Search Account Value Estimation via Keyword Segmentation
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Solution Overview
Problem
Current search engine server technologies inaccurately forecast the estimated value of an entire account by simply accumulating individual search keyword values, often resulting in costs exceeding client expectations, leading to increased client and server workload and network resource consumption.
Innovation Solution
The search engine server generates an estimated value for an entire account by modifying individual search keyword values based on historical data and parameter settings, ensuring the total value aligns with client expectations, thereby reducing the need for further client adjustments and minimizing server workload and network communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search engine server simply accumulates individual search keyword estimated values to obtain the entire account estimated value, then the calculation process is simple and fast, but the accuracy of the estimated value is low and often exceeds client expectations
Solution Approach 1:
The patent segments the entire account estimation into individual search keyword estimations, then further segments each keyword estimation into multiple candidate values. By processing and selecting values at this granular level rather than simply accumulating, the system achieves higher accuracy while managing complexity through structured segmentation of the estimation process.
Solution Approach 2:
The patent changes the parameter representation from single fixed values to multiple candidate values with associated probabilities or weights. This parameter transformation allows the system to capture uncertainty and variability in keyword performance, enabling more accurate overall account estimation through sophisticated value selection rather than simple accumulation.
2Loss of time
If the search engine server returns inaccurate estimated values to clients, then the initial processing is quick, but clients must remove and adjust search keywords, creating additional communication rounds and increasing network resource consumption
Solution Approach 1:
The patent performs preliminary action by providing clients with accurate estimated values that already account for keyword interactions and account-level constraints. This upfront accuracy prevents the need for clients to iteratively remove and adjust keywords, eliminating additional communication rounds and reducing both time loss and network energy consumption.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from client responses and historical data to improve future estimations. By using feedback to refine the estimation algorithm, the system continuously improves accuracy, reducing the frequency of client adjustments and associated communication overhead over time.
3Measurement precision
If the search engine server performs multiple estimated value forecasts to meet client requirements, then the accuracy improves, but the workload of the search engine server increases significantly
Solution Approach 1:
The patent performs preliminary computation of keyword interactions and account-level constraints during the initial estimation phase. By pre-calculating these factors before returning results to clients, the system achieves high accuracy without requiring multiple forecast iterations, thus maintaining server productivity while improving measurement precision.
Solution Approach 2:
The patent implements self-service mechanisms where the estimation system automatically adjusts for keyword interactions and account constraints without requiring manual intervention or multiple client-server interaction cycles. This automated self-adjustment achieves accurate estimations in a single pass, reducing server workload while maintaining high precision.
Data Source
AI summary
Techniques for returning estimated value of search keywords of an entire account include, for the entire account, obtaining one or more selected search keywords and their respective forecast periods and parameter settings. An estimated value of a respective search keyword in the respective forecast period is forecasted. Based on stored historical data and parameter settings of the respective search keyword, the estimated value of the respective search keyword is modified to obtain a modified estimated value. The modified estimated value of each search keyword is added up to generate an estimated value of the entire account. The estimated value of the entire account is returned to a client terminal from which the entire account is logged in. The present disclosure modifies the respective search keyword's estimated value so that the estimated value of the entire account satisfies the expected value of the client.