Conduent Business Services, LLC

Simarjot Kaur

Arun Rajkumar

Nupur Labh

Narayanan Unny

Saurabh Srivastava

Sandya Srivilliputtur Mannarswamy

Satish Prasad Rath

Anuj Mahajan

Jayati Bandyopadhyay

Tuhin Bhattacharya

Saurav Bhattacharya

Neha Singh

Sahil Manchanda

James Macurak

Sachin Mittal

An autonomous automated intent mining system extracts intents from stored conversations in a text-based chat corpus for use in an AI-driven dialog system. The intent mining system includes an intelligent pre-processor for extracting the conversation data from a computer memory and operating on the conversation data to identify relevant utterances. The system additionally includes a representation processor for operating on the identified relevant utterances to create a numerical representation. The system also includes a clustering processor for generating clusters of utterances and classifying each of the identified relevant utterances into a cluster based on the numerical representation of each utterance. Each generated cluster represents a unique intent for use in the AI-driven dialog system.

Method and system for automated autonomous intent mining

A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Trained pattern analyzer for roll out decisions

System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.

Personalizing application interfaces based on usage

Methods and systems for personalized trust recommendation based on a dynamic trust model. In an example embodiment, a trust framework can be derived from a set of use-case specific factors. Invasive data and non-invasive data can be collected from a user (or a group of users) based on activity data and profile data associated with the user. A dynamic trust profile can be created (or learned) based on the invasive data and the non-invasive data collected from the user. A recommended level of trustworthiness can be then provided to the user respect to a particular situation and/or entity (e.g. other users) within the trust framework based on the dynamic trust profile of the user and which is personalized for the user.

Method and system for dynamic trust model for personalized recommendation system in shared and non-shared economy

Growing community of inventors

Bengaluru, India

Simarjot Kaur