Automatic Memory Content Item Provisioning via Natural Language Matching
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Solution Overview
Problem
Users face difficulties in locating specific memory content items, such as photos or videos, especially when they need to be shared during conversations, as existing content item storage and organization systems require manual searching and lack efficient retrieval methods.
Innovation Solution
An automatic memory system that uses natural language input to identify and select matching content items by computing match scores through image/NL matching and tag matching processes, allowing for the automatic presentation of relevant content items during conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual searching and organizing methods are used, then users can access their memory content items, but the time required to locate specific content items increases significantly
Solution Approach 1:
The system automatically indexes and tags memory content items using machine learning models, enabling the system to serve itself in organizing content without requiring user intervention. When a user provides natural language input, the system autonomously retrieves matching content items based on pre-computed embeddings and similarity scores, eliminating manual searching.
Solution Approach 2:
The system performs preliminary indexing and embedding generation for all memory content items in advance, storing these representations for rapid retrieval. This pre-processing allows the system to quickly compute similarity scores and retrieve relevant content items when queried, rather than searching through raw data at retrieval time.
2Productivity
If automated content item selection is implemented, then retrieval speed improves, but system complexity increases
Solution Approach 1:
The patent replaces manual mechanical searching with automated computational processes. Machine learning models generate embeddings and compute similarity scores algorithmically, substituting human cognitive effort with automated information processing. This enables rapid retrieval of content items based on natural language queries without manual intervention.
Solution Approach 2:
The system introduces embedding vectors as an intermediary representation between raw memory content items and natural language queries. By converting both content items and queries into comparable embedding spaces, the system enables efficient similarity-based retrieval without directly comparing raw data, simplifying the matching process while improving speed.
Data Source
AI summary
An automatic memory system can automatically identify and present content items that match a user's natural language (NL) input. The automatic memory system can compute a match score between the NL input and each of multiple potential memory content items. The automatic memory system can compute the match score using a variety of algorithms and/or machine learning models such as an image/NL matching process to get a first match score, a tag matching process to get a second match score, and/or a combination match score from the first and second match scores. The automatic memory system can select one or more of the content items with the highest match score(s). The automatic memory system can provide the selected content items, such as by suggesting them to the NL providing user, automatically displaying or playing them, inserting them into the conversation thread, etc.