Abstract: The invention includes a method of testing a mobile device with a robotic testing device having a holder, a first motor operatively connected to a first robotic implement, and a second motor operatively connected to a second robotic implement. The method includes securing, via the holder, the mobile device to the robotic testing device; actuating, via the first motor, the first robotic implement to contact the mobile device at a first location at a first time; actuating, via the second motor, the second robotic implement to contact the mobile device at a second location at a second time; and recording, via a data processor of the robotic testing device, a response of the mobile device in memory of the robotic testing device. The robotic testing device includes a pressure feedback sensor configured to sense a pressure of one or more of the robotic implements while interacting with the mobile device.

Abstract: Systems and methods for detecting anomalous data traffic over proxy servers in a data communications network. The method includes receiving, by a server computing device, network log data corresponding to data traffic during a timeframe. The method further includes normalizing the network log data using at least one of timestamp data of the network log data or IP address data of the network log data. The method also includes extracting risk-based data features from the network log data. The method further includes calculating using an isolation forest algorithm, anomaly scores for the normalized network log data based on the extracted risk-based features. The method also includes determining at least one anomaly event based on the calculated anomaly scores. The method further includes identifying at least one host device and at least one timestamp corresponding to the at least one anomaly event.

Abstract: Methods and systems are described for automatically rendering a customized user interface (UI). A server receives a request to generate a UI, the request including a user identifier and a workflow identifier. The server determines a template based upon the workflow identifier, comprising areas for insertion of custom UI elements. The server captures a list of UI nodes, each UI node comprising metadata. The server organizes the UI nodes into (i) a tree data structure comprising an anchor UI node and child UI nodes, and (ii) a table data structure of identifiers for the nodes in the tree structure, values, constraints, and the metadata for the corresponding UI node. The server inserts the metadata into the areas for insertion using the tree data structure and the table data structure. The server renders a graphical UI based upon the metadata.

Abstract: A computerized method is provided for automatically authorizing an access request from a user to perform a task with respect to a computing application of an application lifecycle management (ALM) system. The computerized method includes receiving, by a computing device, the access request from the user to perform the task with respect to the application in the ALM system and calculating, by the computing device, a risk score associated with the access request. Calculating the risk score includes determining a plurality of factors including: (i) at least one factor associated with the application indicative of an importance level of the application and (ii) at least one factor characterizing technical experience of the user, and computing the risk score as a weighted sum of the plurality of factors. The method further includes determining, by the computing device, whether to authorize the user access request based on the risk score.

Abstract: Methods and apparatuses are described for secure authentication and network access management for mobile computing devices. A server establishes a communications session with a mobile device. The server receives a network access request from the mobile device, the network access request including user access class data based upon authentication credentials provided by a user. The server determines a level of network access to provide to the mobile device based upon the user access class data. The server generates network configurations based upon the determined level of network access. The server applies the network configurations to the communications session with the mobile device. The server transmits an acknowledgment of the network access request to the mobile device. The mobile device routes network traffic originating from the mobile device to external network devices according to the applied network configurations.

Abstract: Methods and apparatuses are described for generating a cryptographic authentication key. A computing device receives a request to generate a cryptographic key. The device generates a defined sequence of security questions, each question associated with a difficulty value. The device generates the key using the defined sequence of questions, comprising: a) presenting a first question and receiving an answer to the first question, b) generating a hash string corresponding to the received answer, c) determining a next question based upon the hash string, d) presenting the next question and receiving an answer to the next question, e) concatenating the answer to the next question with the hash string, f) applying a hash function to the hash string with the concatenated answer to update the hash string, g) repeating steps c-f until the difficulty values of the questions reaches a threshold, and h) generating the key from the updated hash string.

Abstract: The Seed Splitting and Firmware Extension for Secure Cryptocurrency Key Backup, Restore, and Transaction Signing Platform Apparatuses, Methods and Systems (“SFTSP”) transforms transaction signing request, key backup request, key recovery request inputs via SFTSP components into transaction signing response, key backup response, key recovery response outputs. A transaction signing request message for a transaction is received by a first HSM and includes an encrypted second master key share from a second HSM whose access is controlled by M-of-N authentication policy. The encrypted second master key share is decrypted. A first master key share is retrieved. A master private key is recovered from the master key shares. A transaction hash and a keychain path is determined. A signing private key for the keychain path is generated using the recovered master private key. The transaction hash is signed using the signing private key, and the generated signature is returned.

Abstract: Methods and apparatuses are described for automated execution of computer software based upon determined empathy of a communication participant. A server captures a digitized voice segment from a remote computing device. The server analyzes vocal cues of the digitized voice segment to generate a voice empathy score. The server converts speech in the digitized voice segment into text and determines empathy keywords in the text to generate a keyword empathy score. The server captures digitized images of the participant's face and analyzes physical expressions of the face to identify emotions and generate a facial empathy score. The server generates an overall empathy confidence score for the communication participant based upon the voice empathy score, the keyword empathy score, and the facial empathy score. The server generates recommended changes for the user based upon the overall empathy confidence score and executes a software application that displays the recommended changes.

Abstract: A computerized method of testing a mobile application stored on a mobile test device includes executing, by a computing device, a test script stored in memory of the computing device; running, by the computing device, according to the test script, a human behavior simulation module; retrieving, by the computing device, an event list stored in a server, the event list including one or more electronic human behavior simulations; receiving, by the computing device an electronic human behavior simulation from the event list; providing, by the computing device, selected data of the electronic human behavior simulation to memory of the server for recordation; executing, by the computing device, the electronic human behavior simulation on a mobile test device in electronic communication with the computing device; and executing, by the computing device, according to the test script, a test case stored in memory of the computing device on the mobile test device.

Abstract: Methods and apparatuses are described for automated secure software development management, risk assessment and risk remediation. A server generates security requirements for a software application under development based upon a plurality of technical attributes and a threat model. The server creates a first set of development tasks based upon the generated security requirements. The server scans source code to identify one or more security vulnerabilities and creates a second set of development tasks based upon the identified vulnerabilities. The server generates a security risk score based upon the generated security requirements and the identified vulnerabilities. The server deploys the software application under development to a production computing system upon determining that the security risk score satisfies a criterion.

Abstract: Methods and apparatuses are described for time-series database user authentication and access control. A server computing device receives a request from a remote computing device to access a time-series database coupled to the server computing device, wherein the request includes one or more authentication credentials associated with the remote computing device. The server computing device validates the one or more authentication credentials associated with the remote computing device. The server computing device connects to an access control layer associated with the time-series database. The access control layer authorizes the remote computing device to access data in the time-series database based upon an access profile associated with the validated authentication credentials. The server computing device retrieves data from the time-series database in response to the request.

Abstract: Methods and apparatuses are described for automated analysis, categorization, and behavior prediction of computer scripts using rules-based pattern matching. A server receives from a client device, a request for analysis of a process flow decision. The server identifies computer scripts that execute to generate the process flow decision. For each identified computer script: the server identifies text patterns in the computer script and categorizes the computer script based upon the identified patterns; the server converts the computer script into an expression tree data structure; the server traverses the expression tree to determine logical expressions that return a true result; the server parses each logical expression that returns a true result to identify parameters that contribute to the true result; the server generates a response to the request for analysis of the process flow decision and transmits the response to the request to the client computing device.

Abstract: Methods and apparatuses are described for automatic authentication switching in online chat sessions. A server receives a request to establish an online chat session from a first client device, including authentication credentials associated with the first client device. The server establishes an online chat session between the first client device and a second client device via a first authenticated communications channel. The server transmits one or more chat messages between the first client device and the second client device using the first authenticated communications channel. The first client device determines that the online chat session has lost authentication. The first client device automatically switches the online chat session to a second unauthenticated communications channel. The server maintains the established online chat session between the first client device and the second client device via the second unauthenticated communications channel.

Abstract: Methods and apparatuses are described for detecting vulnerabilities associated with a software application build. A server generates a software application build based upon a source code repository, including determining application dependencies of the software application build. The server identifies vulnerabilities associated with the application dependencies. For each identified vulnerability, the server creates an aspect class based upon a package file associated with the vulnerability, the aspect class comprising vulnerability logging code. The server integrates the created aspect classes into libraries of the application dependencies, generates a new package file based upon the application dependencies, and integrates the new package file into the software application build. The server executes the software application build, including generating log statements by calling the aspect classes in the new package file.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for identification and classification of complaint-specific interactions. A computer data stores unstructured text. A server computing device splits the unstructured text into phrases of words. The server generates a set of tokens from each phrase and removes tokens that are stopwords. The server generates a normalized sentiment score for each set of tokens. The server uses a rules-based classification engine to generate a rules-based complaint score for each set of tokens. The server uses an artificial intelligence machine learning model to generate a model-based complaint score for each set of tokens. The server determines determine whether each set of tokens corresponds to a complaint-specific interaction based upon the rules-based complaint score and the model-based complaint score.

Abstract: Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server trains a word embedding model using one-hot vectors of word pairs from a filtered first corpus of unstructured computer text and a filtered second corpus of unstructured computer text, using an artificial intelligence neural network. The server trains a long short-term memory model using vector matrices that correspond to sentences in the filtered second corpus of unstructured computer text, and labels. The server receives a message, generates a matrix for each sentence in the message by applying the trained word embedding model, generates one or more labels, and a probability for each label, for each sentence in the message by applying the trained long short-term memory model, and routes the message to a second client computing device based upon an assigned label.

Abstract: A computerized method of authenticating a user includes receiving, by a computing device, first biometric data from the user via a first biometric input device; extracting one or more first features from the first biometric data; generating a first vector based on the first features; computing a first similarity score reflecting a geometric distance between the first vector and a first reference vector stored in memory; receiving second biometric data from the user via a second biometric input device; extracting one or more second features from the second biometric data; generating a second vector based on the second features; computing a second similarity score reflecting a geometric distance between the second vector and a second reference vector stored in memory; computing a confidence score based on the first and second similarity scores; and determining a right of the user to access restricted information based on the confidence score.

Abstract: A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.

Abstract: The Firmware Extension for Secure Cryptocurrency Key Backup, Restore, and Transaction Signing Platform Apparatuses, Methods and Systems (“SFTSP”) transforms transaction signing request, key backup request, key recovery request inputs via SFTSP components into transaction signing response, key backup response, key recovery response outputs. A key backup request that includes an encrypted master key associated with a hosting HSM is received by a backup HSM from a backup utility. A private key decryption key corresponding to a public key encryption key previously provided by the backup HSM to the backup utility for the hosting HSM is retrieved from the backup HSM's tamper-proof storage and used to decrypt the encrypted master key. A specified number of master key shares to generate for the decrypted master key is determined and generated using a secret sharing method. The generated master key shares are provided to the backup utility.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for domain-specific stopword identification and removal. A computer data store stores unstructured text. A server computing device splits the unstructured text into phrases and generates tokens from the phrases. The server computing device generates a set of bootstrap keywords using the tokens. An artificial intelligence neural network executing on the server computing device generates a stopword training model. The server computing device generates a first set of candidate stopwords using the bootstrap keywords and the stopword training model. The server computing device generates regular expressions using the bootstrap keywords, and generates a second set of candidate stopwords using the regular expressions. The server computing device stores the candidate stopwords in the data store, and removes stopwords from the unstructured text using the data store.