## 4 Results and Discussion
### Research Questions' Findings
* Recommendation Based Retrieval: This method provides tailored recommendations to users based on their search queries by utilizing methods from information retrieval, data mining, and machine learning [41; 53];
* Nearest-neighbor (NN) technique: This technique scans a set of patent documents to identify patents (nearest neighbors) that resemble a given query patent based on certain features. The similarity metric is typically determined by the distance between the feature vectors of the patent documents [54];
* Dimensionality reduction model: This process renders a more manageable dataset for research or representation by reducing high-dimensional data to a lower-dimensional space while retaining key features (important information) [55];
* Ensemble method: This method integrates several models to enhance the overall effectiveness of the patent retrieval system. It involves training multiple models individually and then combining their predictions to produce an improved final result [56];
* Fuzzy Logic: This technique facilitates adaptable decision-making in situations with incomplete information by accommodating ambiguity and imprecision in patent retrieval operations [47];
* Genetic Algorithm: This approach iteratively modifies search query parameters, including keyword combinations, and weights to better reflect the user's intent and produce more effective and comprehensive search results [57; 58];
* Heuristic Meaning Comparison: This method utilizes patterns and similarities found in patent documents to provide domain-specific insights in the form of rules to steer the search process. These approaches can be used to find synonyms, related concepts, or typical patent structures that could aid in finding more relevant patent documents [59];
* Deep Learning Techniques: These techniques use deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), to identify complex structures and semantic correlations within patent documents to improve overall search results [60; 61; 62];
* Clustering: This process organizes patent documents into relevant clusters based on shared characteristics in their citation networks, metadata, or content, to aid in managing large patent databases. Additionally, it also facilitates the exploration and navigation within the network, in order to locate relevant patent documents [63; 64; 65]. * Natural Language Processing-Based Techniques
* Topic Modeling: Algorithms such as Latent Dirichlet Allocation (LDA) are used to identify generic topics or concepts in a group of patent documents by examining the density of words or terms. These algorithms categorize patent documents into topic clusters using word co-occurrence patterns, facilitating easy investigation and comprehension of key topics and trends within patent documents [61; 66];
* Contextual Modelling: These modeling techniques are employed to comprehend and generate patent textual data, analyzing technical language and domain specific vocabulary, and to interpret search requests. This enhances query comprehension, text summarization, and relevance scoring, ultimately improving the effectiveness of patent search and retrieval processes. Contextual modelling includes approaches such as n-gram models, Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT) [40, 44, 53, 61, 66, 67, 68, 69];
* Semantic Analysis: This process examines the relationships between words, phrases, and sentences to comprehend and interpret the semantics (meaning) of input text. Methods used include named entity recognition, sentiment analysis, and semantic similarity [48, 56, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83];
* Statistical Analysis: This technique uncovers the statistical characteristics and connections found in textual data. It supports tasks such as language modeling, information retrieval, and topic extraction by analyzing statistical traits present in the data.