EPG Data Recurrence Pattern Optimization
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Solution Overview
Problem
Conventional electronic program guide (EPG) data compression techniques fail to identify and optimize recurrence patterns in scheduling information, leading to inefficiencies in bandwidth usage and data redundancy, especially in scenarios with repeated programs across different days, channels, or time intervals.
Innovation Solution
A method and system that identify recurrence patterns in EPG data by associating properties of schedule objects, generating base schedule objects and metadata to represent these patterns, and substituting redundant schedule objects with optimized groupings, thereby reducing data redundancy and optimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional EPG data compression techniques are used, then data transmission is possible, but data redundancy is not reduced and bandwidth usage is inefficient
Solution Approach 1:
The patent merges multiple identical or similar schedule objects into a single base schedule object with recurrence pattern metadata. Instead of transmitting separate data for each occurrence of the same program, the system combines them into one representative object that encapsulates the recurring pattern, thereby reducing data redundancy and improving bandwidth usage efficiency.
Solution Approach 2:
The base schedule object serves multiple functions: it represents the program content, timing, and recurrence pattern simultaneously. The recurrence pattern metadata enables the system to generate multiple schedule objects from a single base object, making the base object universal and multi-functional, which reduces the overall data volume required.
2Loss of information
If all schedule objects are transmitted individually, then complete scheduling information is provided, but bandwidth consumption increases and transmission efficiency decreases
Solution Approach 1:
The patent extracts the common recurrence pattern from multiple schedule objects and places it in metadata associated with the base schedule object. By taking out the redundant information and storing it efficiently in metadata, the system maintains complete scheduling information while reducing the data volume that needs to be transmitted.
Solution Approach 2:
The system changes the parameter representation by introducing recurrence pattern metadata that describes how schedule objects repeat. Instead of transmitting full schedule objects for each occurrence, the system transmits a base object with parameters that define the recurrence pattern, thereby reducing bandwidth consumption while preserving information completeness.
3Device complexity
If recurrence patterns are not identified, then data processing is simpler, but data redundancy remains high and optimization is not achieved
Solution Approach 1:
The patent performs preliminary identification of recurrence patterns in schedule objects before data transmission. By detecting and marking recurring patterns in advance, the system prepares the data in an optimized format that reduces redundancy. This preliminary action adds processing complexity but enables significant reduction in data redundancy and improves overall efficiency.
Data Source
AI summary
Methods and systems of optimizing EPG data include: (i) receiving EPG data comprising a plurality of schedule objects; (ii) identifying a recurrence pattern in a subset of the schedule objects based on an association between properties of the schedule objects in the subset; (iii) based on the recurrence pattern, identifying at least one base schedule object in the subset, each at least one base schedule object comprising properties that are common to two or more of the schedule objects in the subset; (iv) generating at least one recurrence pattern property for identifying the recurrence pattern; (v) generating an optimized schedule grouping comprising the at least one base schedule object and a metadata object, wherein the metadata object comprises the at least one recurrence pattern property; and (vi) providing optimized EPG data in which the subset of schedule objects is substituted with the optimized schedule grouping.