Tcl Research America Inc.

Lifan Guo

Haohong Wang

Mengwen Liu

Yue Shang

Wanying Ding

Dae Hoon Park

Xiaobo Ren

Guanghan Ning

Xiaoli Song

Wenqiang Bo

Bo Song

Jianliang Yi

Ziju Feng

Yizhou Zang

An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Experience-aware anomaly processing system and method

A method is provided for recommending a desired func sequence schedule to a user. The method includes obtaining at least one user intention inputted by the user; creating a func sequence including a plurality of funcs for fulfilling the user intention; and determining user preference on scheduling of the plurality of funcs in the func sequence. The method also includes determining a dependency relationship among the plurality of funcs in the func sequence; and determining scheduling constraints to the scheduling of the plurality of funcs in the func sequence. The method further includes, based on the user preference, the dependency relationship, and the scheduling constraints, performing an optimization process to determine a desired schedule for the plurality of funcs in the func sequence to fulfill the user intention; and recommending the func sequence with the desired schedule to the user.

Personalized FUNC sequence scheduling method and system

A method for an app search engine leveraging user reviews is provided. The method includes receiving an app search query from a user, determining a plurality of relevant apps based on the received app search query, and extracting app descriptions and user reviews associated with the plurality of relevant apps from an app database. The method also includes preprocessing the extracted app descriptions and user reviews of each of the plurality of relevant apps to generate a text corpus and creating a topic-based language model for each of the plurality of relevant apps based on the generated text corpus. Further, the method includes ranking a list of relevant apps using the topic-based language model and providing the ranked app list for the user.

Method and system for app search engine leveraging user reviews

A method is provided for providing desired application usage experience to a user on a user device. The method includes automatically converting a plurality of App pages into FUNC descriptors to be stored, wherein each FUNC descriptor includes at least non-functional properties of a FUNC, capability of the FUNC, and an interface of the FUNC to invoke the FUNC. The method also includes obtaining at least one user intention inputted by the user; determining at least one FUNC descriptor from the FUNC descriptors for fulfilling the user intention; recommending to the user with at least one FUNC corresponding to the at least one FUNC descriptor by rendering the at least one FUNC descriptor on the user device; and invoking the at least one FUNC directly from the user device without going through a launching page of an App page providing the at least one FUNC.

Method and system for FUNC-based experience framework with cross-user intelligence

A method for a conversational recommender system includes receiving at least one user preference from a user, generating an abstract explanation reflecting the at least one user preference and based on the abstract explanation, and retrieving a list of items to be evaluated by the user from an item database. The items on the list have a relevance with the user preference. The method further includes determining an initial ranking order of the items to be evaluated by the user, and ranking the items to be evaluated by the user based on a random walk algorithm and the initial ranking order. Items not ranked are remaining items. The method further includes receiving a user feedback of the ranked item, and re-ranking the remaining items based on the user feedback of the ranked items and the random walk algorithm.

Least-ask: conversational recommender system with minimized user interaction

A method for scalable user intent mining is provided. The method includes detecting named entities from a plurality of query logs in a public query log dataset and generating features of the plurality of query logs based on the detected named entities. The method also includes applying a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs to train a public multimodal RBM and generating a plurality of public query representations. Further, the method includes receiving a search query from a user, determining whether there are a plurality of history queries of the user. When there is no history query, user intent is predicted using the public multimodal RBM. When there are the history queries, the public multimodal RBM is applied on the plurality of history queries to train a personalized multimodal RBM, and the user intent is predicted using the personalized multimodal RBM.

Scalable user intent mining using a multimodal restricted boltzmann machine

A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.

Unified attractiveness prediction framework based on content impact factor

A method for multimodal clue based personalized app function recommendation is provided. The method includes receiving an app search query from a user, obtaining a plurality of real-time clues collected from the user and extracting a plurality of features to represent the collected clues, wherein the plurality of features include structured features and text features. The method also includes generating a joint representation of the multimodal clues based on the plurality of extracted features and creating a logistic regression model based on the joint representation of the multimodal clues. Further, the method includes ranking a list of app functions using the logistic regression model and displaying the ranked app function list for the user.

Method and system for multimodal clue based personalized app function recommendation

The present invention provides a method for providing augmented product specifications based on user reviews. The method obtains input data of specifications and user reviews on a plurality of products, each specification including at least a pair of a feature and a feature-value of the product. The method concatenates the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product. The method further employs a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics. The topics include specifications of the products. The topic model is a type of statistical model for discovering topics that occur in a collection of product-documents. Based on the topic model, the method can provide augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product.

Method and system for generating augmented product specifications

A method and a non-transitory computer-readable medium having computer program for optimizing a system function-flow of a system capable of performing at least one function and comprising a plurality of components by utilizing application programming interface (API) profiling are provided. The method comprises receiving a request to perform the at least one function of the system from a user; obtaining at least one component required to realize the at least one function requested by the user from the plurality of the components; providing a plurality of APIs to the at least one obtained component to choose from; profiling each of the plurality of APIs to get a performance score of each API; based on the profiled APIs, optimizing the system function-flow of the system; determining corresponding APIs for the optimized system function-flow; and performing the at least one function requested by the user based on the determined APIs.

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San Jose, CA, United States of America

Lifan Guo