ML Presentation Media Generation for Onboarding
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Solution Overview
Problem
In large organizations, onboarding new personnel requires retrieving and compiling vast amounts of information from dynamic data sources, which is labor-intensive and often results in outdated information due to frequent updates in the underlying data.
Innovation Solution
A computer-implemented method using a machine learning model to retrieve and synthesize data from multiple dynamic data sources, generating an intermediate text sequence, and producing presentation media that is up-to-date and relevant to the new personnel's role.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual retrieval and compilation of information is used, then information can be customized for new personnel, but the process is extremely labor intensive and time consuming
Solution Approach 1:
The system enables self-service by automatically retrieving and compiling information from multiple data sources without human intervention. The machine learning model autonomously processes data sources, identifies relevant information, and generates presentation media, eliminating the need for manual compilation while maintaining customization capabilities.
Solution Approach 2:
The patent replaces the mechanical manual process of information retrieval and compilation with an automated machine learning system. The ML model substitutes human operators by automatically querying data sources, processing information, and generating presentation materials, thereby dramatically increasing productivity while reducing labor intensity.
2Reliability
If comprehensive information is retrieved from multiple data sources, then the presentation media is complete and relevant, but the process becomes extremely complex
Solution Approach 1:
The machine learning model serves as an intermediary between multiple data sources and the final presentation media. It simplifies the complex interaction by automatically querying, filtering, and synthesizing information from various sources based on the user prompt, managing the complexity while ensuring comprehensive and relevant information retrieval.
Solution Approach 2:
The system employs a universal machine learning model that can handle multiple data sources and generate different types of presentation media through a single interface. This multi-functional approach reduces overall system complexity by using one versatile component instead of separate specialized systems for each data source or output type.
3Reliability
If frequent updates to underlying data are made, then the data remains current and relevant, but the presentation media becomes outdated before delivery
Solution Approach 1:
The system performs preliminary action by generating presentation media on-demand at the moment of user request rather than pre-generating and storing it. This ensures the information is retrieved and compiled fresh from current data sources, guaranteeing currency while eliminating the time loss associated with updating pre-generated materials.
Solution Approach 2:
The system adopts a dynamic approach where presentation media is generated dynamically based on real-time data from multiple sources. Instead of static pre-generated materials that become outdated, the ML model dynamically queries current data and generates up-to-date presentation media, adapting to frequent data updates without time delays.
4Reliability
If specialized knowledge of new personnel's role is required, then the presentation media is highly relevant, but this knowledge is not feasible for any individual to acquire
Solution Approach 1:
The patent replaces the need for human expertise with an automated machine learning system. The ML model substitutes specialized human knowledge by automatically understanding the user prompt, identifying relevant information across multiple data sources, and compiling appropriate presentation media without requiring operators to possess domain-specific expertise.
Solution Approach 2:
The system performs self-service by autonomously acquiring the knowledge needed to generate relevant presentation media. The ML model independently analyzes the user prompt, determines what information is relevant based on the personnel's role, and retrieves appropriate data without human intervention, eliminating the need for operators to have specialized knowledge.
Data Source
AI summary
A computer-implemented method may include causing display of a user interface to a user, the user interface prompting the user to enter a user instruction, receiving the user instruction, wherein the user instruction may include one or more parameters for desired media, retrieving, using a machine learning model, based on the user instruction, a plurality of data sets from a plurality of data sources, wherein the machine learning model may be trained to associate data stored in the plurality of data sources with parameters for desired media, generating, using the machine learning model, an intermediate text sequence, wherein the intermediate text sequence may be representative of the plurality of data sets, and generating, based on the intermediate text sequence, a presentation media output, wherein the presentation media output may be representative of the one or more parameters for desired media.