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049099a a86802e 049099a a86802e 049099a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import os
from .linguistic import LinguisticAnalyzer
from .embeddings import EmbeddingSimilarity
from .cross_encoder import CrossEncoderSimilarity
from .heuristics import DescriptivenessHeuristic
class TrademarkAnalyzer:
"""
High-level API for trademark descriptiveness analysis.
Initializes all sub-modules and provides a unified analyze() method.
"""
def __init__(self, descriptive_keywords_path=None):
"""
Args:
descriptive_keywords_path: Path to JSON file with class‑specific descriptive terms.
"""
# Ensure models are cached in the runtime disk (if not already set)
if "HF_HOME" not in os.environ:
os.environ["HF_HOME"] = "/tmp/huggingface"
# Initialize sub-modules (models are lazy-loaded inside each class)
self.linguistic = LinguisticAnalyzer(descriptive_keywords_path)
self.embedding = EmbeddingSimilarity() # uses sentence-transformers
self.cross_encoder = CrossEncoderSimilarity() # uses cross-encoder
self.heuristic = DescriptivenessHeuristic(
self.linguistic,
self.embedding,
self.cross_encoder
)
def analyze(self, mark, goods, goods_class=None):
"""
Perform full descriptiveness analysis.
Args:
mark (str): The trademark text.
goods (str): Description of goods/services.
goods_class (str, optional): USPTO class (e.g., "30").
Returns:
dict: Contains descriptive_score, generic_score, reasons, explanation, details.
"""
# Load descriptive terms for the class (if any)
descriptive_terms = None
if goods_class and self.linguistic.descriptive_keywords:
class_key = f"class_{goods_class.zfill(3)}" # e.g., class_030
descriptive_terms = self.linguistic.descriptive_keywords.get(class_key, [])
# Run the heuristic assessment
result = self.heuristic.assess(
mark=mark,
goods=goods,
goods_class=goods_class,
descriptive_terms=descriptive_terms
)
return result |