International Business Machines Corporation

Xue Han

Ya Bin Dang

Qi Cheng Li

Lijun Mei

Xin Zhou

Zi Ming Huang

Lian Xue Hu

Li Jun Mei

Jaydeep Sen

Yan Fei Wang

Bu Yu Gao

New question and answer (QA) pairs can be automatically discovered from a corpus of data such as online chats and conversations. Newly discovered QA pairs can augment QA database, which can be used by a computer processor or device, e.g., by a chatbot, an automated machine, and/or another. Existing QA knowledge can be used to learn the structures of QA knowledge distribution in conversations, and new QA knowledge can be automatically learned through the structure of learned QA knowledge distribution in conversations. The structure of learned QA knowledge distribution can be refined by adding more semantics based on labeled data.

Discovering new question and answer knowledge from conversation

Mechanisms are provided for processing a sequential database natural language query. A process model is preprocessed to generate mapping data structure(s). The mapping data structure(s) map elements of the sequential process to other elements of the sequential process to thereby identify sequential and dependent characteristics of the sequential process. A sequential database natural language (SDNL) query interpretation engine is configured with the mapping data structure(s) and natural language processing is performed on a query to generate extracted features. The configured SDNL query interpretation engine operates on the extracted features and the mapping data structure(s) to generate intent information for the query. Executable database queries are generated based on the intent and executed on a database to return a response to the query.

Natural language querying of sequential process automation data

Methods, systems and computer program products for providing automated robotic process automation design generation are provided. Aspects include receiving a set of stored automated sub-processes. Aspects also include receiving a process description document that describes a desired computer automated task (CAT) process. Aspects also include identifying a plurality of candidate sub-processes based on the process description document. Aspects also include identifying at least one matching sub-process based on a comparison of each of the plurality of candidate sub-processes to the set of stored automated sub-processes. The at least one matching sub-process is a stored automated sub-process that exceeds a threshold level of similarity to a candidate sub-process. Aspects also include generating a recommendation to automatically populate a portion of a CAT design file for the desired CAT process based on the at least one matching sub-process.

Automated generation of robotic computer program code

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 chatbot system receives utterances of a conversation. The chatbot system constructs a conversation knowledge graph comprising one or more dialogue segments that correspond to utterances of the conversation. The chatbot system identifies a dialogue segment in the conversation knowledge graph having a contextual uncertainty that is detected based on a context model. The chatbot system generates a clarifying question for the identified dialogue segment having the contextual uncertainty. The chatbot system receives a clarifying answer from a user interface of the computing device to the clarifying question. The chatbot system updates the context model and the conversation knowledge graph based on the clarifying question and the clarifying answer to resolve the contextual uncertainty of the identified dialogue segment.

Conversation-based chatbot training

Methods, computer program products, and systems for Robotic Process Automation (RPA) upgrade of an application are provided. A first mapping indicating relationships between UI elements in a first version of the application and UI elements in a second version of the application and a second mapping indicating relationships between one or more UI elements in a first version of an RPA code corresponding to the first version of the application and UI elements in the first version of the application are respectively obtained. A third mapping indicating relationships between the one or more UI elements in the second version of the application and one or more UI elements in a second version of the RPA code corresponding to the second version of the application is determined and based on which the second version of the RPA code corresponding to the second version of the application is generated.

Growing community of inventors

Beijing, China

Xue Han