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1. WO2019211437 - COMPUTATIONAL EFFICIENCY IN SYMBOLIC SEQUENCE ANALYTICS USING RANDOM SEQUENCE EMBEDDINGS

Publication Number WO/2019/211437
Publication Date 07.11.2019
International Application No. PCT/EP2019/061374
International Filing Date 03.05.2019
IPC
G06N 5/02 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
02Knowledge representation
G06N 20/10 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines
G06N 3/04 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G06F 16/285
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
28Databases characterised by their database models, e.g. relational or object models
284Relational databases
285Clustering or classification
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines [SVM]
G06N 3/0445
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0445Feedback networks, e.g. hopfield nets, associative networks
G06N 5/022
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
02Knowledge representation
022Knowledge engineering; Knowledge acquisition
Applicants
  • INTERNATIONAL BUSINESS MACHINES CORPORATION [US]/[US]
  • IBM UNITED KINGDOM LIMITED [GB]/[GB] (MG)
Inventors
  • WU, Lingfei
  • XU, Kun
  • CHEN, Pin-Yu
  • CHEN, Chia-Yu
Agents
  • GRAHAM, Timothy
Priority Data
15/972,10804.05.2018US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) COMPUTATIONAL EFFICIENCY IN SYMBOLIC SEQUENCE ANALYTICS USING RANDOM SEQUENCE EMBEDDINGS
(FR) EFFICACITÉ DE CALCUL DANS UNE ANALYSE D'UNE SÉQUENCE SYMBOLIQUE UTILISANT DES INCORPORATIONS DE SÉQUENCES ALÉATOIRES
Abstract
(EN) 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.
(FR) L'invention concerne un procédé et un système d'analyse d'une séquence symbolique. Des métadonnées d'une séquence symbolique sont reçues en provenance d'un dispositif informatique d'un propriétaire. Un ensemble de R séquences aléatoires est généré sur la base des métadonnées reçues et envoyé au dispositif informatique du propriétaire de la séquence symbolique en vue du calcul d'une matrice de caractéristiques sur la base de l'ensemble de R séquences aléatoires et de la séquence symbolique. La matrice de caractéristiques est reçue en provenance du dispositif informatique du propriétaire. Lorsqu'il est déterminé qu'un produit interne de la matrice de caractéristiques est inférieur à une précision seuil, le processus itératif revient à la génération des R séquences aléatoires. Lorsqu'il est déterminé que le produit interne de la matrice de caractéristiques est supérieur ou égal à la précision seuil, la matrice de caractéristiques est catégorisée sur la base d'un apprentissage machine. La matrice de caractéristiques globale catégorisée est envoyée de manière à être affichée sur une interface utilisateur du dispositif informatique du propriétaire.
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