Abstract: A method for processing information by an intelligent agent and the intelligent agent, where the method comprises: a first intelligent agent sends a request message to a second intelligent agent, where the request message includes an invitation message or a recommendation message; the first intelligent agent receives a decision message fed back by the second intelligent agent, where the decision message is determined according to the invitation message or the recommendation message and a knowledge model of the second intelligent agent; and the first intelligent agent updates, according to the decision message, a knowledge model of the first intelligent agent or sends a notification message to a first user account corresponding to the first intelligent agent. By using these technical solutions, information on a social network may be learned and processed by means of interaction with another intelligent agent, thereby implementing mining of data on the social network.

Abstract: A method for processing information by an intelligent agent and the intelligent agent, where the method comprises: a first intelligent agent sends a request message to a second intelligent agent, where the request message includes an invitation message or a recommendation message; the first intelligent agent receives a decision message fed back by the second intelligent agent, where the decision message is determined according to the invitation message or the recommendation message and a knowledge model of the second intelligent agent; and the first intelligent agent updates, according to the decision message, a knowledge model of the first intelligent agent or sends a notification message to a first user account corresponding to the first intelligent agent. By using these technical solutions, information on a social network may be learned and processed by means of interaction with another intelligent agent, thereby implementing mining of data on the social network.

Abstract: A method for predicting information propagation in a social network includes acquiring target information to be predicted, and acquiring influences of K clusters, where the target information is posted or forwarded by a first user at a first moment, and K is a positive integer; determining a role probability distribution of the first user, and determining a second user who has not propagated the target information, where the role probability distribution of the first user is used to indicate probabilities that the first user belongs separately to the K clusters; and determining, according to the influences of the K clusters and the role probability distribution of the first user, a probability that the second user forwards the target information from the first user. In the embodiments of the present application, propagation of target information in a social network can be predicted by using influences of K clusters.

Abstract: A method includes storing documents of different fields, and a relationship between any two documents of different fields, performing word segmentation and stop word removal on the documents of different fields, to obtain a vocabulary data set for the documents of different fields, constructing an incidence matrix between the documents of different fields according to the relationship between the any two documents of different fields, obtaining a topic cluster of the documents of different fields according to the vocabulary data set, obtaining a probability that any topic in the topic cluster appears in any document and a matching weight of the any topic for any two different fields according to the incidence matrix and the topic cluster, and computing a similarity between the any two documents according to the probabilities and the matching weight of the any topic for the fields to which the any two documents belong.

Abstract: A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.

Abstract: The data mining method disclosed in the present invention is used for mining mixed-type data by mining subject information from image data and mining scene information or sentiment information from text data and categorizing and aggregating the obtained information, so as to obtain the correlation between specific subject information and specific scene information or sentiment information. Since the present invention is based on mixed-type data, it is possible to effectively avoid information loss caused by mining only one type of data. Meanwhile, it is possible to accurately obtain information correlation and reduce interference of irrelevant information.

Abstract: A mobile terminal alarm method and apparatus are provided. The method includes collecting environmental data of a mobile terminal; determining, according to the environmental data, a scenario in which the mobile terminal is located; and sending, when the scenario meets a preset alarm condition, and the mobile terminal is in a preset state, an alarm in an alarm manner corresponding to the preset state.

Abstract: A method for predicting information propagation in a social network includes acquiring target information to be predicted, and acquiring influences of K clusters, where the target information is posted or forwarded by a first user at a first moment, and K is a positive integer; determining a role probability distribution of the first user, and determining a second user who has not propagated the target information, where the role probability distribution of the first user is used to indicate probabilities that the first user belongs separately to the K clusters; and determining, according to the influences of the K clusters and the role probability distribution of the first user, a probability that the second user forwards the target information from the first user. In the embodiments of the present application, propagation of target information in a social network can be predicted by using influences of K clusters.

Abstract: A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.

Abstract: A mobile terminal alarm method and apparatus are disclosed, and are used to resolve a problem in the prior art that a mobile terminal is incapable of warning a user to avoid an unexpected situation. The method includes collecting environmental data of a mobile terminal; determining, according to the environmental data, a scenario in which the mobile terminal is located; and sending, when the scenario meets a preset alarm condition, and the mobile terminal is in a preset state, an alarm in an alarm manner corresponding to the preset state.

Abstract: A method for processing information by an intelligent agent and the intelligent agent, where the method comprises: a first intelligent agent sends a request message to a second intelligent agent, where the request message includes an invitation message or a recommendation message; the first intelligent agent receives a decision message fed back by the second intelligent agent, where the decision message is determined according to the invitation message or the recommendation message and a knowledge model of the second intelligent agent; and the first intelligent agent updates, according to the decision message, a knowledge model of the first intelligent agent or sends a notification message to a first user account corresponding to the first intelligent agent. By using these technical solutions, information on a social network may be learned and processed by means of interaction with another intelligent agent, thereby implementing mining of data on the social network.

Abstract: Embodiments of the present invention disclose an eye tracking method and apparatus, where the method includes: acquiring eye movement information and terminal status information; matching a preset eye tracking scenario by using the acquired eye movement information and terminal status information; and performing, in the matched preset eye tracking scenario, an operation corresponding to the eye movement information. By using the embodiments of the present invention, eye tracking may be performed according to the eye movement information and the terminal status information, which increases the number of eye tracking dimensions, and improves precision of eye tracking and efficiency of eye movement-based interaction.