Invention Grant
- Patent Title: Computational efficiency in symbolic sequence analytics using random sequence embeddings
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Application No.: US15972108Application Date: 2018-05-04
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Publication No.: US11227231B2Publication Date: 2022-01-18
- Inventor: Lingfei Wu , Kun Xu , Pin-Yu Chen , Chia-Yu Chen
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Intelletek Law Group, PLLC
- Agent Gabriel Daniel, Esq.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F16/28

Abstract:
A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner.
Public/Granted literature
- US20190340542A1 Computational Efficiency in Symbolic Sequence Analytics Using Random Sequence Embeddings Public/Granted day:2019-11-07
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