Abstract: A system and computer-implemented method for approving a dynamic mortgage application using a blockchain. In one aspect, the method may include determining a customer is approved for a mortgage (“mortgage ready”) and determining a real estate property is mortgage ready. The method may include comparing a calculated amount in which the customer is approved for a mortgage loan with a calculated appraisal value of the real estate property, and approving the mortgage application of the customer when the calculated amount the customer is approved for the mortgage loan is equal to, or exceeds, the calculated appraisal value of the real estate property, reducing a processing time and closing time of the mortgage.

Abstract: A system and computer-implemented method for approving a dynamic mortgage application. In one aspect, the method may include determining a customer is approved for a mortgage and determining a real estate property is mortgage ready, including calculating an appraisal value for the real estate property based upon the information about the real property retrieved from a computer file or memory location/address (such as by using a machine learning or artificial intelligence algorithm). The method may still further include comparing the calculated amount the customer is approved for a mortgage loan with the calculated appraisal value of the real estate property; and approving the mortgage application of the customer for the real estate property when the calculated amount the customer is approved for the mortgage loan meets, or exceeds, the calculated appraisal value of the real estate property.

Abstract: A system and computer-implemented method of identifying multiple mortgage ready properties. The method includes receiving, via one or more processors, information from a customer approved for a mortgage about preferences for a real estate property and accessing, at a memory coupled to one or more processors, a memory storage location to retrieve a plurality of mortgage ready real estate properties meeting one or more of the customer preferences information. The method further includes transmitting, via the one or more processors, the plurality of real estate properties meeting one or more of the customer preferences information to the customer.

Abstract: Aspects of the technology described herein make legacy hydrocarbon studies accessible to modern computer analysis. Whatever the initial format, the technology described herein analyzes the studies to identify characteristics that are interesting to people who study hydrocarbon environments. As an initial process, various segments within a hydrocarbon study received by the technology described herein are identified. The various segments can include text, maps, charts, and tables. Within each of these segments, specific types of text segments, maps, charts, and tables may be identified. For each segment identified, characteristics of interest may be determined through analysis of the segment. In one aspect, segment-specific analysis is performed on each type of segment. Different technologies may be used for different segments.

Abstract: Aspects of the technology described herein identify geologic features within seismic data using modern computer analysis. An initial step is the development of training data for the machine classifier. The training data comprises an image of seismic data paired with a label identifying points of interest that the classifier should identify within raw data. Once the training data is generated, a classifier can be trained to identify areas of interest in unlabeled seismic images. The classifier can take the form of a deep neural network, such as a U-net. Aspects of the technology described herein utilize a deep neural network architecture that is optimized to detect broad and flat features in seismic images that may go undetected by typical neural networks in use. The architecture can include a group of layers that perform aspect ratio compression and simultaneous comparison of images across multiple aspect ratio scales.