International Business Machines Corporation

Tsinghua University

Shao Chun Li

Hao Chen

Qi Cheng Li

Xin Zhou

Ya Bin Dang

Li Jun Mei

Steven M Kagan

Feng Li

He H Liu

Zhong Xian Li

Hua Fang Tan

Kathryn Allyn Bassin

Howard M Hess

Susan Eileen Skrabanek

Jian Wang

Jian Ding

Jie Ma

Sheng Huang

Jun Zhu

Guang Tai Liang

Yi Peng Yu

Juhnyoung Lee

Rong Liu

Lijun Mei

Li Wang

Wei Sun

Yi-Min Chee

Wei Ye Chen

Fang Quan Xie

Susan E Smith

Yipeng Yu

Claudio Cozzi

Ying Liu

Ning Duan

Florian Pinel

David Nahamoo

Peng Ji

Jing Chang Huang

Yi Qin Yu

Rangachari Anand

Jing Mei

Xu Min

Guotao Zhao

Bo Zhang

Xin Zhang

Qiang Zhang

Wei Zhao

Xiao Lin Sun

Jie Cui

Shiwan Zhao

Zhenyu Zhang

Soumitra Sarkar

Yi Qi

Pankaj Dhoolia

Anindya Neogi

Chang Li

Santhosh Babu Kumaran

Shivali Agarwal

Girish Bhimrao Chafle

Arjun Natarajan

Wei Zhu

Fenno F Heath, Iii

Renuka Sindhgatta

Yuan Zhou

Sreeram Ramakrishnan

Tian J Chao

Zhuoxuan Jiang

Hao Ac Chen

Guang Tai Lt Liang

Ming Lou

Jeffrey L Robinson

Ningbo Zhang

Embodiments of the present disclosure relate to a method, system, and computer program product for domain adaptation. According to the method, a source model of a source domain is obtained, where the source model is trained to generate a label indicating a predicted category of data from the source domain. A training sample from a target domain is obtained, where the training sample comprises training data from the target domain and a true label indicating a true category of the training data from the target domain. A first label is generated for the training data by using the source model. The first label indicates a predicted category of the training data. A target model of the target domain is trained based on the training data, the true label and the first label.

Domain adaptation

The present disclosure relates to a method, system and computer program product for electronic health record (EHR) data synthetization. According to the method, an original EHR dataset X is captured. A latent space Z is generated from the original EHR dataset X, wherein dimensionality of Z is lower than that of X. A stochastic process prior module is applied to the latent space Z. Synthetic EHR dataset X' is reconstructed from the latent space Z after being applied with the stochastic process prior.

Electronic health record data synthesization

A computer-implemented authentication method, the method comprising: matching a brain pattern sequence with a predetermined password to allow access to a system, wherein the brain pattern sequence is calculated by analyzing a signal slope of a slope threshold of the brain activity to determine a timing and a duration of the brain activity.

Electroencephalography (EEG) based authentication

Embodiments of the present invention relate to computer-implemented methods, systems, and computer program products for managing a conversational system. In one embodiment, a computer-implemented method comprises: obtaining, by a device operatively coupled to one or more processors, a first message sequence comprising messages involved in a conversation between a user and a conversation server; obtaining, by the device, a conversation graph indicating an association relationship between messages involved in a conversation; and in response to determining that the first message sequence is not matched in the conversation graph, updating, by the device, the conversation graph with a second message sequence, the second message sequence being generated based on a knowledge library including expert knowledge that is associated with a topic of the conversation.

Conversational system management

Techniques for response generation are provided. One computer-implemented method comprises: receiving, by an electronic device operatively coupled to a processing unit, a query from a user. The computer-implemented method also comprises generating, by the electronic device, a dialog candidates set based on user context, wherein the user context comprises the query. The computer-implemented method can also comprise generating, by the electronic device, a response based on the dialog candidates set.

Response generation

A computer-implemented method is presented for enabling hierarchical conversational policy learning for sales strategy planning. The method includes enabling a user to have a conversation with a robot via a conversation platform, employing a plan database to store general plans used in the conversation, employing an industry database to store a plurality of candidate plans pertaining to sales promotions, and employing a plan and policy optimizer to allow the robot to select and output an optimal plan from the plurality of candidate plans, the optimal plan determined by hierarchical reinforcement learning via a first learner and a second learner, the first leaner selecting the optimal plan and the second learner selecting an optimal action.

Hierarchical conversational policy learning for sales strategy planning

Computer-implemented methods, computer program products, and system facilitating evaluation of a progress of a diagnosis process are provided. The computer-implemented method comprises: obtaining, by a device operatively coupled to one or more processing units, a list of symptom(s); retrieving, by the device, a sub-graph associated with the list of symptom(s) from a knowledge graph; extracting, by the device, a list of feature(s) from the retrieved sub-graph; estimating, by the device, an average number of inquiries based on the extracted list of feature(s); and calculating, by the device, the progress of the diagnosis process based on the estimated average number of inquiries.

Progress evaluation of a diagnosis process

A vehicle occupant comfort system, comprises a processor that stores computer executable components stored in memory. A plurality of sensors sense ambient conditions associated with exterior and interior conditions of a vehicle. A context component infers or determines context of an occupant of the vehicle. A comfort model component implicitly and explicitly trained on occupant comfort related data analyzes information from the plurality of sensors and context component. A comfort controller adjusts environmental conditions of a passenger compartment of the vehicle based at least in part on output of the comfort model component.

Facilitating personalized vehicle occupant comfort

Methods and systems for automating user interactions include annotating chat logs to identify user intent in question-answer pairs. A classifier is trained, using the annotated chat log, to identify user intent in automated conversations. Chat flows are formed, using the annotated chat logs, that provide responses to user statements based on identified user intent. An automated conversation is conducted with a user, using the chat flows and the classifier, to provide automated responses to user statements.

Transformation of chat logs for chat flow prediction

This disclosure provides a method for road condition prediction. The method comprises receiving, for at least one source vehicle, sensor data collected by a sensor associated with the source vehicle. The method further comprises identifying, based on the sensor data, at least a location with an abnormal road condition and a responsive action taken by the source vehicle. The method further comprises notifying, the location with the abnormal road condition and the responsive action to at least one target vehicle that is expected to pass the location. This disclosure also provides a computer system and a computer software product for road condition prediction.

Road condition prediction

The present disclosure provides a method, computer system and computer program product facilitating generation of a conversation content. A computer-implemented method comprises: obtaining, by a device operatively coupled to one or more processors, history conversation data of an user; determining, by the device, a professional level of the user based on the history conversation data; and generating, by the device, a conversation content for the user based on the professional level of the user.

Conversation content generation based on user professional level

Systems and method for automated contextual dialog generation for cognitive conversations include embedding a natural language sentence input by a user into a corresponding sentence vector using a sentence embedder. A context array is generated using a contextual sentence embedder to embed the sentence vector and previous sentence vectors of a conversation history into a context array. Response words are predicted from the sentence vector by performing sequence-to-sequence dialog prediction with a dialog prediction network. Context of the input sentence is quantified by extracting context features from the context array using a situation quantification network. A response dialog is generated in natural language to display to a user, the response dialog responding to the input sentence with a response generator by determining a dialog state including the response words and the quantified context and optimizing the response dialog with reinforcement learning corresponding to the dialog state.

Automated contextual dialog generation for cognitive conversation

A method, system, and computer program product, include obtaining window status decision information based on vehicle interior background noise and determining switch status of the vehicle window(s) based on the obtained window status decision information.

System, method and computer program product for detecting switch status of vehicle window(s)

A computer-implemented method, computer program product, and computer processing system are provided for generating inferences from a forest of predefined problem determination trees using a processor-based conversation platform. The method includes selecting a tree from among the forest of predefined problem determination trees, responsive to user utterances uttered during an inference generating session. The method further includes navigating the tree to allocate a relevant tree node to generate a problem diagnosis question or a problem resolution action by understanding the user utterances among common interaction patterns in problem diagnosis and problem resolution dialogs. The method also includes providing speech for uttering the problem diagnosis question or the problem resolution action to a user.

Forest inference engine on conversation platform

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Beijing, China

Shao Chun Li