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1. WO2020227147 - CENTRALIZED MACHINE LEARNING PREDICTOR FOR A REMOTE NETWORK MANAGEMENT PLATFORM

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

What is claimed is:

1. A remote network management platform comprising:

an end-user computational instance comprising a first set of computational resources of the remote network management platform and dedicated to a managed network;

a training computational instance comprising a second set of computational resources of the remote network management platform, and configured to perform operations including:

receiving, from the end-user computational instance, a corpus of textual records, training, based on the corpus of textual records, a machine learning (ML) model to determine a degree of numerical similarity between input textual records and textual records in the corpus of textual records, and

transmitting, to the end-user computational instance, the ML model; and a prediction computational instance comprising a third set of computational resources of the remote network management platform, and configured to perform operations including:

receiving, from the end-user computational instance, an additional textual record, receiving, from the end-user computational instance, the ML model, determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records, and

based on the respective numerical similarities, transmitting, to the end-user computational instance, representations of one or more of the textual records in the corpus of textual records.

2. The remote network management platform of claim 1, wherein the third set of

computational resources includes more memory than the first set of computational resources.

3. The remote network management platform of claim 1, wherein the third set of computational resources includes a memory, and wherein the operations performed by the prediction computational instance additionally include:

in response to receiving the additional textual record, determining that the ML model is not stored in the memory; and

requesting, from the end-user computational instance, the ML model.

4. The remote network management platform of claim 1, wherein determining, by the ML model, respective numerical similarities between the additional textual record and one or more of the textual records in the corpus of textual records comprises using the ML model to determine at least one of (i) word vectors that describe, in a first semantically-encoded vector space, a meaning of respective words of the additional textual record, or (ii) a paragraph vector that describes, in a second semantically-encoded vector space, a meaning of multiple words of the additional textual record.

5. The remote network management platform of claim 4, wherein the ML model represents a set of clusters of the textual records in the corpus of textual records, and wherein determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records further comprises selecting, for the additional textual record, a cluster from the set of clusters based on at least one of the word vectors or the paragraph vector.

6. The remote network management platform of claim 1, wherein the ML model represents a set of clusters of textual records in the corpus of textual records, and wherein determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records comprises selecting, for the additional textual record, a cluster from the set of clusters.

7. The remote network management platform of claim 1, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for an input textual record and for a given textual record in the corpus of textual records, respective vector representations in a vector space; and

determine a degree of numerical similarity between the input textual record and the given textual record by determining a distance, within the vector space, between the vector representations of the input textual record and the given textual record.

8. The remote network management platform of claim 1, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for an input textual record and for a given textual record in the corpus of textual records, respective vector representations in a vector space; and

determine a degree of numerical similarity between the input textual record and the given textual record by determining an angle, within the vector space, between the vector representations of the input textual record and the given textual record.

9. The remote network management platform of claim 1, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for a cluster of textual records in the corpus of textual records, an aggregate vector representation in a vector space;

determine, for an input textual record, a vector representation in the vector space; and determine a degree of numerical similarity between the input textual record and the cluster of textual records in the corpus of textual records by determining an angle, within the vector space, between the vector representation of the input textual record and the aggregate vector representation.

10. The remote network management platform of claim 1, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for a cluster of textual records in the corpus of textual records, an aggregate vector representation in a vector space;

determine, for an input textual record, a vector representation in the vector space; and determine a degree of numerical similarity between the input textual record and the cluster of textual records in the corpus of textual records by determining a distance, within the vector space, between the vector representation of the input textual record and the aggregate vector representation.

11. The remote network management platform of claim 1, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for a cluster of textual records in the corpus of textual records, a representative volume within a vector space;

determine, for an input textual record, a vector representation in the vector space; and determine a degree of numerical similarity between the input textual record and the cluster of textual records in the corpus of textual records by determining whether the vector representation of the input textual record is within the representative volume.

12. The remote network management platform of claim 1, further comprising:

an additional end-user computational instance comprising a fourth set of computational resources of the remote network management platform and dedicated to an additional managed network;

wherein the operations performed by the training computational instance additionally include:

receiving, from the additional end-user computational instance, an additional corpus of textual records,

training, based on the additional corpus of textual records, an additional ML model to determine a degree of similarity between input textual records and textual records in the additional corpus of textual records, and

transmitting, to the additional end-user computational instance, the additional ML model; and

wherein the operations performed by the prediction computational instance additionally include:

receiving, from the additional end-user computational instance, a further textual record,

receiving, from the additional end-user computational instance, the additional ML model,

determining, by the additional ML model, additional respective numerical similarities between the further textual record and the textual records in the additional corpus of textual records, and

based on the additional respective numerical similarities, transmitting, to the additional end-user computational instance, representations of one or more of the textual records in the additional corpus of textual records.

13. An article of manufacture including a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a remote network management platform, cause the remote network management platform to perform operations comprising:

operating a first set of computational resources of the remote network management platform to provide an end-user computational instance that is dedicated to a managed network; operating a second set of computational resources of the remote network management platform to provide a training computational instance that is configured to perform operations including: (i) receiving, from the end-user computational instance, a corpus of textual records, (ii) training, based on the corpus of textual records, a machine learning (ML) model to determine a degree of numerical similarity between input textual records and textual records in the corpus of textual records, and (iii) transmitting, to the end-user computational instance, the ML model; and

operating a third set of computational resources of the remote network management platform to provide a prediction computational instance that is configured to perform operations including: (i) receiving, from the end-user computational instance, an additional textual record, (ii) receiving, from the end-user computational instance, the ML model, (iii) determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records, and (iv) based on the respective numerical similarities, transmitting, to the end-user computational instance, representations of one or more of the textual records in the corpus of textual records.

14. The article of manufacture of claim 13, wherein the operations performed by the prediction computational instance additionally include:

in response to receiving the additional textual record, determining that the ML model is not stored in a memory of the third set of computational resources; and

requesting, from the end-user computational instance, the ML model.

15. The article of manufacture of claim 13, wherein determining, by the ML model, respective numerical similarities between the additional textual record and one or more of the textual records in the corpus of textual records comprises using the ML model to determine at least one of (i) word vectors that describe, in a first semantically-encoded vector space, a meaning of respective words of the additional textual record, or (ii) a paragraph vector that describes, in a second semantically-encoded vector space, a meaning of multiple words of the additional textual record.

16. The article of manufacture of claim 15, wherein the ML model represents a set of

clusters of the textual records in the corpus of textual records, and wherein determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records further comprises selecting, for the additional textual record, a cluster from the set of clusters based on at least one of the word vectors or the paragraph vector.

17. The article of manufacture of claim 13, wherein the ML model represents a set of clusters of textual records in the corpus of textual records, and wherein determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records comprises selecting, for the additional textual record, a cluster from the set of clusters.

18. The article of manufacture of claim 13, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for an input textual record and for a given textual record in the corpus of textual records, respective vector representations in a vector space; and

determine a degree of numerical similarity between the input textual record and the given textual record by determining a distance, within the vector space, between the vector representations of the input textual record and the given textual record.

19. The article of manufacture of claim 13, wherein training the ML model to determine the degree of numerical similarity between input textual records and textual records in the corpus of textual records comprises training the ML model to:

determine, for an input textual record and for a given textual record in the corpus of textual records, respective vector representations in a vector space; and

determine a degree of numerical similarity between the input textual record and the given textual record by determining an angle, within the vector space, between the vector representations of the input textual record and the given textual record.

20. A method compri sing :

operating a first set of computational resources of a remote network management platform to provide an end-user computational instance that is dedicated to a managed network; operating a second set of computational resources of the remote network management platform to provide a training computational instance that is configured to perform operations including: (i) receiving, from the end-user computational instance, a corpus of textual records, (ii) training, based on the corpus of textual records, a machine learning (ML) model to determine a degree of numerical similarity between input textual records and textual records in the corpus of textual records, and (iii) transmitting, to the end-user computational instance, the ML model; and

operating a third set of computational resources of the remote network management platform to provide a prediction computational instance that is configured to perform operations including: (i) receiving, from the end-user computational instance, an additional textual record, (ii) receiving, from the end-user computational instance, the ML model, (iii) determining, by the ML model, respective numerical similarities between the additional textual record and the textual records in the corpus of textual records, and (iv) based on the respective numerical similarities, transmitting, to the end-user computational instance, representations of one or more of the textual records in the corpus of textual records.