The present disclosure relates to systems and methods for creating and using personas. The method includes receiving a first set of input signals associated with data from one or more source; receiving a second set of input signals associated with data from one or more source; converting the first set of input signals and the second set of input signals to a wavelet; constructing a persona based on the wavelet; storing the persona in a ledger; receiving a request for a decision related to a transaction; converting the request to a new wavelet; determining a difference between the new wavelet and the stored persona; generating a score based on the difference; and authorizing the transaction based on the score.
Patent number: 11556927
Grant Date: January 17, 2023
Sensor data analysis may include obtaining video data, detecting facial data within the video data, extracting the facial data from the video data, detecting indicator data within the video data, extracting the indicator data from the video data, transforming the extracted facial data into representative facial data, and determining a mood of the person by associating learned mood indicators derived from other detected facial data with the representative facial data. The analysis may include determining that the representative facial data is associated with a complex profile, and determining a context regarding the person within the environment by weighting and processing the determined mood, at least one subset of data representing information about the person of the complex profile, and the indicator data. The analysis may include determining a user experience for the person, and communicating the determined user experience to a device associated with the person
Patent number: 11341515
Grant Date: May 24, 2022
The present disclosure relates to systems and methods for creating and training neural networks. The method includes collecting a set of signals from a database; applying a transform to each signal to create a modified set of signals, wherein signals of the modified set of signals are wavelets; iteratively, for each of a subset of the modified signals: training the neural network using a modified signal of the subset by adding at least one node to the neural network in response to an error function of an analysis of the modified signal exceeding a threshold; removing nodes from the neural network with activation rates below an activation rate threshold; and grouping each node into a lobe among a plurality of lobes, wherein nodes belonging to a lobe have a common characteristic.
Patent number: 11182675
Grant Date: November 23, 2021
The present disclosure relates to systems and methods for detecting and identifying anomalies within a discrete wavelet database. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new wavelet, convert the net transaction to a wavelet, convert the wavelet to a tensor using an exponential smoothing average, calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors, perform a weighted summation of the difference field to produce a difference vector, apply one or more models to the difference vector to determine a likelihood of the new wavelet representing an anomaly, and add the new wavelet to the field when the likelihood is below a threshold.
Patent number: 11036824
Grant Date: June 15, 2021
Powered by DeepDecision®
Copyright © 2024 Deep Labs, Inc - All Rights Reserved.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.