Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Various embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and wearable computing and audio devices for monitoring and managing health and wellness. More specifically, disclosed are methods, interfaces, and computer-readable media to generate predictive consumable recommendations and indicators to determine nutrient density of health-promoting nutrients in consumables, such as food, drink, supplements, and the like. In one or more embodiments, a flow includes identifying nutritional content of one or more consumables, selecting a first element of the one or more consumables, identifying the first element as a first nutrient, and identifying a second element of the one or more consumables. Further, the flow includes characterizing an association between the first nutrient and the second element, and determining an indicator indicative of a nutrient density of at least the first element included in the one or more consumables.

Abstract: An electronic device may include hardware and software configured to detect device failures in the electronic device and log a device error code for detected device failures. Device error codes may be electronically communicated over a communications link to a system external to the electronic device. A client device (e.g., a smartphone, tablet, pad, or PC) may be linked with the electronic device and may be configured to receive the communicated device error codes. The client device may be operative to communicate the received device error codes to another system (e.g., a backend system). The electronic device may link with the backend system and communicate the device error codes to the backend system. Networked computing resources in communication with the backend system may analyze the received error codes to determine whether or not to communicate a device replacement notice. A customer profile may be used as part of the analysis.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Systems and methods for providing career recommendations to a member of a social network are described. In some example embodiments, the systems and methods receive input associated with a professional or aspirational goal from a member of a social network, determine a recommendation based on information stored by the social network, and provide the recommendation to the member of the social network, among other things.

Abstract: Systems and methods for providing career recommendations to a member of a social network are described. In some example embodiments, the systems and methods receive input associated with a professional or aspirational goal from a member of a social network, determine a recommendation based on information stored by the social network, and provide the recommendation to the member of the social network, among other things.

Abstract: Disclosed is a task classification system that interacts with a user. The task classification system may include a recognizer that may recognize symbols in the user's input communication, and a natural language understanding unit that may determine whether the user's input communication can be understood. If the user's input communication can be understood, the natural language understanding unit may generate understanding data. The system may also include a communicative goal generator that may generate communicative goals based on the symbols recognized by the recognizer and understanding data from the natural language understanding unit. The generated communicative goals may be related to information needed to be obtained from the user. The system may further include a sentence planning unit that may automatically plan one or more sentences based on the communicative goals generated by the communicative goal generator with at least one of the sentences plans being output to the user.

Abstract: Disclosed is a task classification system that interacts with a user. The task classification system may include a recognizer that may recognize symbols in the user's input communication, and a natural language understanding unit that may determine whether the user's input communication can be understood. If the user's input communication can be understood, the natural language understanding unit may generate understanding data. The system may also include a communicative goal generator that may generate communicative goals based on the symbols recognized by the recognizer and understanding data from the natural language understanding unit. The generated communicative goals may be related to information needed to be obtained from the user. The system may further include a sentence planning unit that may automatically plan one or more sentences based on the communicative goals generated by the communicative goal generator with at least one of the sentences plans being output to the user.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for presenting career information are described. Consistent with some embodiments, the profile data of members of a social network service is analyzed to generate a set of probabilities for use in predicting career transitions. Based on some profile data (e.g., academic major, academic degree, desired industry, etc.) provided by a user, the derived probabilities are used to predict a set of job titles likely to be of interest to the user. By repeating this process, the user can generate a career path, which is displayed in a visual and interactive manner, enabling the user to explore various aspects of different careers, industries and jobs.

Abstract: The invention relates to a method for sentence planning (120) in a task classification system that interacts with a user. The method may include recognizing symbols in the user's input communication and determining whether the user's input communication can be understood. If the user's communication can be understood, understanding data may be generated (220). The method may further include generating communicative goals (3010) based on the recognized symbols and understanding data. The generated communicative goals (3010) may be related to information needed to be obtained form the user. The method may also include automatically planning one or more sentences (3020) based on the generated communicative goals and outputting at least one of the sentence plans to the user (3080).

Abstract: The invention relates to a system that interacts with a user in an automated dialog system (100). The system may include a communicative goal generator (210) that generates communicative goals based on a first communication received from the user. The generated communicative goals (210) may be related to information needed to be obtained from the user. The system may further include a sentence planning unit (220) that automatically plans one or more sentences based on the communicative goals generated by the communicative goal generator (210). At least one of the planned sentences may be then output to the user (230).

Abstract: The invention relates to a task classification system (900) that interacts with a user. The task classification system (900) may include a recognizer (920) that may recognize symbols in the user's input communication, and a natural language understanding unit (900) that may determine whether the user's input communication can be understood. If the user's input communication can be understood, the natural language understanding unit (930) may generate understanding data. The system may also include a communicative goal generator that may generate communicative goals based on the symbols recognized by the recognizer (920) and understanding data from the natural language understanding unit (930). The generated communicative goals may be related to information needed to be obtained from the user.

Abstract: The invention relates to a task classification system (900) that interacts with a user. The task classification system (900) may include a recognizer (920) that may recognize symbols in the user's input communication, and a natural language understanding unit (900) that may determine whether the user's input communication can be understood. If the user's input communication can be understood, the natural language understanding unit (930) may generate understanding data. The system may also include a communicative goal generator that may generate communicative goals based on the symbols recognized by the recognizer (920) and understanding data from the natural language understanding unit (930). The generated communicative goals may be related to information needed to be obtained from the user.

Abstract: The invention relates to a task classification system (900) that interacts with a user. The task classification system (900) may include a recognizer (920) that may recognize symbols in the user's input communication, and a natural language understanding unit (900) that may determine whether the user's input communication can be understood. If the user's input communication can be understood, the natural language understanding unit (930) may generate understanding data. The system may also include a communicative goal generator that may generate communicative goals based on the symbols recognized by the recognizer (920) and understanding data from the natural language understanding unit (930). The generated communicative goals may be related to information needed to be obtained from the user.