Abstract: A method includes receiving, at a search toolbar, a search query from a machine in a network. The machine has an associated machine profile for participating in the network as an entity. The machine profile includes a machine identifier and machine metadata. A query type is determined from the search query. A search context for the machine is determined using a semantic graph of the network. From a set of services for the network, one or more relevant services to respond to the search query are identified based on the query type and the search context. The search query is applied to the one or more relevant services to obtain a set of responses. A set of relevant results for the search query is determined from the set of responses. The set of relevant results is transmitted to the machine.

Abstract: A method includes receiving a message query from an entity identifier participating in a social network. The message query specifies one or more entities, one or more requirements, and one or more constraints. A set of message query parameters is generated based on the message query. A set of queries for a semantic graph of the social network is generated based on the set of message query parameters. The set of queries is applied to the semantic graph to obtain a set of query results. A message context of the entity identifier is determined based on the set of query results and the set of message query parameters. A set of messages from a message repository is determined based on the message context. The set of messages can be presented on a client computer associated with the entity identifier.

Abstract: Systems, methods, and computer media for determining compatible users through machine learning are provided herein. Previous interactions between some users in a group can be used to determine a first set of user-to-user compatibility scores. Both the first set of compatibility scores and attributes for the users in the group can be provided as inputs to a machine learning model that can be used to determine a second set of user-to-user compatibility scores for user pairs who do not have an interaction history. Along with input constraints, the first and second sets of user-to-user compatibility scores can be used to select compatible user groups.

Abstract: A method for adaptive voice interaction includes monitoring voice communications between a service recipient and a service representative, measuring a set of voice communication features based upon the voice communications between the service recipient and the service representative, analyzing the set of voice communication features to generate emotion metric values, and generating a response based on the analysis of the set of voice communication features.

Abstract: The invention described here uses a Mouth Phoneme Model that relates phonemes and visemes using audio and visual information. This method allows for the direct conversion between lip movements and phonemes, and furthermore, the lip reading of any word in the English language. Speech API was used to extract phonemes from audio data obtained from a database which consists of video and audio information of humans speaking a word in different accents. A machine learning algorithm similar to WEKA (Waikato Environment for Knowledge Analysis) was used to train the lip reading system.

Abstract: A method for adaptive voice interaction includes monitoring voice communications between a service recipient and a service representative, measuring a set of features based upon the voice communications, and analyzing the set of features to generate emotion metric values.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system, and the probable emotional state is communicated to a user.

Abstract: A computerized method, software, and system for recognizing emotions from a speech signal, wherein statistical and MFCC features are extracted from the speech signal, the MFCC features are sorted to provide a basis for comparison between the speech signal and reference samples, the statistical and MFCC features are compared between the speech signal and reference samples, a scoring system is used to compare relative correlation to different emotions, a probable emotional state is assigned to the speech signal based on the scoring system, and the probable emotional state is communicated to a user.