Convolutional neural network (CNN)-based suggestions for anomaly input
Assigneesalesforce.com, inc.
InventorsChang Lu, Lingtao Zhang
Life of this patent
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Abstract
The technology disclosed determines one or more field values in a set of field values for a particular field in a fielded dataset that are similar to an input value using six similarity measures. A factor vector is generated per similarity measure and combined to form an input matrix. A convolutional neural network processes the input matrix to generate evaluation vectors. A fully-connected network evaluates the evaluation vectors to generate suggestion scalars for similarity to a particular input value. Thresholding is applied to suggestions scalars to determine one or more suggestion candidates for the particular input value.
Claims20
A neural network-based suggestion method, including:
- determining that one or more field values in a set of field values are similar to an input value for a particular field in a fielded dataset, including
- comparing a particular input value to unique field values for the particular field by
- applying a plurality of similarity measures and
- generating a factor vector that has one scalar for each of the unique field values;
- evaluating the factor vector using convolution filters in a convolutional neural network (CNN) to generate evaluation vectors for similarity to the unique field values;
- further evaluating the evaluation vectors using a fully-connected (FC) neural network to produce suggestion scalars for similarity to the particular input value;
- and
- using the suggestion scalars to determine one or more suggestion candidates for the particular input value.
A system including one or more processors coupled to memory, the memory loaded with computer instructions to provide neural network-based suggestions, the instructions, when executed on the processors, implement actions comprising:
- determining that one or more field values in a set of field values are similar to an input value for a particular field in a fielded dataset, including
- comparing a particular input value to unique field values for the particular field by
- applying a plurality of similarity measures and
- generating a factor vector that has one scalar for each of the unique field values;
- evaluating the factor vector using convolution filters in a convolutional neural network (CNN) to generate evaluation vectors for similarity to the unique field values;
- further evaluating the evaluation vectors using a fully-connected (FC) neural network to produce suggestion scalars for similarity to the particular input value;
- and
- using the suggestion scalars to determine one or more suggestion candidates for the particular input value.
A non-transitory computer readable storage medium impressed with computer program instructions to provide neural network-based suggestions, the instructions, when executed on a processor, implement a method comprising:
- determining that one or more field values in a set of field values are similar to an input value for a particular field in a fielded dataset, including
- comparing a particular input value to unique field values for the particular field by
- applying a plurality of similarity measures and
- generating a factor vector that has one scalar for each of the unique field values;
- evaluating the factor vector using convolution filters in a convolutional neural network (CNN) to generate evaluation vectors for similarity to the unique field values;
- further evaluating the evaluation vectors using a fully-connected (FC) neural network to produce suggestion scalars for similarity to the particular input value;
- and
- using the suggestion scalars to determine one or more suggestion candidates for the particular input value.
Dependencies are inferred from the claim text.