Clari Inc.

Mahesh Subedi

Venkat Rangan

Lei Tang

Kurt Leafstrand

Sanjay Kumar Mahalingam

MohamadAli Torkamani

The disclosure describes various embodiments for determining emails that each need a response based on data from a customer relationship (CRM) system. In one embodiment, a method of determining such emails includes the operations of retrieving open tasks assigned to a user from a task database; determining one or more source email domains for one or more source contacts, and one or more target email domains for one or more target contacts; and determining one or more threads emails exchanged between the source contacts and the target contacts based on the source email domains and the target email domains. The method further includes the operations of creating an email list from the threads of emails, including a latest email from a group that was sent by a target contact; and generating a subset of the list of emails by analyzing contents of each of the list of emails using a machine learning model.

Method for determining and notifying users of pending activities on CRM data

According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Method and system for identifying emails and calendar events associated with projects of an enterprise entity

Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.

Method and system for summarizing user activities of tasks into a single activity score using machine learning to predict probabilities of completeness of the tasks

According to some embodiments, described herein is a system and method for classifying and grouping users of a task database system based on their activities in completing tasks defined in the task database system. A cloud server communicatively coupled to the task database system receives task data from the task database system, receives a request for ranking users under a particular level in a user hierarchy, gathers user activity data from an activity data server, uses a predetermined algorithm calculate a raw score for each user based on the user's activity, normalizes the raw score, and generates a ranking of the users based on their normalized scores. The ranking can be used as an indicator for determining which users need additional training in communicating with outside parties in completing assigned tasks.

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Sunnyvale, CA, United States of America

Mahesh Subedi