Dish Wireless L.l.c.

Sling Media Inc.

Dish Network Technologies India Private Limited

Madhukar Pulluru

Yongjoon Oh

Kalleri Faizel Rehiman

Sisong Bei

Amith Kumar Ullur Raghavendra

Christine Chu

Mikhail L Arbuzov

Christine Michelle Chu

Vinay Tiwari

Faizel Rehiman Kalleri

Vignesh Narayanaswamy

Qi Chu

The system obtains a first low-level information representing a first multiplicity of activities. The first low-level information includes a first log including a first multiplicity of entries representing the first activity. An entry among the first multiplicity of entries can represent various activities. The system obtains a second low-level information representing a second multiplicity of activities. The second low-level information includes a second log comprising a second multiplicity of entries. The system obtains a correspondence between the second multiplicity of activities and the second low-level information. The correspondence maps a second activity among the second multiplicity of activities to a subset of entries in the second log. The system determines that the subset of entries in the second log and a subset of entries in the first log are similar and the subset of entries in the first log indicates the second activity.

Determining an activity associated with a mobile device based on a low-level information representing the activity

The system obtains data associated with UE, representing interaction between the UE and a network. The data includes: previous bandwidth usage associated with the UE, previous CDR associated with the UE, anticipated geolocation of the UE, a plan associated with the UE, one or more media events, a number of lines associated with the UE, a length of time the UE has been associated with the network, and a unique identifier associated with the UE. The system obtains multiple plans associated with the network, where a plan indicates the bandwidth usage associated with the UE within a predetermined period. Based on the data, the system predicts the bandwidth usage associated with the UE within the predetermined period to obtain a predicted bandwidth usage and determines the plan among the multiple plans accommodating the predicted bandwidth usage. The system requests the bandwidth usage associated with the plan from the network.

Predicting a bandwidth usage associated with a mobile device operating on a wireless telecommunication network

Methods and apparatuses that utilize machine learning techniques to dynamically adjust the placement of secondary content that is displayed across numerous user devices over time are described. The user devices may comprise electronic computing devices, such as a mobile phones and digital televisions. The secondary content may be displayed within open slots of webpages or display screens in response to being selected for display during a real-time bidding process for the open slots. In some cases, in response to a bid request for an open slot within a webpage or display screen, a computer-implemented bid generation system for determining the selection and placement of secondary content may identify the secondary content to be displayed within the open slot, determine a bid amount for the identified secondary content, and transmit a bid response that includes the bid amount and the identified secondary content.

Adaptive placement of audiovisual content on user devices

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Foster City, CA, United States of America

Madhukar Pulluru