International Business Machines Corporation

Akihiro Kishimoto

Radu Marinescu

Yufang Hou

Beat Buesser

Bei Chen

Toshiyuki Hama

Hiroshi Kajino

Hsiang Han Hsu

Djallel Bouneffouf

Masataro Asai

A system receives messaging, video and/or audio input streams including dialogue spoken by users at a group meeting. From these inputs, the system obtains single or multiple interaction records including natural language text memorializing content spoken by each speaker at a meeting, analyzes the content, and identifies single or multiple action item tasks in the interaction records. The system then generates summaries indicating the action item tasks for the users. From the dialogue content, the system further detects whether each action item is addressed, and whether the action item for a user has a solution, or not. The system further detects whether one action item is a precondition for resolving another action item by the user or in conjunction with another user. Using a pre-configured template, the system generates action item summaries, any associated solution, and any relationship or precondition between action items and presents the summary to a user.

Generating summary and next actions in real-time for multiple users from interaction records in natural language

A computer implemented method of modifying molecular structures constrained by a budget is provided. The computer implemented method includes receiving from a user a subset of molecules, where each molecule is represented as a generation path, and receiving from the user an allotted budget for modifying a selection of molecules from the subset of molecules. The computer implemented method further includes testing a first molecule, and reducing the allotted budget based on the resources expended to test the first molecule. The computer implemented method further includes testing a second molecule, and reducing the allotted budget based on the resources expended to test the second molecule. The computer implemented method further includes determining a remaining amount of the allotted budget, and testing additional molecules from the subset of molecules until the allotted budget is exhausted. The computer implemented method further includes presenting the tested molecules to the user.

Optimization of multiple molecules

In an approach for building an automated customer support system, a processor receives a set of sentences extracted from a natural language conversation occurring between an IT support system and a user. A processor extracts an initial state and a goal state from the set of sentences using a Natural Language Classifier. A processor extracts one or more actions from the set of sentences. A processor creates a formal planning model. A processor determines the one or more formal actions are not complete using a first machine learning model. A processor completes the one or more formal actions with one or more missing parts. A processor produces an executable plan using a planner. A processor implements one or more executable scripts according to a sequence of the one or more formal actions of the executable plan using a plan executor.

Automated system for customer support

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating code development by predicting one or more code attributes and/or code portions for use in a project code to be written. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a dialogue component that generates a query based on a natural language request comprising a code-related attribute, and a prediction component that predicts another attribute or a code portion to satisfy the request. In an embodiment, an input dataset employed to support the influence mapping can comprise time-stamped tuple data comprising a state, an action and a reward. The code-related attribute can at least partially define a project code, of code to be written.

Dialogue- and machine learning-facilitated code development

Techniques regarding generating and/or training one or more symbolic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train a symbolic model via active machine learning. The symbolic model can characterize a formal planning language for a planning domain as a plurality of digital image sequences.

Symbolic model training with active learning

In an approach for recommending a safe path for people to navigate an environment, a processor generates a contact graph for a user in a group of users in an area, based on a pre-defined distance measurement between the user and another user in a same timestamp during a pre-defined time period. A processor builds a profile for the user based on the contact graph, the profile including a probability model corresponding to the contact graph to estimate an infection probability for the user. A processor calculates an initial route for the user from a first location to a second location in the area. A processor analyzes an infection risk for the user based on the profile and other users within a pre-defined distance to the user in the initial route. A processor updates the initial route based on the analysis to minimize the risk to be infected.

Growing community of inventors

Setagaya, Japan

Akihiro Kishimoto