Solution recommendation based on incomplete data sets

• US 20080275834A1
• Filed: 07/15/2008
• Published: 11/06/2008
• Est. Priority Date: 01/30/2006
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Sun, Tong, Zhong, Ming, Shepherd, Michael D., Cole, Alan T.
• Granted Patent: US 8,037,003 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

In accordance with one aspect of the present exemplary embodiment, a system determines a solution based on received data. An intake component receives an incomplete data set from one or more sources. A recommendation system transforms the incomplete data set into a semantic data set via latent semantic indexing, classifies the semantic data set into an existing cluster and provides one or more solutions of the existing cluster as one or more recommendations.

Claims

1. A computer based system that identifies one or more workflows for a print process, comprising:

an intake component that receives an incomplete data set from one or more sources, wherein the incomplete data set is related to a production workflow for a print process;

a case classification and recommendation component that maps the incomplete data set into a case log vector in a case constraint space, utilizes a latent semantic index transformation to map the case log vector into a semantic vector with reduced dimensionality, classifies the semantic vector into an existing case cluster whose cluster centroid vector has the largest cosine product with the semantic vector, and returns one or more representative workflows of the existing case cluster as one or more recommended workflow solutions;

at least one predefined cluster that is created by mapping previously collected data into one or more data vectors, mapping the one or more data vectors into one or more semantic data vectors and clustering the one or more semantic data vectors into at least one group based at least in part upon one or more mutual correlations, wherein the each of the at least one cluster is associated with at least one solution; and

an online recommendation system that provides one or more solutions via correlation of the incomplete data set to one or more predefined data clusters, wherein the solution is a workflow that completely defines a print process automated by at least one automation device.

2. The computer based system according to claim 1, further including a solution bank that stores and presents the one or more recommended solutions.

3. The computer based system according to claim 1, wherein the incomplete data set is at least one case log that contains one or more user constraints that relate to one or more user requirements.

4. The computer based system according to claim 1, wherein the recommendation system further includes an offline recommendation system that receives data from a database and clusters the data into groups based on their mutual correlation, wherein each cluster is associated with one or more recommended solutions.

5. The computer based system according to claim 1, wherein the one or more recommended workflow solutions are assigned a confidence score, wherein the confidence score is defined as Score

6. The computer based system according to claim 7, wherein the confidence score indicates the cosine product between the semantic vector related to the incoming case and its associated centroid vector, wherein 0 represents the minimal confidence when d(x,c(x))=−

1 or x=−

c(x) and 1 represents the maximal confidence when d(x,c(x))=1 or x=c(x).

7. The computer based system according to claim 1, further including a case database that stores at least one record of at least one previous case, wherein the at least one record includes one or more customer constraints, at least one generated workflow and at least one interested workflow, wherein the at least one generated workflow is provided by the recommendation system and the at least one interested workflow is selected from one of the generated workflows.

11. The computer based system according to claim 12, further including a cluster characterization component that calculates the representative workflows for each cluster in the final clustering scheme generated by the case clustering component.

8. The computer based system according to claim 9, further including an LSI initialization and transformation component that maps the one or more case logs into one or more case log vectors based at least in part on one or more attribute values and the importance of each attribute, and maps the case log vectors into semantic case log vectors.

9. The computer based system according to claim 10, wherein the case log vectors are mapped into semantic case log vectors wherein d randomly sampled case logs are represented by a t×

d matrix M, where each column vector is the vector corresponding to the j th case log, and wherein r is the rank of M, where the singular value decomposition (SVD) of M is M=T×

S×

D′

, where T, a t×

r matrix, and D, a d×

r matrix, have orthonormal column vectors and S is a diagonal matrix with singular values ranked in a descending order, and wherein T_k, S_k, D_k, are the resulted matrices by keeping only the first k columns of T, S, D, where k should be the number of semantic concepts in the case logs which produces a case log vector x that is folded into the k-dimensional semantic space by x_k=x×

T_k, which maps x, a t-dimensional case log vector, into x_k, a k-dimensional semantic case log vector.

10. The computer based system according to claim 11, further including a case clustering component that clusters the semantic case log vectors into groups based on their mutual correlations, wherein the number of case log vectors is n and the maximum number of case clusters is K_max, for K=1 to K_max, the clustering algorithm a) clusters n vectors into K clusters by using K-means algorithm with refined initial centroids, b) evaluates the performance of the K clusters by Bayesian Information Criterion (BIC) scores, and c) stores the above K cluster centroids and their evaluation score as a clustering scheme, wherein the clustering scheme with the best evaluation score is output.