Abstract: A method for signal modulation includes: receiving network signals from a plurality of participants; analyzing the received network signals to detect latent signals; processing the network signals based on one or more external event data; processing the network signals to exclude sensitive data in the network signals and the latent signals; and encapsulating pan-network signals based on the processed network signals.

Abstract: A method for event detection includes: obtaining a subpopulation data from a graph structure for performing a graph analysis, wherein the subpopulation data is associated with personality and demographic characteristics of users; obtaining a user profile associated with a target user; inferring psychological traits of the user by performing the graph analysis based on the user profile and the subpopulation data; performing an outcome linkage analysis based on labeled event outcome profiles and the inferred psychological traits to generate personalized knowledge graph data associated with the target user; and profiling, monitoring, or performing an anomaly detection for event data streams based on the personalized knowledge graph data.

Abstract: A data processing method includes receiving input data associated with a plurality of transactions; generating, based on the input data, a plurality of wavelets corresponding to the plurality of transactions; storing the plurality of wavelets and corresponding keys associated with the wavelets in a key-value database; and outputting, based on the plurality of wavelets, one or more indicators.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to systems and methods for generating a persona and detecting anomalies using a hash tree. 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 plurality of data structures related to an individual, convert the data structures into a plurality of Bayesian wavelets, group the Bayesian wavelets into a tree structure, using transitions between the Bayesian wavelets within the tree structure, generate a plurality of Markovian wavelets representing the transitions, replace one or more of the Bayesian wavelets with hashes, and output the tree structure as a persona representing the individual. The instructions may also include training a neural network.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.