Personalized News Generation via Text Segmentation and Editing
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Solution Overview
Problem
Radio listeners often find broadcast content unappealing due to its lack of personalization, as it is typically designed to appeal to a broad audience rather than individual interests.
Innovation Solution
A system comprising a server and client computing devices that generate and deliver personalized news programs by selecting and editing media content items based on user attributes, using a four-phase technique to extract, edit, and format text data into a tailored sequence for real-time streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If broadcast content is designed to appeal to a broad audience, then it can reach more listeners, but it fails to be tailored to individual interests and becomes unappealing to specific listeners
Solution Approach 1:
The broadcast content is segmented into multiple media content items, each representing a discrete unit of information that can be independently selected and personalized for individual listeners based on their attributes and interests
Solution Approach 2:
The system dynamically generates personalized news programs by selecting and sequencing media content items in real-time based on user attributes, transforming static broadcast content into dynamic, personalized streams for each listener
2Manufacturing precision
If text is extracted and edited for personalized content, then content relevance improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying grammatical break positions and preparing text extraction rules before actual content generation, enabling faster processing when personalized news programs need to be created in real-time
Solution Approach 2:
The system replaces manual text editing and content selection with automated computational processes that use algorithms to identify grammatical breaks, extract relevant text portions, and sequence media content items based on user attributes
3Stability of the object's composition
If grammatical break positions are identified to create proper subsets of text, then content coherence improves, but processing complexity increases
Solution Approach 1:
The text processing system uses inherent grammatical structures within the text itself to automatically identify appropriate break positions, allowing the text to guide its own segmentation without requiring complex external analysis or manual intervention
Data Source
AI summary
An example method involves (i) accessing first data defining multiple portions of a content item, wherein at least a plurality of the portions represent text; (ii) selecting, from the plurality of portions representing text, a subset of the portions representing text, wherein the selecting is based on each portion of the selected subset having a particular characteristic; (iii) based on the text represented by the portions of the selected subset, generating second data that represents a concatenation of the text represented by the portions of the selected subset; and (iv) providing output based on the generated second data.