International Business Machines Corporation

Li Jun Mei

Qi Cheng Li

Shao Chun Li

Jian Wang

Xin Zhou

Hao Chen

Jie Ma

Ya Bin Dang

Yi Peng Yu

Feng Li

Guoqiang Hu

Zi Yu Zhu

Bo Yang

Rong Liu

Yu Fen Yuan

Yi Min Wang

Wei Sun

Xin Zhang

Juhnyoung Lee

Wei Ye Chen

Fang Quan Xie

Guang Tai Liang

HuaFang Tan

David Nahamoo

Guo Qiang Hu

Yi-Min Chee

Xin Hao Cheng

Maja Vukovic

Jing Li

Jinho Hwang

Zhenyu Zhang

Fan Jing Meng

Anup Kalia

Ajay Gupta

John Joseph Rofrano

Yan Gao

Shi Wan Zhao

Rangachari Anand

Chen Lin

Xue Han

Lian Xue Hu

Chen Hao

Hao Ac Chen

Zhuoxuan Jiang

Guang Tai Lt Liang

Methods and systems for treating a disease include navigating a symptom-centric decision tree which accounts for probabilities for symptoms and diseases and emergency values for the symptoms and diseases, based on information provided by a user, to determine a disease. A treatment is provided to the user based on the determined disease.

Providing medical treatment using optimized symptom-centric decision trees based on disease-centric knowledge graphs

A system for automating a process using robotic process automation code includes a memory device for storing program code, and at least one processor device operatively coupled to the memory device. The at least one processor device is configured to execute program code stored on the memory device to process, based on a contextual dictionary, a process description document associated with a process to be automated by a robotic process automation system, automatically generate robotic process automation code based on the processing, and execute the robotic process automation code for automating the process.

Automating a process using robotic process automation code

A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.

Generating three-dimensional virtual scene

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

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

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

Systems and methods for facilitating an automatic adjustment of a braking system is provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a braking curve model based on braking usage pattern data corresponding to one or more vehicles. The computer-implemented method can also comprise adjusting, by the system, a supplemental braking component of the first vehicle based on a simulation of one or more braking components corresponding to the one or more vehicles, wherein the one or more braking components is represented by the braking curve model.

Facilitation of automatic adjustment of a braking system

Systems and methods for prioritizing interview questions, including predicting answers to questions in a candidate question set using a neural network model, and generating a ranking for each of the questions in the candidate question set by determining answer confidence, completeness, and ambiguity for each of the answers, and incorporating user preferences using a personalized preference matrix. A top ranked question is automatically selected and presented to the user, and the personalized preference matrix is iteratively adjusted for subsequent ranking of questions based on an answer to the top ranked question by the user to reduce computational resources expended.

Predicting user preferences and requirements for cloud migration

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 computer-implemented method is provided for processing a set D of conversation logs. The method includes learning, by a processor device, a set R of entity relation mining rules from a set K of known entity utterances uttered by known entities. The method further includes automatically recommending, by the processor device, extended utterances of the known entities from unrecognized ones of the known entities based on the set R.

Extending system entities for conversational system

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Beijing, China

Li Jun Mei