Techniques are described herein for assembling/evaluating automated assistant responses for privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.

Techniques are described herein for assembling/evaluating automated assistant responses for privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.

A system of multi-modal transmission of packetized data in a voice activated data packet based computer network environment is provided. A natural language processor component can parse an input audio signal to identify a request and a trigger keyword. Based on the input audio signal, a direct action application programming interface can generate a first action data structure, and a content selector component can select a content item. An interface management component can identify first and second candidate interfaces, and respective resource utilization values. The interface management component can select, based on the resource utilization values, the first candidate interface to present the content item. The interface management component can provide the first action data structure to the client computing device for rendering as audio output, and can transmit the content item converted for a first modality to deliver the content item for rendering from the selected interface.

A system of multi-modal transmission of packetized data in a voice activated data packet based computer network environment is provided. A natural language processor component can parse an input audio signal to identify a request and a trigger keyword. Based on the input audio signal, a direct action application programming interface can generate a first action data structure, and a content selector component can select a content item. An interface management component can identify first and second candidate interfaces, and respective resource utilization values. The interface management component can select, based on the resource utilization values, the first candidate interface to present the content item. The interface management component can provide the first action data structure to the client computing device for rendering as audio output, and can transmit the content item converted for a first modality to deliver the content item for rendering from the selected interface.

A method includes generating a text file related to media content. The media content comprises a first segment and a second segment. A first portion of the text file includes a first time of the first segment and represents audio in the first segment or video in the first segment. A second portion of the text file includes a second time of the second segment and represents audio in the second segment or video in the second segment. A search query is received at a playback device, and the playback device plays the media content starting at the second time in response to the search query matching the second portion of the text file.

A computer-implemented method for generating a first set of longest common sequences from a plurality of known malicious webpages, the first set of longest common sequences representing input data from which a human generates a set of regular expressions for detecting phishing webpages. There is included obtaining HTML source strings from the plurality of known malicious webpages and transforming the HTML source strings to reduce the number of at least one of stop words and repeated tags, thereby obtaining a set of transformed source strings. There is further included performing string alignment on the set of transformed source strings, thereby obtaining at least a scoring matrix. There is additionally included obtaining a second set of longest common sequences responsive to the performing the string alignment. There is further included filtering the second set of longest common sequences, thereby obtaining the first set of longest common sequences.

A computer-implemented method for generating a first set of longest common sequences from a plurality of known malicious webpages, the first set of longest common sequences representing input data from which a human generates a set of regular expressions for detecting phishing webpages. There is included obtaining HTML source strings from the plurality of known malicious webpages and transforming the HTML source strings to reduce the number of at least one of stop words and repeated tags, thereby obtaining a set of transformed source strings. There is further included performing string alignment on the set of transformed source strings, thereby obtaining at least a scoring matrix. There is additionally included obtaining a second set of longest common sequences responsive to the performing the string alignment. There is further included filtering the second set of longest common sequences, thereby obtaining the first set of longest common sequences.

A machine learning system executed by a processor may generate predictions for a variety of natural language processing (NLP) tasks. The machine learning system may include a single deployment implementing a parameter efficient transfer learning architecture. The machine learning system may use adapter layers to dynamically modify a base model to generate a plurality of fine-tuned models. Each fine-tuned model may generate predictions for a specific NLP task. By transferring knowledge from the base model to each fine-tuned model, the ML system achieves a significant reduction in the number of tunable parameters required to generate a fine-tuned NLP model and decreases the fine-tuned model artifact size. Additionally, the ML system reduces training times for fine-tuned NLP models, promotes transfer learning across NLP tasks with lower labeled data volumes, and enables easier and more computationally efficient deployments for multi-task NLP.

Systems, methods, and devices for managing privacy policies are disclosed. In one embodiment, a method for management of a user's privacy preferences may include: identifying a computer application installed on a user electronic device, or a website accessed using a browser executed by the user electronic device; retrieving a privacy policy document analysis for a privacy policy document associated with the computer application or the website, the privacy policy document analysis comprising a valuation of a plurality of privacy policy segments within the privacy policy document; receiving a privacy preference analysis for the user, the privacy preference analysis comprising a valuation of a plurality of privacy preferences for the user; identifying a recommended action in response to the valuation of one of the privacy policy segments being outside the scope of the valuation of one of the plurality of privacy preferences; and executing the recommended action.

Systems, methods, and devices for managing privacy policies are disclosed. In one embodiment, a method for management of a user's privacy preferences may include: identifying a computer application installed on a user electronic device, or a website accessed using a browser executed by the user electronic device; retrieving a privacy policy document analysis for a privacy policy document associated with the computer application or the website, the privacy policy document analysis comprising a valuation of a plurality of privacy policy segments within the privacy policy document; receiving a privacy preference analysis for the user, the privacy preference analysis comprising a valuation of a plurality of privacy preferences for the user; identifying a recommended action in response to the valuation of one of the privacy policy segments being outside the scope of the valuation of one of the plurality of privacy preferences; and executing the recommended action.