Open Text Corporation

Emc Corporation

Amazon Technologies, Inc.

Edward C Bueche

Marc Brette

Sunita Casula

Shu-Shang Sam Wei

Shreyas Satyanand Kaushik

Frederic Ciminera

Petr Pleshachkov

Shanshan Quan

Peter W Madany

Christopher H Stacey

Francisco Borges

Venkatesan Chandrasekaran

Daniel S Collins

Francois Mairesse

Allen B Hubbe

Navaneeth Ranganna

Suvidya S Daftardar

Amina Shabbeer

Oluwasoga T Oni

Umer Toor

Toby Ray Latin-Stoermer

Brandon Scott Durham

Fabian Moerchen

Panagiota Karanasou

Jonathan Barnett Feinstein

Alok Verma

Catherine Breslin

Fabian Triefenbach

Tao Ye

Klaus Reiter

Judith Gaspers

David Hikaru Nakayama

Yun Suk Paik

Traci Wei-Fien Tsai Gadow

James Wade Hoelter

Torbjorn Vik

Jonathan Pollack

Meng (Joseph) Hsien Hsieh

Charles Lee Thorp

Warren D Freitag

Anmol Tiwari

Robert Matthew Cowherd

Amaury Gutierrez Acosta

Sheperd Huang

A method for providing a dormant state for content management servers is provided. Client devices are allowed to conduct transactions with servers when the servers are active. However, in a dormant state, the servers are not allowed to accept new transactions. Thus, by utilizing the dormant state, software upgrades can be made to one server at a time. Alternatively, all servers can be taken down for major upgrades, with the servers still operated in a read-only mode based on a file image from a point in time just prior to the shutdown. When the upgrade is completed, the servers can be returned to the active state.

Method and system for virtual server dormancy

A system for providing a search service application is disclosed and includes an application builder component that provides a search model for a first object of a plurality of objects. The search model is based at least on an end-user input field corresponding to a first attribute of the first object and a search result output field corresponding to a second attribute of the first object. The search model is also associated with a backend data store that supports a storage structure that stores information relating to the first object. The system also includes a deployment engine that automatically configures a search engine system associated with the backend data store to place a portion of indexed data into a first partition and to place another portion of indexed data into at least another partition based on the search model.

Methods and systems for providing a search service application

Dialog acts (e.g., questions) are selected for voice browsing by a model trained to identify a dialog act that is most likely to lead to a desired outcome. Upon receiving an invocation to begin a conversation, a score indicative of a level of confidence that the conversation will have a successful outcome is determined, and a dialog act is selected based on the score. Subsequently, at each turn of the conversation, the score is updated or a new score is calculated, and a dialog act is selected based on the updated or new score. Confidence scores are calculated based on input features that are determined based on the user who uttered the invocation or responses to dialog acts, as well as a context of the conversation, and provided to a linear model or a machine learning model as inputs.

Confidence scoring for selecting tones and text of voice browsing conversations

Voice data from a current conversation between a user and a voice-controlled user device can be used to determine a search constraint for searching a database. Other search constraints can be determined based at least in part on the current conversation, a previous conversation, and/or a previous action. Properties can be associated with the search constraints. Once the search constraints have been determined, a plurality of search query plans is determined and a first search query plan is executed to query the database.

Voice conversational search constraint management

Dialog acts (e.g., questions) are selected for voice browsing by a machine learning model trained to identify a dialog act that is most likely to lead to a desired outcome. When an invocation is received from a user, a context of the invocation is determined, and a pool of dialog acts is scored based on the context by a machine learning model. Dialog acts are selected from the pool and presented to the user in accordance with a randomization policy. Data regarding the dialog acts and their success in achieving a desired outcome is used to train one or more machine learning models to select dialog acts in response to invocations.

Selecting dialog acts using controlled randomness and offline optimization

Techniques are provided for training a target language model based at least in part on data associated with a reference language model. For example, language data utilized to train an English language model may be translated and provided as training data to train a German language model to recognize utterances provided in German. By utilizing the techniques herein, the efficiency of training a new language model may be improved due at least in part to replacing labor-intensive operations conventionally performed by specialized personnel with machine-generated data. Additionally, techniques discussed herein provide for reducing the time required for training a new language model by leveraging information associated with utterances of one language to train the new language model associated with a different language.

Techniques for model training for voice features

Techniques are provided for training a language recognition model. For example, a language recognition model may be maintained and associated with a reference language (e.g., English). The language recognition model may be configured to accept as input an utterance in the reference language and to identify a feature to be executed in response to receiving the utterance. New language data (e.g., other utterances) provided in a different language (e.g., German) may be obtained. This new language data may be translated to English and utilized to retrain the model to recognize reference language data as well as language data translated to the reference language. Subsequent utterances (e.g., English utterances, or German utterances translated to English) may be provided to the updated model and a feature may be identified. One or more instructions may be sent to a user device to execute a set of instructions associated with the feature.

A system for providing a search service application is disclosed and includes an application builder component that provides a search model for a first object of a plurality of objects. The search model is based at least on an end-user input field corresponding to a first attribute of the first object and a search result output field corresponding to a second attribute of the first object. The search model is also associated with a backend data store that supports a storage structure that stores information relating to the first object. The system also includes a deployment engine that automatically configures a search engine system associated with the backend data store to generate new search indexes based on the search model, and to place a portion of indexed data into a first partition and to place another portion of indexed data into at least another partition based on the search model.

A method for providing a dormant state for content management servers. Client devices are allowed to conduct transactions with servers when the servers are active. However, in a dormant state, the servers are not allowed to accept new transactions. Thus, by utilizing the dormant state, software upgrades can be made to one server at a time. Alternatively, all servers can be taken down for major upgrades, with the servers still operated in a read-only mode based on a file image from a point in time just prior to the shutdown. When the upgrade is completed, the servers can be returned to the active state.

Technologies are disclosed herein for providing a media application service for predictive caching of media content on a mobile device. The media application service is configured to consider usage data related to media content playback by a user on one or more devices, a connectivity profile of the mobile device, and to generate a list of media content to cache on the mobile device when power and network connectivity requirements of the mobile device are met or exceeded.

Predictive caching of media content

Growing community of inventors

Pleasanton, CA, United States of America

Edward C Bueche