Dynamic Narrated Analytics Playlist Playback Control
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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Processing large volumes of data from diverse sources is cumbersome and time-consuming, often resulting in outdated or inaccurate insights due to incompatible data formats and complex data structures, making it difficult for users to extract meaningful information.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, allowing for contextual information and user interactions to modify the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large volumes of data are used for analysis, then the quality and accuracy of insights improve, but the complexity of computations and difficulty of extracting meaningful information increase
Solution Approach 1:
The system segments the analysis process into distinct phases: automated data collection from multiple sources, machine learning-based pattern recognition, and structured presentation of insights. This segmentation reduces computational complexity by handling different aspects separately rather than requiring simultaneous complex processing of all data at once.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw data and user interpretation. This intermediary uses machine learning algorithms to process large volumes of data, extract patterns, and present simplified insights, thereby reducing the computational burden on users while maintaining high accuracy.
2Measurement precision
If large volumes of data are used for analysis, then the quality of insights improves, but the time required for processing increases
Solution Approach 1:
The system performs preliminary actions by automatically collecting and pre-processing data from multiple sources before analysis is needed. Data is structured, cleaned, and organized in advance using automated processes, so when analysis is required, the system can quickly generate insights without time-consuming manual preparation.
Solution Approach 2:
The automated analysis system operates autonomously to collect, process, and analyze data without requiring continuous user intervention. The machine learning models self-adjust and self-optimize based on the data patterns they detect, reducing the time users need to spend on manual data processing while maintaining high accuracy.
3Adaptability or versatility
If data from multiple diverse sources is integrated, then the comprehensiveness of analysis improves, but the difficulty of handling incompatible formats and structures increases
Solution Approach 1:
The system employs universal data processing capabilities that can handle multiple data formats and structures through a unified approach. The automated collection and analysis infrastructure is designed to work with diverse data sources (databases, APIs, files, streams) using common protocols and machine learning techniques, eliminating the need for separate processing pipelines for each data type.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the characteristics of each data source. Machine learning algorithms automatically detect data formats and structures, then adapt processing methods accordingly, allowing comprehensive integration of diverse data sources without requiring manual configuration for each format.
4Ease of operation
If manual data extraction and analysis is performed, then user control over the process is maintained, but the effort and time required increase significantly
Solution Approach 1:
The system performs data collection, processing, and analysis automatically without requiring user intervention for each step. Users simply define their analysis goals, and the system self-manages the entire workflow including data gathering from multiple sources, machine learning analysis, and result presentation, dramatically reducing the time and effort users must invest.
Solution Approach 2:
The system provides continuous feedback to users through automated presentation of insights in various formats (text, visualizations, alerts). Users can review results and provide feedback to refine analysis parameters, maintaining control over the process while the system handles the time-consuming execution automatically.
Data Source
AI summary
Techniques described modify playback of a narrated analytics playlist in a personalized analytics system. In some implementations, audible input is received during playback of the narrated analytics playlist. The audible input can be used to control the behavior of a playback module playing out the narrated analytics playlist. Alternately or additionally, user input can be received, where the user input corresponds to modifying an original scene included in the narrated analytics playlist. Some implementations generate synchronized audible output that be output with the modified original scene of the narrated analytics playlist. Alternately or additional, implementations can automatically determine to visually apply an auto-pointer to portions of the narrated analytics playlist. In generating the narrated analytics playlist, implementations can calculate a respective playback duration of each scene in a plurality of scenes to include in the narrated analytics playlist, and generate the narrated analytics playlist based on the calculating.