Apparatus and methods for generating data structures to represent and compress data profiles

1. A method, comprising:

receiving, at a processor, stimulus-response data including a digital representation of a stimulus and a digital representation of a response;

identifying, via the processor, a distribution type from a plurality of distribution types, based on the digital representation of the stimulus;

identifying, via the processor, a range of inclination values (“RI”) of the distribution type, based on the digital representation of the response, the RI defined as:

RI={{w m ,inc m },{x m,i ] for i= 1,10/inc m },

where subscript “m” is an integer value, w m =a weight from a plurality of weights associated with the stimulus-response data, inc m =an increment, and x m,i =a value in a range of inclination values from a set of ranges of inclination values; and

compiling, via the processor, a compressed multidimensional data profile associated with an object of the stimulus-response data based on the RI.

2. The method of claim 1 , wherein the distribution type is a first distribution type, the RI is a first RI, the method further comprising:

identifying, via the processor, a second distribution type from the plurality of distribution types, based on the digital representation of the stimulus; and

identifying, via the processor, a second RI of the second distribution type, based on the digital representation of the response,

wherein compiling the compressed multidimensional data profile is based on the first distribution type, the second distribution type, the first RI and the second RI.

3. The method of claim 1 , further comprising:

calculating, via the processor, the plurality of weights, the calculating based on a rule, the rule being a function of a credibility of a source of the stimulus-response data,

the compiling the compressed multidimensional data profile being based on the weight.

4. The method of claim 1 , wherein the stimulus-response data is a first stimulus-response data, the stimulus is a first stimulus, the response is a first response, the method further comprising:

receiving, via the processor, a second stimulus-response data including a digital representation of a second stimulus and a digital representation of a second response; and

modifying the compressed multidimensional data profile based on the second stimulus-response data.

5. The method of claim 1 , wherein the RI has a multimodal distribution.

6. The method of claim 1 , wherein:

the RI represents behavioral leanings of the individual; and the compressed multidimensional data profile is associated with a mindset profile of the individual.

7. The method of claim 1 , further comprising:

sending, to a compute device of the individual and after compiling the compressed multidimensional data profile, a signal representing a plurality of recommendations for the individual; and

receiving updated stimulus-response data after sending the signal representing the plurality of recommendations for the individual.

8. A method, comprising:

calculating, via a processor, a plurality of weights, each weight from the plurality of weights being uniquely associated with a stimulus-response pair from a plurality of stimulus-response pairs;

retrieving, via the processor, a plurality of ranges of inclination values (“RIs”), each RI from the plurality of RIs being uniquely associated with a response from a plurality of responses and a distribution type from a plurality of distribution types, each distribution type from the plurality of distribution types being associated with a stimulus from a plurality of stimuli;

compiling, via the processor, a compressed multidimensional data profile based on the plurality of weights, the plurality of distribution types, and the plurality of ranges of inclination values; and

in response to receiving, at the processor, a stimulus-response signal, modifying at least one RI from the plurality of RIs according to:

R

⁢

I

1

(

X

,

I

)

=

R

⁢

I

0

(

X

,

I

)

⁢

(

+

)

⁢

SD

⁢

(

X

,

I

)

,

where RI 0 (X,I) is a current leaning distribution, RI 1 (X,I) is a modified leaning distribution, SD (X,I) is a leaning distribution of the stimulus-response signal, X is a vector representing a range of values of a dimension, I is an identifier of an individual, and (+) represents evidence aggregation.

9. The method of claim 8 , wherein the plurality of distribution types includes at least one distribution of: a desire-to-improve type, a willingness-to-learn type, a judgment-style type, or a learning-style type.

10. The method of claim 8 , wherein each RI from the plurality of RIs includes an array of integer values between 0 and 1.

11. The method of claim 8 , wherein:

RI={{w m ,inc m },{x m,i } for i= 1,10/inc m },

where w m =a weight from the plurality of weights, inc m =an increment, and x m,i is a value in a RI from the plurality of RIs.

12. The method of claim 8 , wherein:

the plurality of ranges of inclination values represents behavioral leanings of the individual; and

the compressed multidimensional data profile is associated with a mindset profiles of the user.

13. The method of claim 8 , further comprising:

sending, to a user interface and after compiling the compressed multidimensional data profile, a signal representing a plurality of recommendations for the individual; and

receiving an updated plurality of stimulus-response pairs after sending the signal representing the plurality of recommendations for the individual.

14. The method of claim 8 , wherein at least one RI from the plurality of RIs has a multimodal distribution.

15. The method of claim 8 , wherein the calculating the plurality of weights is based on a credibility of a source of the plurality of stimulus-response pairs.

16. A method, comprising:

retrieving, via a processor, a plurality of distribution types, each distribution type from the plurality of distribution types being uniquely associated with a stimulus from a plurality of stimuli;

retrieving, via the processor, a plurality of ranges of inclination values (“RIs”), each RI from the plurality of RIs being uniquely associated with a response from a plurality of responses and a distribution type from the plurality of distribution types, each RI from the plurality of RIs defined as:

RI={{w m ,inc m },{x m,i ] for i= 1,10/inc m },

where w m =a weight from a plurality of weights associated with the stimulus-response data, inc m =an increment, and x m,i =a value in a range of inclination values from a set of ranges of inclination values; and

defining, via the processor, a compressed multidimensional data profile based on a plurality of weights, the plurality of distribution types, and the plurality of ranges of inclination values.

17. The method of claim 16 , wherein:

the plurality of RIs represents behavioral leanings of the individual; and

the compressed multidimensional data profile is associated with a mindset profile of the user.

18. The method of claim 16 , further comprising:

sending, to a user interface and after defining the compressed multidimensional data profile, a signal representing a plurality of recommendations for the individual; and

receiving an updated plurality of stimulus-response pairs after sending the signal representing the plurality of recommendations for the individual.

19. The method of claim 16 , wherein at least one range of inclination values from the plurality of ranges of inclination values has a multimodal distribution.

20. The method of claim 16 , wherein the plurality of weights is associated with a credibility of a source of the plurality of responses.