Abstract: Techniques for training a classification model to improve the classification of open banking transactions are presented. The techniques include receiving raw training data from a data source. The raw training data includes historical transaction data made up of a plurality of individual transactions. The raw training data is input into the classification model. The raw training data is processed by performing a data preparation operation on the raw training data. The data preparation operation includes removing numerical characters, repeating special characters, and accent words from the textual data of each transaction. Vocabulary training is then performed on the processed training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The classification model is then trained using a transformer model, which uses the tokenized text. The trained classification model is then stored in a database.

Abstract: Techniques for training an entity resolution model are presented. The techniques include inputting raw training data into the entity resolution model. The training data includes historical transaction data including a plurality of transactions. A label dictionary is generated by performing natural language processing (NLP) on the training data. The NLP includes scanning text of each transaction, extracting one or more entities from the text, and storing the label dictionary in a database. The label dictionary includes the extracted entities. Tagged data is generated from the training data using the label dictionary. Vocabulary training is performed on the training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The entity resolution model is then trained using a transformer model, which uses the tokenized text and the tagged data. The trained entity resolution model is then stored in a database.

Abstract: A system is provided for manufacturing physical goods. The system includes an input device configured to generate or receive input data, the input data describing parameters of one or more production facilities. The system includes a computer processor configured to map the input data onto a graph, wherein each vertex of the graph comprises one or more solution elements. The computer processor is configured to apply one or more graph pruning algorithms to the graph. The computer processer is configured to determine one or more of the graph vertices as candidate solutions. The system includes a display device configured to display a graphical representation of the candidate solutions. The system includes at least one production machine configured to received configuration parameters according to a selected one of the candidate solutions. The configuration parameters are effective to control the operation of the production machine to manufacture a physical good.