the initial food item.

18. The method of claim 1, wherein (3) comprises developing the initial personalized nutrition advice based additionally on a food intake history of the user.

19. The method of claim 18, wherein the food intake history of the user includes a record of food eaten by the user, a record of food rejected by the user, and a record of food leftover by the user after eating a meal.

20. The method of claim 1, wherein (3) comprises developing the initial personalized nutrition advice based additionally on a particular location.

21. The method of claim 20, wherein the particular location comprises a current geographic location of the device.

22. The method of claim 21, wherein (3) further comprises identifying the current geographic location of the device using a global positioning system (GPS) function within the device.

23. The method of claim 20, wherein the particular location comprises a geographic location specified by the user which differs from the current geographic location of the device.

24. The method of claim 1, wherein all components which perform (1)-(3) are contained within the device.

25. The method of claim 1, wherein the personalized food data associated with the user include at least one of allergies, dietary restrictions, medical conditions, taste preferences, and food intolerances associated with the user.

26. The method of claim 1, wherein the personalized food data associated with the user include at least one of the following quantities associated with the user:

a minimum amount of calories, a maximum amount of calories, a minimum amount of proteins, a maximum amount of proteins, a minimum amount of fiber, a maximum amount of fiber, a minimum amount of sugar, a maximum amount of sugar, a minimum amount of salt, a maximum amount of salt, a minimum amount of trans fat, a maximum amount of trans fat, a minimum amount of saturated fat, and a maximum amount of saturated fat.

27. The method of claim 1, wherein the initial personalized nutrition advice comprises advice not to eat the initial food item because the initial food item is inconsistent with the personalized food data associated with the user.

28. The method of claim 1, wherein the initial personalized nutrition advice comprises advice to eat the initial food item because the initial food item is consistent with the personalized food data associated with the user.

29. The method of claim 1, further comprising:

(4) providing the user with information about at least one of contents, ingredients, and nutrients of the initial food item.

30. The method of claim 1, wherein (3) comprises:

(3)(a) identifying at least one minimum or maximum personalized periodic nutritional intake amount associated with the user;

(3)(b) determining the impact of the user eating the initial food item on the at least one minimum or maximum personalized periodic nutritional intake amount within a particular period of time; and

(3)(c) developing the initial personalized nutrition advice for the user, indicating whether the user should eat the initial food item, based on the determined impact on the at least one minimum or maximum personalized periodic nutritional intake amount associated with the user.

31. The method of claim 30, wherein the initial personalized nutrition advice indicates what the user'"'"'s nutritional intake amounts will be for the particular period of time if the user eats the initial food item.

32. The method of claim 30, wherein the initial personalized nutrition advice indicates whether any of the user'"'"'s periodic nutritional intake amounts will exceed their minimum or maximum, if the user eats the initial food item.

33. The method of claim 30, wherein (3)(c) comprises:

(3)(c)(i) developing initial personalized nutrition advice which advises the user not to eat the initial food item;

(3)(c)(ii) automatically identifying at least one alternative food item; and

(3)(c)(iii) developing alternative personalized nutrition advice which advises the user to eat the at least one alternative food item.

34. The method of claim 30, wherein (3) further comprises:

(3)(d) receiving input from the user indicating that the user has chosen to eat the initial food item; and

(3)(e) updating nutritional intake amounts associated with the particular period of time based on nutrition information associated with the initial food item.

35. The method of claim 30, wherein (3) further comprises:

(3)(d) updating the current values of the user'"'"'s personalized periodic nutritional intake amounts to reflect physical activity of the user.

36. The method of claim 35, wherein the updating is performed in response to input received from the user.

37. The method of claim 35, wherein the updating is performed without input of the user.

38. The method of claim 37, wherein the updating is performed using a global positioning system (GPS) to track the distance and speed traveled by the user in a particular period of time.

39. The method of claim 30, wherein (3) further comprises:

(3)(d) receiving input from the user indicating that the user has decided not to completely eat the initial food item;

(3)(e) updating the current values of the user'"'"'s personalized periodic nutritional intake amounts to reflect the quantity of food leftovers of the user.

40. A computer system including at least one processor and at least one computer readable medium tangibly storing computer-readable instructions, wherein the at least one processor is adapted to execute the computer-readable instructions to perform a method for use with a device being used by a user, the method comprising:

(1) receiving input from a user representing a presentation from the user of an initial food item within the vicinity of a particular location;

(2) using the device to;

(c) sense the initial food item; and

(d) develop food identification data descriptive of the initial food item; and

(3) developing initial personalized nutrition advice for the user related to the initial food item, based on at least one of;

(c) the food identification data; and

(d) personalized food data associated with the user.

41. The computer system of claim 40, wherein the method further comprises:

(4) providing the initial personalized nutrition advice to the user.

42. The computer system of claim 41, wherein (4) comprises providing the initial personalized nutrition advice to the user using at least one of text, voice, photo, video, light, vibration, and ring tone.

48. The computer system of claim 40, wherein the user presents the initial food item to the device by taking at least one of a picture and a video of the initial food item.

49. The computer system of claim 40, wherein the user presents the initial food item to the device by reading a bar code associated with the initial food item.

50. The computer system of claim 40, wherein the user presents the initial food item to the device by reading an RFID tag associated with the initial food item.

51. The computer system of claim 40, wherein the user presents the initial food item to the device by providing a description of the initial food item to the device.

52. The computer system of claim 40, wherein (2)(a) comprises sensing the initial food item to obtain food sensed data, and wherein (2)(b) comprises identifying the food identification data based on the food sensed data.

53. The computer system of claim 40, wherein (2)(a) comprises sensing the initial food item using at least one of the following technologies:

Gas chromatography (GC), GC-mass spectrometry (GCMS), mass spectrometry in non-vacuum environment, Atmospheric Pressure Chemical Ionization (APCI), Micro Electro-Mechanical Systems (MEMS), ion mobility spectroscopy, dielectrophoresis, infrared spectroscopy, near-infrared spectroscopy, chemical and conductometric sensors, electronic nose sensors, synthetic olfaction sensors, solid state sensors, Raman sensors, photo analysis, 3D photo modeling, video analysis, biosensors, bio-mimetic systems, photometric sensors, bar code scanning, reading of Radio Frequency Identification (RFID) tags, micro-cantilevers, nano-cantilevers, and any miniaturized equipment developed to smell gas molecules such as volatile organic compounds and peptides.

54. The computer system of claim 40, wherein (2)(a) comprises sensing the initial food item using at least one of the above technologies in multivariate analysis.

55. The computer system of claim 40, wherein the initial personalized nutrition advice comprises advice to eat the initial food item.

56. The computer system of claim 40, wherein the initial personalized nutrition advice comprises advice not to eat the initial food item.

57. The computer system of claim 40, wherein (3) comprises developing the initial personalized nutrition advice based additionally on a food intake history of the user.

58. The computer system of claim 57, wherein the food intake history of the user includes a record of food eaten by the user, a record of food rejected by the user, and a record of food leftover by the user after eating a meal.

59. The computer system of claim 40, wherein (3) comprises developing the initial personalized nutrition advice based additionally on a particular location.

60. The computer system of claim 59, wherein the particular location comprises a current geographic location of the device.

61. The computer system of claim 60, wherein (3) further comprises identifying the current geographic location of the device using a global positioning system (GPS) function within the device.

62. The computer system of claim 59, wherein the particular location comprises a geographic location specified by the user which differs from the current geographic location of the device.

63. The computer system of claim 40, wherein all components which perform (1)-(3) are contained within the device.

64. The computer system of claim 40, wherein the personalized food data associated with the user include at least one of allergies, dietary restrictions, medical conditions, taste preferences, and food intolerances associated with the user.

65. The computer system of claim 40, wherein the personalized food data associated with the user include at least one of the following quantities associated with the user:

a minimum amount of calories, a maximum amount of calories, a minimum amount of proteins, a maximum amount of proteins, a minimum amount of fiber, a maximum amount of fiber, a minimum amount of sugar, a maximum amount of sugar, a minimum amount of salt, a maximum amount of salt, a minimum amount of trans fat, a maximum amount of trans fat, a minimum amount of saturated fat, and a maximum amount of saturated fat.

66. The computer system of claim 40, wherein the initial personalized nutrition advice comprises advice not to eat the initial food item because the initial food item is inconsistent with the personalized food data associated with the user.

67. The computer system of claim 40, wherein the initial personalized nutrition advice comprises advice to eat the initial food item because the initial food item is consistent with the personalized food data associated with the user.

68. The computer system of claim 40, wherein the method further comprises:

(4) providing the user with information about at least one of contents, ingredients, and nutrients of the initial food item.

69. The computer system of claim 40, wherein (3) comprises:

(3)(a) identifying at least one minimum or maximum personalized periodic nutritional intake amount associated with the user;

(3)(b) determining the impact of the user eating the initial food item on the at least one minimum or maximum personalized periodic nutritional intake amount within a particular period of time; and

(3)(c) developing the initial personalized nutrition advice for the user, indicating whether the user should eat the initial food item, based on the determined impact on the at least one minimum or maximum personalized periodic nutritional intake amount associated with the user.

70. The computer system of claim 69, wherein the initial personalized nutrition advice indicates what the user'"'"'s nutritional intake amounts will be for the particular period of time if the user eats the initial food item.

71. The computer system of claim 69, wherein the initial personalized nutrition advice indicates whether any of the user'"'"'s periodic nutritional intake amounts will exceed their minimum or maximum, if the user eats the initial food item.

72. The computer system of claim 69, wherein (3)(c) comprises:

(3)(c)(i) developing initial personalized nutrition advice which advises the user not to eat the initial food item;

(3)(c)(ii) automatically identifying at least one alternative food item; and

(3)(c)(iii) developing alternative personalized nutrition advice which advises the user to eat the at least one alternative food item.

73. The computer system of claim 69, wherein (3) further comprises:

(3)(d) receiving input from the user indicating that the user has chosen to eat the initial food item; and

(3)(e) updating nutritional intake amounts associated with the particular period of time based on nutrition information associated with the initial food item.

74. The computer system of claim 69, wherein (3) further comprises:

(3)(d) updating the current values of the user'"'"'s personalized periodic nutritional intake amounts to reflect physical activity of the user.

75. The computer system of claim 74, wherein the updating is performed in response to input received from the user.

76. The computer system of claim 74, wherein the updating is performed without input of the user.

77. The computer system of claim 76, wherein the updating is performed using a global positioning system (GPS) to track the distance and speed traveled by the user in a particular period of time.

78. The computer system of claim 69, wherein (3) further comprises:

(3)(d) receiving input from the user indicating that the user has decided not to completely eat the initial food item;

(3)(e) updating the current values of the user'"'"'s personalized periodic nutritional intake amounts to reflect the quantity of food leftovers of the user.

43. The computer system of claim A41, wherein the method further comprises:

(5) receiving, from the user, an input indicating whether the user accepts the initial personalized nutrition advice.

44. The computer system of claim 43, wherein the method further comprises:

(6) recording the user'"'"'s input indicating whether the user accepts the initial personalized nutrition advice in a food intake history of the user.

45. The computer system of claim 43, wherein the user'"'"'s input indicating whether the user accepts the initial personalized nutrition advice indicates that the user rejects the initial food item, and wherein the method further comprises:

(6) identifying alternative food identification data descriptive of at least one alternative food item within the vicinity of the particular location;

(7) developing alternative personalized nutrition advice for the user related to the at least one alternative food item, based on at least one of;

(c) the alternative food identification data descriptive of the at least one alternative food item; and

(d) the personalized food data associated with the user;

(8) providing the alternative personalized nutrition advice for the at least one alternative food item to the user.

46. The computer system of claim 45, wherein (6) comprises identifying the at least one alternative food item using data from an external source.

47. The computer system of claim 46, wherein (6) comprises identifying the at least one alternative food item by:

(6)(a) identifying the current geographic location of the device; and

(6)(b) identifying at least one alternative food item within the vicinity of the current geographic location of the device using an external data source of geo-referenced food data.

79. A computer-implemented method comprising:

(1) identifying first personalized food data of a first user associated with a first device;

(2) identifying second personalized food data of at least one second user associated with at least one second device; and

(3) developing, based on the first and second personalized food data, a database containing data representing the first personalized food data and the second personalized food data.

80. The method of claim 79, further comprising:

(4) developing, based on the database, personalized nutrition advice associated with the first user.

81. The method of claim 80, wherein the advice is developed in (4) by:

(4)(a) identifying a subset of the second users whose personalized food data are similar to the first user'"'"'s personalized food data;

(4)(b) identifying a first food item indicated as preferred by the personalized food data of the subset of the second users; and

(4)(c) advising the first user to eat the identified first food item.

82. The method of claim 80, wherein the advice is developed in (4) by:

(4)(a) identifying a subset of the second users whose personalized food data are similar to the first user'"'"'s personalized food data;

(4)(b) identifying a first food item indicated as not preferred by the personalized food data of the subset of the second users; and

(4)(c) advising the first user not to eat the identified first food item.

83. The method of claim 80, wherein the advice is developed in (4) based on both the database and food intake history of at least one of the users reflected in the database.

84. The method of claim 83, wherein the advice is developed in (4) by:

(4)(a) identifying a first food item previously eaten by the second users; and

(4)(b) advising the first user to eat the first food item.

85. The method of claim 83, wherein the advice is developed in (4) by:

(4)(a) identifying a first food item not previously eaten by the second users; and

(4)(b) advising the first user not to eat the first food item.

86. The method of claim 79, wherein (3) includes transmitting the first and second personalized food data between the first and second devices.

87. The method of claim 79, wherein (3) includes transmitting the first and second personalized food preferences to a server.

88. The method of claim 79, wherein each user may specify restrictions on which other users may access the user'"'"'s personalized food data.

89. The method of claim 79, further comprising modifying a menu based on the first and second personalized food data.

90. The method of claim 79, further comprising modifying a meal based on the first and second personalized food data.

91. The method of claim 80, wherein the advice is developed in (4) based on both the database and geographic locations of at least one of the users reflected in the database.

92. The method of claim 91, wherein the advice is developed in (4) by:

(4)(a) identifying second users whose geographic locations are within the vicinity of a particular location;

(4) (b) identifying a first food item previously eaten by the second users; and

(4) (c) advising the first user to eat the identified first food item.

93. The method of claim 92, wherein the particular location comprises a current geographic location of the first device.

94. The method of claim 93, wherein the particular location comprises a geographic location specified by the first user.

95. The method of claim 91, wherein the advice is developed in (4) by:

(4)(a) identifying second users whose geographic locations are within the vicinity of the particular location;

(4)(b) identifying a first food item not previously eaten by the second users; and

(4)(c) advising the first user not to eat the identified first food item.

96. A computer system including at least one processor and at least one computer readable medium tangibly storing computer-readable instructions, wherein the at least one processor is adapted to execute the computer-readable instructions to perform a method comprising:

(1) identifying first personalized food data of a first user associated with a first device;

(2) identifying second personalized food data of at least one second user associated with at least one second device; and

(3) developing, based on the first and second personalized food data, a database containing data representing the first personalized food data and the second personalized food data.

97. The computer system of claim 96, wherein the method further comprises:

(4) developing, based on the database, personalized nutrition advice associated with the first user.

98. The computer system of claim 97, wherein the advice is developed in (4) by:

(4)(a) identifying a subset of the second users whose personalized food data are similar to the first user'"'"'s personalized food data;

(4)(b) identifying a first food item indicated as preferred by the personalized food data of the subset of the second users; and

(4)(c) advising the first user to eat the identified first food item.

99. The computer system of claim 97, wherein the advice is developed in (4) by:

(4)(a) identifying a subset of the second users whose personalized food data are similar to the first user'"'"'s personalized food data;

(4)(b) identifying a first food item indicated as not preferred by the personalized food data of the subset of the second users; and

(4)(c) advising the first user not to eat the identified first food item.

100. The computer system of claim 97, wherein the advice is developed in (4) based on both the database and food intake history of at least one of the users reflected in the database.

101. The computer system of claim 100, wherein the advice is developed in (4) by:

(4)(a) identifying a first food item previously eaten by the second users; and

(4)(b) advising the first user to eat the first food item.

102. The computer system of claim 100, wherein the advice is developed in (4) by:

(4)(a) identifying a first food item not previously eaten by the second users; and

(4)(b) advising the first user not to eat the first food item.

103. The computer system of claim 96, wherein (3) includes transmitting the first and second personalized food data between the first and second devices.

104. The computer system of claim 96, wherein (3) includes transmitting the first and second personalized food preferences to a server.

105. The computer system of claim 96, wherein each user may specify restrictions on which other users may access the user'"'"'s personalized food data.

106. The computer system of claim 96, wherein the method further comprises modifying a menu based on the first and second personalized food data.

107. The computer system of claim 96, wherein the method further comprises modifying a meal based on the first and second personalized food data.

108. The computer system of claim 97, wherein the advice is developed in (4) based on both the database and geographic locations of at least one of the users reflected in the database.

109. The computer system of claim 108, wherein the advice is developed in (4) by:

(4)(a) identifying second users whose geographic locations are within the vicinity of a particular location;

(4)(b) identifying a first food item previously eaten by the second users; and

(4)(c) advising the first user to eat the identified first food item.

110. The computer system of claim 109, wherein the particular location comprises a current geographic location of the first device.

111. The computer system of claim 109, wherein the particular location comprises a geographic location specified by the first user.

112. The computer system of claim 108, wherein the advice is developed in (4) by:

(4)(a) identifying second users whose geographic locations are within the vicinity of the particular location;

(4)(b) identifying a first food item not previously eaten by the second users; and

(4)(c) advising the first user not to eat the identified first food item.