100 results about "Behavioral traits" patented technology

Disclosed are methods in which a user is compensated for viewing advertisements. A user expresses interest in viewing a particular advertisement, but the variant of that advertisement that the user views is based on the user's demographic, psychographic, or behavioral traits. Thus, two different users with different traits who express interest in viewing the same advertisement may view different variants of that advertisement tailored to their individual traits. Rules specifying which users view which advertisement variants may be re-defined while an advertising campaign is underway in order to increase the effectiveness of an advertising campaign.

The invention provides a behavior characteristic similarity-based malicious code homology analysis method, which comprises the steps of: firstly extracting and quantifying behavior characteristics representing malicious codes based on a dynamic binary pile platform, on this base, measuring the similarity of the behavior characteristics among different malicious codes, and reflecting a homology judgment result of the malicious codes with the similarity of the behavior characteristics. By using the invention, the malicious codes collected in a network can be subject to homology analysis, and a powerful support is provided for tracing of a subsequent attack source. The method can correctly reflects the homology among samples of the malicious codes, correctly differentiates the samples of the malicious codes without homology, and has important guidance and reference significances in analysis work of the homology of the malicious codes.

The invention discloses a microblog water army identifying method based on a probabilistic graphical model. The microblog water arm identifying method comprises three steps as follows: quantizing user attributes and conventional microblog using behaviors into characteristic indexes which are respectively attribute characteristics and behavior characteristics of the user; regarding probability that the user belongs to the water army as a hidden variable of a graph by constructing a probability graph structure between the attribute characteristics and the behavior characteristics; and predicating the probability that other users belong to the water army by utilizing parameters of a sample learning graph model as well as the learned probability graph model. The invention provides the microblog water arm identifying method which is automatic, effective and quick by searching how to automatically identity water army users in the microblog platform, so that the identification is effectively carried out under a large data volume condition of the microblog. Moreover, the microblog water army identifying method has a good practical value and an extensive application prospect in the technical field of data mining.

The invention relates to an identity authentication system and method based on user habit behavior features. The identity authentication system comprises a configuration module, a collecting muddle, a sample processing module, a sample quantity judging module, a variance calculation module, a combined dimension module, a habit behavior dimension composition module and an authentication module, wherein besides dimensions such as gestures and shaking, dimensions such as activity regions, acting tracks, touch force and trading behaviors are also collected by the collecting module; then, a plurality of behavior habits are extracted through data mining for identity recognition. The problems that in the prior art, the user behavior features are collected only through once collection at the initial stage, and the generated slight change of the user habit behaviors cannot be caught along with the timelapse, so that the user per se cannot be recognized when the behavior change exceeds the critical value are solved. The risk that the gesture action is imitated or an unauthenticated user passes through the authentication due to sampling is further reduced.

An advertisement brokering system for social media-aware advertisement brokering is provided. The advertisement brokering system includes a processor and a memory, wherein the memory comprises a rules database and plurality of modules. The memory further comprises a data mining module for mining data from predetermined resources based upon predefined characteristics and behavioral traits of users. The memory further comprises an analysis module for identifying users based on match of the mined data with the predefined characteristics and/or behavioral traits. The memory further comprises a brokering module for offering advertising services to the identified users. The memory further comprises a feedback module for notifying an advertiser corresponding to change in behavioral activities of the identified users after offering advertising services. The memory further comprises a calibration module for calibrating advertising services based on change noticed in behavioral activities of the identified users.

The invention provides a personality prediction method based on microblog user behaviors. The method comprises the steps that first, an id list of microblog active users is acquired, personality questionnaires filled on line by tested users are acquired according to the id list of the active users through a 'talk to him' function of the microblog; second, according to the list of the tested users who fill the personality questionnaires, microblog data of the users are downloaded, and according to an established microblog network behavior system, corresponding static behavior features and dynamic behavior features are extracted from the microblog data; third, numeralization is conducted on the extracted dynamic behavior features of the users according to a time sequence analysis method to achieve a complete microblog feature set; fourth, according to a stepwise regression algorithm, a biggest obvious feature set is extracted from the microblog feature set to achieve feature selection; a personality prediction regression model is established for the selected features, and personality psychology indexes of the users are predicted.

The invention belongs to the field of user behavior analysis and data mining, and relates to a method for mining potential purchased commodities and categories of a user based on user behavior characteristics, which comprises the following steps of: performing data coding on preprocessed data, and performing characteristic engineering processing to obtain user behavior characteristic data; carrying out positive and negative sample analysis and classification on the sample data, and carrying out dynamic undersampling processing on positive and negative samples to generate a plurality of samplesubsets as trained positive and negative sample data; training the decision tree model through the positive and negative sample data, training a plurality of single prediction models, and fusing the single prediction models in a stacking mode to generate a plurality of fused prediction models; and predicting the potential purchased commodities and categories of the user based on the plurality of fusion prediction models, and processing and analyzing the prediction result of each fusion prediction model to obtain the potential purchased commodities and categories of the user with weights. According to the invention, merchants can be helped to explore users with high potential purchase intention, and the user consumption conversion rate of marketing is improved.

The invention relates to the field of network information security, and aims to provide a user risk assessment and analysis method based on a multi-dimensional behavior model. The user risk assessmentand analysis method based on the multi-dimensional behavior model can evaluate the user risk of an application with a log. Users are acquired by a product log; behavior features of the users are searched; behavior data form sample data; a cluster of normal behaviors is obtained by birch; lastly, normal behavior probability distribution of the user is obtained through a mixed Gauss model; and thenBayesian linear regression is adopted to obtain a linear model capable of accurately generalizing normal behaviors of the users. Through adoption of the method, risk assessment can be performed on online user states accurately, efficiently and intelligently.

A system and method for identifying and analyzing an entity's motivations and associated characteristics, by classifying and measuring the presence of sensory block indicators. The system includes the means for determining predetermined patterns of sensory block combinations; thus the presence of motivational information, including behavioral traits and other vital knowledge.

An apparatus and a method are provided for detecting and recording, in an electronic device, evidence of a person's situation. Situation data is detected using a situation data sensor in the electronic device. Situation data can be data representing evidence of a person's situation, wherein such evidence can show a person's place in relation to another person or thing, or the state of a person being able to interact with another person or thing. Next, biometric data of the user of the electronic device is detected. Biometric data can be data that represents one or more intrinsic physical or behavioral traits of a person. Thereafter, a report is created using the situation data and the biometric data, wherein the report includes evidence of the situation of the user of the electronic device.

The invention discloses a driver risk preference behavior diagnosis system. The driver risk preference behavior diagnosis system comprises a scene module, a driving simulator and a diagnosis module, wherein the scene module is used for setting multiple design scenes of a specified task object in a driving task normal form; the driving simulator is used for carrying out simulation driving according to the specified task object; the diagnosis module is used for measuring the decision result, the behavior feature index and vehicle dynamics parameters of a driver under the driving task normal form and evaluating the internal risk preference of the driver according to the measurement result to generate a diagnosis report. The diagnosis system is based on the simulation driving task normal form of a virtual reality technology, the driving simulator serves as an evaluation platform, the internal risk preference of the driver is diagnosed by measuring the driver decision behavior and the vehicle dynamics indexes under the standard driving task normal format, the diagnosis reliability is improved, and the road traffic safety is better ensured.

One embodiment of the present invention sets forth a technique for transporting both behavior and related geometric information for an animation asset between different animation environments. A common virtual machine specification with a specific instruction set architecture is defined for executing behavioral traits of the animation asset. Each target animation environment implements the instruction set architecture. Because each virtual machine runtime engine implements an identical instruction set architecture, animation behavior can identically reproduced over any arbitrary platform implementing the virtual machine runtime engine. Embodiments of the present invention beneficially enable reuse of animation assets without compatibility restrictions related to platform or application differences.

The invention discloses an animal emotion recognition method and device and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: obtainingbehavior data of a to-be-identified target animal, wherein the behavior data comprises video picture data and/or sound data; depending on the behavior data, determining a target identification modelcorresponding to the type of the target animal, preprocessing the behavior data to obtain at least one behavior feature vector, calling the target recognition model, inputting the at least one behavior feature vector into the target recognition model, and outputting the emotion label of the target animal, wherein the at least one behavior feature vector comprises a limb feature vector and/or a sound feature vector. According to the embodiment of the invention, emotion recognition can be carried out based on any behavior of the target animal, and the problem that some infrequent behaviors of the animal may not be recognized due to limited artificial experience when artificial recognition is adopted is solved.

The invention discloses a data dynamic anti-leakage and warning method and system. The method comprises the following steps of binding user authentication information and a user biological characteristic image; integrating user behavior data, integrating a user behavior identification result, an operation body identification result and a body behavior identification result, and converting the results into user behavior characteristic data; calculating a user behavior, on the basis of a machine learning technology, calculating a user behavior abnormal index by taking the user behavior characteristic data as input and taking a user behavior model as a reference; warning the user behavior, comparing the user behavior abnormal index with a set warning threshold value, and if the user behavior abnormal index is greater than the warning threshold value, generating warning information to a manager. Through application of the scheme provided by the invention, under the condition that a user has no sense, real-time audit is carried out on the terminal user behavior, a potential suspicious user behavior is identified and warned timely, and a control method over the whole data anti-leakage system is perfected.

The invention discloses a behavior analysis method, a behavior analysis device, a server, a behavior analysis system and a storage medium. The method includes the following steps that: behavior data and behavior time parameters corresponding to the behavior data which are transmitted by a smart wearable device are received in real time; all behavior data during preset periods are obtained every preset time period according to the behavior time parameters, all the behavior data are preprocessed, so that behavior characteristic data are generated; behavior types corresponding to the time periodsare obtained through a pre-established behavior recognition model according to the behavior characteristic data; and behavior recognition analysis results corresponding to the time periods are generated through a pre-established behavior law table according to the behavior types and the corresponding time periods, wherein the behavior law table stores the behavior types corresponding to each timeperiod, and the behavior recognition analysis results include normal behavior results and abnormal behavior results. With the behavior analysis method, the behavior analysis device, the server, the behavior analysis system and the storage medium of the invention adopted, intelligent care for patients with mental disorders can be realized.

The invention belongs to the technical field of teaching behavior analysis, and discloses a teaching behavior analysis system and analysis method based on artificial intelligence, which are used for realizing data operation and processing and controlling a main control module of each module to work normally through a main control computer. The teaching video acquisition module is connected with the main control module and is used for acquiring teaching video data through camera shooting; and the teaching behavior identification module is connected with the main control module and is used for identifying the teaching behavior of the teaching video through an identification program. The classroom teaching behavior cloud model is constructed by taking the speech and the activity behavior of the behavior subject as basic units through the teaching interaction analysis module, the cloud model quantitative analysis method for the classroom teaching interaction behavior is provided, and a foundation is laid for finely describing teaching interaction behavior characteristics; meanwhile, evaluation difference caused by personal evaluation is avoided through the teaching quality evaluation module, so that evaluation of the teaching design quality has more basis and credibility.

The embodiment of the invention discloses a behavior prediction method and device and a related product. The invention relates to the technical field of machine learning. According to the method, a feature sequence of target content and N historical behavior sequences of a target user are obtained, a shared feature extraction result is extracted based on the N historical behavior sequences and the feature sequence of the target content, and personalized feature extraction results in one-to-one correspondence are extracted based on the N historical behavior sequences. When any behavior of the target user in the target content is predicted, a prediction result is obtained based on the previously obtained shared feature extraction result and the personalized feature extraction result of the corresponding historical behavior sequence. According to the scheme, the difference of behavior characteristics of various historical behavior sequences is considered, personalized characteristic extraction is performed in a targeted manner, and prediction of different behaviors is not executed only based on a shared characteristic extraction result any more. Even in a scene in which the relevancy of the to-be-predicted behavior is relatively low, relatively high prediction accuracy can be ensured.