US 2014/0229407 A1Published application

Distributing relevant information to users of an enterprise network

Assigneesalesforce.com, inc.

InventorScott Douglas White

Life of this patent

Estimating the term…

Loading patent…

Abstract

Various implementations are directed to systems, apparatus, computer-implemented methods and storage media for identifying a target set of users of an enterprise network to which to distribute a communication of enterprise-related information. For example, when a communication system receives a request to distribute a communication, the communication system analyzes the communication to identify a set of enterprise users that are predicted to find the information in the communication relevant, and especially, relevant from the enterprise's perspective. For example, the communication system can include a machine learning system that can construct, update and maintain a machine learning model of induction. In some implementations, the machine learning system trains the machine learning model by identifying contextual features of previously distributed communications, user traits of recipients of the previously distributed communications, and actions or inactions that indicate whether the recipients found the information in the communications relevant.

Claims32

Dependent claims

A computer implemented method for updating a machine learning model for determining one or more decision boundaries, the method comprising:

analyzing, by one or more computing systems, an enterprise-related user communication;
determining, by the one or more computing systems, one or more contextual features for the user communication based on one or more of the content, subject, purpose, source, and recipients of the user communication;
determining, by the one or more computing systems, one or more relevancy scores for each of one or more respective recipients of the user communication, each relevancy score based on actions taken or not taken by the respective recipients based on the communication, each relevancy score representing a relevance of enterprise-related information included with the communication to the respective recipient;
determining, by the one or more computing systems, one or more user traits associated with the one or more recipients of the user communication;
analyzing, by a machine learning system executing within the one or more computing systems, the one or more determined contextual features, the one or more determined relevancy scores and the one or more determined user traits associated with the one or more recipients;
updating, by the machine learning system, a machine learning model based on the analysis of the one or more determined contextual features, the one or more determined relevancy scores and the one or more determined user traits, the machine learning model being stored in one or more databases accessible by the one or more computing systems, the updating including determining one or more relevancy values for the machine learning model based at least in part on the one or more determined relevancy scores and the one or more determined user traits;
and
determining one or more decision boundaries for the machine learning model based on the determined relevancy values, each decision boundary separating at least a portion of the machine learning model into a first class of user trait values having respective relevancy values above a threshold from a respective second class of user trait values having respective relevancy values below the threshold.

5 more elements

A computer implemented method for using a machine learning model to identify a set of enterprise users to receive a communication, the method comprising:

analyzing, by one or more computing systems, an enterprise-related user communication or a request to distribute an enterprise-related user communication;
determining, by the one or more computing systems, one or more contextual features for the user communication based on one or more of the content, subject, purpose and source of the user communication;
providing, by the one or more computing systems, the one or more determined contextual features to a machine learning model stored in one or more databases accessible by the one or more computing systems, the machine learning model including n dimensions for representing n respective user traits, each user trait having two or more possible values, the machine learning model further including a plurality of relevancy values associated with respective user trait values;
identifying, by the one or more computing systems, a target set of enterprise users to receive the user communication, the identifying including, for each of one or more of the determined contextual features:
determining, based on the relevance values in the machine learning model, one or more enterprise users of a plurality of candidate enterprise users that are associated with user trait values having respective relevancy values above a threshold;
and
selecting the determined one or more enterprise users as the target set of enterprise users;
and
distributing the user communication to the target set of enterprise users.

6 more elements

Dependencies are inferred from the claim text.