Apple Inc.

Liveperson, Inc.

Ravikiran Chittari

Eswar Priyadarshan

Dan Grigorovici

Jayasurya S Vadrevu

Kenley Sun

Omar T Abdala

Joe Bradley

Alan Gilchrest

Prasenjit Mukherjee

Irfan Khasim Mohammed

Bodhi Deb

Matthew Dunn

Jeremy W Rothman-Shore

Robert LoCascio

Jonathan Altschuler

Suresh Ramakrishnaiah

Joseph Cuccinelli

Htet Win

Chuqing He

Andrew Miller

Eswar Priyadershan

Ravi Gadekarla

Akhila Ravela

Manoj Potturu

Ranjith Gampa

Deepali Kale

Snigdha Sivadas

Trupti Chinchghare

Jesse Wang

James Poulin

Systems and method are provided for artificial-intelligence-based formatting of data into interface-specific representations. A computing device may receive datasets including information structured for presentation through various interfaces. The computing device may train a machine-learning model using feature vectors defined from the datasets. The machine-learning model may be trained to generate interface-specific representations of data. The computing device may then receive a query through a first type of interface and execute the trained machine-learning model using the query and an identification of the first type of interface. The machine-learning model may generate a response to the query that includes a structure tailored for interfaces that correspond to the first type of interface. The computing device may then facilitate a transmission of the response to the query through an interface that corresponds to the first type of interface.

Methods and systems for implementing a unified data format for artificial intelligence systems

The present disclosure relates generally to facilitating routing of communications. More specifically, techniques are provided to dynamically transfer messaging between a network device and a terminal device to a type of bot based on intents identified from the messaging. Further, techniques are provided to track performance of the selected type of bot during automation.

Systems and methods for transferring messaging to automation

Methods and systems for transcript-based bot creation are provided. Information may be stored in memory regarding different automation templates associated with different statement types. A transcript may be imported that includes statements, which may be analyzed and classified as one or more of the different statement types. The imported transcript may be displayed in a graphic user interface with its statements displayed in accordance with the automation templates associated with the respective statement type. User input may be received, including modification input that modifies at least one automation template associated with at least one statement of the displayed transcript designated as an integration point. A custom bot may thereafter be generated based on the modification input and configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the modified automation template.

Automatic bot creation based on scripts

A method for facilitating the rendering of a web page on a mobile device includes providing an enhanced DOM tree for the web page that includes web page components and their display coordinates. A rule set is applied to the DOM tree, thereby selecting components for display on the mobile device. A mobile-device-specific version of the source code is generated based on the selected components.

Displaying content on a mobile device

A present technology intelligently retargets invitational content to a user by maintaining a user profile that includes data descriptive of the user's interaction with invitational content previously presented to the user and making decisions regarding retargeting based on an analysis of the user profile. The information in the user profile is not limited to whether or not the user completed a conversion action associated with invitational content presented. Rather, the profile is expanded to include such information as where on the conversion continuum the user abandoned the process and details of how the invitational content was presented to the user, e.g. colors used, situation, and message. When presented with a request for invitational content, the system uses the profile information to influence which content is selected and how it is presented to the user. This enables the system to re-present invitational content in a form and/or situation where the user is more likely to complete the conversion action.

Presenting content packages based on audience retargeting

Systems and methods are provided for selecting contexts for new invitational content and invitational content for new contexts. In particular, a performance history of delivered invitational content in known contexts is combined with similarity measures for the delivered invitational content, with respect to a new invitational content, to generate a list of potential contexts for the new invitational content. Similarly, a performance history of in known contexts with delivered invitational content can combined with similarity measures for known contexts, with respect to a new context, to generate a list of potential content for the new context. Further, a combination of these methods can be used to pair new invitational content with new contexts.

Predicting content and context performance based on performance history of users

Segments used to select content to be targeted to a user are recursively refined based on continuously derived user characteristics. Based on information gathered from new requests for targeted content and/or user interaction with previously delivered content the user is assigned to one or additional targeted segments. The targeted segments can be used to select content to be delivered to the user based on the user's assignment to the targeted segments. Accordingly, each user is grouped into one or more targeted segments and based on the user's inclusion in those segments, requests for targeted content can be served to the user.

Population segmentation

Systems and methods are provided for audience targeting based on a performance history of invitational content from secondary content providers, such as advertisements. In operation, responsive to request from a user for content package, a queue of available invitational content is retrieved. Thereafter, based at least on a performance history of the available invitational content in the queue, a context of the primary content, and a performance history of invitational content previously presented to other users, the queue can be at least partially reordered to provide an adjusted queue. The invitational content for the content package can then be selected based on the adjusted queue.

Network-based targeted content delivery based on queue adjustment factors calculated using the weighted combination of overall rank, context, and covariance scores for an invitational content item

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Acton, MA, United States of America

Ravikiran Chittari