Abstract: Method and system for classifying and labeling images, which can perform segmentation based on features of each part of images, classify and match the image and the segmented image based on a classification model built by the machine learning method. Meanwhile, each image is assigned with labels and text descriptions. The system also includes a string module assigning the image with a plurality of matching labels and text descriptions that are the most relevant in recent times. Furthermore, the classification model is trained by machine learning method such as an unsupervised learning, a self-supervised learning, or a heuristic algorithms. In addition, a character recognition module is provided to extract characters in the image for comprehensive learning and calculations to facilitate classification and labeling of the image.

Abstract: Method and system for extracting valuable words and forming a valuable word net, wherein a server is employed to collect contents of articles from internet sources, EDM texts (email direct marketing), product descriptions and other texts, and extract valuable words by machine learning. Each valuable word performs connection weight based on the number of times the text has been read, the number of times the text has been clicked, the number of times the text has been cited, the correlation of external sites, the conversion of expert knowledge, the probability space, the Shannon entropy, the spatial distribution and other values and their conversion by machine learning; then the linked valuable words are integrated to form a valuable word net. When the valuable words are needed to be used, the valuable words and the valuable word net can be retrieved from the database for subsequent applications.

Abstract: An electronic marketing system and an electronic marketing method, wherein, by retrieving the user's browsing traces, browsing history and other de-identified information on the website or the Internet, the similar users can be still effectively grouped without using the user's personal information. The product can be matched with the user to filter out the candidate products that the user may purchase. Alternatively, the product to be sold can be selected first to conduct the matching process, thereby creating a candidate user group. Thereafter, the product leaflet can be generated from candidate products and sent to each user in the candidate user group. Moreover, the user information can be adjusted in real time after the user clicks on the product leaflet. The targeted marketing can still be achieved without use of personal information. Furthermore, the user information can be adjusted in real time to achieve the optimal electronic marketing effect.

Abstract: A system for awakening non-shopping consumers and an implementation method thereof employ artificial intelligence to grouping consumers, and then determine consumers who have not shopped for a long time, and provide products that meet consumers' needs by analyzing the results of the grouping, as the basis for consumers to shop. Moreover, the desire of consumers to buy goods can be promoted so as to awaken consumers who have not shopped for a long time.

Abstract: A method for behavior vectorization of information de-identification, through which data concerning browsing traces, link paths, trigger events, clicks, and operation behaviors of network users on the Internet are selected by a server, a client device, or an edge device for performing a conversion/integration process. Then, the integrated data are converted into a vector. The vector represents the profile of the usage behavior of the network users. Moreover, because vectors can be quickly grouped and classified to find similar groups, it can quickly identify the network users. The server uses the supervised learning method as the base method, and uses pre-defined network behaviors for training. Also, the semi-supervised learning method or the unsupervised learning method can be employed to modify undefined network behaviors to better conform to the profile description of the network users.

Abstract: Method and system for classifying and labeling images, which can perform segmentation based on features of each part of images, classify and match the image and the segmented image based on a classification model built by the machine learning method. Meanwhile, each image is assigned with labels and text descriptions. The system also includes a string module assigning the image with a plurality of matching labels and text descriptions that are the most relevant in recent times. Furthermore, the classification model is trained by machine learning method such as an unsupervised learning, a self-supervised learning, or a heuristic algorithms. In addition, a character recognition module is provided to extract characters in the image for comprehensive learning and calculations to facilitate classification and labeling of the image.

Abstract: Method and system for extracting valuable words and forming a valuable word net, wherein a server is employed to collect contents of articles from internet sources, EDM texts (email direct marketing), product descriptions and other texts, and extract valuable words by machine learning. Each valuable word performs connection weight based on the number of times the text has been read, the number of times the text has been clicked, the number of times the text has been cited, the correlation of external sites, the conversion of expert knowledge, the probability space, the Shannon entropy, the spatial distribution and other values and their conversion by machine learning; then the linked valuable words are integrated to form a valuable word net. When the valuable words are needed to be used, the valuable words and the valuable word net can be retrieved from the database for subsequent applications.

Abstract: Method and system for determining and reclassifying valuable words, wherein a large amount of text and valuable words are pre-inputted into a word processing server for machine learning. Moreover, the word processing server is trained on the valuable words and many labels associated with the valuable words such that it can learn and determines the valuable words in the text that meet the definition of the valuable word. The valuable word is further extracted from the text and re-classified after extraction. In addition, each valuable word is provided with various relevance labels to facilitate the subsequent application of the valuable words.