Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Systems and methods are described for linking records from different databases. A search may be performed for each record of a received record set for similar records based on having similar field values. Recommended records of the record set may be assigned with the identified similar records to sub-groups. Pairs of records may be formed for each record of the sub-group, and comparative and identifying features may be extracted from each field of the pairs of records. Then, a trained model may be applied to the differences to determine a similarity score. Cluster identifiers may be applied to records within each sub-group having similarity scores greater than a predetermined threshold. In response to a query for a requested record, all records having the same cluster identifier may be output on a graphical interface, allowing users to observe linked records for a person in the different databases.

Systems and methods are described for linking records from different databases. A search may be performed for each record of a received record set for similar records based on having similar field values. Recommended records of the record set may be assigned with the identified similar records to sub-groups. Pairs of records may be formed for each record of the sub-group, and comparative and identifying features may be extracted from each field of the pairs of records. Then, a trained model may be applied to the differences to determine a similarity score. Cluster identifiers may be applied to records within each sub-group having similarity scores greater than a predetermined threshold. In response to a query for a requested record, all records having the same cluster identifier may be output on a graphical interface, allowing users to observe linked records for a person in the different databases.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

Provided is a data amount sufficiency determination device capable of determining the sufficiency of the data amount of learning data with higher accuracy.

A data amount sufficiency determination device according to the present disclosure includes a time series data acquisition unit to acquire time series data, a data division unit to divide the time series data into a plurality of pieces of substring data, a data set generation unit to generate a plurality of substring data sets that are sets of substring data, a feature amount calculation unit to calculate a feature amount of the substring data, a probability distribution generation unit to generate probability distribution of the feature amount for each substring data sets, and a determination unit to determine whether or not the probability distribution has converged.

Provided is a data amount sufficiency determination device capable of determining the sufficiency of the data amount of learning data with higher accuracy.

A data amount sufficiency determination device according to the present disclosure includes a time series data acquisition unit to acquire time series data, a data division unit to divide the time series data into a plurality of pieces of substring data, a data set generation unit to generate a plurality of substring data sets that are sets of substring data, a feature amount calculation unit to calculate a feature amount of the substring data, a probability distribution generation unit to generate probability distribution of the feature amount for each substring data sets, and a determination unit to determine whether or not the probability distribution has converged.