Abstract: The present invention provides a robust and effective solution to an entity or an organization for creating and standardizing an Automotive Data Quality Marker (ADQM) to determine/evaluate/predict the quality of automotive data such as Telematics, Body Control, ADAS, Diagnostics, Dashcams, and In-Vehicle Infotainment but not limited to the like generated by the vehicle (i.e., data source) using a machine learning (ML) engine associated with a processing unit. The machine learning engine comprises an amalgamation of machine learning algorithms to determine ADQM for a particular dataset. Data pertaining to vehicles is huge and repetitive. Re-training of the model for improved accuracy is a requirement as automotive data can be augmented with additional signals and data received and stored as trip objects on a regular basis.

Abstract: The present invention provides a robust and effective solution to an entity or an organization by allowing to visualize the flow of automotive data signals from a source system to a sink system with different metric signals at each step and creating a mechanism for creating automotive signal quality marker (ASQM) for high quality automotive data signals by determining high value data parameters, inter-parameter correlation, data quality variation with trip duration, and frequency of data collection/distribution across datasets. The quality criterion thus developed is applied to data generated from a plurality of data sources. The ASQM may be calculated by converting the developed automotive data signals quality criterion into a numeric value on a specified scale. It can be used by all stakeholders in the value chain for consistent measurement of automotive data signals quality.

Abstract: The present disclosure provides a system and a method facilitating optimized distribution of automotive signals across various consumers and providers. The system provides real-time data distribution to consumers, where the data is collected from a plurality of sources within a vehicle as well as different data providers. The system creates a profile pyramid, where basic signals in the incoming data are mapped to a base profile of the pyramid and includes additional and more advanced signals. The signal definition in each layer of the pyramid is based on common ontology and is agnostic of the signals defined by data providers. Each data consumer is tagged to a profile layer within the profile pyramid. A distributor distributes the data at a chosen frequency and sends multiple signals to different consumers without maintaining multiple copies of the configuration or incoming data.

Abstract: A portable media content device for providing media content to remote devices external to a home network, the portable media content device may include a processor in communication with a memory configured to maintain media content, and a transceiver. The processor may be configured to recognize a home network provided at a first location, instruct the transceiver to transmit a media request to an in-home media recorder connected to the home network and at the first location, receive, in response to the media request, media content from the media recorder and to store the media content in the memory.

Abstract: The present invention provides a robust and effective solution to an entity or an organization by allowing aggregation of a set of automotive data packets pertaining to single trip using one or more common identifiers and one or more timestamps to enable enhanced data analytics and data correlation. The set of automotive data packets may be then segregated based on a plurality of sensor signals and a plurality of calculated signals to obtain a set of derived signals. The set of derived signals can provide an option of moving calculation of derived signals outside of vehicle to reduce computation cost on the vehicle. The size of the data packets may be optimized by sending the plurality of sensor signals outside of minimum signal set, only when one or more values change from the last stored value.

Abstract: The present disclosure relates to a system (102) for managing bulk consent, the system includes a processor (202) operatively coupled to a memory, the memory storing instructions executable by the processor to receive, from a first entity (108), a bulk consent request for one or more vehicles, wherein the bulk consent request comprises a plurality of categories of automotive data, each category comprises a set of parameters associated with corresponding one or more vehicles, indicate, by the first entity (108), a set of control values associated with corresponding one or more vehicles, the set of control values control access to the set of parameters, receive, by a second entity (110), the bulk consent request from the first entity through a distributor (104) and modify the set of parameters based on the set of control values specified by the first entity in the bulk consent request.

Abstract: The present invention provides a robust and effective solution to an entity or an organization for creating and standardizing an Automotive Data Quality Marker (ADQM) to determine/evaluate/predict the quality of automotive data such as Telematics, Body Control, ADAS, Diagnostics, Dashcams, and In-Vehicle Infotainment but not limited to the like generated by the vehicle (i.e., data source) using a machine learning (ML) engine associated with a processing unit. The machine learning engine comprises an amalgamation of machine learning algorithms to determine ADQM for a particular dataset. Data pertaining to vehicles is huge and repetitive. Re-training of the model for improved accuracy is a requirement as automotive data can be augmented with additional signals and data received and stored as trip objects on a regular basis.

Abstract: The present invention provides a robust and effective solution to an entity or an organization by allowing to visualize the flow of automotive data signals from a source system to a sink system with different metric signals at each step and creating a mechanism for creating automotive signal quality marker (ASQM) for high quality automotive data signals by determining high value data parameters, inter-parameter correlation, data quality variation with trip duration, and frequency of data collection/distribution across datasets. The quality criterion thus developed is applied to data generated from a plurality of data sources. The ASQM may be calculated by converting the developed automotive data signals quality criterion into a numeric value on a specified scale. It can be used by all stakeholders in the value chain for consistent measurement of automotive data signals quality.

Abstract: A portable media content device for providing media content to remote devices external to a home network, the portable media content device may include a processor in communication with a memory configured to maintain media content, and a transceiver. The processor may be configured to recognize a home network provided at a first location, instruct the transceiver to transmit a media request to an in-home media recorder connected to the home network and at the first location, receive, in response to the media request, media content from the media recorder and to store the media content in the memory.

Abstract: A data collator is useful in a scenario where multiple entities/applications, which are unaware of the data processing of each other, want to collate their data before passing it to the external entity. Each entity informs the data collator about its dependency for a given common reference value on other entities in the system while storing the data generated with the data collator. The data generated and the dependency data form an input stream into the data collator which then stores the data generated while verifying the dependencies. The data collator then delivers the complete generated data values as a bunch to the internal/external receiving/requesting entity when the required dependencies are satisfied. Thus, a complete match is attained before the data generated is transmitted to the receiving/requesting addressee by the data collator.

Abstract: The present invention relates to a data collator useful in a scenario where multiple entities/applications, which are unaware of the data processing of each other, want to collate their data before passing it to the external entity. Each entity informs the data collator about its dependency for a given common reference value on other entities in the system while storing the data generated with the data collator. The data generated and the dependency data form an input stream into the data collator which then stores the data generated while verifying the dependencies. The data collator then delivers the complete generated data values as a bunch to the internal/external receiving/requesting entity when the required dependencies are satisfied. Thus, a complete match is attained before the data generated is transmitted to the receiving/requesting addressee by the data collator.