17544759. Role-Based Cross Data Source Actionable Conversation Summarizer simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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Role-Based Cross Data Source Actionable Conversation Summarizer

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Angelo Danducci Ii of Austin TX (US)

Kirti A. Apte of Austin TX (US)

Olga Saprycheva of Austin TX (US)

Tyrome Sweet of Merced CA (US)

Role-Based Cross Data Source Actionable Conversation Summarizer - A simplified explanation of the abstract

This abstract first appeared for US patent application 17544759 titled 'Role-Based Cross Data Source Actionable Conversation Summarizer

Simplified Explanation

The patent application describes a mechanism in a data processing system that summarizes conversations from multiple sources based on roles and identifies actionable items using machine learning.

  • Aggregates conversation data from different sources
  • Applies a computerized summarization process to generate role-based summaries
  • Uses a machine learning classifier to determine if each sentence is an actionable item
  • Adds actionable items to the role-based summaries

Potential Applications

This technology can be applied in various domains where there is a need to summarize and extract actionable items from conversations, such as:

  • Customer service: Summarizing customer support conversations and identifying actionable tasks for agents.
  • Project management: Extracting actionable tasks from team discussions and assigning them to relevant members.
  • Legal proceedings: Summarizing legal conversations and identifying actionable steps for lawyers.

Problems Solved

This technology addresses the following problems:

  • Information overload: By summarizing conversations, it helps users quickly grasp the key points and actionable items without going through the entire conversation.
  • Actionable item identification: It automates the process of identifying actionable items, saving time and effort for users.
  • Cross-source aggregation: It enables the consolidation of conversation data from multiple sources, providing a comprehensive view for analysis and summarization.

Benefits

The benefits of this technology include:

  • Improved productivity: Users can quickly identify and act upon actionable items without spending excessive time on reading and analyzing conversations.
  • Enhanced collaboration: By assigning and tracking actionable items, teams can work more efficiently and ensure tasks are completed.
  • Better decision-making: Summarized conversations provide a concise overview, enabling users to make informed decisions based on key information.


Original Abstract Submitted

A mechanism is provided in a data processing system for role-based cross data source actionable conversation summarization. The mechanism aggregates conversation data from a plurality of conversation data sources. The mechanism applies a computerized summarization process to the aggregated conversation data to generate at least one role-based summary of the aggregated conversation data. The mechanism applies a machine learning classifier to the at least one role-based summary to determine if each sentence in the at least one role-based summary is an actionable item. Responsive to detecting an actionable item, the mechanism adds the actionable item to the at least one role-based summary.

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