Targeted Data Dissemination System for Enterprise Information Delivery
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Solution Overview
Problem
Coordinating the dissemination of relevant information across different departments and locations within an enterprise is resource-intensive due to varying policies and employee-specific needs, making it challenging to provide tailored information to employees effectively.
Innovation Solution
An IT system with a knowledge base and campaign database that uses metadata and trigger data to dynamically curate and deliver targeted content to employees based on their characteristics and specific events, utilizing a cloud computing system and multi-instance cloud architecture for efficient information management and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If information is manually coordinated across different departments and locations, then employees can receive relevant information, but the process becomes resource-intensive and difficult to scale
Solution Approach 1:
The system enables self-service by allowing employees to automatically receive relevant information based on their profile data and the policies they are associated with. The automated matching system compares employee characteristics with policy metadata without requiring manual intervention, thus eliminating the resource-intensive manual coordination process while maintaining information delivery effectiveness.
Solution Approach 2:
The patent replaces the mechanical manual coordination process with an automated computational system. The system uses algorithms to match employee filter data with policy metadata, automatically generating and delivering personalized information packets. This substitution of manual mechanical processes with automated computational processes dramatically improves productivity while reducing resource consumption.
2Loss of energy
If generic information is sent to all employees, then resource intensity is reduced, but information relevance to individual employees decreases
Solution Approach 1:
The system applies local quality by customizing information content for each employee based on their specific characteristics stored in filter data. Instead of uniform generic information, the system dynamically assembles personalized information packets by matching employee attributes (such as location, department, role) with corresponding policy metadata, ensuring each employee receives locally optimized relevant information.
Solution Approach 2:
The system changes parameters by dynamically adjusting information content based on employee-specific variables. The metadata associated with each policy includes parameters such as geographic region, department, job level, and other characteristics. The system varies the information delivered by changing these parameters according to the recipient's profile, thereby maintaining high relevance while operating efficiently.
3Loss of information
If personalized information is manually created for each employee, then information relevance is maximized, but device complexity and resource requirements increase significantly
Solution Approach 1:
The system applies segmentation by breaking down the information delivery process into discrete manageable components: employee filter data, policy metadata with associated characteristics, matching algorithms, and personalized information packet assembly. This segmentation allows the complex personalization task to be handled through systematic comparison and matching of discrete data elements rather than requiring complex manual creation processes.
Solution Approach 2:
The system achieves universality through a centralized platform that handles information delivery for multiple departments, locations, and employee types simultaneously. The same core matching logic and metadata structure serve diverse information needs across the entire organization, reducing overall system complexity compared to maintaining separate manual processes for each department or location.
4Loss of information
If comprehensive policies are made available to all employees, then information completeness is improved, but information overload and difficulty in finding relevant information increases
Solution Approach 1:
The system extracts only the relevant portion of comprehensive policies that applies to each specific employee. By comparing employee filter data with policy metadata characteristics, the system extracts and delivers only the applicable information segments, eliminating the need for employees to navigate through complete but largely irrelevant policy documents. This extraction process maintains information completeness for relevant topics while dramatically improving accessibility.
Solution Approach 2:
The system performs preliminary action by pre-processing and tagging policies with metadata characteristics that describe their applicability criteria. This preliminary organization of information by characteristics (such as geographic region, department, job level) enables rapid matching and delivery of relevant information without requiring employees to search through comprehensive unorganized policy repositories, thereby improving ease of operation while maintaining completeness.
Data Source
AI summary
The present approach relates to providing targeted content to a user (e.g., employee) of an enterprise. In certain implementations, the present techniques involve receiving filter data (e.g., data identifying an employee) from a user and providing content in the form of knowledge blocks to a user based on metadata of the knowledge blocks that is associated with the filter data. In other implementations, the present techniques involve receiving trigger data (e.g., data of an employee satisfying a condition) and providing a set of activities (e.g., social activities or work related) to a user based on the trigger data.