Abstract: Techniques for facilitating natural language interactions with visual interactive content are described. During a build time, a system analyzes various websites and applications relating to a particular user goal to understand website and application navigation and information relating to the user goal. The learned information is used to store configuration data. During runtime, when a user request performance of an action, the system engages in a dialog with the user to complete the user's goal. The system uses the stored configuration data to determine actions to be performed at a website or application to complete the user's goal, and determines system responses to present to the user to facilitate completion of the goal. Such system responses may request information from the user, may inform the user of information displayed at the website or application, etc.

Abstract: Techniques for facilitating natural language interactions with visual interactive content are described. During a build time, a system analyzes various websites and applications relating to a particular user goal to understand website and application navigation and information relating to the user goal. The learned information is used to store configuration data. During runtime, when a user request performance of an action, the system engages in a dialog with the user to complete the user's goal. The system uses the stored configuration data to determine actions to be performed at a website or application to complete the user's goal, and determines system responses to present to the user to facilitate completion of the goal. Such system responses may request information from the user, may inform the user of information displayed at the website or application, etc.

Abstract: Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.

Abstract: A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.

Abstract: Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: Techniques for storing and using multi-session context are described. A system may store context data corresponding to a first interaction, where the context data may include action data, entity data and a profile identifier for a user. Later the stored context data may be retrieved during a second interaction corresponding to the entity of the second interaction. The second interaction may take place at a system different than the first interaction. The system may generate a response during the second interaction using the stored context data of the prior interaction.

Abstract: A system that is capable of resolving anaphora using timing data received by a local device. A local device outputs audio representing a list of entries. The audio may represent synthesized speech of the list of entries. A user can interrupt the device to select an entry in the list, such as by saying “that one.” The local device can determine an offset time representing the time between when audio playback began and when the user interrupted. The local device sends the offset time and audio data representing the utterance to a speech processing system which can then use the offset time and stored data to identify which entry on the list was most recently output by the local device when the user interrupted. The system can then resolve anaphora to match that entry and can perform additional processing based on the referred to item.

Abstract: A dialog system receives a user request corresponding to a dialog with a user. The dialog system processes the user request to determine multiple service providers capable of responding to the user request. The dialog system selects one service provider based on a request-to-handle score, and selects another service provider based on a satisfaction rating. The dialog system updates the dialog state based on further input provided by the user to determine an output responsive to the user request.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to create a training data set from at least one image, the request to include an indication of the at least one image and at least one indication of an operation to perform on the at least one image to generate a plurality of images from the at least one image; creating a training dataset by extracting one or more chunks from a first at least one image according to the request; and receiving one or more requests to train an anomaly detection machine learning model using the created training dataset; and training an anomaly detection machine learning model according to one or more requests using the created training data.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: Techniques for feedback-based training are described.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to create a training data set from at least one image, the request to include an indication of the at least one image and at least one indication of an operation to perform on the at least one image to generate a plurality of images from the at least one image; creating a training dataset by extracting one or more chunks from a first at least one image according to the request; and receiving one or more requests to train an anomaly detection machine learning model using the created training dataset; and training an anomaly detection machine learning model according to one or more requests using the created training data.

Abstract: A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.

Abstract: A system that is capable of resolving anaphora using timing data received by a local device. A local device outputs audio representing a list of entries. The audio may represent synthesized speech of the list of entries. A user can interrupt the device to select an entry in the list, such as by saying “that one.” The local device can determine an offset time representing the time between when audio playback began and when the user interrupted. The local device sends the offset time and audio data representing the utterance to a speech processing system which can then use the offset time and stored data to identify which entry on the list was most recently output by the local device when the user interrupted. The system can then resolve anaphora to match that entry and can perform additional processing based on the referred to item.

Abstract: A system can operate a speech-controlled device in a mode where the speech-controlled device determines that an utterance is directed at the speech-controlled device using image data showing the user speaking the utterance. If the user is directing the user's gaze at the speech-controlled device while speaking, the system may determine the utterance is system directed and thus may perform further speech processing based on the utterance. If the user's gaze is directed elsewhere, the system may determine the utterance is not system directed (for example directed at another user) and thus the system may not perform further speech processing based on the utterance and may take other actions, for example discarding audio data of the utterance.

Abstract: A technology is described for deploying datasets to a production data store. An example of the technology may include receiving a request at a data deployment service to submit a dataset to a production data store which is accessible to services that utilize datasets stored in the production data store. A temporary dataset may be created in a preproduction staging store and a first consistency check may be performed against the temporary dataset to determine whether the temporary dataset complies with dataset specifications. The temporary dataset may be included in a batch of temporary datasets cached on the preproduction staging store when the first consistency check is successful, and a second consistency check may be performed against the batch to determine whether the at least one temporary dataset complies with the dataset specifications. The temporary datasets may be stored to the production data store when the second consistency check is successful.

Abstract: A method and apparatus for adaptive test time reduction is provided. The method begins with running a predetermined number of structural tests on wafers or electronic chips. Pass/fail data is collected once the predetermined number of structural tests have been run. This pass/fail data is then used to determine which of the predetermined number of structural tests are consistently passed. The consistently passed tests are then grouped into slices within the test vectors. Once the grouping has been performed, the consistently passed tests are skipped when testing future production lots of the wafers or electronic chips. A sampling rate may be modulated if it is determined that adjustments in the tests performed are needed. In addition, a complement of the tests performed on the wafers may be performed on the electronic chips to ensure complete test coverage.

Abstract: Systems and methods for preparing an application testing environment and for executing an automated test script in an application testing environment are disclosed. According to an aspect, a method includes providing graphical user interface (GUI) test automation objects. The method also includes classifying each of the GUI test automation objects as one of a test essential object and a test navigation object. Further, the method includes serializing the GUI test automation objects classified as a test navigation object for subsequent testing in a testing environment.

Abstract: Various processes or modules described herein enable the schematic design tools to obtain physical data of a physical design and to perform one or more simulations in the schematic domain with such physical data such that the schematic design tools are made electrically aware of the physical data. Various types of data in the physical domain may be transferred to the schematic domain for the performance of one or more schematic simulations with the transferred data. The schematic designs are thus made electrically aware of such data from the physical domain and may incorporate any layout induced effects early in the schematic design stage or even at the time a schematic instance of a physical module is to be created in the schematic domain.