Decho Corporation

Tignis, Inc.

Oregon State University

Delta Design, Inc.

Mozy, Inc.

Jonathan L Herlocker

John Bruce Forbes

Thomas G Dietterich

Paul Maritz

Matt McLaughlin

Ryan J Stoddard

Thomas G Dletterich

Alexander Fry

Adam Ashenfelter

Steven Herchak

A method is disclosed for generating a corrective film pattern for reducing wafer bow in a semiconductor wafer fabrication process. The method inputs to a neural network a wafer bow signature for a predetermined semiconductor fabrication step. The neural network generates from the input a corrective film pattern corresponding to the wafer bow signature. The neural network is trained with a training dataset of wafer shape transformations and corresponding corrective film patterns.

Method for determining corrective film pattern to reduce semiconductor wafer bow

A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.

Method for physical system anomaly detection

A method for analyzing time series sensor data of a physical system represented by a process graph retrieves sensor data streams from stored sensor time series data. Each of the sensor data streams comprises a sequence of time-value pairs and is associated with a sensor identifier, a time offset, and a sampling period. A metric data stream is produced from the retrieved sensor data streams in accordance with a stored physics model of the physical system. Producing the metric data stream includes i) synchronizing the sensor data streams by adjusting time offsets of the sensor data streams and adding interpolated values and times to the sensor data streams to produce synchronized streams with equal sampling periods; and ii) performing a point-wise computation over values of the sensor data streams in accordance with the physics model.

Analysis of time series sensor measurements in physical systems

A method for automatically presenting digital content to a user of a computer computes task-related metadata from data which may include i) a most recent event record, ii) a most recent specification received from the user of a task being performed by the user, iii) an automatic prediction of the current task being executed by the user, iii) past event records and associated task identifiers stored in a database and/or iv) content in resources associated with a given task. The task-related metadata is communicated to a digital content service provider. Digital content relevant to the user based on the task-related metadata is then selected, sent to the computer, and presented to the user. Rules and filters control the metadata going out and the content coming in, allowing automatic adaptation based on the current task, characteristics of the content, and other factors.

Methods for delivering task-related digital content based on task-oriented user activity

Methods for using task-related information to enhance digital searching are provided. A task-oriented user activity system maintains task-related information about resources accessed by a user and current user task. This task-related information is used to enhance search queries to include task-related search criteria that improve relevance of search results. The task-related information can also be used to include task-related metadata in search engine index, e.g., by storing the metadata in the index or by storing it in resources which are subsequently indexed. Task-related information can also be used to enhance search results by filtering and ranking results to increase relevance with respect to a user's current task.

Methods for enhancing digital search query techniques based on task-oriented user activity

Methods for using task-related information to enhance digital searching are provided. A task-oriented user activity system maintains task-related information about resources accessed by a user and current user task. This task-related information is used to enhance search results by filtering and ranking results to increase relevance with respect to a user's current task. The task-related information can also be used to include task-related metadata in search engine index, e.g., by storing the metadata in the index or by storing it in resources which are subsequently indexed. Task-related information can also be used to enhance search results by enhancing search queries to include task-related search criteria.

Methods for enhancing digital search results based on task-oriented user activity

Methods for using task-related information to enhance digital searching are provided. A task-oriented user activity system maintains task-related information about resources accessed by a user and current user task. This task-related information is used to include task-related metadata in search engine index, e.g., by storing the metadata in the index or by storing it in resources which are subsequently indexed. Task-related information can also be used to enhance search results by filtering and ranking results to increase relevance with respect to a user's current task and/or to enhance search queries to include task-related search criteria.

Methods for generating search engine index enhanced with task-related metadata

A method for assisting multi-tasking computer users includes receiving from a user a specification of a task being performed by the user or an indication of completion of a task, collecting state changes in multiple executing programs, predicting a current task being performed by the user based on a recent state change event, a past specification of a task being performed by the user, and past events and associated tasks. Based on the predicted current task, user interface elements in multiple executing programs are adapted to facilitate performance of the task. The method may also allow a user to specify a new task based on a task template derived from a completed task to facilitate completion of the new task. The task templates also may be shared among users, and active tasks may also be team tasks shared among users.

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Seattle, WA, United States of America

Jonathan L Herlocker