Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved table identification in a spreadsheet. One method including: receiving a spreadsheet including at least one table; identifying, using machine learning, one or more classes of a plurality of classes for each cell of the received spreadsheet, wherein the plurality of classes include corners and not-a-corner; and inducing at least one table in the received spreadsheet based on the one or more identified classes for each cell of the received spreadsheet.

Abstract: A processing system is described which induces a context free grammar (CFG) based on a set of descriptions. The descriptions pertain to a particular subject. Thus, the CFG targets the particular subject, and is accordingly referred to as a subject-targeted context free grammar (ST-CFG). The processing system can use the ST-CFG to determine whether a new description is a proper description of the subject. The processing system also provides synthesizing functionality for building an ST-CFG based on one or more smaller component ST-CFGs.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user such as the creator of the content item or an administrator. The microtasks can be assigned to one or more workers including the creator of the content item. When a determination is made that an assigned worker is available to complete a microtask (e.g., when the worker is waiting in line, has just closed an application or file, or has just completed a phone call, etc.), the assigned microtask is presented to the worker for completion.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.

Abstract: Application programming interface (API) for starting and accessing distributed routing table (DRT) functionality. The API facilitates bootstrapping into the DRT by one or more devices of a group of devices (a mesh) seeking to collaborate over a serverless connection, establishing a node of the DRT, where each node is an instance of an application that is participating in the mesh, and node participation by allowing the application to search for keys published by other nodes in the mesh, or by becoming part of the mesh by publishing a key. The API facilitates optimization of the routing table for quickly finding a root of a specific key in the mesh by finding the key directly in a cache or by asking a root node of the key that is in the local routing table that is closest numerically to the key being searched.

Abstract: A processing system is described which induces a context free grammar (CFG) based on a set of descriptions. The descriptions pertain to a particular subject. Thus, the CFG targets the particular subject, and is accordingly referred to as a subject-targeted context free grammar (ST-CFG). The processing system can use the ST-CFG to determine whether a new description is a proper description of the subject. The processing system also provides synthesizing functionality for building an ST-CFG based on one or more smaller component ST-CFGs.

Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.

Abstract: The universal text input technique described herein addresses the difficulties of typing text in various languages and scripts, and offers a unified solution, which combines character conversion, next word prediction, spelling correction and automatic script switching to make it extremely simple to type any language from any device. The technique provides a rich and seamless input experience in any language through a universal IME (input method editor). It allows a user to type in any script for any language using a regular qwerty keyboard via phonetic input and at the same time allows for auto-completion and spelling correction of words and phrases while typing. The technique also provides a modeless input that automatically turns on and off an input mode that changes between different types of script.

Abstract: The universal text input technique described herein addresses the difficulties of typing text in various languages and scripts, and offers a unified solution, which combines character conversion, next word prediction, spelling correction and automatic script switching to make it extremely simple to type any language from any device. The technique provides a rich and seamless input experience in any language through a universal IME (input method editor). It allows a user to type in any script for any language using a regular qwerty keyboard via phonetic input and at the same time allows for auto-completion and spelling correction of words and phrases while typing. The technique also provides a modeless input that automatically turns on and off an input mode that changes between different types of script.

Abstract: Application programming interface (API) for starting and accessing distributed routing table (DRT) functionality. The API facilitates bootstrapping into the DRT by one or more devices of a group of devices (a mesh) seeking to collaborate over a serverless connection, establishing a node of the DRT, where each node is an instance of an application that is participating in the mesh, and node participation by allowing the application to search for keys published by other nodes in the mesh, or by becoming part of the mesh by publishing a key. The API facilitates optimization of the routing table for quickly finding a root of a specific key in the mesh by finding the key directly in a cache or by asking a root node of the key that is in the local routing table that is closest numerically to the key being searched.

Abstract: Application programming interface (API) for starting and accessing distributed routing table (DRT) functionality. The API facilitates bootstrapping into the DRT by one or more devices of a group of devices (a mesh) seeking to collaborate over a serverless connection, establishing a node of the DRT, where each node is an instance of an application that is participating in the mesh, and node participation by allowing the application to search for keys published by other nodes in the mesh, or by becoming part of the mesh by publishing a key. The API facilitates optimization of the routing table for quickly finding a root of a specific key in the mesh by finding the key directly in a cache or by asking a root node of the key that is in the local routing table that is closest numerically to the key being searched.

Abstract: Application programming interface (API) for starting and accessing distributed routing table (DRT) functionality. The API facilitates bootstrapping into the DRT by one or more devices of a group of devices (a mesh) seeking to collaborate over a serverless connection, establishing a node of the DRT, where each node is an instance of an application that is participating in the mesh, and node participation by allowing the application to search for keys published by other nodes in the mesh, or by becoming part of the mesh by publishing a key. The API facilitates optimization of the routing table for quickly finding a root of a specific key in the mesh by finding the key directly in a cache or by asking a root node of the key that is in the local routing table that is closest numerically to the key being searched.