## 2 Background
### Related Works  
[PERSON] et al. [18] presented a study in which supervised machine learning techniques classified documents related to judicial decisions in order to ascertain the opinion trends that Brazilian courts have. The authors applied a methodology to process the judicial decisions from the Regional Labor Court (TRT) of the 3 rd Region, located in the Brazilian state of Minas Gerais, for data mining to extract and process the information present in the judicial documents and made use of natural language to perform the automatic classification of the documents, reaching more than 90% accuracy when indicating the tendency of each judge in a sentence. [PERSON] and [PERSON] [19] presented a study related to the use of a text search tool of the Superior Court of Justice (STJ) and presented the performance of searches based on Boolean queries with logical and proximity operators. The authors concluded that the court's system could be improved to facilitate the search for decisions already made by the STJ, optimize access to jurisprudence, and follow the evolution of the court's understanding on several themes. The improvement was possible because the Text Retrieval Conference (TREC) technique compares textual similarities. In addition, the authors found that the Best Match 25 (BM25) and Term Frequency Inverse Document Frequency (TF-IDF) modelsenabled an improvement in search performance, obtaining better results than semantic models based on prediction such as Word2 Vec and Bidirectional Encoder Representations from Transformers (BERT). [PERSON] et al. [20] used Artificial Intelligence to assist legal professionals in searching for jurisprudences in quality databases on judicial decisions. The authors' textbase was powered by relevant legal cases and identification of appropriate jurisprudence for retention, with automatic extraction of information from the document into the database, integrated with a thesaurus based on standard legal terms and with retrieval based on similar terms. [PERSON] et al. [21] presented the PROJLAW application with support for Natural Language Processing (NLP) to analyze the texts that make up a court judgment. NLP and linked used the data for document identification, indexing, and recommendation. After seeking validation of the system through user experience, the application produced answers for the searches performed efficiently and with keyword insertion during the search. The authors concluded that the more keywords used, the greater the search accuracy. [PERSON] et al. [22] addressed the use of Artificial Intelligence with natural language processing for the analysis of judicial decisions in building predictive models that reveal patterns that guide judicial decisions in order to be able to predict possible future decisions. The authors proposed to build a tool to predict patterns from the European Convention on Human Rights (ECHR) using the supervised machine learning (SVM) algorithm [23]. [PERSON] et al. [24] presented their research and development project, called _VICTOR_, aimed at solving recognition pattern problems in texts from court cases belonging to the Supreme Court (STF). Differently from previous researches, in this work, the authors proposed a solution to speed up the analysis of judicial decisions directed to the STF and identify which cases are linked to particular subjects of general repercussions, such as competition, price taking, etc., using Convolutional Neural Network (CNN) [24] and Natural Processing Language. The main difference between existing jurisprudence Search Systems and the system proposed in this research is that the developed system applied evaluation techniques and iterative redefinitions in the verification and validation of all the functionalities of the proposed solution and used the accessibility and usability guidelines proposed in the literature during the system development process. Thus, we can infer that the developed system follows the best practices used in existing jurisprudence Search systems.