Abstract: A computer-implemented method is provided for facilitating live communication between a potential customer and an enterprise in relation to a product. The method includes causing to display, by a computing device on a webpage over a communications network, an interactive banner located on the webpage that includes advertisement content related to the product. The advertisement content includes an offer to the potential customer to communicate live with the enterprise about the product. The method includes adding to the context information, by the computing device, information related to an interaction over the communications network between the potential customer and the interactive banner. The method further includes adjusting, by the computing device over the communications network, the advertisement content in the interactive banner on the webpage if the potential customer accepts the offer.

Abstract: The Database, Data Structure and Framework Transformer Apparatuses, Methods and Systems (“DDSFT”) transforms variable list request, population selection, base table transform extract data inputs via DDSFT components into transformed, merged data outputs. The DDSFT includes a database structure that stores data used in the framework operations. A macro-tool includes one or more macros that control a sequence of database queries that extract the data from the database structure and then perform transformations on the extracted data. The macro-tool includes a series of binary flags indicative of whether or not statements are executed.

Abstract: Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server captures historical story data from an Agile development tracking system. For each completed story, the server generates a vector based upon story-specific features and assigns a label to the vector based upon a cycle time associated with the story. The server trains a classification model using a neural network on the vectors and labels. The server captures new story data from the Agile development tracking system. For each new story, the server generates a vector based upon story-specific features and executes the trained model on the vector to generate a cycle time prediction for the new story. Based upon the cycle time prediction, the server identifies deficiencies in the new story and generates an alert message.

Abstract: Methods and apparatuses are described for intelligent automation of computer software testing log aggregation, analysis, and error remediation. A client device generates test log files for software automation testing tools, each test log file comprising errors generated by execution of test scripts. The client device creates an archive file with the log files and transmits the files to a server. The server extracts the files from the archive file and parses each of the files to identify errors. The server aggregates errors from at least two of the files and transmits the aggregated errors to a log management message queue and a development message queue. The server generates a user interface and transmits the user interface to a remote device. The server also generates development change orders that, when executed by the server, create tasks in a development tracking system to resolve errors by changing development source code.

Abstract: Methods and apparatuses are described for of recognizing handwritten characters in digital images using context-based machine learning. A server captures an image of a document that comprises one or more handwritten data fields, the document associated with a user identifier. The server identifies a field type for each handwritten data field in the image. The server creates a pixel intensity array for each character in each handwritten data field and determines whether a user-specific character map exists for the user identifier. If a map exists, the server retrieves the map and generates digital form data by executing a user-specific handwriting classifier using the map, the pixel intensity arrays, and the field types. If a map does not exist, the server builds a map based upon the pixel intensity arrays and generates digital form data by executing a baseline handwriting classifier using the map, the pixel intensity arrays, and the field types.

Abstract: A computer-implemented method is described for authenticating the identity of a caller. A mobile computing device receives, via a wireless communications network, an incoming call setup request initiated by a phone system of a caller, and executes an identity service application upon receipt of the incoming call setup request. The call includes a plurality of data packets. The mobile computing device extracts a caller identifier from the plurality of data packets. The caller identifier includes one or more bits at a first predetermined position within the plurality of data packets. The mobile computing device forwards the caller identifier to an identity service computing device. The identity service computing device determines an identity status of the caller. The mobile computing device receives, from the identity service computing device, an authentication token and the identity status of the caller. The mobile computing device displays the identity status of the caller.

Abstract: A system is provided for monitoring a data center that includes at least one rack cabinet configured to store at least one computing asset. The system includes a radio-frequency identification (RFID) tag attached to the computing asset, where the RFID tag stores inventory data related to the computing asset. The system also includes a plurality of tracking modules disposed inside of the rack cabinet without being physically attached to the computing asset. Each tracking module is adapted to monitor a condition inside of the rack cabinet. At least one of the plurality of tracking modules includes an RFID reader configured to communicate with the RFID tag to obtain the inventory data of the computing asset. The RFID tag and reader maintain a line of sight relative to each other inside of the rack cabinet to minimize metallic interference during communication therebetween.

Abstract: A computer-implemented system is provided for visualizing and analyzing security threats in a suite of software applications. The system includes a visualization module for rendering, on a computer display, a map with components representative of the suite of software applications and relationships among the software applications. The components are displayed in a base layer of the map. The system also includes a threat modeling module configured to automatically identify one or more security threats in the suite of software applications. The threat modeling module is adapted to interact with the visualization module to graphically depict on the computer display the one or more security threats on the map in a threat modeling layer. The system further includes a security controls module configured to implement security controls for mitigating the one or more security threats identified by the threat modeling module.

Abstract: The Social Aggregated Fractional Equity Transaction Partitioned Acquisition Apparatuses, Methods and Systems, (“SAFETPA”) transforms fractional equity ownership purchase request inputs via SAFETPA components into transaction confirmation outputs. A Fractional Order Generation component receives a purchase request for a fractional order of an equity via a data communication network interface from a client terminal of a client having a source of funds and having a public encryption key, determines a purchase price for the fractional order of the equity by retrieving a quote for a price of a share of the equity, confirms that the source of funds contains at least the purchase price; and generates a trade order for the share of the equity.

Abstract: Methods and apparatuses are described for coordinated scaling of a cloud-based software application and a backend resource in a networked computing environment. A server captures resource usage metrics associated with cloud-based applications. The server captures resource usage metrics associated with backend resources that correspond to the cloud-based applications. The server aggregates the resource usage metrics to generate a set of integrated application-level resource usage metrics. The server determines a current resource consumption level based upon the integrated application-level resource usage metrics for the cloud-based software application. The server compares the current resource consumption level to a desired resource consumption level. The server computing device changes resource availability for the cloud-based software application, based upon a difference between the current resource consumption level and the desired resource consumption level.

Abstract: Methods and apparatuses are described for automatic validation of applications on synchronized mobile devices. A server receives a command associated with a mobile application and a first image file corresponding to a current view of the application from a client computing device. The server transmits the command to a plurality of mobile devices coupled to the server. Each mobile device accesses the application and performs the command against the application. The server receives a second image file from each mobile device that corresponds to a current view of the mobile application on the mobile device. The server extracts a first set of text segments from predefined areas of the first image file and second sets of text segments from predefined areas of each second image file. The server compares the first set and each second set to determine whether a difference exists between the first set and each second set.

Abstract: Systems and methods for monitoring one or more servant regions of an application server. A processor in electronic communication with the application server identifies one or more servant regions of the application server; requests to the application server to receive one or more performance metrics for each of the one or more servant regions, the performance metrics included in a virtual resource statistics module for collecting and reporting available resource data; receives from the application server first information including the one or more performance metrics for each of the one or more servant regions; extracts from the first information the one or more performance metrics for each of the one or more servant regions of the application server; and determines, based on the one or more performance metrics, whether to generate an alert message.

Abstract: Methods and apparatuses are described for monitoring usage and adjusting operational characteristics of physical assets. Asset sensors capture temperature data and x-y-z coordinate displacement data associated with a physical asset. A reference sensor captures temperature data of a defined physical area. For each asset sensor, a server computing device monitors the temperature data and the x-y-z coordinate displacement data. The server applies triggers to the temperature data and the displacement data to determine whether a change event occurred. The server records the temperature data and the displacement data when a change event occurred for the physical asset. The server generates instructions comprising a change to: an operational state or an availability of the physical asset. The server transmits the instructions to a remote device associated with the physical asset. The remote device executes the instructions to change: the operational state or the availability of the physical asset.

Abstract: Methods, systems and apparatuses, including computer program products, are described for detecting a proximity of a mobile computing device during a user session. The mobile computing device enters a first operational mode during the user session and obtains sensor data from at least one sensor responsive to a property of the location. The mobile computing device forwards to a server computing device, a baseline profile including the sensor data and a time stamp indicating a time the sensor data was obtained. The mobile computing device receives from the server computing device, a differential trigger threshold including a range of permissible values for sensor data obtained from the at least one sensor. The mobile computing device obtains updated sensor data from the at least one sensor. The mobile computing device enters a second operational mode when the updated sensor data is outside of the differential trigger threshold.

Abstract: A computer-implemented method is described for authenticating an identity of a user requesting execution of a computerized transaction via a first client computing device. The first device and a second client computing device in proximity each execute applications for communicating with a server. The first and second devices contemporaneously capture voice sequences including at least one audible sound vocalized by the user, and a timestamp indicating when the sound is captured. The identity of the user is validated based upon a determination that the user vocalized the pass phrase, and a difference between the first timestamp and the second timestamp is below a predetermined threshold. The first device executes a computerized transaction with the server computing device upon receiving validation of the identity of the user.

Abstract: A method is described for dynamically determining availability of a computing resource. An application session is established, and an application session data matrix is generated including parameters specifying aspects of the application session, an application associated with the application session, and a computing resource on which the application session depends. A component signature block is derived based on the parameters of the application session data matrix. The component signature block identifies the computing resource on which the application session depends and a total number of sessions dependent on the computing resource. A metric signature block is generated based on one or more parameters of the application session data matrix and the component signature block. The metric signature block is classified according to one or more probability models. A computing resource availability rating for the computing resource is derived based on an output of the one or more probability models.

Abstract: The Secure Firmware Transaction Signing Platform Apparatuses, Methods and Systems (“SFTSP”) transforms transaction signing request inputs via SFTSP components into transaction signing response outputs. A transaction signing request message for a transaction may be received at a first HSM. An encrypted master private key associated with the transaction may be obtained from a second HSM. A private key decryption key associated with the first HSM may be retrieved from the first HSM's tamper-proof storage. The encrypted master private key may be decrypted using the private key decryption key. A transaction hash and a keychain path associated with the transaction signing request message may be determined. A signing private key for the keychain path may be generated by the first HSM using the decrypted master private key. The transaction hash may be signed by the first HSM using the signing private key, and the generated signature may be returned.

Abstract: Techniques for searching for products in an inventory of product are disclosed. The techniques receive data that specifies attributes of a product, search inventory of products to determine potential matches or closely related matches to at least some of the attributes of the products, generate messages based on the search of inventory to determine the potential matches, generate positional information associated with the messages and generate a set of informational depictions of the generated messages, with the informational depictions of the messages when rendered on a display device are rendered in juxtaposition with representations of the products according to the generated positional information.

Abstract: A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments an

Abstract: Methods and systems are described for data lineage identification and change impact prediction. Servers capture metadata that defines data objects associated with data sources. The servers determine direct relationships between data sources based upon the captured metadata. The servers identify indirect relationships between the data sources. The servers generate a data lineage across the data sources for the data objects. The servers extract unstructured text from database incident tickets and match the unstructured text to the metadata. The servers generate a multidimensional vector for the data objects based upon the data lineage and the unstructured text. The servers train a classification model using the vectors to predict a change impact score for each data object. The servers receive a request to change a data object. The servers determine a change impact score for the data object. When the score is below a threshold, the servers execute the change.