Semantics-based interests in computing environments and systems

• US 8,175,902 B2
• Filed: 12/23/2008
• Issued: 05/08/2012
• Est. Priority Date: 12/23/2008
• Status: Expired due to Fees

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Song, Yu, Jeong, Sangoh, Cheng, Doreen, Kalasapur, Swaroop S.
• Primary Examiner(s): Gort, Elaine
• Assistant Examiner(s): Hayles, Ashford S
• Application Number:
  US12/343,393
• Publication Number:
  US 20100161381A1
• Time in Patent Office: 1,232 Days
• Field of Search: 705/7.11, 705/14, 701/302
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements

Abstract

An input situation can be represented by at least a first context variable. Data that includes interest values for multiple context variables can be provided and obtained. The obtained data can include a first data pertaining to the input situation and a second data pertaining to one or more other situations. It can be determined whether the first context variable is associated with a discrete range of values or a continuous range of values. At least a portion of data pertaining to the situations can be determined to be proximate data when the first context variable is associated with a continuous range of values. Based on the input situation and the proximate data, an interest value for the first input situation can be determined as a prediction of the interest in the input situation.

Claims

1. A computer-implemented method of generating an interest value for a first input situation, comprising:

obtaining a first input situation represented by at least a first context variable of a plurality of context variables;

obtaining data including interest values for said plurality of context variables, wherein said data includes first data pertaining to the first input situation and other data pertaining to one or more other situations;

(a) determining whether the first context variable is associated with a discrete range of values or a continuous range of values;

(b) determining at least a portion of said other data pertaining to one or more other situations to be proximate data proximate to said first input situation when said determining (a) determines that said first context variable is associated with a continuous range of values; and

generating, based on said proximate data pertaining to said one or more other situations and said first data pertaining to the first input situation, an interest value for said first input situation wherein;

said first context variable of said first input situation has a first input situation context value from a range of possible context values;

said data includes a plurality of data associations, each data association including a first data association context variable and a data association interest value, said first data association context variable having a value from said range of possible context values;

said proximate data includes a proximate data association subset of the plurality of data associations, each data association in said proximate data association subset having a value for said first data association context variable that is in close proximity to said first input situation context value along the range of possible context values; and

the interest value for said first input situation is based on the data association interest values of the data associations in said proximate data association subset.

2. The computer-implemented method of claim 1, wherein the first input situation comprises a second context variable of the plurality of context variables and wherein the method further comprises:

(c) determining whether the second context variable is associated with a discrete range of values or a continuous range of values;

(d) determining whether said portion of said other data pertaining to one or more other situations pertains to the value of said second variable of said first input situation,

wherein determining (b) comprises determining at least a portion of said other data pertaining to one or more other situations to be proximate data proximate to said first input situation when said determining (a) determines that said first context variable is associated with a continuous range of values and when determining (c) determines that said second context variable is associated with a discrete range of values and determining (d) determines that said portion of said other data pertains to the value of said second context variable of said first input situation.

3. The computer-implemented method of claim 2, wherein the first context variable pertains to time and the second context variable pertains to a location.

4. The computer-implemented method of claim 1, wherein the first context variable pertains to time.

5. The computer-implemented method of claim 1, wherein said interest value for said first input situation is based on an average of the data association interest values of the data associations in said proximate data association subset.

6. The computer-implemented method of claim 1, wherein values in close proximity to the first context value are values in said range of possible context values that form a continuous, sequential band around or on one side of the first context value in said range of possible context values.

13. The computer-implemented method of claim 1 wherein said interest value generated based on said proximate data is a computed interest value for a generated situation-based interest rating component, said computed interest value indicative of interest in said object of interest in said first input situation and wherein the method further comprises:

obtaining a plurality of situation-based interest rating components for a plurality of situations, said plurality of situation-based interest rating component including said generated situation-based interest rating component, wherein each one of said plurality of situation-based interest rating components includes an interest value indicative of interest in said object of interest in one of said plurality of situations, each one of the plurality of situations effectively represented by said plurality of context variables, each one of said plurality of context variables having an associated plurality of possible context values;

obtaining a second input situation that is effectively represented by a plurality of input context values;

identifying, based on said second input situation, one of said plurality of situation-based interest rating components as a matching situation-based interest rating component for said input situation;

determining the relevance of at least a portion of one or more other situation-based interest rating components to said matching situation-based interest rating component; and

determining, based on said one or more relevancies, a projected interest value indicative of a projected interest in said object of interest in said second input situation.

14. The computer-implemented method of claim 13, wherein said determining of relevance comprises one or more of the following:

determining the relevance of situations of said one or more other situation based interest rating components to a matching situation of said matching situation-based interest rating component;

determining the relevance of interest values of said one or more other situation-based interest rating components to a matching interest value of said matching situation-based interest rating component; and

determining the relevance of said one or more other situation-based interest rating components as a whole to said matching situation-based interest rating component considered as a whole.

15. The computer-implemented method of claim 14, wherein each of said plurality of situation-based interest ratings components are represented in a vector form.

16. The computer-implemented method of claim 15, wherein determining the relevance of said one or more other situation-based interest rating components as a whole comprises considering entire said vector forms.

17. The computer-implemented method of claim 14, wherein:

at least one of said situation-based interest rating components is an unknown interest rating component that includes an unknown interest value; and

said determining of the relevance of interest values of said one or more other situation-based interest rating components comprises;

not considering said unknown interest value.

18. The computer-implemented method of claim 13, wherein at least one of said situation-based interest rating components is an average-based interest rating component that includes an average interest value.

19. The computer-implemented method of claim 18, wherein said method further comprises:

determining said average interest value by considering only a plurality of known interest values associated with occurrence of a situation represented by said average-based interest rating component.

20. The computer-implemented method of claim 13, wherein said method further comprises:

assigning an initial value to an unknown interest value, said unknown interest value having a range of possible interest values;

updating said initial value based on one or more of the following;

a) the projected interest value; and

b) an approximate midpoint of the range of possible interest values.

21. The computer-implemented method of claim 13, wherein said method further comprises:

obtaining data relating to a set of data associations, each one of the set of data associations including an associated data association situation and an associated data association interest value, the associated data association situation including two or more data association context variables, each one of said data association context variables having a plurality of possible data association context values, wherein said obtaining of the plurality of situation-based interest rating components includes averaging the data association interest values associated with a subset of the set of data associations, the subset including two or more data associations that have the same situation.

22. The computer-implemented method of claim 13, wherein:

a first one of the plurality of situation-based interest rating components has at least two context variables, values of the at least two context variables encoded as at least a portion of a first multi-dimensional vector;

at least two of the input context values of the second input situation are encoded as at least a portion of a second multi-dimensional vector;

the predicting of the projected interest level uses a weighted sum, the weighted sum being based at least partly on the calculating of a distance between the first multi-dimensional vector and the second multi-dimensional vector.

23. The computer-implemented method of claim 22, wherein the calculating of the distance includes at least one of a group consisting of:

a) calculating a cosine distance; and

b) calculating a Euclidean distance.

24. The computer-implemented method of claim 13, wherein a first one of the context variables is encoded using 1-in-N encoding, N being an integer equal to the number of possible context values associated with the first one of the context variables, such that the first one of the context variables is encoded as a vector with N values.

28. The computer-implemented method of claim 1 further comprising displaying information to a user based on the generated interest value for the said first input situation.

7. A computer-implemented method of generating an interest value for a first input situation, comprising:

obtaining a first input situation represented by at least first and second context variables;

obtaining data including interest values for a plurality of context variables including said first and second context variables, wherein said data includes first data pertaining to the first input situation and other data pertaining to one or more other situations;

(a) determining whether said first context variable is associated with a discrete range of values or a continuous range of values;

(b) determining whether values of said second context variable of said other data are within an acceptable range of the value of said second variable of said first input situation when said determining (a) determines that said first context variable is associated with said discrete range of values;

(c) determining at least a portion of said other data pertaining to one or more other situations to be proximate data proximate to said first input situation when said determining (b) determines that said values of said second variable of said other data are within an acceptable range of the value of said second variable of said first input situation; and

generating, based on said proximate data pertaining to said one or more other situations and said first data pertaining to the first input situation, an interest value for said first input situation wherein;

said second context variable for the first input situation has a first input situation-based context value from a range of possible context values;

said data includes a plurality of data associations, each data association including a first data association context variable, a second data association context variable and a data association interest value, said second data association context variable having a value from said range of possible context values;

said proximate data includes a proximate data association subset of the plurality of data associations, each data association in said proximate data association subset having a value for said second data association context variable that is in close proximity to said first input situation-based context value on the range of possible context values; and

the interest value for said first input situation is based on the data association interest values of the data associations in said proximate data association subset.

8. The computer-implemented method of claim 7, wherein the second context variable pertains to time.

9. The computer-implemented method of claim 8, wherein the first context variable pertains to a location.

10. The computer-implemented method of claim 7, wherein the interest value for said first input situation is based on an average of the data association interest values of the data associations in said proximate data association subset.

11. The computer-implemented method of claim 7, wherein values for said first context variable in each one of the data associations of said proximate data association subset are the same.

12. The computer-implemented method of claim 7, wherein values in close proximity to the first context value are values in said range of possible context values that form a continuous, sequential band around or on one side of the position of the first situation-based context value in said range of possible context values.

29. The computer-implemented method of claim 7 further comprising displaying information to a user based on the generated interest value for said first input situation.

25. A computing system, wherein said computing system is operable to:

obtain a first input situation represented by at least a first context variable;

obtain data including interest values for a plurality of context variables including said first context variable, wherein said data includes first data pertaining to the first input situation and other data pertaining to one or more other situations;

(a) determine whether the first context variable is associated with a discrete range of values or a continuous range of values;

(b) determine at least a portion of said other data pertaining to one or more other situations to be proximate data to said first input situation when said determining (a) determines that said first context variable is associated with a continuous range of values;

generate, based on said proximate data pertaining to said one or more other situations and said first data pertaining to the first input situation, an interest value for said first input situation wherein;

said first context variable of said first input situation has a first input situation context value from a range of possible context values;

said data includes a plurality of data associations, each data association including a first data association context variable and a data association interest value, said first data association context variable having a value from said range of possible context values;

said proximate data includes a proximate data association subset of the plurality of data associations, each data association in said proximate data association subset having a value for said first data association context variable that is in close proximity to said first input situation context value along the range of possible context values; and

the interest value for said first input situation is based on the data association interest values of the data associations in said proximate data association subset.

26. The computing system of claim 25, wherein at least one of the context variables is based on one or more of the following:

a) an environmental factor and/or element;

b) an environmental factor and/or element associated with one or more humans interacting with one or more applications on the computing system;

c) environmental context of use associated with an environment of one or more humans as they interact with one or more active applications on the computing system;

d) a geographical and/or physical factor and/or element;

e) time, date, location, mode, mode of operation, condition, event, temperature, speed and/or acceleration of movement, power and/or force;

f) presence of one or more external components and/or devices;

g) presence of one or more active components operating on one or more external devices in a determined proximity of said device; and

h) one or more physiological and/or biological conditions associated with one or more persons interacting with the computing system.

27. A computer readable storage medium embodied in a tangible form that includes executable computer code operable to generating an interest value for a first input situation, including:

executable computer code operable to obtain a first input situation represented by at least a first context variable;

executable computer code operable to obtain data including interest values for a plurality of context variables including said first context variable, wherein said data includes first data pertaining to the first input situation and other data pertaining to one or more other situations;

executable computer code operable to (a) determine whether the first context variable is associated with a discrete range of values or a continuous range of values;

executable computer code operable to (b) determine at least a portion of said other data pertaining to one or more other situations to be proximate data to said first input situation when said determining (a) determines that said first context variable is associated with a continuous range of values; and

generating, based on said proximate data pertaining to said one or more other situations and said first data pertaining to the first input situation, an interest value for said first input situation wherein;

said first context variable of said first input situation has a first input situation context value from a range of possible context values;

said data includes a plurality of data associations, each data association including a first data association context variable and a data association interest value, said first data association context variable having a value from said range of possible context values;

said proximate data includes a proximate data association subset of the plurality of data associations, each data association in said proximate data association subset having a value for said first data association context variable that is in close proximity to said first input situation context value along the range of possible context values; and

the interest value for said first input situation is based on the data association interest values of the data associations in said proximate data association subset.

30. A computing system, wherein said computing system is operable to:

obtain a first input situation represented by at least a first context variable;

obtain data including interest values for a plurality of context variables including said first context variable, wherein said data includes first data pertaining to the first input situation and other data pertaining to one or more other situations;

(a) determine whether the first context variable is associated with a discrete range of values or a continuous range of values;

(b) determine at least a portion of said other data pertaining to one or more other situations to be proximate data to said first input situation when said determining (a) determines that said first context variable is associated with a continuous range of values;

generate, based on said proximate data pertaining to said one or more other situations and said first data pertaining to the first input situation, an interest value for said first input situation wherein;

said first context variable of said first input situation has a first input situation context value from a range of possible context values;

said data includes a plurality of data associations, each data association including a first data association context variable and a data association interest value, said first data association context variable having a value from a range of possible context values;

said proximate data includes a proximate data association subset of the plurality of data associations, each data association in said proximate data association subset having a value for said first data association context variable that is in close proximity to said first input situation context value; and

the interest value for said first input situation is based on the data association interest values of the data associations in said proximate data association subset.

31. The computer system of claims 30 wherein the computing system is further operable to display information to a user based on the generated interest value for said first input situation.