Abstract: A method and architecture for encoding image data solely based on pixel intensities, eliminating the need for encoding pixel positions. This is achieved by utilizing low-discrepancy (LD) sequences, such as Sobol or Van Der Corput (VDC) sequences, for deterministic encoding of the image intensity values. The position hypervectors, which were previously required for representing pixel positions, are no longer needed. The corresponding index of the LD sequence is used to encode the intensity values.

Abstract: The present invention relates to systems and methods for ingesting of raw client data into a supply chain optimization system. The client enterprise data system (EDS) provides raw data, typically in tabular format across many files, to a data management module of the optimization system. This data is then profiled. and a set of mapping AI models is applied to the profiled data. Each of the plurality of mapping AI models corresponds to a single input feature of the optimization model. As the mapping models are applied, a live preview is generated. This live preview is reviewable by a human operator, and human input can be provided. When there is input, the individual mapping model is updated (without impacting the other mapping models), and a new live preview for that given feature is generated. The resulting standardized feature set may be consumed by the supply chain optimization model.