Industrial Technology Research Institute

Chieh-Jen Wang

Chung-Jen Chiu

Ci-Jie Lin

Chieh-Jen Hsiao

A graph-based natural language optimization method and an electronic apparatus are provided. The method is adapted for an electronic apparatus with a processor. In the method, an input sentence submitted by a user is received, and multiple domain-related entities are extracted from the input sentence. The domain-related entities are input to a graph database to analyze a connection relationship between the domain-related entities, and filling data is obtained from the graph database based on the connection relationship. The input sentence and the filling data are integrated via a natural language processing technology to generate an optimized natural language sentence.

Graph-based natural language optimization method and electronic apparatus

A drug-screening system includes an encoding module, a candidate-drug generating module and a drug-ranking module. The encoding module is configured to encode a drug expression and at least one drug-ranking indicator to generate a first encoding variable. The candidate-drug generating module is configured to train a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The drug-ranking module is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs.

Drug-screening system and drug-screening method

An interactive recommendation system and an interactive recommendation method are provided. The interactive recommendation method includes the following steps. Firstly, a server receives a user request from a user device through the Internet. Then, the server analyzes the user request and generates at least one subsidiary request corresponding to the user request. Then, the server issues an interactive request to the user device according to the at least one subsidiary request. The server generates a recommended application program list according to the at least one subsidiary request. After the user device responds to the interactive request, the server recommends at least one application program in the recommended application program list to the user device.

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Kaohsiung, Taiwan

Chieh-Jen Wang