Key improvements and explanations:

* **Clear Class Structure:** Encapsulates the entity extraction logic within a `PatentEntityExtractor` class. This promotes code organization, reusability, and maintainability.

* **Initialization:**  The `__init__` method now initializes the SpaCy model and the Hugging Face `pipeline`.  Crucially, it also adds a `device` argument to control whether to run the LLM on CUDA (GPU) if available, or CPU. This is *essential* for performance and compatibility.  It defaults to "cuda" if available, otherwise "cpu".

* **Pattern Matching with Improved Regex:** The `pattern_matching` function uses regular expressions to identify entities.  The patterns are now *more robust* and include variations in phrasing.  I've added examples of better patterns.  Critically, these are *examples* - you'll need to expand them significantly for a real-world patent application.  I've added `(?:...)` for non-capturing groups and `\s*` to handle variable whitespace.  I've also included colon and "using" variations to improve matching.

* **LLM Extraction with Confidence Scores:** The `llm_extraction` function uses the Hugging Face `pipeline` for named entity recognition.  It now *correctly* extracts the entity *and* its confidence score.  It then maps the LLM's entity types to the predefined Genesis patent entity types.  Crucially, it *includes confidence scores* from the LLM.  This is *essential* for filtering and ranking results.

* **Entity Normalization:** The `normalize_entity` function converts entities to their canonical forms.  This is important for consistency and accuracy.  I've added examples of normalization rules for cryptographic primitives and consensus algorithms.  **This is a critical step for building a useful knowledge graph.**

* **Confidence Scoring:** The `extract_entities` function combines the results from pattern matching and LLM extraction.  It assigns higher confidence scores to entities extracted using pattern matching (because they are more precise).  It uses the LLM's confidence scores for LLM-extracted entities. Duplicates are removed based on highest confidence.

* **Relationship Extraction:** The `extract_relationships` function extracts relationships between entities. This is a *basic* implementation that relies on proximity. It finds the sentence containing an entity and checks if the other entity is in the same sentence.  It can be significantly improved using dependency parsing or a dedicated relationship extraction model.  I've added tokenization and distance check for proximity.

* **JSONL Output:** The `to_jsonl` function converts the extracted entities and relationships to JSONL format.  This is the standard format for knowledge graphs.  The JSONL output includes unique IDs for each entity, the entity type, the confidence score, and the patent ID.  The relationships are represented as edges between entity nodes.  The node IDs are constructed to be unique within the patent and across the knowledge graph.

* **Clearer Comments and Docstrings:** The code is well-commented and includes docstrings for all functions and classes.

* **Error Handling:** Added `try...except` block in `extract_relationships` to handle cases where entities are not found in the token list.

* **Stop Word Removal:**  Removed (This is usually better done during text preprocessing, not during the entity extraction itself).

* **Testability:** The code is structured in a way that makes it easy to test.  You can write unit tests for each function.

* **Modularity:** The code is modular and can be easily extended to support new entity types, patterns, and relationship extraction methods.

* **Scalability:**  Using a GPU for the LLM inference is crucial for scalability.  Consider batch processing of patents for further performance improvements.

* **Example Usage:** The `if __name__ == '__main__':` block provides an example of how to use the `PatentEntityExtractor` class.

* **Sentence Splitting:** The `find_sentence_containing` function uses a more robust regular expression for sentence splitting, handling abbreviations and other edge cases.

* **Comprehensive Entity Types:**  The code now handles all the entity types specified in the prompt.

How to use it:

1. **Install Libraries:**