SYSTEM AND METHOD FOR IDENTIFYING AN OBSERVED PHENEMENON

• US 20110055121A1
• Filed: 09/29/2008
• Published: 03/03/2011
• Est. Priority Date: 09/29/2008
• Status: Abandoned Application

• Current Assignee: Mobilefusion, Inc.
• Original Assignee: Mobilefusion, Inc.
• Inventors: Yoon, Kevin Myungho, SHARMA, Abhishek, Datta, Ankur, Siddiqi, Sajid Mahmood, Bauer, Michael Scott
• Application Number:
  US12/240,796
• Publication Number:
  US 20110055121A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 18/21 Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
• CPC Class Codes: G06F 18/2178 based on feedback of a supervisor
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

A system for identifying an observed phenomenon. The system includes a computing device configured for receiving disparate data streams associated with disparate data sources. The system also includes a feature extraction module communicably connected to the computing device, a classification module communicably connected to the computing device, and a consensus module communicably connected to the computing device. The feature extraction module is configured for generating a set of attributes for each data stream. The classification module is configured for soft associating labels with attributes for each set of attributes, and for generating a confidence value for each soft association. The consensus module is configured for generating an output indicative of the phenomenon. The consensus module includes a standardization module and a sequential data module. The standardization module is configured for standardizing the confidence values. The sequential data module is configured for generating the output based on the standardized confidence values.

Claims

1. A system for identifying an observed phenomenon, the system comprising:

a computing device configured for receiving disparate data streams associated with disparate data sources;

a feature extraction module communicably connected to the computing device, wherein the feature extraction module is configured for generating a set of attributes for each data stream;

a classification module communicably connected to the computing device, wherein the classification module is configured for;

soft associating labels with attributes for each set of attributes; and

generating a confidence value for each soft association; and

a consensus module communicably connected to the computing device, wherein the consensus module is configured for generating an output indicative of the phenomenon, wherein the consensus module comprises;

a standardization module configured for standardizing the confidence values; and

a sequential data module configured for generating the output based on the standardized confidence values.

2. The system of claim 1, wherein the sequential data module comprises:

a set of model parameters; and

an online estimation module communicably connected to the set of model parameters, wherein the online estimation module is configured for;

receiving the labels and corresponding standardized confidence values; and

generating the output based on the standardized confidence values and a previous output.

3. The system of claim 2, wherein the sequential data module is further configured for maintaining a belief state indicative of the output.

4. The system of claim 1, further comprising a feedback learning module communicably connected to the computing device, wherein the feedback learning module is configured for:

receiving information associated with the output; and

initiating modification of at least one of the following;

the standardization module; and

at least one model parameter of the sequential data module.

5. A method, implemented at least in part by a computing device, for identifying an observed phenomenon, the method comprising:

receiving disparate data streams associated with disparate data sources;

generating a set of attributes for each data stream;

soft associating labels with attributes for each set of attributes generating a confidence value for each soft association;

standardizing the confidence values; and

generating an output indicative of the phenomenon based on the standardized confidence values.

6. The method of claim 5, wherein generating the attributes comprises applying a transform to each data stream.

7. The method of claim 6, wherein applying the transform comprises applying at least one of the following:

a spatial transform;

a temporal transform; and

a frequency-based transform.

8. The method of claim 6, further comprising reducing a dimensionality of at least one of the transformed data streams.

9. The method of claim 8, wherein reducing the dimensionality comprises applying at least one of the following to the at least one of the transformed data streams:

a linear dimensionality reduction algorithm; and

a non-linear dimensionality reduction algorithm.

10. The method of claim 5, wherein soft associating the labels comprises applying at least one of the following to at least one of the attributes:

a parametric classification algorithm; and

a non-parametric classification algorithm.

11. The method of claim 5, wherein generating the confidence values comprises determining a distance of at least one of the labels from a decision boundary.

12. The method of claim 5, wherein standardizing the confidence values comprises, for each data stream, standardizing the confidence values associated with the data stream to a value between zero and one.

13. The method of claim 5, wherein generating the output further comprises generating the output based on a parameter model and a previous output.

14. The method of claim 5, further comprising maintaining a belief state based on the output.

15. The method of claim 5, further comprising receiving feedback regarding the output.

16. The method of claim 15, further comprising at least one of the following based on the received feedback:

modifying a process utilized for the standardization of the confidence values; and

modifying at least one parameter model utilized to generate the output.