Automated Narrated Analytics Playlist Generation
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Solution Overview
Problem
Processing large volumes of data from diverse sources is cumbersome and time-consuming, often resulting in outdated or inaccurate insights due to incompatible data formats and complex data structures, making it difficult for users to extract meaningful information.
Innovation Solution
An automated system generates narrated analytics playlists by curating data from multiple sources, identifying attributes and relational data models, and using machine-learning algorithms to extract insights, which are then presented in a user-friendly format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large volumes of data are used for analysis, then better representations of performance are generated, but the computations become more complex and harder to process
Solution Approach 1:
The patent introduces an automated analytics system that acts as an intermediary between raw data and user interpretation. This system includes components for automated data collection, processing, and generation of narrated analytics playlists that translate complex computational results into accessible insights, thereby resolving the contradiction between using large data volumes and managing computation complexity.
Solution Approach 2:
The system implements self-service capabilities through automated data processing pipelines that continuously collect, clean, and analyze data without requiring manual intervention. The automated generation of insights and narrated playlists allows the system to serve itself in transforming raw data into actionable information, reducing the burden of complex computations on users.
2Loss of information
If large volumes of data are used for analysis, then more comprehensive insights are obtained, but the ability to identify and extract desirable information is obfuscated
Solution Approach 1:
The patent employs extraction mechanisms that selectively pull out relevant insights from large volumes of data. The automated analytics system identifies and extracts desirable information through structured processing pipelines that filter, aggregate, and highlight key findings, making it easier to identify valuable information without being overwhelmed by the total data volume.
Solution Approach 2:
The system replaces manual information extraction mechanisms with automated computational processes. Machine learning algorithms and automated analysis tools substitute for human efforts in sifting through data, enabling the system to efficiently identify and extract desirable information from large datasets without the obfuscation that plagues manual approaches.
3Ease of operation
If data analysis is performed manually, then user control is maintained, but the process is cumbersome and time-consuming
Solution Approach 1:
The patent implements preliminary action by pre-configuring data collection pipelines, processing rules, and analysis parameters before data arrives. The system prepares analytics frameworks in advance, so when data is collected, it can be processed efficiently through pre-established workflows. This maintains user control over the analysis approach while dramatically improving processing productivity through automated execution of pre-planned operations.
4Productivity
If automated systems are used for data analysis, then processing efficiency is improved, but contextual information may be lost in the output
Solution Approach 1:
The patent incorporates feedback mechanisms where the automated analytics system continuously monitors its own output and adjusts its processing to preserve contextual information. The narrated analytics playlists include contextual framing that explains the significance of findings, and the system uses feedback loops to ensure that automated processing maintains the contextual relevance of the original data, preventing information loss while preserving high processing efficiency.
Data Source
AI summary
Techniques described herein provide automated generation of a narrated analytics playlist. Various implementations curate data from various data sources, where curating the data includes identifying attributes and relational data models. One or more implementations base the curating upon anecdotal data associated with a user. In response to receiving a trigger event to perform a query analysis, one or more implementations identify keywords to use in the query analysis, and extract information from the curated data based, at least in part on the one or more keywords. The extracted information is then analyzed to identify insights. In turn, one or more implementations generate a narrated analytics playlist using the insights. Some implementations utilize machine-learning algorithms to curate, extract and/or process data to generate insights. Various implementations abstract the data used to teach the machine-learning algorithms and share the abstracted data to other devices.