Query Context Translation Engine Session Adaptation
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Solution Overview
Problem
Conventional e-commerce systems fail to provide high-quality machine translations of user queries due to their short nature and lack of context, leading to suboptimal user experiences in multi-language platforms.
Innovation Solution
The Query Context Translation Engine collects and utilizes a user's search query history within a session to dynamically adapt translations, building a topic model that identifies relevant translations by concatenating recent queries and applying statistical methods like Latent Dirichlet allocation to improve translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine translation is used for short user queries, then translation speed is maintained, but translation quality deteriorates due to lack of context
Solution Approach 1:
The system performs preliminary actions by collecting and storing user search query history before the actual translation task. This historical context is prepared in advance and made available when translation is needed, allowing the system to enhance short queries with relevant contextual information from previous searches within the same session.
Solution Approach 2:
The patent introduces an intermediary component (translation engine with context adaptation) that sits between the short user query and the translation output. This intermediary enriches the brief query by incorporating contextual information from search history, effectively mediating between the limited input and the need for high-quality translation without requiring the user to provide additional context.
2Measurement precision
If user search query history is collected and analyzed to improve translation, then translation relevance improves, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by having the translation engine perform both standard translation tasks and context analysis simultaneously. The same system components handle both the translation of queries and the analysis of search history patterns, eliminating the need for separate dedicated systems and reducing overall complexity despite the enhanced capabilities.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting translation parameters based on analyzed context from search history. Rather than building a completely new complex system, the approach modifies existing translation parameters (such as topic selection, terminology preferences) based on contextual analysis, achieving improved accuracy through parameter adaptation rather than structural complexity.
3Adaptability or versatility
If topic model is applied to concatenate and analyze recent queries, then translation adaptability improves, but processing time increases
Solution Approach 1:
The system applies partial action by selecting only the most recent and relevant queries from the search history for topic modeling, rather than analyzing the entire history. This selective approach concatenates a limited subset of queries (e.g., last 5-10 searches) to establish context, achieving sufficient adaptability without the computational overhead of processing complete search histories.
Solution Approach 2:
The patent implements dynamics by making the topic modeling process adaptive and session-based. The system dynamically determines which queries to include in the topic model based on recency and relevance, and the topic model itself is regenerated for each translation task based on current session context. This dynamic approach allows the system to adapt translation behavior to current user intent while avoiding unnecessary processing of outdated or irrelevant historical data.
Data Source
AI summary
According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.