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US 2022/0092073 A1Published application
Natural language keyword tag extraction
AssigneeCapital One Services, LLC
InventorsMicah PRICE, Qiaochu TANG, Jason HOOVER, Geoffrey DAGLEY, Avid GHAMSARI
Life of this patent
Estimating the term…
Abstract
Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. Various techniques, methods, systems, and apparatuses include utilizing one or more tags generated by application of a machine learning model to a data source, where the data source may include generic and specific language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile in response to a user request for a suggestion.
Claims20
A method, comprising:
- receiving a request for an automobile selection;
- accessing a first plurality of tags related to a first automobile make and model of a plurality of automobile make and models, wherein the first plurality of tags are based on:
- a term frequency-inverse document frequency (TF-IDF) model applied to a plurality of terms in a data source comprising a corpus of automobile reviews to compute a respective score for each of the terms, wherein the respective scores of a first subset of the plurality of terms exceed a threshold score;
- and
- an aggregation of the terms of the first subset, wherein each tag of the first plurality of tags is associated with a respective plurality of probability distributions generated by the TF-IDF model, wherein each probability distribution associated with one of a plurality of vehicular features, and wherein the first plurality of tags are further based on the probability distributions;
- determining a relationship between a first tag of the first plurality of tags and a second tag of a second plurality of tags;
- returning the first tag as a first suggestion responsive to the request based on the relationship and the first tag having a higher score than the second tag;
- and
- updating, by the TF-IDF model based on a selection of the first automobile make and model, the plurality of probability distributions of each tag of the first plurality of tags.
6 more elements
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to:
- receive a request for an automobile selection;
- access a first plurality of tags related to a first automobile make and model of a plurality of automobile make and models, wherein the first plurality of tags are based on:
- a term frequency-inverse document frequency (TF-IDF) model applied to a plurality of terms in a data source comprising a corpus of automobile reviews to compute a respective score for each of the terms, wherein the respective scores of a first subset of the plurality of terms exceed a threshold score;
- and
- an aggregation of the terms of the first subset, wherein each tag of the first plurality of tags is associated with a respective plurality of probability distributions generated by the TF-IDF model, wherein each probability distribution associated with one of a plurality of vehicular features, and wherein the first plurality of tags are further based on the probability distributions;
- determine a relationship between a first tag of the first plurality of tags and a second tag of a second plurality of tags;
- return the first tag as a first suggestion responsive to the request based on the relationship and the first tag having a higher score than the second tag;
- and
- update, by the TF-IDF model based on a selection of the first automobile make and model, the plurality of probability distributions of each tag of the first plurality of tags.
6 more elements
A computing apparatus comprising:
- a processor;
- and
- a memory storing instructions that, when executed by the processor, cause the processor to:
- receive a request for an automobile selection;
- access a first plurality of tags related to a first automobile make and model of a plurality of automobile make and models, wherein the first plurality of tags are based on:
- a term frequency-inverse document frequency (TF-IDF) model applied to a plurality of terms in a data source comprising a corpus of automobile reviews to compute a respective score for each of the terms, wherein the respective scores of a first subset of the plurality of terms exceed a threshold score;
- and
- an aggregation of the terms of the first subset, wherein each tag of the first plurality of tags is associated with a respective plurality of probability distributions generated by the TF-IDF model, wherein each probability distribution associated with one of a plurality of vehicular features, and wherein the first plurality of tags are further based on the probability distributions;
- determine a relationship between a first tag of the first plurality of tags and a second tag of a second plurality of tags;
- return the first tag as a first suggestion responsive to the request based on the relationship and the first tag having a higher score than the second tag;
- and
- update, by the TF-IDF model based on a selection of the first automobile make and model, the plurality of probability distributions of each tag of the first plurality of tags.
9 more elements
Dependencies are inferred from the claim text.