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BACKGROUND

Biometrics offer the opportunity for identity assurance and identity validation. Many conventional uses for biometrics currently exist for identity and validation. These conventional approaches suffer from many flaws. For example, the IPHONE facial recognition service limits implementation to a one to one match. This limitation is due to the inability to perform one to many searching on the biometric, let alone on a secure encrypted biometric. In fact, most conventional approaches search or match biometrics using unencrypted information, and attempt to perform the search in secure computing spaces to avoid compromise of the biometrics.

SUMMARY

It is realized that there is a need for a solution that provides one to many searching, and that provides for operations on encrypted biometric information. There is a further need to establish such searches that accomplish one to many matching in polynomial time. Various embodiments of the privacy-enabled biometric system provide for scanning of multiple biometrics to determine matches or closeness. Further embodiments can provide for search and matching across multiple types of encrypted biometric information improving accuracy of validation over many conventional approaches, while improving the security over the same approaches.

According to another aspect, conventional approaches are significantly burdened not only in biometric data that is to be searched in the clear but also by key management overhead that is needed for securing those biometrics in storage. Using APPLE as an example, a secure enclave is provided on the IPHONE with encryption keys only available to the secure enclave such that facial biometrics never leave a respective device or the secure enclave. Various embodiments described herein completely change this paradigm by fully encrypting the reference biometric, and executing comparisons on the encrypted biometrics (e.g., encrypted feature vectors of the biometric).

According to one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) a system can determine matches or execute searches on the encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values.

According to one embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

According to another aspect, encrypted search can be executed on the system in polynomial time, even in a one to many use case. This feature enables scalability that conventional systems cannot perform and enables security/privacy unavailable in many conventional approaches.

According to one aspect a privacy-enabled biometric system is provided. The system comprises at least one processor operatively connected to a memory; a classification component executed by the at least one processor, comprising a classification network having a deep neural network (“DNN”) configured to classify feature vector inputs during training and return a label for person identification or an unknown result during prediction; and the classification component is further configured to accept as an input feature vectors that are Euclidean measurable and return the unknown result or the label as output.

According to one embodiment, a set of biometric feature vectors is used for training in the DNN neural network for subsequent prediction. According to one embodiment, biometrics are morphed a finite number of times to create additional biometrics for training of the second (classification) neural network. The second neural network is loaded with the label and a finite number of feature vectors based on an input biometric. According to one embodiment, the classification component is configured to accept or extract from another neural network Euclidean measurable feature vectors. According to one embodiment, the another neural network comprises a pre-trained neural network. According to one embodiment, this network takes in a plaintext biometric and returns a Euclidean measurable feature vector that represents a one-way encrypted biometric. According to one embodiment, the classification neural network comprises a classification based deep neural network configured for dynamic training with label and feature vector input pairs to training. According to one embodiment, a feature vector is input for prediction.

According to one embodiment, the system further comprises a preprocessing component configured to validate plaintext biometric input. According to one embodiment, only valid images are used for subsequent training after the preprocessing. According to one embodiment, the classification component is configured with a plurality of modes of execution, including an enrollment mode configured to accept, as input, a label and feature vectors on which to train the classification network for subsequent prediction. According to one embodiment, the classification component is configured to predict a match, based on a feature vector as input, to an existing label or to return an unknown result. According to one embodiment, the classification component is configured to incrementally update an existing model, maintaining the network architecture and accommodating the unknown result for subsequent predictions. According to one embodiment, wherein the system is configured to analyze the output values and based on their position and the values, determine the label or unknown.

According to one embodiment, the classification network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of classes at least equal to the number of dimensions of the feature vector input, first and a second hidden layers, and an output layer that generates an array of values. According to one embodiment, the fully connected neural network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of nodes at least equal to the number of dimensions of the feature vector input, a first hidden layer of at least 500 dimensions, a second hidden layer of at least twice the number of input dimensions, and an output layer that generates an array of values that based on their position and the values, determine the label or unknown. According to one embodiment, a set of biometric feature vectors is used for training the DNN neural network for subsequent prediction.

According to one aspect a computer implemented method for executing privacy-enabled biometric training is provided. The method comprises instantiating, by at least one processor, a classification component comprising classification network having a deep neural network (“DNN”) configured to classify feature vector inputs during training and return a label for person identification or an unknown result during prediction; accepting, by the classification component, as an input feature vectors that are Euclidean measurable and a label for training the classification network; and Euclidean measurable feature vectors for prediction functions with the classification network; and classifying, by a classification component executed on at least one processor, the feature vector inputs and the label during training.

According to one embodiment, the method further comprises accepting or extracting, by the classification component, from another neural network the Euclidean measurable feature vectors. According to one embodiment, the another neural network comprises a pre-trained neural network. According to one embodiment, the classification neural network comprises a classification based deep neural network configured for dynamic training with label and feature vector input pairs. According to one embodiment, the method further comprises an act of validating input biometrics used to generate a feature vector. According to one embodiment, the method further comprises an act of triggering a respective one of a plurality of modes of operation, including an enrollment mode configured to accept a label and feature vectors for an individual. According to one embodiment, the method further comprises an act of predicting a match to an existing label or returning an unknown result responsive to accepting a biometric feature vector as input.

According to one embodiment, method further comprises an act of updating the classification network with respective vectors for use in subsequent predictions. To handle the case of a person's looks changing over time, the input for prediction, may be used to re-train the individual. According to one embodiment, the method further comprises an act of updating, incrementally, an existing node in the classification network and maintaining the network architecture to accommodate the feature vector for subsequent predictions. According to one embodiment, the classification network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of nodes at least equal to the number of dimensions of the feature vector input, a first and second hidden layer and an output layer that generates an array of values.

According to one aspect a non-transitory computer readable medium containing instructions when executed by at least one processor cause a computer system to execute a method for executing privacy-enabled biometric analysis, the method is provided. A method comprises an instantiating, a classification component comprising a classification network having a deep neural network (“DNN”) configured to classify feature vector and label inputs during training and return a label for person identification or an unknown result during prediction; accepting, by the classification component, as an input feature vectors that are Euclidean measurable as an input and a label for training the classification network, and Euclidean measurable feature vectors for prediction functions with the classification network; and classifying, by a classification component executed on at least one processor, the feature vector inputs and the label during training.

According to one embodiment, the method further comprises an act of accepting or extracting, by the classification component, from another neural network Euclidean measurable feature vectors. According to one embodiment, the another neural network comprises a pre-trained neural network. According to various embodiments, the computer readable medium contains instructions to perform any of the method steps above, individually, in combination, or in any combination.

According to one aspect a privacy-enabled biometric system is provided. The system comprises a classification means comprising a classifying deep neural network (“DNN”) executed by at least one processor the FCNN configured to: classify feature vector inputs and return a label for person identification or an unknown result as a prediction; and accept as an input, feature vectors that are Euclidean measurable and a label as an instance of training.

According to one aspect, a privacy-enabled biometric system is provided. The system comprises at least one processor operatively connected to a memory; a classification component executed by the at least one processor, including a classification network having a deep neural network (“DNN”) configured to classify feature vector inputs during training and return a label for person identification or an unknown result during prediction, wherein the classification component is further configured to accept as an input feature vectors that are Euclidean measurable; a feature vector generation component comprising a pre-trained neural network configured to generate Euclidean measurable feature vectors as an output of a least one layer in the neural network responsive to input of an unencrypted biometric input.

According to one embodiment, the classification component is further configured to accept one way homomorphic, Euclidean measurable vectors, and labels for person identification as input for training. According to one embodiment, the classification component is configured to accept or extract from the pre-trained neural network the feature vectors. According to one embodiment, the pre-trained neural network includes an output generation layer which provides Euclidean Measurable feature vectors. According to one embodiment, the classification network comprises a deep neural network suitable for training and, for prediction, output of a list of values allowing the selection of labels or unknown as output. According to one embodiment, the pre-trained network generates feature vectors on a first biometric type (e.g., image, voice, health data, iris, etc.); and the classification component is further configured to accept feature vectors from another neural network that generates Euclidean measurable feature vectors on another biometric type.

According to one embodiment, the system is configured to instantiate multiple classification networks each associated with at least one different biometric type relative to another classification network, and classify input feature vectors based on executing at least a first or second classification network. According to one embodiment, the system is configured to execute a voting procedure to increase accuracy of identification based on multiple biometric inputs or multiple types of biometric input. According to one embodiment, the system is configured to maintain at least an executing copy of the classifying network and an updatable copy of classification network that can be locked or put in an offline state to enable retraining operations while the executing copy of the classifying network handles any classification requests. According to one embodiment, the classification component is configured with a plurality of modes of execution, including an enrollment mode configured to accept a label for identification and the input feature vectors for an individual from the feature vector generation component.

According to one embodiment, the classification component is configured to predict a match to an existing label or to return an unknown result based on feature vectors enrolled in the classification network. According to one embodiment, the classification component is configured to incrementally update an existing node in the neural network maintaining the network architecture and accommodating the unknown result for subsequent predictions. According to one embodiment, the classification network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of nodes at least equal to the number of dimensions of the feature vector input, a first hidden layer, a second hidden layer, and an output layer that generates hat generates an array of values that based on their position and the values, determine the label or unknown. According to one embodiment, the classification network further comprises a plurality of layers including two hidden layers and an output layer having a number of nodes at least equal to the number of dimensions of the feature vector input.

According to one aspect a computer implemented method for executing privacy-enabled biometric analysis, the method is provided. The method further comprises instantiating, by at least one processor, a classification component comprising a deep neural network (“DNN”) configured to classify feature vector inputs during training and return a label for person identification or an unknown result during prediction, and a feature vector generation component comprising a pre-trained neural network; generating, by the feature vector generation component Euclidean measurable feature vectors as an output of a least one layer in the pre-trained neural network responsive to input of an unencrypted biometric input; accepting, by the classification component, as an input feature vectors that are Euclidean measurable generated by the feature vector generation component and a label for training the classification network, and Euclidean measurable feature vectors for prediction functions with the classification network; and classifying, by a classification component executed on at least one processor, the feature vector inputs and the label during training.

According to one embodiment, the method further comprises accepting or extracting, by the classification network the Euclidean measurable feature vectors from the pre-trained neural network. According to one embodiment, the second neural network comprises a pre-trained neural network. According to one embodiment, the method further comprises an act of validating input feature vectors as Euclidean measurable. According to one embodiment, the method further comprises generating, by the classification component feature vectors on a first biometric type (e.g., image, voice, health data, iris, etc.); and accepting, by the classification component, feature vectors from another neural network that generates Euclidean measurable feature vectors on a second biometric type.

According to one embodiment, method further comprises: instantiating multiple classification networks each associated with at least one different biometric type relative to another classification network, and classifying input feature vectors based on applying at least a first or second classification network. According to one embodiment, the method further comprises executing a voting procedure to increase accuracy of identification based on multiple biometric inputs or multiple types of biometric input and respective classifications. According to one embodiment, for a biometric to be considered a match, it must receive a plurality of votes based on a plurality of biometrics. According to one embodiment, the method further comprises instantiating multiple copies of the classification network to enable at least an executing copy of the classification network, and an updatable classification network that can be locked or put in an offline state to enable retraining operations while the executing copy of the classification network handles any classification requests. According to one embodiment, the method further comprises predicting a match to an existing label or to return an unknown result based, at least in part, on feature vectors enrolled in the classification network. According to one embodiment, the method further comprises updating, incrementally, an existing model in the classification network maintaining the network architecture and accommodating the unknown result for subsequent predictions.

According to one aspect a non-transitory computer readable medium containing instructions when executed by at least one processor cause a computer system to execute a method for executing privacy-enabled biometric analysis, the method is provided. The method comprises instantiating a classification component comprising a deep neural network (“DNN”) configured to classify feature vector and label inputs during training and return a label for person identification or an unknown result during prediction, and a feature vector generation component comprising a pre-trained neural network; generating, by the feature vector generation component Euclidean measurable feature vectors as an output of a least one layer in the pre-trained neural network responsive to input of an unencrypted biometric input; accepting, by the classification component, as an input feature vectors that are Euclidean measurable generated by the feature vector generation component and a label for training the classification network, and Euclidean measurable feature vectors for prediction functions with the classification network; and classifying, by a classification component executed on at least one processor, the feature vector inputs and the label during training. According to various embodiments, the computer readable medium contains instructions to perform any of the method steps above, individually, in combination, or in any combination.

According to one aspect a privacy-enabled biometric system is provided. The system comprises a feature vector generation means comprising a pre-trained neural network configured to generate Euclidean measurable feature vectors responsive to an unencrypted biometric input; a classification means comprising a deep neural network (“DNN”) configured to: classify feature vector and label inputs and return a label for person identification or an unknown result for training; and accept feature vectors that are Euclidean measurable as inputs and return a label for person identification or an unknown result for prediction.

According to one aspect a privacy-enabled biometric system is provided. The system comprises at least one processor operatively connected to a memory; a classification component executed by the at least one processor, including a classification network having a deep neural network (“DNN”) configured to classify feature vector and label inputs during training and return a label for person identification or an unknown result during prediction, wherein the classification component is further configured to accept as an input feature vectors that are Euclidean measurable; the classification network having an architecture comprising a plurality of layers: at least one layer comprising nodes associated with feature vectors, the at least one layer having an initial number of identification nodes and a subset of the identification nodes that are unassigned; the system responsive to input of biometric information for a new user is configured to trigger an incremental training operation for the classification network integrating the new biometric information into a respective one of the unallocated identification nodes usable for subsequent matching.

According to one embodiment, the system is configured to monitor allocation of the unallocated identification nodes and trigger a full retraining of the classification network responsive to assignment of the subset of unallocated nodes. According to one embodiment, the system is configured to execute a full retraining of the classification network to include additional unallocated identification nodes for subsequent incremental retraining of the DNN. According to one embodiment, the system iteratively fully retrains the classification network upon depletion of unallocated identification nodes with additional unallocated nodes for subsequent incremental training. According to one embodiment, the system is further configured to monitor matching of new biometric information to existing identification nodes in the classification network.

According to one embodiment, the system is further configured trigger integration of new biometric information into existing identification nodes responsive to exceeding a threshold associated with matching new biometric information. According to one embodiment, the pre-trained network is further configured to generate one way homomorphic, Euclidean measurable, feature vectors for the individual. According to one embodiment, the classification component is further configured to return a set of probabilities for matching a set of existing labels. According to one embodiment, the classification component is further configured to predict an outcome based on a trained model, a set of inputs for the prediction and a result of a class or unknown (all returned values dictating UNKNOWN).

According to one embodiment, the classification component is further configured to accept the feature vector inputs from a neural network model that generates Euclidean measurable feature vectors. According to one embodiment, the classification component is further configured to extract the feature vectors from the neural network model from layers in the model. According to one embodiment, the system further comprising a feature vector component executed by the at least one processor comprising a neural network. According to one embodiment, the feature vector component is configured to extract the feature vectors during execution of the neural network from layers. According to one embodiment, the neural network comprises of a set of layers wherein one layer outputs Euclidean Measurable Feature Vectors. According to one embodiment, the system further comprising a retraining component configured to monitor a number of new input feature vectors or matches of new biometric information to a label and trigger retraining by the classification component on the new biometric information for the label. This can be additional training on a person, using predict biometrics, that continues training as a biometric changes over time. The system may be configured to do this based on a certain number of consecutive predictions or may do it chronologically, say once every six months.

According to one embodiment, the classification component is configured to retrain the neural network on addition of new feature vectors. According to one embodiment, the neural network is initially trained with unallocated people classifications, and the classification component is further configured to incrementally retrain the neural network to accommodate new people using the unallocated classifications. According to one embodiment, the system further comprises a retraining component configured to: monitor a number of incremental retraining; trigger the classifier component to fully retrain the neural network responsive to allocation of the unallocated classifications. According to one embodiment, the classification component is configured to fully retrain the neural network to incorporate unallocated people classifications, and incrementally retrain for new people using the unallocated classifications. According to one embodiment, the classification component further comprises multiple neural networks for processing respective types of biometric information. According to one embodiment, the classification component is further configured to generate an identity of a person responsive to at least two probable biometric indicators that may be used simultaneously or as part of a “voting” algorithm.

According to one aspect a computer implemented method for privacy-enabled biometric analysis is provided. The method comprises instantiating, by at least one processor, a classification component comprising a classification network having a deep neural network (“DNN”) configured to classify feature vector and label inputs during training and return a label for person identification or an unknown result during prediction, and wherein the classification component is further configured to accept as an input feature vectors that are Euclidean measurable and return the unknown result or the label as output; instantiating the classification component includes an act of allocating within at least one layer of the classification network, an initial number of classes and having a subset of the class slots that are unassigned; triggering responsive to input of biometric information for a new user incremental training operation for the classification network integrating the new biometric information into a respective one of the unallocated class slots usable for subsequent matching.

According to one embodiment, the method further comprises acts of accepting, by the classification component, as an input feature vectors that are Euclidean measurable generated by a feature vector generation component; classifying, by the classification component executed on at least one processor, the feature vector inputs; and returning, by the classification component, a label for person identification or an unknown result. According to one embodiment, the method further comprises acts of instantiating a feature vector generation component comprising a pre-trained neural network; and generating, by the feature vector generation component Euclidean measurable feature vectors as an output of a least one layer in the pre-trained neural network responsive to input of an unencrypted biometric input. According to one embodiment, the method further comprises an act of monitoring, by the at least one processor, allocation of the unallocated identification classes and triggering an incremental retraining of the classification network responsive to assignment of the subset of unallocated nodes to provide additional unallocated classes.

According to one embodiment, the method further comprises an act of monitoring, by the at least one processor, allocation of the unallocated identification nodes and triggering a full retraining or incremental of the classification network responsive to assignment of the subset of unallocated nodes. According to one embodiment, the method further comprises an act of executing a full retraining of the classification network to include additional unallocated classes for subsequent incremental retraining of the DNN. According to one embodiment, the method further comprises an act of fully retraining the classification network iteratively upon depletion of unallocated identification nodes, the full retraining including an act of allocating additional unallocated nodes for subsequent incremental training. According to one embodiment, the method further comprises an act of monitoring matching of new biometric information to existing identification nodes. According to one embodiment, the method further comprises an act of triggering integration of new biometric information into existing identification nodes responsive to exceeding a threshold associated with matching new biometric information. According to one embodiment, the method further comprises an act of generating one way homomorphic, Euclidean measurable, labels for person identification responsive to input of Euclidean measurable feature vectors for the individual by the classification component.

According to one aspect a non-transitory computer readable medium containing instructions when executed by at least one processor cause a computer system to execute a method instantiating a classification component comprising a classification network having a deep neural network (“DNN”) configured to classify feature vector and label inputs during training and return a label for person identification or an unknown result during prediction, and wherein the classification component is further configured to accept as an input feature vectors that are Euclidean measurable and return the unknown result or the label as output; instantiating the classification component includes an act of allocating within at least one layer of the classification network, an initial number of classes and having a subset of additional classes that are unassigned; triggering responsive to input of biometric information for a new user incremental training operation for the classification network integrating the new biometric information into a respective one of the unallocated identification nodes usable for subsequent matching. According to various embodiments, the computer readable medium contains instructions to perform any of the method steps above, individually, in combination, or in any combination.

According to one aspect a privacy-enabled biometric system is provided. The system comprises at least one processor operatively connected to a memory; a classification component executed by the at least one processor, comprising classification network having a deep neural network configured to classify Euclidean measurable feature vectors and label inputs for person identification during training, and accept as an input feature vectors that are Euclidean measurable and return an unknown result or the label as output; and an enrollment interface configured to accept biometric information and trigger the classification component to integrate the biometric information into the classification network.

According to one embodiment, the enrollment interface is accessible via uri, and is configured to accept unencrypted biometric information and personally identifiable information (“PII”). According to one embodiment, the enrollment interface is configured to link the PII to a one way homomorphic encryption of an unencrypted biometric input. According to one embodiment, the enrollment interface is configured to trigger deletion of the unencrypted biometric information. According to one embodiment, the system is further configured to enroll an individual for biometric authentication; and the classification component is further configured to accept input of Euclidean measurable feature vectors for person identification during prediction. According to one embodiment, the classification component is further configured to return a set of probabilities for matching a feature vector. According to one embodiment, the classification component is further configured to predict an outcome based on a trained model, a set of inputs for the prediction and a result of a class (persons) or UNKNOWN (all returned values dictating UNKNOWN).

According to one embodiment, the system further comprises an interface configured to accept a biometric input and return and indication of known or unknown to a requesting entity. According to one embodiment, requesting entity includes any one or more of: an application, a mobile application, a local process, a remote process, a method, and a business object. According to one embodiment, the classification component further comprising multiple classification networks for processing different types of biometric information. According to one embodiment, the classification component is further configured to match an identity of a person responsive to at least two probable biometric indicators that may be used simultaneously or as part of a voting algorithm. According to one embodiment, the classification network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of classes at least equal to the number of dimensions of the feature vector input, a first and second hidden layer, and an output layer that generates an array of values.

According to one aspect a computer implemented method for privacy-enabled biometric analysis, the method is provided. The method comprises instantiating, by at least one processor, a classification component comprising a full deep neural network configured to classify feature vectors that are Euclidean measurable and a label inputs for person identification during training, and accept as an input feature vectors that are Euclidean measurable and return an unknown result or the label as output during prediction, and an enrollment interface; accepting, by the enrollment interface, biometric information associated with a new individual; triggering the classification component to train the classification network on feature vectors derived from the biometric information and a label for subsequent identification; and return the label through for subsequent identification.

According to one embodiment, an instantiating the enrollment interface included hosting a portal accessible via uri, and the method includes accepting biometric information and personally identifiable information (“PII”) through the portal. According to one embodiment, the method further comprises linking the PII to a one way homomorphic encryption of unencrypted biometric input. According to one embodiment, the method further comprises triggering deletion of unencrypted biometric information on a submitting device. According to one embodiment, method further comprises enrolling individuals for biometric authentication; and mapping labels and respective feature vectors for person identification, responsive to input of Euclidean measurable feature vectors and a label for the individual. According to one embodiment, the method further comprises returning a set of probabilities for matching a set of existing labels.

According to one embodiment, the method further comprises predicting an outcome based on a trained model, a set of inputs for the prediction and a result of a class (e.g., persons) or unknown (e.g., all returned values dictating UNKNOWN). According to one embodiment, the method further comprises accepting via an authentication interface a biometric input and returning and indication of known or unknown to a requesting entity. According to one embodiment, the requesting entity includes any one or more of: an application, a mobile application, a local process, a remote process, a method, and a business object. According to one embodiment, the method further comprises processing different types of biometric information using multiple classification networks. According to one embodiment, the method further comprises generating an identity of a person responsive to at least two probable biometric indicators that may be used simultaneously or as part of a voting algorithm.

According to one embodiment, the classification network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of classes at least equal to the number of dimensions of the feature vector input, a second hidden layer of at least twice the number of input dimensions, and an output layer that generates an array of values. According to one embodiment, the fully connected neural network further comprises an input layer for accepting feature vectors of a number of dimensions, the input layer having a number of nodes at least equal to the number of dimensions of the feature vector input, a first hidden layer of at least 500 dimensions, a second hidden layer of at least twice the number of input dimensions, and an output layer that generates an array of values that based on their position and the values, determine the label or unknown.

Still other aspects, examples, and advantages of these exemplary aspects and examples, are discussed in detail below. Moreover, it is to be understood that both the foregoing information and the following detailed description are merely illustrative examples of various aspects and examples, and are intended to provide an overview or framework for understanding the nature and character of the claimed aspects and examples. Any example disclosed herein may be combined with any other example in any manner consistent with at least one of the objects, aims, and needs disclosed herein, and references to “an example,” “some examples,” “an alternate example,” “various examples,” “one example,” “at least one example,” “this and other examples” or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the example may be included in at least one example. The appearances of such terms herein are not necessarily all referring to the same example.

