Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.

Abstract: This application relates to systems and methods for automating document creation from digital or analog user input, coupled with data tracking and artificial intelligence (AI) validation. The methods include multiple or single user input of text and documents into a portal; cloud storage of data and documents; automated evaluation and verification of user-inputted material; generation of documents from user-inputted information; tracking of progress of user-inputted and other materials; and compilation of documents to facilitate users and their representatives. The systems include an AI module capable of identifying key features of government documents; a module configured for adjusting document dimensions, skew, and resolution based on recognized features; a second AI module capable of extracting text, code, or other symbols from a the modified document; and a module configured for transforming those data into useable information. Other embodiments are described.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes analysis systems and methods, comprising impinging radiation from a source on an analyte, detecting energy interactions resulting from the impinging radiation using a detector, adjusting a signal emitted from the radiation detector using a pre-processing method to emphasize specific features of that signal, using a machine learning module to interpret specific parts of the adjusted signal, producing a quantitative and/or qualitative model using the machine leaning module, and applying the quantitative and/or qualitative model to a separate energy interaction. The quantitative and qualitative models derived from this training can be applied to new detector inputs from the same or similar instruments. Other embodiments are described.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.

Abstract: Disclosed is a method, system, and process of analyzing script for scanning mass internet content, comprising news websites, blogs, social media sites, and other forms of internet-based content posted online by separate users and organizations. The script is arrayed in filter modules and performs staged, multiple keyword searching and collects/assigns opinion, temporal, and geographic information to create individual synthetic units. These synthetic units can be displayed in a variety of ways including as geospatial maps, timelines, and in a variety of more advanced analytical charts. The filter modules are easily replicated and represent a flexible and powerful data collection and processing tool. The script allows for rapid and massive content analysis online and the preparation of synthetic units for display to interested parties.