Abstract: Human speech signals that are uttered within an environment are transcribed; the environment includes one or more avatars representing one or more software agents; the human speech signals are directed to at least one of the avatars. At least one non-speech behavioral trace is obtained within the environment; the trace is representative of non-speech behavior directed to the at least one of the avatars. The transcribed human speech signals and the at least one non-speech behavioral trace are forwarded to the one or more software agents. A proposed act is obtained from at least one of the agents; responsive thereto, a command is issued to cause the avatar corresponding to the software agent from which the proposed act is obtained to emit synthesized speech and to act visually in accordance with the proposed act.

Abstract: Human speech signals that are uttered within an environment are transcribed; the environment includes one or more avatars representing one or more software agents; the human speech signals are directed to at least one of the avatars. At least one non-speech behavioral trace is obtained within the environment; the trace is representative of non-speech behavior directed to the at least one of the avatars. The transcribed human speech signals and the at least one non-speech behavioral trace are forwarded to the one or more software agents. A proposed act is obtained from at least one of the agents; responsive thereto, a command is issued to cause the avatar corresponding to the software agent from which the proposed act is obtained to emit synthesized speech and to act visually in accordance with the proposed act.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A computer-implemented method for constructing an ontology includes using a chatbot program to elicit an utterance from a user. An utterance is received from the user. The received utterance is parsed to detect a concept/property/relationship therein. It is determined whether the identified concept/property/relationship is represented within an ontology and the identified concept/property/relationship is added to the ontology when it is determined that the it is not present therein. A response to the received utterance is generated using the ontology. The generated response is presented to the user using the chatbot.

Abstract: A computer-implemented method includes extracting a plurality of topics from a plurality of unlabeled social media posts, mapping the plurality of topics to a plurality of frequencies, each frequency in the plurality of frequencies indicating how often a corresponding topic in the plurality of topics occurs in the plurality of unlabeled social media posts, and predicting, based in part on the plurality of frequencies, a future social media posting behavior of a specific social media user, wherein the future social media posting behavior includes a specific topic about which the specific social media user is likely to post at a time in the future and a frequency with which the specific topic is likely to occur in posts of the specific social media user that are created at the time in the future.

Abstract: A computer-implemented method for constructing an ontology includes using a chatbot program to elicit an utterance from a user. An utterance is received from the user. The received utterance is parsed to detect a concept/property/relationship therein. It is determined whether the identified concept/property/relationship is represented within an ontology and the identified concept/property/relationship is added to the ontology when it is determined that the it is not present therein. A response to the received utterance is generated using the ontology. The generated response is presented to the user using the chatbot.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: The present disclosure relates generally to the field of segmenting social media users (such as users of a social media network) by means of life event detection (such as based upon social media messages and/or postings) and entity matching. In various embodiments, systems, methods and computer program products are provided.

Abstract: An image capture device includes a camera lens capturing image data, a microphone receiving a voice command from a user, a network interface, a memory storing a computer program, and a processor configured to execute the computer program. The computer program is configured to identify a target subject and an image capture function based on the voice command and user preference data. The computer program is configured to search a first database for the target subject and search a second database for the target subject, extract a visual representation of the target subject, detect the target subject in an environment viewed through camera lens, and apply the image capture function to the target subject detected in the environment. It is determined if image data is captured by the user and the user preference data is updated based on whether the image data is captured or not.

Abstract: A computer-implemented method includes extracting a plurality of topics from a plurality of unlabeled social media posts, mapping the plurality of topics to a plurality of frequencies, each frequency in the plurality of frequencies indicating how often a corresponding topic in the plurality of topics occurs in the plurality of unlabeled social media posts, and predicting, based in part on the plurality of frequencies, a future social media posting behavior of a specific social media user, wherein the future social media posting behavior includes a specific topic about which the specific social media user is likely to post at a time in the future and a frequency with which the specific topic is likely to occur in posts of the specific social media user that are created at the time in the future.

Abstract: An image capture device includes a camera lens. A microphone receives a voice command from a user. A network interface establishes a network connection. A memory stores a computer program. A processor executes the computer program to receive the voice command, parse at least one image capture goal of the user and capture image data of a scene observed by a camera lens. Image features are extracted from the scene including a target subject. A first local database is searched for the target subject. A second remote database is searched for the target subject when the target subject is not found. First quality features are extracted from the scene. Second quality features are loaded from a features database. The first and second quality features are compared. Image capture settings adjustment advice is generated based on the image capture goal, the image features, and the first and second quality features.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: An image capture device includes a camera lens configured to capture image data, a microphone configured to receive a voice command from a user, a network interface configured to establish a network connection, a memory storing a computer program, and a processor configured to execute the computer program. The computer program is configured to identify a target subject and an image capture function included in the voice command using natural language processing (NLP). The computer program is configured to search a first database for the target subject and search a second database for the target subject, extract a visual representation of the target subject, detect the target subject in an environment viewed through camera lens, apply the image capture function to the target subject detected in the environment, and capture the image data. The image data includes the target subject with the image capture function applied to the target subject.

Abstract: A system for coordinating multiple chatbots in a group conversation using natural dialog systems, including a creation unit that enables a user to create a group chat with chatbots; a response unit that allows the user to reply to any utterance extracted from a message received from a member of the group chat; a transmission unit that sends messages to every member of the group chat; a development unit that develops chatbots that understand natural language and interact in a group chat using natural dialogue; a network connection; a first database that stores a knowledge base extracted from all the utterances exchanged by members of the group chat; a second database that stores all interactions between the user and the group chat, and a third database that stores all interaction protocols used by the members of the group chat.

Abstract: An image capture device includes a camera lens capturing image data, a microphone receiving a voice command from a user, a network interface, a memory storing a computer program, and a processor configured to execute the computer program. The computer program is configured to identify a target subject and an image capture function based on the voice command and user preference data. The computer program is configured to search a first database for the target subject and search a second database for the target subject, extract a visual representation of the target subject, detect the target subject in an environment viewed through camera lens, and apply the image capture function to the target subject detected in the environment. It is determined if image data is captured by the user and the user preference data is updated based on whether the image data is captured or not.

Abstract: An image capture device includes a camera lens configured to capture image data, a microphone configured to receive a voice command from a user, a network interface configured to establish a network connection, a memory storing a computer program, and a processor configured to execute the computer program. The computer program is configured to identify a target subject and an image capture function included in the voice command using natural language processing (NLP). The computer program is configured to search a first database for the target subject and search a second database for the target subject, extract a visual representation of the target subject, detect the target subject in an environment viewed through camera lens, apply the image capture function to the target subject detected in the environment, and capture the image data. The image data includes the target subject with the image capture function applied to the target subject.

Abstract: An image capture device includes a camera lens. A microphone receives a voice command from a user. A network interface establishes a network connection. A memory stores a computer program. A processor executes the computer program to receive the voice command, parse at least one image capture goal of the user and capture image data of a scene observed by a camera lens. Image features are extracted from the scene including a target subject. A first local database is searched for the target subject. A second remote database is searched for the target subject when the target subject is not found. First quality features are extracted from the scene. Second quality features are loaded from a features database. The first and second quality features are compared. Image capture settings adjustment advice is generated based on the image capture goal, the image features, and the first and second quality features.

Abstract: The present disclosure relates generally to the field of segmenting social media users (such as users of a social media network) by means of life event detection (such as based upon social media messages and/or postings) and entity matching. In various embodiments, systems, methods and computer program products are provided.

Abstract: A system for managing items. The system includes a sensor for identifying a first physical object and a second physical object in a building. A computer interface in communication with the sensor is configured to receive a first identification of the first physical object and a second identification of the second physical object. A computer with at least one central processing unit is configured to automatically search for trend information about the first physical object, determine a first interaction between the first physical object and the second physical object based on the first identification of the first physical object and the second identification of the second physical object, and recommend disposition of the first physical object based on the first interaction between the first physical object and the second physical object, and the trend information about the first physical object.