Server Task Data Processing Using Dynamic Selection
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Solution Overview
Problem
Existing online game systems lack diversity and novelty in task data, leading to reduced user engagement and inefficient communication between clients and servers, as they rely on fixed task data orders that fail to meet the varied needs of users.
Innovation Solution
A method and system where a server processes task data based on type identifiers, such as daily, mainline, or random task data, using preset processing schemes to generate and return customized results to clients, allowing for diverse task types and improved communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the server sends task data to the client in a fixed order, then the task data transmission is simple and efficient, but the task data become monotonous and uninteresting, reducing user engagement
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed task data transmission order to a dynamic selection mechanism. The server randomly selects task data from multiple categories (daily tasks, mainline tasks, random tasks) based on user requests, making the transmission order variable and adaptable. This resolves the contradiction by maintaining transmission efficiency while introducing diversity through dynamic selection rather than fixed sequencing.
Solution Approach 2:
The patent changes the parameter of task data selection from fixed order to random selection based on category identifiers. By introducing a selection parameter that randomly chooses from different task categories rather than following a predetermined sequence, the system maintains efficient transmission while achieving diverse task data delivery, thus resolving the contradiction between efficiency and diversity.
2Device complexity
If the server uses fixed task data orders, then the system structure is simple, but it cannot meet the diverse needs of users, reducing communication efficiency
Solution Approach 1:
The patent segments task data into multiple distinct categories (daily tasks, mainline tasks, random tasks) with separate identifiers. This segmentation allows the server to selectively transmit different task types based on user needs while maintaining a relatively simple overall system structure. The segmentation enables diverse user requirement satisfaction without requiring a completely complex system redesign.
Solution Approach 2:
The patent implements a universal task data processing mechanism that can handle multiple task types through a single random selection framework. The server uses a unified approach to select from different task categories, providing multi-functionality that satisfies diverse user needs without proportionally increasing system complexity. This resolves the contradiction by achieving versatility through a generalizable selection mechanism rather than separate specialized systems.
3Adaptability or versatility
If the server processes task data according to type identifiers, then task data diversity and user engagement are improved, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining task data categories and their corresponding identifiers (daily-task-data identifier, mainline-task-data identifier, random-task-data identifier) before user requests. The server uses these pre-established categories to guide the random selection process, reducing real-time processing complexity while maintaining task data diversity. The classification structure is prepared in advance, simplifying the actual processing operation.
Data Source
AI summary
Various embodiments provide methods, servers, and systems for processing task data. In an exemplary method, a task-data-processing request sent by a client can be received. The task-data-processing request can contain a type identifier of task data. The type identifier of the task data can include a daily-task-data identifier, a mainline-task-data identifier, a random-task-data identifier, or a combination thereof. The task data corresponding to the type identifier of the task data can be processed to generate processing results, according to the type identifier of the task data and a preset processing scheme corresponding to each type of the task data. The processing results can be returned to the client.