Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include retrieving software development data associated with a software application; receiving modular configuration data from a client system; determining application usage data based at least in part on a user profile of the client system that is associated with the software application; generating a release note data object based at least in part on at least one of the software development data, the modular configuration data, and the application usage data; and performing one or more software application release operations by transmitting the release note data object to the client system.

Abstract: There is a need for more effective and efficient predictive data analysis. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method for generating a neutralized prediction model includes accessing an initial prediction model generated using an initial training data object, performing a randomized shuffling of the initial training data object to generate a shuffled training data object, generating randomized predictions by processing the shuffled training data object using the initial prediction model, performing a neutralization of the initial training data object to generate a neutralized training data object, and generating the neutralized prediction model based at least in part on the neutralized training data object and the randomized predictions.

Abstract: There is a need for solutions that generates a temporally dynamic prediction for a particular prediction input. This need can be addressed by, for example, processing the prediction input using each of a plurality of temporally trained machine learning models to generate a corresponding model-specific prediction inference of a plurality of model-specific prediction inferences and processing the plurality of model-specific prediction inferences using an ensemble model to generate the temporally dynamic prediction for the prediction input.

Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include determining per-application configuration data associated with a software application; determining per-client configuration data associated with a client system; determining per-application per-client configuration data associated with the software application and the client system; generating modular configuration data based at least in part on at least one of the per-application configuration data, the per-client configuration data, and the per-application per-client configuration data; generating a releasable software application based at least in part on the modular configuration data; and performing one or more software release operations by transmitting the releasable software application to the client system.

Abstract: There is a need for more effective and efficient secure data transmission. This need can be addressed by, for example, solutions for secure data transmission that utilize per-user-functionality secret shares. In one example, a method includes generating a hashed user identifier based on a received user identifier; transmitting the hashed user identifier to an external computing entity; and receiving a data retrieval secret share from the external computing entity, wherein: (i) the data retrieval secret share is selected from a plurality of per-user-functionality secret shares, (ii) the plurality of per-user-functionality secret shares are generated based on a secret value, (iii) the secret value is generated based on the hashed user identifier, (iv) the secret value is used to generate a user data private key, and (v) the external computing entity is configured to encrypt user-provided data using the user data private key prior to transmission of the encrypted user-provided data.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for selecting a test suite for an API. In one embodiment, a computing entity or apparatus is configured to receive test patterns and heuristics, receive an input API, the input API comprising API specifications, parse the input API to extract the API specifications, and based at least in part on the extracted API specifications and the test patterns and heuristics, select a test suite, wherein the test suite is programmatically generated using a machine learning model and comprises one or more test routines, one or more data values, and one or more expected results.

Abstract: An apparatus comprising a communications interface configured to communicate over one or more networks, at least one processor and at least one memory including program code is provided. The at least one memory and the program code configured to, with the processor, cause the apparatus to at least receive, via the communications interface, an interparty transaction request. The interparty transaction request corresponds to an interparty transaction and identifying a first party and a second party. The apparatus generates a smart contract based on the interparty transaction request; stores a data entry indicating the generation of the smart contract to a distributed ledger; executes the smart contract to cause settlement of the interparty transaction between the first party and the second party; and stores a data entry indicating the settlement of the interparty transaction to the distributed ledger.

Abstract: A system is engineered to facilitate calculation of predicted perils connected with a ridesharing vehicle. The system facilitates premium endorsement that provides higher coverage than is provided under conventional products and allows drivers to work directly with their personal lines insurer in reviewing their coverage and filing a claim in the event of an accident. The subject matter facilitates higher limits while including a post-policy period audit that allows a reconciliation of high-use or high-risk driving with appropriate retroactive rate adjustments. The system also facilitates retroactive rate adjustments that would compare a policyholder’s time/distance driven or collected telematics data (including time or distance driven in each driving period) to the group average or other historical underwriting data.

Abstract: Home telematics devices are engineered to identify unique device signatures for all appliances, fixtures, and so on that generate voltage noise, pressure waves, and acoustic responses throughout a property. The device signatures comprise the inventory of devices in the insured's home and are used to create an electronic record of the devices that assists him in filing a claim with an insurer that is quick and easy after a theft or total loss. Using the device signatures provided by home telematics devices' sensing an itemization technology, fraud detection is also possible. Additionally, analytics software predicts possible failure by analyzing the device signatures.

Abstract: Embodiments are disclosed for automatically processing a claim provided by a user. Responsive to receiving a notice of loss associated with a claim of a user, a set of customer identity validation data are collected. The set of customer identity validation data may be determined to meet a pre-defined identity validation criteria. Responsive to determining that the set of customer identity validation data meets the pre-defined identity validation criteria, current claim evaluation data for the claim may be accessed. A set of predictive impact assessment scores associated with the current claim evaluation data may be determined using a predictive model. The set of current claim evaluation data may be determined to meet pre-defined claim data criteria by comparing the predictive impact assessment scores with a set of impact assessment thresholds. Responsive to determining that the set of current claim evaluation data meets the pre-defined claim data criteria, a reactive action to the claim may be determined.

Abstract: Drones are engineered with sensors for use in insurance applications. After locating an object of interest, a drone performs an investigation by probing the object of interest. Sensors receive feedback from the object of interest. An electronic fingerprint of the drone is produced. Afterward, perils are computed based on the feedback and the fingerprint of the drone is used in insuring the object of interest. The act of probing includes thumping, drumming, or radiating ultrasound waves against the object of interest. The sensors can be turned off when they are within a geographic zone of prohibited operations.

Abstract: With graph compression, there is a need to significantly reduce the size of the graph information/data for routing determinations. The disclosed graph compression allow for more efficient and less resource-intensive routing determinations while maintain complete graph fidelity.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting property damage restoration estimates.

Abstract: There is a need for more effective and efficient secure data transmission. This need can be addressed by, for example, solutions for secure data transmission that utilize per-user-functionality secret shares. In one example, a method includes generating a hashed user identifier based on a received user identifier; transmitting the hashed user identifier to an external computing entity; and receiving a data retrieval secret share from the external computing entity, wherein: (i) the data retrieval secret share is selected from a plurality of per-user-functionality secret shares, (ii) the plurality of per-user-functionality secret shares are generated based on a secret value, (iii) the secret value is generated based on the hashed user identifier, (iv) the secret value is used to generate a user data private key, and (v) the external computing entity is configured to encrypt user-provided data using the user data private key prior to transmission of the encrypted user-provided data.

Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include determining per-application configuration data associated with a software application; determining per-client configuration data associated with a client system; determining per-application per-client configuration data associated with the software application and the client system; generating modular configuration data based at least in part on at least one of the per-application configuration data, the per-client configuration data, and the per-application per-client configuration data; generating a releasable software application based at least in part on the modular configuration data; and performing one or more software release operations by transmitting the releasable software application to the client system.

Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include retrieving software development data associated with a software application; receiving modular configuration data from a client system; determining application usage data based at least in part on a user profile of the client system that is associated with the software application; generating a release note data object based at least in part on at least one of the software development data, the modular configuration data, and the application usage data; and performing one or more software application release operations by transmitting the release note data object to the client system.

Abstract: Methods, systems, computer program products, and apparatus relating to an item inventory and item replacement are provided. In one embodiment a method for automatic processing of an insurance claim is provided. The method comprises (a) receiving one or more item indicators, each item indicator associated with an item in an insurance claim for an insured party; (b) for a particular item indicator of the one or more item indicators, automatically identifying an item listing associated with the particular item indicator from an item inventory, the item listing identified based at least in part on the particular item indicator; and (c) indicating the item listing as a replacement item for the particular item. Indicating the item listing as a replacement item for the particular item comprises adding the item listing to an electronic shopping cart.