Crossword Puzzle Generator Using Validity Prediction
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Solution Overview
Problem
Conventional crossword puzzle generators are resource-intensive and inefficient, often requiring repeated attempts to create valid puzzles, and fail to provide user-engaging content tailored to individual interests.
Innovation Solution
A data processing system that generates crossword puzzles by using a dictionary of clue-word pairs, evaluates the likelihood of a crossword layout being valid, and only compiles puzzles with high likelihood, while also providing user profiles to tailor content and include hints for user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional crossword puzzle generators compile crosswords by generating a crossword layout and then inserting multiple words from among clue-word pairs, then a crossword puzzle can be created, but the process is resource intensive and requires repeated attempts to create valid puzzles
Solution Approach 1:
The patent applies preliminary action by pre-evaluating crossword layouts using a trained model to predict validity before actually compiling words into the puzzle. This allows the system to discard invalid layouts early, avoiding the resource-intensive process of attempting to compile words only to discover the layout is invalid. The model is trained on historical crossword data to recognize patterns of valid layouts, enabling this preliminary assessment.
Solution Approach 2:
The patent extracts the validity prediction function from the overall crossword generation process by using a separate trained machine learning model to assess layout validity. This separates the layout evaluation task from the word compilation task, allowing the system to quickly filter out invalid layouts without performing the full resource-intensive compilation process, thereby reducing overall computing resource usage.
2Adaptability or versatility
If conventional crossword puzzle generators use generic content, then puzzles can be created, but they fail to provide user-engaging content tailored to individual interests
Solution Approach 1:
The patent applies preliminary action by pre-computing user interest profiles based on their browsing history and content interactions before puzzle generation. This allows the system to have user-specific content preferences ready in advance, enabling personalized puzzle content selection without complex real-time analysis during puzzle creation. The system stores and retrieves these profiles efficiently to match content with user interests.
Solution Approach 2:
The patent introduces user interest profiles as an intermediary layer between the user and the crossword content selection process. These profiles act as mediators that translate user browsing behavior into content preferences, which then guide the selection of clues and answers that match user interests. This intermediary simplifies the personalization process by pre-processing user data into actionable preference profiles.
3Reliability
If the system evaluates every crossword layout for validity, then valid puzzles can be ensured, but computing resources are wasted on layouts with low likelihood of being valid
Solution Approach 1:
The patent applies preliminary action by using a trained machine learning model to predict layout validity before full compilation. The model, trained on historical crossword data, provides a quick probability assessment that filters out low-likelihood layouts early in the process. This preliminary prediction step ensures that only high-probability layouts proceed to resource-intensive compilation, maintaining reliability while conserving computing resources.
Solution Approach 2:
The patent changes the parameter of layout evaluation from a binary valid/invalid determination to a probabilistic assessment. By outputting a likelihood score, the system can prioritize layouts with higher validity probabilities and skip or de-prioritize those with low scores. This parameter transformation enables more efficient resource allocation while maintaining high reliability in the final puzzle output.
Data Source
AI summary
Methods and systems that facilitate generating and presenting a crossword puzzle. Methods include obtaining, from a content source, a plurality of data items. Using the plurality of data items, a dictionary of clue-word pairs are generated. A crossword layout is generated using a random crossword layout generator. The generated crossword layout is input to a model that outputs a likelihood that the input crossword layout results in a valid crossword. If the likelihood that the crossword layout results in a valid crossword puzzle satisfies a first threshold, a plurality of words in a set of clue-word pairs from among the plurality of clue-word pairs is inserted into the crossword layout. If the plurality of words inserted into the crossword layout results in a valid crossword puzzle, the crossword puzzle is provided to a user on a user device using the set of clue-word pairs.