31results about How to "Effective and efficient and scalable and convenient solution" patented technology

A vehicle computing platform may receive driver sensor data indicating whether a driver seat in a vehicle is occupied. The vehicle computing platform may determine, based on the driver sensor data, an identity of a driver of the vehicle. The vehicle computing platform may receive passenger sensor data indicating whether a passenger seat in the vehicle is occupied. The vehicle computing platform may, based on the passenger sensor data indicating that the passenger seat in the vehicle is occupied, obscure information on a screen of the vehicle.

A system for creating a synthetic voice identifier may include a plurality of synthesized voice authorization (SVA) devices and a biometric combinatory device (BCD). The SVAs may be communicatively coupled to the BCD via a network and may communicate utilizing a markup language. The SVA devices may capture an audio signal of a voice of a user, modify the audio signal with a randomized audio frequency signal to generate a modified audio signal, and communicate, the modified audio signal as a synthesized voice signal associated with the user. The BCD may receive biometric information corresponding to a user, the biometric information comprising at least audio information associated with a voice of the user, receive, at an integration module, location information corresponding to a location of the user, combine, the location information and audio signal information associated with the user to generate a synthesized voice identifier associated with the user, and communicate the synthesized voice identifier to a remote device for use in an authentication process of the user.

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.

Aspects of the disclosure relate to using vehicle telematics data to assess parameters associated with vehicle operation. In some instances, a driving assessment system may include a first computing device associated with a user in a vehicle and a second computing located remotely from the first computing device. The first computing device may collect, by way of one or more of an accelerometer and global positioning system (GPS), vehicle operational data and vehicle locational information associated with the vehicle and corresponding to a trip of the vehicle and may transmit such information to the second computing device. The second computing device may identify actionable and second order actionable trip data from the vehicle operational data and vehicle locational information and may calculate a behavior score for the trip.

Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and / or multilevel platforms can request, receive, and / or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. The big data dataset can be uncompressed based on a codec and uncompressed data blocks can be distributed for processing. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and / or another computer machine and can be decrypted back into the sanitized dataset.

A system for creating a synthetic voice identifier may include a plurality of synthesized voice authorization (SVA) devices and a biometric combinatory device (BCD). The SVAs may be communicatively coupled to the BCD via a network and may communicate utilizing a markup language. The SVA devices may capture an audio signal of a voice of a user, modify the audio signal with a randomized audio frequency signal to generate a modified audio signal, and communicate, the modified audio signal as a synthesized voice signal associated with the user. The BCD may receive biometric information corresponding to a user, the biometric information comprising at least audio information associated with a voice of the user, receive, at an integration module, location information corresponding to a location of the user, combine, the location information and audio signal information associated with the user to generate a synthesized voice identifier associated with the user, and communicate the synthesized voice identifier to a remote device for use in an authentication process of the user.

Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and / or multilevel platforms can request, receive, and / or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. State point information for previously reached safe points can be stored and progressively released such that only the incomplete portion(s) of task(s) need to be resubmitted. The encrypted data can be transmitted, in response to the request, to a source, a target, and / or another computer machine and can be decrypted back into the sanitized dataset.

Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and / or multilevel platforms can request, receive, and / or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. Crashed executor(s) can be detected and caged to prevent further use. Uncompleted task(s) for crashed executor(s) can be reassigned. The encrypted data can be transmitted, in response to the request, to a source, a target, and / or another computer machine and can be decrypted back into the sanitized dataset.

A highway detection system may include a telematics device associated with a vehicle having one or more sensors arranged therein, a mobile device associated with a user traveling in the vehicle, and a server computer. The server computer may receive traveling data for a trip of the user from the one or more sensors and via the telematics device. The server computer may then determine whether the user is traveling within a city or on a highway based on analysis of the traveling data for the trip of the user, which may include a statistical analysis to calculate standard deviations of metrics in the traveling data. In response to the determination, the server computer may generate a notification to transmit to the user based on whether the user is traveling within a city or on a highway and transmit the notification to the mobile device associated with the user.

Aspects of the disclosure relate to resource allocation and rebating during in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and / or multilevel platforms can request, receive, and / or authenticate requests for a big data dataset, containing sensitive and non-sensitive data. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. Secure connection(s) to the data store can be established. Sensitive information can be redacted into a sanitized dataset based on one or more data obfuscation types. RAM requirements and current RAM allocation can be diagnosed. Portion(s) of the current RAM allocation exceeding the RAM requirements can be rebated. The encrypted data can be transmitted, in response to the request, to a source, a target, and / or another computer machine and can be decrypted back into the sanitized dataset.

Aspects of the disclosure relate to in-flight data masking and on-demand encryption of big data on a network. Computer machine(s), cluster managers, nodes, and / or multilevel platforms can request, receive, and / or authenticate requests for a big data dataset, containing sensitive and non-sensitive data, in a data store based on credentials received from a source. Profiles can be auto provisioned, and access rights can be assigned. Server configuration and data connection properties can be defined. A secure connection to the data store can be established. The sensitive information in the big data dataset can be redacted into a sanitized dataset based on one or more data obfuscation types. The encrypted data can be transmitted, in response to the request, to a source, a target, and / or another computer machine and can be decrypted back into the sanitized dataset.

Aspects of the disclosure relate to information security by identifying unique or related factors in common between individuals subject to a common threat vector. Data mining and data acquisition of public and non-public user information is performed to prevent, disrupt, and / or address criminal, cyber, and fraudulent threats. The information can be normalized into template(s) to align information across disparate datasets and enable efficient storage of the big data into appropriate fields to be tracked. The information can be stored in data warehouse(s) or in multidimensional data structure(s) for investigation if a threat vector against a group of individuals is detected. The multidimensional data can be analyzed to identify direct connections, common connecting entities, and / or connectivity clusters between individuals who were attacked or who may be attacked in the future. Remediation, machine learning, enhanced security, and / or vulnerability assessments may be implemented based on the results of the analysis.