Baidu USA LLC

Midea Group Co., Ltd.

Yu Zhu

Liangliang Zhang

Xin Chen

Hua Zhou

Shiyuan Fang

Lin Li

Yuxiang Gao

Jing Qian

In response to receiving a user inquiry describing one more symptoms of a medical condition, an online hospital visit prediction system can determine whether the user will visit the hospital for treatment related to the medical condition the day following the user inquiry, termed 'hospital day.' Query logs are stored for a large plurality of users. Using the query logs, a prediction model can be trained using predetermined features extracted from the query logs of each of a plurality of users for a predetermined period of time prior to the day the user visited the hospital. The predetermined features can be used to train the prediction model with a high accuracy to determine whether a future user will visit the hospital the day following a query about a medical condition.

Methods of feature extraction and modeling for categorizing healthcare behavior based on mobile search logs

Described is a technique for associating words used in a search query with categories. This technique aims to produce potentially more relevant search results by improving the associations with words used for a search. A machine learning technique is implemented to train a classification model, which may include a word embedding model. The classification model is trained to receive words as input and to create vectors of the words as output. These word vectors may then be mapped to a vector space and the technique may then perform a cluster analysis of the vectors. Based on the cluster analysis, clusters may be identified and each cluster may be associated with a corresponding category.

Method and system of determining categories associated with keywords using a trained model

Laundry appliances use machine learning models and/or personalization to provide better treatments. As one example, a laundry appliance has a chamber in which laundry items are placed for treatment. Sensor(s) are positioned to sense contents of the chamber or to sense the laundry items as they are loaded into the chamber. The machine learning model uses data from these sensors to determine various attributes of the laundry items and/or the treatment, such as the type of fabric and/or how dirty the items are, and the treatment process is controlled accordingly. Personalized data, such as an individual's preferences for laundry treatments or his sensitivities and allergies, may also be used to personalize the treatment process.

Personalized laundry appliance

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San Jose, CA, United States of America

Yu Zhu