User behavior segmentation using latent topic detection
First Claim
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1. A method of artificial intelligence guided segmentation of event data, the method comprising:
- accessing, from a data store, a plurality of event records associated with respective users of a plurality of users, wherein a first plurality of event records associated with a first user are stored using a first quantity of storage;
accessing an event categories data structure indicating a plurality of event categories and, for each event category, attribute criteria usable to identify events associated with respective event categories;
for the event records,identifying one or more attributes of the event record,comparing the identified one or more attributes of the event record to the attribute criteria of respective event categories, andbased on said comparing, assigning, to the event record, an event category having attribute criteria matching the identified one or more attributes of the event record;
generating, for the first user, first compressed event data using the event records associated with the first user and a latent feature identification model, wherein the latent feature identification model takes the event records for the first user and the event categories assigned thereto as an input, and provides association values for the first user for respective event topics identified by the first compressed event data,wherein first compressed event data associated with the first user is stored using a second quantity of storage, the second quantity of storage being less than the first quantity of storage for storing the event records of the first user;
assigning the first user to one of a plurality of data clusters included in a clustering model using the first compressed event data for the first user; and
generating, for the first user, second compressed event data using a comparison between the first compressed event data for the first user and an average latent feature identification value for a latent feature included in the data cluster to which the first user has been assigned, wherein the second compressed event data associated with the first user is stored using a third quantity of storage, the third quantity of storage being less than the second quantity of storage.
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Abstract
The features relate to artificial intelligence directed compression of user event data based on complex analysis of user event data including latent feature detection and clustering. Further features are described for reducing the size of data transmitted during event processing data flows and devices such as card readers or point of sale systems. Machine learning features for dynamically determining an optimal compression as well as identifying targeted users and providing content to the targeted users based on the compressed data are also included.
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Citations
20 Claims
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1. A method of artificial intelligence guided segmentation of event data, the method comprising:
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accessing, from a data store, a plurality of event records associated with respective users of a plurality of users, wherein a first plurality of event records associated with a first user are stored using a first quantity of storage; accessing an event categories data structure indicating a plurality of event categories and, for each event category, attribute criteria usable to identify events associated with respective event categories; for the event records, identifying one or more attributes of the event record, comparing the identified one or more attributes of the event record to the attribute criteria of respective event categories, and based on said comparing, assigning, to the event record, an event category having attribute criteria matching the identified one or more attributes of the event record; generating, for the first user, first compressed event data using the event records associated with the first user and a latent feature identification model, wherein the latent feature identification model takes the event records for the first user and the event categories assigned thereto as an input, and provides association values for the first user for respective event topics identified by the first compressed event data, wherein first compressed event data associated with the first user is stored using a second quantity of storage, the second quantity of storage being less than the first quantity of storage for storing the event records of the first user; assigning the first user to one of a plurality of data clusters included in a clustering model using the first compressed event data for the first user; and generating, for the first user, second compressed event data using a comparison between the first compressed event data for the first user and an average latent feature identification value for a latent feature included in the data cluster to which the first user has been assigned, wherein the second compressed event data associated with the first user is stored using a third quantity of storage, the third quantity of storage being less than the second quantity of storage. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of compressing transaction data, the method comprising:
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receiving a plurality of transaction records each identifying a transaction by one of a plurality of users; assigning a category to each of the plurality of transaction records; generating first compressed transaction records using a latent feature identification model, wherein the latent feature identification model takes the transaction records for the one of the plurality of users and categories assigned thereto as an input, and provides association values for the one of the plurality of users for respective topics identified in the first compressed event data; identifying a clustering compression model for the one of the plurality of users; and generating second compressed transaction records using the first compressed transaction records and the clustering compression model. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A transaction data compression system comprising:
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one or more computer processors configured to execute software instructions; a non-transitory tangible storage device storing the software instructions executable by the one or more processors to at least; access transaction data associated with a plurality of users; for a plurality of transactions in the transaction data; assign a transaction category based on one or more attributes of the transaction; and assign a transaction category level for the transaction category based at least in part on on spend levels of individual users associated with the plurality of transactions; and generate, for each user, first compressed transaction data using the transaction categories assigned to the transaction records for a respective user and a latent feature identification model, wherein the latent feature identification model takes the event records for the first user and the event categories assigned thereto as an input, and provides association values for the first user for respective event topics identified by the first compressed event data, wherein the first compressed transaction data associated with the one of the respective users is stored using a second quantity of storage, the second quantity of storage being less than the first quantity of storage; identify a clustering compression model for users included in the plurality of users; assign each of the users to one of a plurality of data clusters included in the respective clustering compression model using respective first compressed transaction data for the user; and generate, for each user, second compressed transaction data using a comparison between the first compressed transaction data for a user and an average for the data cluster to which the user has been assigned, wherein the second compressed transaction data is stored using a third quantity of storage, the third quantity of storage being less than the second quantity of storage. - View Dependent Claims (17, 18, 19, 20)
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Specification