E-book Server Complete-Reading Probability and Time Prediction
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Solution Overview
Problem
Users face difficulties in selecting appropriate electronic books due to limited information on reading preferences, as conventional methods lack data on complete-reading probability and expected time, making it hard to find suitable books within vast collections.
Innovation Solution
A method and server system that acquire and provide information on complete-reading probability and expected time by analyzing user data, including reference pages and read times, and adjust this information based on user and book similarity, to help users select books that match their preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only basic book information (cover, title, author, publisher, category, publication date) is provided, then the information structure remains simple, but users cannot accurately determine whether the book matches their preferences
Solution Approach 1:
The system pre-collects reading behavior data (pages read, time spent) from multiple users before the user makes a selection. This preliminary data gathering enables the calculation of complete-reading probability and expected time metrics that will later assist the user in making informed decisions without requiring them to manually evaluate each book.
Solution Approach 2:
The patent introduces intermediate metrics (complete-reading probability, expected time to complete reading) that mediate between raw reading behavior data and user decision-making. These intermediate metrics translate complex behavioral patterns into actionable insights that directly address user preferences for books suitable to their reading capacity.
2Adaptability or versatility
If the number of available electronic books is increased, then user selection variety improves, but the time and effort required to find a suitable book increases
Solution Approach 1:
The system utilizes feedback from multiple users' reading behaviors (whether they completed reading, how long it took them) to generate predictive metrics. This feedback loop allows the system to learn from collective user experiences and provide accurate predictions about a specific user's likelihood of completing a book, thereby reducing their selection time despite increased variety.
Solution Approach 2:
The system enables users to serve themselves by providing them with personalized metrics (complete-reading probability, expected time) that they can use to independently evaluate books according to their own reading habits and preferences, eliminating the need for time-consuming manual evaluation or reliance on traditional review systems.
3Measurement precision
If only total page count is provided, then the book description remains concise, but users cannot accurately estimate the time required to complete reading
Solution Approach 1:
The system transforms the static parameter of total page count into dynamic predictive metrics by incorporating reading behavior data from multiple users. Instead of merely displaying page count, the system calculates expected time to complete reading based on actual user reading speeds and patterns, providing a more accurate and personalized time estimation that adapts to individual user characteristics.
Data Source
AI summary
Information on an e-book is provided. The server acquires information on a first reference read page to an n-th reference read page and information on a first required time to an n-th required time; and in response to detecting that a specific electronic book is selected, (i) acquires information on a specific complete-reading probability and information on a specific complete-reading expected time by referring to a specific reference total page of the specific electronic book and at least part of information on a specific reference read page which is a page read by the users among the specific reference total page and information on a specific required time which is time taken by the users for reading the specific reference read page and (ii) matches at least part of the information on the specific complete-reading probability and the information on the specific complete-reading expected time with the specific electronic book.