[24]7.ai, Inc.

24/7 Customer, Inc.

Subhash Ramchandra Kulkarni

Kranthi Mitra Adusumilli

Ravi Vijayaraghavan

Ravi Prakash

Darshan Hegde

User intent is identified while the user browses online and recommendations are provided to the user. The recommendations are based on the identified intent, interests, and preferences of the user who is performing the searches. The determination of user intent and interests is based on a statistical model derived from data compiled from the user and a plurality of other users. Other resources may also be determined to be relevant, for example, because of past interactions of the user, memberships of the user in ecommerce websites, the user's interests and preferences are similar to those of other users, and so on. The result of the user search is a ranked set of recommendations that is provided to the user.

Intent prediction based recommendation system using data combined from multiple channels

Online browsing behavior is used to predict the intent of online users dynamically. The category of online user is predicted and the prediction can be used to provide assistance to the user, if required. Such prediction is based on a combination of a Na&#xef;ve's Bayes classifier and a Markov model.

Dynamic prediction of online shopper's intent using a combination of prediction models

A method and apparatus enables identification of customer characteristics and behavior, and predicts the customer's intent. Such prediction can be used to adopt various business strategies to increase the chances of conversion of customer interaction to a sale, and thereby can increase revenue, and/or enhance the customer's experience.

Method and apparatus for intent modeling and prediction

Embodiments of the invention relate to managing user interactions and, more particularly, to performing analysis on data generated by user interactions. Embodiments of the invention use text mining to extract personal information of users from user interactions automatically. A topic model is used to reduce the number of dimensions required to represent the text, yet all the information of interest is highly pronounced. This enables a lower dimensional representation of the data leading to significantly faster computations.

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Bengaluru, India

Subhash Ramchandra Kulkarni