51 results about "Artificial intelligence marketing" patented technology

The invention provides an artificial intelligence-based object pushing method and apparatus. The method comprises the steps of inputting historical click behaviors of a target user to a built user model for performing learning to obtain multi-dimensional preference eigenvectors of a target user; obtaining eigenvectors of all to-be-pushed objects; inputting the eigenvectors of the to-be-pushed objects and the multi-dimensional preference eigenvectors to a trained deep neural network model for performing prediction to obtain prediction probabilities of the to-be-pushed objects; and pushing the to-be-pushed objects to the target user according to the obtained prediction probabilities. According to the method and the apparatus, the preferences of the target user are obtained through the user model built by a neural network, and the probability of possibly purchasing to-be-pushed group orders by the target user is obtained based on the deep neural network and the preferences of the target user, so that the pushing is more effective; and preference features are selected through training by the user model, so that a large amount of manpower does not need to be consumed for selection and the pushing efficiency is improved.

The invention provides a man-machine interaction method and device based on artificial intelligence. The man-machine interaction method based on artificial intelligence comprises the steps that a problem input by a user is received; the problem is processed according to a pre-generated model to obtain an answer corresponding to the problem and having a human dialogue style; the model is generated according to human dialogue corpus data; the answer is fed back to the user. The method can make man-machine interaction have a real human dialogue and interaction effect.

The invention discloses single-index prediction of a big data platform based on artificial intelligence in the technical field of artificial intelligence, and the method comprises the following steps:(1) a first layer is a data exchange center, and is responsible for the collection work of data, serves as a basic corpus of big data, and carries out the data mining; (2) the second layer is used for storing data and is responsible for storing the collected data; (3) the third layer is data processing and is responsible for calculating data stored in the HDFS and the HBASE; (4) the fourth layeris used for data display and is responsible for displaying mined data; And (5) the fifth layer is constructed by adopting a mixed model data center based on the main data warehouse and the auxiliary big data processing platform, and the index of the big data platform is pre-warned, so that the fault operation and maintenance efficiency is improved, the loss caused by a major fault is reduced, andthe processing capability of mass data is improved.

The invention discloses a financial risk control system based on big data and artificial intelligence. The system include a risk information collection module, an information management module, a riskassessment module, a risk processing module, and a risk storage module. A risk information management server comprises a risk classification unit, a risk identification unit and a key information extraction unit. A risk assessment server comprises a risk management unit, an assessment unit and a risk pre-storage unit. The risk processing module comprises a processing node, a statistical unit anda generation unit. The system classifies risk items in the risk management server by keyword extraction and comparison methods, and at the same time, the classified risk items are assessed in the risk assessment server, so as to improve risk management and control efficiency. The system can automatically control risk in real time, thereby preventing occurrence of major risk items, and the systemcan improve and update risk control rules, so as to make risk control of a transaction process more comprehensive and save human resources.

The invention relates to a system and a method for marketing inspection analysis based on artificial intelligence, the system comprises a big data platform, an AI model and a customer volume expense service risk analysis system based on artificial intelligence. The AI model comprises a data preprocessing module, an input graph processing module and a graph convolution network. The customer volumeexpense service risk analysis system based on artificial intelligence comprises a preprocessing layer, a neural network layer and a view layer. The method is based on an artificial intelligence marketing inspection and analysis system, by building big data platforms, AI model and customer volume cost business risk analysis system based on artificial intelligence, AI model algorithm self-learning gradually refines the audit rules, and fast and efficient positioning problems, reduce the manual workload, improve the efficiency of inspection, through artificial intelligence technology and big datacalculation, to achieve automatic analysis of marketing risk, accurate positioning, accurate targeting problem prevention, to achieve the purpose of high intelligence, high accuracy and easy to use.

The invention discloses an artificial intelligence comprehensive service platform, which comprises a platform resource layer used for uniformly accessing hardware resources, storing the hardware resources and calculating the hardware resources, a platform engine layer which is used for providing technical support for artificial intelligence, big data, cloud computing and industry application, an application development layer which is used for providing a quick development tool, an intelligent service generator and a development interface, and an application template layer which is used for providing templates of artificial intelligence applications of all walks of life. The artificial intelligence comprehensive service platform has the advantages of scalability, customizability, expandability, maintainability, usability, quick development and the like.

The invention discloses a visual programming tool system based on artificial intelligence and Internet-of-things. According to the invention, the artificial intelligence technology, the Internet-of-things technology and the edge computing technology are combined. Primary and secondary school students who do not understand how to program can build application scenes of artificial intelligence, theInternet-of-things and edge computing in a node flow-based building block type visual programming mode; a user who is learning artificial intelligence programming can use computing power resources inthe system to carry out online programming development and artificial intelligence model training.

An artificial intelligence question and answer robot development method can perform unsupervised learning on question topics by uploading a dialogue corpus to a cloud, automatically cluster the question topics, and persist clustering results in various forms to serve as training data of a deep learning classifier. The deep learning classifier carries out topic classification on questions of a user, calculates similarity, compares the similarity with a threshold value, judges that the questions are learned if the similarity exceeds the threshold value, and retrieves and gives corresponding answers. In the training process, a visual interface is provided to measure the training quality, and the question classification accuracy can reach more than 95%. According to the method, some common questions can be answered through training, a manual corpus labeling process of the question and answer robot is not needed, hyper-parameter tuning can be carried out through a visual interface, the development efficiency and classification accuracy of the question and answer robot are greatly improved, and the method can be applied to practical applications such as intelligent customer service and chat assistants.

The invention discloses a college aspiration fill-out decision-making method and system based on artificial intelligence. According to the method and the system, on the basis of the artificial intelligence and big data, through intelligent judgment, major and college selection relatively suitable for own features is provided for an examinee. According to the system, college query, decision-makingsupport and the like can be provided when aspiration fill-out is carried out, the examinee is helped to make a scientific decision, an information platform for the aspiration fill-out of the examineeis provided, and the aspiration fill-out scientificity and high efficiency are improved. The college aspiration fill-out decision-making system comprises a college recommendation module, a self-cognition module, a college performance prediction module, a student portrait module, a occupational interest evaluation system module, a college data module, a college admission data module, an AI data module and a self-learning system module based on industries.

The invention discloses a radiotherapy plan making method based on artificial intelligence and an expert database. On the basis of a traditional radiotherapy plan making method, an automatic organic sketching method based on the artificial intelligence is utilized, a computer automatically sketches common tumor target areas and surrounding organs at risk according to different disease types, the sketching differences caused by clinical experience are reduced, and the sketching time is effectively shortened; meanwhile, based on expert database information, radiotherapy plans are automatically designed according to different disease types, and the radiotherapy plan differences caused by the clinical experience are reduced. Through the mode, with the assistance of the artificial intelligenceand the expert database, the accuracy of sketching of interested areas in radiotherapy plan making is improved, the effectiveness of the plans in radiotherapy plan making is guaranteed, thus the radiotherapy plan making time is obviously shortened, the rely on the experience is reduced, and the limit of the traditional radiotherapy plan making method is lowered.

The invention discloses an auxiliary sports training method based on artificial intelligence. A user model is built according to personal archive data and training strategy data of a trainee; a recommendation model is built through a collaborative filtering recommendation algorithm; and recommendation of training strategies is provided for subsequent trainees. During training, an exercise duration, image data, exercise video data and interactive data of the trainee are collected; the video data and the image data in the exercise training process are analyzed; the physical ability and the exercise intensity of the trainee are evaluated according to an exercise result; and the result obtained by the video data and the image data in the exercise training process and other data in the trainingprocess are added into a personal archive as exercise records. The quality and precision of the recommendation algorithm are improved by updating the user model; and the problems of exercise analysisand action quality evaluation in the exercise training process are solved.

The present invention discloses an artificial intelligence machine based automatic design typesetting method comprising the steps of: 1, establishing a mapping dock framework with a third party e-commerce platform basic information; 2, performing automatic typesetting according to product basic information and pictures; 3, analyzing the semanteme of e-commerce product fields; and 4, performing image identification from an aesthetic angle of view. Compared with artificial typesetting, by using the artificial intelligence machine based automatic design typesetting method, the workload that needs a very long time to be artificially finished can be finished in a very short time, so that the human resource expense is saved, the management cost is reduced, and businessmen can acquire business opportunities in business scenes needing fast exhibition of goods Moreover, compared with the existing typesetting, the artificial intelligence machine based automatic design typesetting method is more standard and professional, is more beautiful and flexible in effect, and has an aesthetic image identifying function.

The invention provides a target remote damage assessment system for different vehicle models established based on an artificial intelligence supervised learning decision tree method and a target remote damage assessment method thereof, belongs to the field of vehicle damage assessment, and aims at solving the problem of target detection of a collided vehicle after vehicle collision. The technical key points are that a target detection subsystem judges the object of collision of the vehicle, wherein the target detection subsystem learns target training data so as to generate a target model, and the intelligent supervised learning decision tree method is applied in establishing of the target model. The beneficial effects are that target detection of vehicle collision can be realized through the technical scheme, a machine learning method is applied in the technical field of remote damage assessment and the judgment accuracy is enhanced in the damage assessment process by the machine learning method.

The invention discloses an adaptive education system based on artificial intelligence. The system comprises a control center, a teacher end and student ends, and also comprises an evaluation unit which is used for evaluating the learning stages of students, a learning evaluation unit used for evaluating the learning progress conditions of the students, a learning path customization unit used for formulating personalized learning schemes according to different learning progresses of the students and a knowledge storage unit used for storing knowledge covering the whole education knowledge system. The control center is connected with the teacher end, the student ends, the evaluation unit, the learning evaluation unit, the learning path customization unit and the knowledge storage unit separately. The invention further provides an adaptive education method based on the artificial intelligence. According to the present invention, the teaching aid can automatically adapt to the learning conditions of the students and dynamically adjust teaching according to the learning conditions, so that the personalized education taking the students as the centers is really realized, and the customized education idea of the Confucius is truly realized.

The embodiment of the invention relates to the field of intelligent robots, and discloses a dialogue generation method and device based on artificial intelligence, network equipment and a storage medium. The dialogue generation method based on artificial intelligence comprises: acquiring a to-be-replied statement, and inputting the to-be-replied statement into a retrieval model, wherein the retrieval model is used for screening K candidate replies responding to the to-be-replied statement from a preset dialogue corpus, and K is a positive integer; obtaining K candidate replies output by the retrieval type model, inputting the to-be-replied statement and the K candidate replies into a generative model, screening out prediction words by utilizing the generative model according to the to-be-replied statement, the K candidate replies and the inverse document frequency of each word in the dictionary, and outputting a prediction reply formed by utilizing the prediction words; and obtaining areply statement according to the prediction reply. According to the dialogue generation method and device based on artificial intelligence, the network equipment and the storage medium provided by the embodiment of the invention, the output precision of the chat robot can be improved.

The invention discloses a product development auxiliary system and method based on artificial intelligence, and the system comprises: a technology analysis module which is used for calculating the scientific and technological strength index of a product; The culture analysis module which is used for calculating a culture trend intensity index of the product; The patent analysis module which is used for judging the frequency of covering the key vocabularies by the patent characteristics by extracting the key vocabularies for patent analysis and comparing the key vocabularies with the patent characteristics in the patent database belonging to the field; The scoring module which is used for calculating a comprehensive evaluation value of product development according to the scientific and technological intensity index, the cultural trend intensity index and the patent feature coverage frequency. On the basis of product technical analysis, cultural analysis and patent analysis, the productdevelopment direction is determined by comprehensively considering the market trend, the product cultural connotation, the technical life cycle and the patentable semantic analysis, so that a reliable basis is provided for an enterprise to carry out product development decision making.

The invention relates to the field of intelligent logistics, discloses a logistics control system and a logistics control method with artificial intelligence. The invention solves the technical problem of intelligently calculating the cargo allocation quantity of each warehouse. The data of regions and clients in each region are used as features. Multiple historical data features are used as a training data set to train a prediction model. The prediction model can predict the number of each kind of clothes stored in each region warehouse. A worker can configure a corresponding number of garments in a corresponding warehouse in advance. The storage space can be fully utilized according to preferences of consumers. The situation of space waste is reduced. The prediction model is a supervisedlearning algorithm in the field of machine learning. When the sales volume of products in each region changes, the information is fed back to the model for adjustment purpose. The model is updated intime. The supervised learning can calibrate the model for real-time purchase data. The prediction model is continuously optimized.

The invention discloses a method for creating an artificial intelligence bookkeeping system with voice, cooperation and analysis functions. The method comprises the steps of creating a new project, introducing a front end framework MUI developed based on JS to serve as a development framework, packaging front end tools into a JS method of development, and packaging and calling a native method; setting configuration parameters; arranging functional modules including voice input, cooperative bookkeeping and account analysis modules in the development framework; and according to an HTML5 page constructed by the functional modules, performing packaging to generate an APP. The method is realized based on HTML5, and is low in learning cost and high in cross-platform development efficiency; bookkeeping is quicker through voices; multi-person bookkeeping operation is realized by adopting cooperative bookkeeping; an account analysis process is customized as needed; display is performed througha chart; and bookkeeping related data of a user is cached to reduce the frequency of requesting to a server, so that the interactive loss is reduced. The method for creating the artificial intelligence bookkeeping system with the voice, cooperation and analysis functions can be widely applied to the field of mobile internet.

The invention discloses an automatic medical question answering system and an automatic medical question answering method based on artificial intelligence and big data. The method comprises the steps:obtaining a medical question inputted by a patient, and automatically recognizing a set keyword contained in the medical question; screening answers according to the number and levels of set keywords; searching a doctor-seeing record of the user after obtaining authorization of the patient, wherein the doctor-seeing doctor is high in frequency; generating a corresponding artificial intelligence model according to the doctor-seeing habit of each doctor; and an artificial intelligence model is selected according to the conclusion of the patient analysis unit, the artificial intelligence model replies the screened answers to the patient according to the doctor-seeing habits of the corresponding doctor-seeing doctors, and if the patient is not authorized or has no doctor-seeing record or hasno doctor-seeing doctors with high frequency, the artificial intelligence model is randomly selected to reply the answers to the patient. According to the invention, the corresponding artificial intelligence model is generated according to the doctor-seeing habits of the doctors. The method is more intelligent than a method adopting a unified template. The required artificial intelligence model can also be provided for patients with preferences.

The invention provides a data integration application system and a method based on block chain and artificial intelligence. The service platform performs artificial intelligence deep learning and trains a consumption demand model based on the first block information, matches the product service information and generates a push message. The invention has the following beneficial effects: the service platform carries out artificial intelligence in-depth learning on the first user information and operation records and trains the consumption demand model, so that the consumption demand of the first user is thoroughly analyzed and the generated push message is more accurate and effective; the service platform performs artificial intelligence in-depth learning on the first user information and operation records and trains the consumption demand model. The privacy and security of all parties are effectively guaranteed by using block chain technology. In addition, cross-industry, cross-regional, cross-field, cross-sectoral and cross-sectoral big data resources are interconnected, shared and interused, fully integrated development and utilization of information resources, and in-depth mining of the value of information resources are achieved to achieve multi-win for consumers, sellers, manufacturers and service providers.

Systems and methods for a cloud-based artificial intelligence engine are provided. In one embodiment, a graph engine identifies an intention based on a loop around one or more subnets, a personal cloud generates an intelligence report based on the identified intention, and a user device performs actions based on the intelligence report and a confidence level of nodes within the loop above a threshold. In this way, a user device may perform actions informed by a global intelligence that understands contextual information while maintaining privacy control.

The invention discloses a Japanese word training method based of artificial intelligence and aimed at enterprise employment posts. The method includes: collecting operation history of a user using mobile equipment, analyzing spare time period of the user, and setting the spare time period as a set T; ordering Japanese words according to using frequency, setting pushing priority from front to rear,and setting the pushing priority as a set W; determining pushing interval and time number within the spare time period of the user according to memory capacity of different users and memorizing difficulty of different words, tracking learning and memorizing time of each word when the user memorizes words, switching gesture of a next word, building a training model, outputting memorizing degree ofwords, and pushing words different in frequency according to the memorizing degree. In the method, pushing strategies of Japanese words are determined according to fragmented time of enterprise staffand personal memory capacity of the staff, so that time utilizing efficiency of the user is improved, and word memorizing efficiency is improved.

The invention discloses an artificial intelligence-based social relationship knowledge graph generation method and a robot system. The method comprises an acquisition step, an extraction step, an intersection step, an intersection information acquisition step, a relationship generation step, a knowledge graph generation step and an entity acquisition step. According to the method and the system, through a user relationship knowledge graph generation technology based on artificial intelligence and big data, the intelligence and high efficiency of user relationship knowledge graph generation areimproved.