Autonomous Collaborative Authoring System for Innovation Workflows
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Solution Overview
Problem
Collaborative sourcing initiatives face challenges such as a lack of relevant content, difficulty in locating and updating information, and manual processes for voting, commenting, and approving intentions, leading to inefficiencies in innovation and decision-making.
Innovation Solution
A cloud-based autonomous and collaborative authoring system that searches public and private data sources in real-time to filter, identify, and connect relevant information, and automatically generates and updates intentions and articles, using a web portal for participant input and an Article and Intention Generation engine to autonomously take actions based on participant inputs and retrieved content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for voting, commenting, and approving intentions, then participants can directly control and evaluate ideas, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system implements autonomous agents that automatically perform voting, commenting, and approval actions on intentions without requiring constant human intervention. These agents autonomously evaluate intentions based on predefined criteria and participant profiles, enabling the system to serve itself and significantly reducing the time participants and managers spend on manual evaluation tasks
Solution Approach 2:
The patent introduces autonomous agents as intermediaries between participants and the intention evaluation process. These agents act on behalf of participants, automatically performing voting and commenting actions based on participant profiles and intention characteristics, thereby mediating the interaction and eliminating the need for direct manual participation in every evaluation step
2Quantity of substance
If collaborative sourcing initiatives start with a blank canvas, then organizations have full control over content creation, but they suffer from data starvation and lack of relevant content
Solution Approach 1:
The system automatically performs preliminary actions by sourcing and populating intentions and supporting content from multiple external data sources before the collaborative initiative begins. This pre-population ensures that participants start with abundant relevant content rather than a blank canvas, making the initiative easier to launch and more engaging from the start
Solution Approach 2:
The patent employs a multi-functional content sourcing system that automatically retrieves intentions and supporting materials from diverse external sources including social media, news outlets, industry databases, and participant-generated content. This universal approach to content acquisition ensures abundant relevant material is available across different types of collaborative initiatives regardless of their specific domain
3Measurement precision
If participants manually research and collect data points for intentions, then accuracy can be ensured, but participants struggle to find and submit new intentions due to busy schedules
Solution Approach 1:
The system replaces the manual mechanical process of researching and collecting data with an automated information retrieval system. Autonomous agents continuously search multiple data sources for new intentions and supporting content, automatically validating and structuring the information. This substitution maintains data accuracy through systematic verification while completely eliminating the time participants would spend on manual research
Solution Approach 2:
The patent implements continuous automated searching and validation of intentions and supporting content from multiple sources. Rather than relying on periodic manual input from participants, the system maintains an ongoing process of discovering, validating, and adding new intentions and content, ensuring both accuracy through continuous verification and ease of operation by removing manual effort entirely
4Reliability
If challenge owners constantly follow up with participants for voting and comments, then idea development can be monitored, but the process becomes burdensome and inefficient
Solution Approach 1:
The system implements automated feedback mechanisms where autonomous agents continuously monitor intention status, participant engagement levels, and development progress. The agents automatically send notifications, reminders, and status updates to both participants and challenge owners, providing reliable monitoring of idea development without requiring manual follow-up. This feedback loop maintains reliability while eliminating the complexity of human coordination
Data Source
AI summary
An autonomous intention, article search and actionable data generation system and method to query public or private as well as internal and external data sources that are available to an organization, tapping into all information in real-time and on an ongoing basis to make recommendations to take at least one action or to autonomously filter, find, identify, connect, merge, support, evaluate, select, and approve intentions and/or articles for a given search context, an instance context (such as one of a challenge, theme, topic, goal, objective, mission, target, focus area, problem, risk, or the like), and an organizational context (such as an industry, line of business, strategy, goals, objectives, areas of expertise, and the like). The system may also take into account a participant's past actions in similar situations, a participant's background, diversity and inclusion attributes, skills, interests, experience, location, and other participant attributes.