Patents by Inventor Hongliang Fei
Hongliang Fei has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20200311519Abstract: Described herein are embodiments for systems and methods to incorporate skip-gram convolution to extract non-consecutive local n-gram patterns for comprehensive information for varying text expressions. In one or more embodiments, one or more recurrent neural networks are employed to extract long-range features from localized level to sequential and global level via a chain-like architecture. Comprehensive experiments on large-scale datasets widely used for the text classification task were conducted to demonstrate the effectiveness of the presented deep skip-gram network embodiments. Performance evaluation on various datasets demonstrates that embodiments of the skip-gram network are powerful for general text classification task set. The skip-gram models are robust and may be generalized well on different datasets, even without tuning the hyper-parameters for specific dataset.Type: ApplicationFiled: March 28, 2019Publication date: October 1, 2020Applicant: Baidu USA LLCInventors: Hongliang FEI, Chaochun LIU, Yaliang LI, Ping LI
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Patent number: 10685319Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.Type: GrantFiled: October 13, 2015Date of Patent: June 16, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: JoAnn Piersa Brereton, Ajay Ashok Deshpande, Arun Hampapur, Miao He, Alan Jonathan King, Xuan Liu, Christopher Scott Milite, Jae-Eun Park, Joline Ann Villaranda Uichanco, Songhua Xing, Steve Igrejas, Hongliang Fei, Vadiraja Ramamurthy, Yingjie Li, Kimberly D. Hendrix, Xiao Bo Zheng
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Patent number: 10679178Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.Type: GrantFiled: December 1, 2015Date of Patent: June 9, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: JoAnn Piersa Brereton, Ajay Ashok Deshpande, Arun Hampapur, Miao He, Alan Jonathan King, Xuan Liu, Christopher Scott Milite, Jae-Eun Park, Joline Ann Villaranda Uichanco, Songhua Xing, Steve Igrejas, Hongliang Fei, Vadiraja Ramamurthy, Yingjie Li, Kimberly D. Hendrix, Xiao Bo Zheng
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Patent number: 10650305Abstract: Presented are relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).Type: GrantFiled: July 8, 2016Date of Patent: May 12, 2020Assignee: Baidu USA LLCInventors: Chaochun Liu, Nan Du, Shulong Tan, Hongliang Fei, Wei Fan
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Patent number: 10540703Abstract: The present disclosure relates generally to the field of product supply networks (e.g., for order fulfillment and inventory control). In one specific example, mechanisms are provided for presenting visualizations to aid in co-locating two or more products in the same location (e.g., at a common order fulfillment facility) based upon associations between the products. In various embodiments, systems, methods and computer program products are provided.Type: GrantFiled: May 13, 2016Date of Patent: January 21, 2020Assignee: Wayfair LLCInventors: Ajay A. Deshpande, Hongliang Fei, Arun Hampapur, Xuan Liu
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Patent number: 10496748Abstract: A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.Type: GrantFiled: September 18, 2018Date of Patent: December 3, 2019Assignee: Baidu Online Network Technology (Beijing) Co., Ltd.Inventors: Jingbo Zhou, Yuan Xia, Hongliang Fei, Chaochun Liu, Weishan Dong, Wei Fan
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Patent number: 10372743Abstract: Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.Type: GrantFiled: July 20, 2016Date of Patent: August 6, 2019Assignee: Baidu USA LLCInventors: Shulong Tan, Hongliang Fei, Yi Zhen, Yu Cao, Bocong Liu, Chaochun Liu, Richard Chun Ching Wang, Dawen Zhou, Wei Fan
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Publication number: 20190114318Abstract: A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.Type: ApplicationFiled: September 18, 2018Publication date: April 18, 2019Applicant: Baidu Online Network Technology (Beijing) Co., Ltd.Inventors: Jingbo Zhou, Yuan Xia, Hongliang Fei, Chaochun Liu, Weishan Dong, Wei Fan
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Publication number: 20180039735Abstract: Presented are systems and methods that allow healthcare providers and governments to infer demand for healthcare resources to ensure effective and timely healthcare services to patients by reducing healthcare supply shortages, emergencies, and healthcare costs. In embodiments, this is accomplished by gathering data from a number of sources to generate labeled records from which entity features and relationships between entities are extracted, correlates, and/or combined with other external healthcare data. In embodiments, this information is used to train a model that predicts healthcare resource demands given a set of input conditions or factors.Type: ApplicationFiled: August 2, 2016Publication date: February 8, 2018Applicant: Baidu USA LLCInventors: Yi Zhen, Hongliang Fei, Shulong Tan, Wei Fan
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Publication number: 20180025008Abstract: Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.Type: ApplicationFiled: July 20, 2016Publication date: January 25, 2018Applicant: Baidu USA LLCInventors: Shulong Tan, Hongliang Fei, Yi Zhen, Yu Cao, Bocong Liu, Chaochun Liu, Richard Chun Ching Wang, Dawen Zhou, Wei Fan
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Publication number: 20180025121Abstract: Systems and methods are disclosed provide improved automated extraction of medical-related information. In embodiments, finer-grained medical-related data, such as medical entities, including symptoms, diseases, dimensions, and temporal information, can be extracted. In embodiments, by extracted finer level medical-related information from an input statement and generating visual displays of that information, a medical professional can readily see relevant medical information that provides medical entities and associated dimension information, as well as evolving history.Type: ApplicationFiled: July 20, 2016Publication date: January 25, 2018Applicant: Baidu USA LLCInventors: Hongliang Fei, Shulong Tan, Yi Zhen, Erheng Zhong, Chaochun Liu, Dawen Zhou, Wei Fan
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Publication number: 20180012121Abstract: Presented are relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).Type: ApplicationFiled: July 8, 2016Publication date: January 11, 2018Applicant: Baidu USA LLCInventors: Chaochun Liu, Nan Du, Shulong Tan, Hongliang Fei, Wei Fan
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Publication number: 20180011979Abstract: Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.Type: ApplicationFiled: July 11, 2016Publication date: January 11, 2018Applicant: Baidu USA LLCInventors: Erheng Zhong, Chaochun Liu, Yusheng Xie, Nan Du, Hongliang Fei, Yi Zhen, Yu Cao, Richard Chun Ching Wang, Dawen Zhou, Wei Fan
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Publication number: 20170330259Abstract: The present disclosure relates generally to the field of product supply networks (e.g., for order fulfillment and inventory control). In one specific example, mechanisms are provided for presenting visualizations to aid in co-locating two or more products in the same location (e.g., at a common order fulfillment facility) based upon associations between the products. In various embodiments, systems, methods and computer program products are provided.Type: ApplicationFiled: May 13, 2016Publication date: November 16, 2017Inventors: Ajay A. DESHPANDE, Hongliang FEI, Arun HAMPAPUR, Xuan LIU
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Publication number: 20170330124Abstract: Causal performance analysis for store merchandising may be provided. A clustering technique may be performed based on target store location data and existing store data. Based on the clustering technique, a peer selection group is determined comprising a group of stores determined to have similar attributes to the target store location. Sales distortions for a plurality of divisions associated with the group of stores in the peer selection group may be determined. A distortion matrix may be generated comprising a ranked list of the plurality of divisions. A merchandise mix recommendation for the target store location may be presented via a user interface device.Type: ApplicationFiled: October 26, 2016Publication date: November 16, 2017Inventors: Ajay A. Deshpande, Hongliang Fei, Arun Hampapur, Hongfei Li, Xuan Liu
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Publication number: 20170228812Abstract: A method and system optimizing source selection of an online order with the lowest fulfillment cost by considering multiple types of parameters, including shipping costs, backlog costs and markdown savings of the order. The method includes obtaining an order from the order retrieval subsystem of the OMS, selecting the candidate sources, and retrieving data from retailers or shipping companies of each selected candidate sources. The system then calculates the costs and savings parameters of the candidate sources from the retrieved data. The system identifies all possible candidate sourcing selections of the order and calculates the total fulfillment cost of each sourcing selection of the order by adding the shipping costs with the backlog costs, and subtracting the markdown savings of all candidate sources in each sourcing selection. The system identifies the optimized sourcing selection of the order with the lowest fulfillment cost and renders the selection to the OMS.Type: ApplicationFiled: February 8, 2016Publication date: August 10, 2017Inventors: JoAnn P. Brereton, Ajay A. Deshpande, Hongliang Fei, Arun Hampapur, Miao He, Kimberly D. Hendrix, Steve Igrejas, Alan J. King, Yingjie Li, Xuan Liu, Christopher S. Milite, Jae-Eun Park, Vadiraja S. Ramamurthy, Joline Ann V. Uichanco, Songhua Xing, Xiao Bo Zheng
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Patent number: 9584836Abstract: Enhanced personalized advertisements may be provided through media such as IPTV, for example, by analyzing data such as subscribers' mobility data (calling and movement data), television (TV) watching history, online browsing data (e.g., Internet, web site browsing), and subscribers' historical purchasing transactions. With the analysis, subscribers' contexts, any information that reflects subscribers' interests and activities, and intents, tendency to buy certain products, items, services, and/or travel to some locations may be inferred.Type: GrantFiled: September 3, 2014Date of Patent: February 28, 2017Assignee: International Business Machines CorporationInventors: Hongliang Fei, Kin H. Lei, Ming Li, Sambit Sahu
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Patent number: 9542510Abstract: Detecting appliance in a building, in one aspect, may comprise receiving meter data associated with energy consumption, the meter data comprising at least energy consumption data associated with usage of the appliance, receiving customer data associated with the meter data, extracting features for training a model for detecting the appliance, based on at least the meter data and the customer data, and constructing the model based on the features.Type: GrantFiled: August 7, 2013Date of Patent: January 10, 2017Assignee: International Business Machines CorporationInventors: Hongliang Fei, John Hutchinson, Younghun Kim, Sanjay K. Mamidipalli, Milind R. Naphade, Sambit Sahu
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Patent number: 9507890Abstract: Detecting appliance in a building, in one aspect, may comprise receiving meter data associated with energy consumption, the meter data comprising at least energy consumption data associated with usage of the appliance, receiving customer data associated with the meter data, extracting features for training a model for detecting the appliance, based on at least the meter data and the customer data, and constructing the model based on the features.Type: GrantFiled: September 30, 2013Date of Patent: November 29, 2016Assignee: International Business Machines CorporationInventors: Hongliang Fei, John Hutchinson, Younghun Kim, Sanjay K. Mamidipalli, Milind R. Naphade, Sambit Sahu
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Publication number: 20160267583Abstract: Generating an electronic data model of a logical supply chain and regenerating the model based on changes to modelling parameters, to guide decision making. The data model is generated using known information about the supply chain. One or more decision scenarios are modelled based on a simulated loan request, and the data model is regenerated to consider the consequences. A recommendation is made based on pre-defined or user-defined criteria.Type: ApplicationFiled: March 9, 2015Publication date: September 15, 2016Inventors: Hongliang Fei, Miao He, Hongfei Li, Buyue Qian