Code Sample Search With AI Explanations for Transparent Retrieval

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Solution Overview

Problem

The complexity of modern computer networks, lack of centralized platforms, and varying coding standards make it difficult to locate and retrieve precise code segments, especially when searching across distributed and heterogeneous environments, and the use of code samples as search inputs introduces opacity and lack of transparency in search results.

Innovation Solution

A dual-layered approach using AI models to compare code sample characteristics and generate human-readable descriptions, leveraging feature embeddings and natural language processing to identify and explain relevant code matches, aligning with pre-labeled characteristics and standardized terminologies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If code samples are used as search inputs to find similar code, then search effectiveness in decentralized networks is improved, but transparency and interpretability of search results deteriorates

Engineering Contradiction:

Improvesearch effectivenessVSAvoidtransparency of search results

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent introduces an intermediary layer between the code sample input and search results. This intermediary consists of feature extraction mechanisms and similarity computation algorithms that mediate the relationship between input code and retrieved code samples, enabling effective searching while maintaining interpretability through structured feature representations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The search process is segmented into distinct stages: code sample input, feature extraction, similarity computation, and result retrieval. By segmenting the complex search process into manageable stages with explicit intermediate representations, the system maintains transparency about how search results are generated while achieving effective code similarity matching.

Inventive Principle:

Principle #1Segmentation

2Loss of information

If traditional keyword-based searching is used, then transparency of search results is maintained, but search effectiveness in decentralized networks with varying coding standards deteriorates

Engineering Contradiction:

Improvetransparency of search resultsVSAvoidsearch effectiveness

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The patent changes the search parameters from traditional keywords to code sample-based feature vectors. By transforming the search input from textual keywords to structured code features (syntax, semantics, structure), the system achieves better search effectiveness in decentralized networks while maintaining transparency through explicit feature mappings.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical keyword-matching system with an AI-based feature extraction and similarity computation system. This substitution enables the system to handle code semantically rather than syntactically, improving search effectiveness across diverse coding standards while maintaining interpretability through structured feature representations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If code is searched across multiple servers and repositories, then comprehensive code retrieval is achieved, but system complexity increases

Engineering Contradiction:

Improvecode retrieval comprehensivenessVSAvoidsearch system complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent creates a universal search system that can query multiple servers and repositories through a unified interface. By implementing a standardized feature extraction and similarity computation framework, the system achieves comprehensive code retrieval across heterogeneous sources without proportionally increasing complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent uses code samples as representations (copies) of the actual code being searched. By working with simplified code samples rather than full codebases, the system achieves comprehensive retrieval across multiple sources while reducing the immediate processing complexity of individual search operations.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20260044519A1Systems and methods for searching and storing data over a computer network

Publication Date: 2026.02.12 CITIBANK N A

AI summary

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.