Intuit, Inc.

Yi Xu Ng

Sricharan Kallur Palli Kumar

Yogish Pai

Conrad De Peuter

Steve George Goyette

Efraim David Feinstein

Robert Bruce MacMartin

Ian Andrew Sebanja

Nagaraj Janardhana

Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.

Integrated machine learning and rules platform for improved accuracy and root cause analysis

A method identifies a plurality of product categories by applying a categorization model to product data that includes product names and product descriptions. The product data is obtained from internal sources and external sources. The method creates a knowledge graph for a plurality of business entities from inputs that include the plurality of product categories, published data that includes merchant categorizations, and geographical data. The method generates cohort clusters of similar business entities by applying a clustering algorithm to the knowledge graph. The method uses the cohort clusters to learn an extrapolation model for predicting inventory. The method makes a prediction as to the inventory level using the extrapolation model for a user of the merchant application. The method determines a confidence level for the user and orders additional inventory for the user without further input, if the confidence level is above a specified threshold.

User behavior confidence level of automation

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Berkley, CA, United States of America

Yi Xu Ng