Abstract: A system includes a user computing device with an application for removal of privacy data. The application obtains vehicle information associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle information, the application determines vehicle parameters associated with the target vehicle. The application obtains a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and presents the instruction set. A user experience feedback associated with the candidate in-vehicle devices is obtained and stored in a database.

Abstract: Generating, using artificial intelligence algorithms and a training dataset, at least one machine learning model that is configured to generate scores for multiple attributes of one or more personal data handling approaches associated with a vehicle and/or an in-vehicle unit of the vehicle that handles personal data of a user. The training dataset comprises labelled documents that define the personal data handling approaches, each labelled document having scores pre-assigned to one or more of the multiple attributes of the respective personal data handling approach. Determining one or more personal data handling approaches associated with a target vehicle linked to received vehicle identification information and at least one in-vehicle unit of the target vehicle that handles personal data of the user. Performing semantical analysis of the personal data handling approaches.

Abstract: Utilizing a user computing device to remove privacy information. The user computing device may obtain a vehicle identification number associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle identification number, the user computing device may obtain vehicle parameters associated with the target vehicle. The user computing device may obtain a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and may present the instruction set. The user computing device may obtain a user feedback experience. The user feedback experience may include a confirmation of removal of the privacy information from the at least one candidate in-vehicle device, user comments, a voice recording, or an image, captured by a camera of the user computing device, of the at least one candidate in-vehicle device.

Abstract: Generating, using artificial intelligence algorithms and a training dataset, at least one machine learning model that is configured to generate scores for multiple attributes of one or more personal data handling approaches associated with a vehicle and/or an in-vehicle unit of the vehicle that handles personal data of a user. The training dataset comprises labelled documents that define the personal data handling approaches, each labelled document having scores pre-assigned to one or more of the multiple attributes of the respective personal data handling approach. Determining one or more personal data handling approaches associated with a target vehicle linked to received vehicle identification information and at least one in-vehicle unit of the target vehicle that handles personal data of the user. Performing semantical analysis of the personal data handling approaches.

Abstract: Robust recognition systems to identify potentially identifying information that is contained within a dataset representing a system target are disclosed, together with related method. In an implementations, the systems and methods receive the dataset, identify features in the dataset that have a characteristic indicative of an expected feature, processes the identified features to yield a candidate feature, wherein the candidate feature is one feature contained within the identified features that has the highest probability of containing potentially identifying information.

Abstract: A system includes a user computing device with an application for removal of privacy data. The application obtains vehicle information associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle information, the application determines vehicle parameters associated with the target vehicle. The application obtains a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and presents the instruction set. A user experience feedback associated with the candidate in-vehicle devices is obtained and stored in a database.

Abstract: A method of translating instructions for operating electromechanical systems includes identifying an operating-language instruction from one or more operating-language instructions at least based on user information that includes a type of an electromechanical system and a desired operation. The method further includes determining whether one or more existing translated parameters are available to the computing device. The method also includes, in response to the one or more existing translated parameters being available to the computing device, identifying an existing translated parameter from among the one or more existing translated parameters at least based on a level of relevancy of the existing translated parameter to one or more user parameters, where the existing translated parameter has a highest level of relevancy to the one or more user parameters. The method also includes generating, by the computing device, a translated instruction from the operating-language instruction.

Abstract: A data privacy and security management system for a vehicle. A privacy server is configured to receive contextual data associated with a vehicle, an in-vehicle module of the vehicle, or a user of the vehicle. The privacy server receives privacy preference information that is representative of user-specific permission settings that define preferences of the user regarding handling of personal data of the user. Privacy action rules can be generated based at least in part on the privacy preference information or the contextual data. The privacy server can determine privacy risk events. For each privacy risk event, the privacy risk server can execute privacy actions, for example, context-based privacy actions, based at least in part on the privacy action rules.

Abstract: A system includes a user computing device with an application for removal of privacy data. The application obtains vehicle information associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle information, the application determines vehicle parameters associated with the target vehicle. The application obtains a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and presents the instruction set. A user experience feedback associated with the candidate in-vehicle devices is obtained and stored in a database.

Abstract: Generating, using artificial intelligence algorithms and a training dataset, at least one machine learning model that is configured to generate scores for multiple attributes of one or more personal data handling approaches associated with a vehicle and/or an in-vehicle unit of the vehicle that handles personal data of a user. The training dataset comprises labelled documents that define the personal data handling approaches, each labelled document having scores pre-assigned to one or more of the multiple attributes of the respective personal data handling approach. Determining one or more personal data handling approaches associated with a target vehicle linked to received vehicle identification information and at least one in-vehicle unit of the target vehicle that handles personal data of the user. Performing semantical analysis of the personal data handling approaches.

Abstract: A method includes utilizing a user computing device to remove privacy information. The user computing device may obtain a vehicle identification number associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle identification number, the user computing device may obtain vehicle parameters associated with the target vehicle. The user computing device may obtain a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and may present the instruction set. The user computing device may obtain a user feedback experience. The user feedback experience may include a confirmation of removal of the privacy information from the at least one candidate in-vehicle device, user comments, a voice recording, or an image, captured by a camera of the user computing device, of the at least one candidate in-vehicle device.

Abstract: Robust recognition systems to identify potentially identifying information that is contained within a dataset representing a system target are disclosed, together with related method. In an implementations, the systems and methods receive the dataset, identify features in the dataset that have a characteristic indicative of an expected feature, processes the identified features to yield a candidate feature, wherein the candidate feature is one feature contained within the identified features that has the highest probability of containing potentially identifying information.

Abstract: A system includes a server that is communicatively coupled to a computing device. The server receives vehicle information associated with a target vehicle that has a target in-vehicle module from which a privacy information of a user is to be removed. Using the vehicle information, the server retrieves and transmits data associated with removing privacy information from a set of candidate in-vehicle modules of vehicles that are related to the target vehicle. The computing device presents an instruction set associated with removing privacy information from the candidate in-vehicle module that corresponds to the selected image. The server receives user feedback associated with the instruction set.

Abstract: A system includes a user computing device with an application for removal of privacy data. The application obtains vehicle information associated with a target vehicle that has a target in-vehicle device from which privacy information of a user is to be removed. Using the vehicle information, the application determines vehicle parameters associated with the target vehicle. The application obtains a privacy information removal file comprising an instruction set associated with removing privacy data from candidate in-vehicle devices, and presents the instruction set. A user experience feedback associated with the candidate in-vehicle devices is obtained and stored in a database.

Abstract: A system includes a server that is communicatively coupled to a computing device. The server receives vehicle information associated with a target vehicle that has a target in-vehicle module from which a privacy information of a user is to be removed. Using the vehicle information, the server retrieves and transmits data associated with removing privacy information from a set of candidate in-vehicle modules of vehicles that are related to the target vehicle. The computing device presents an instruction set associated with removing privacy information from the candidate in-vehicle module that corresponds to the selected image. The server receives user feedback associated with the instruction set.

Abstract: Systems and methods to prevent a recognition system from identifying one or more features contained in a dataset that are associated with potentially identifying information is disclosed. The dataset is received by the recognition system and includes information representing a system target, the recognition system is programmed to identify features in the dataset that are likely to be associated with potentially identifying information by comparing features contained in the dataset against features expected by the recognition system, wherein, upon location of features in the dataset that are similar to the features expected by the recognition system, the recognition system seeks to ascertains information associated therewith in an effort to obtain the potentially identifying information.

Abstract: A system includes a server that is communicatively coupled to a computing device. The server receives vehicle information associated with a target vehicle that has a target in-vehicle module from which a privacy information of a user is to be removed. Using the vehicle information, the server retrieves and transmits data associated with removing privacy information from a set of candidate in-vehicle modules of vehicles that are related to the target vehicle. The computing device presents images associated with the candidate in-vehicle modules in a likely order of resemblance of the candidate in-vehicle modules to the target in-vehicle module. The user selects an image of a candidate in-vehicle module that matches or resembles the target in-vehicle module. The computing device presents an instruction set associated with removing privacy information from the candidate in-vehicle module that corresponds to the selected image. The server receives user feedback associated with the instruction set.

Abstract: A system includes a first device that is communicatively coupled to and synchronized with a second device such that the second device stores privacy information of a user that is contained in a data file of the first device. Upon receiving a request to remove the privacy information of the user from the second device, the first device creates and stores a backup copy of the data file. Then, the first device deletes the data file from the first device. Further, a replacement file for the data file is created. The replacement file comprises non-sensitive information. The replacement file is uploaded to the second device to overwrite the privacy information with the non-sensitive information. Then, a radio frequency communication between the first device and the second device is disabled and the data file is restored using the backup copy.

Abstract: A portable computing device equipped with at least one image capture device and/or a light source captures an image (or a video) of a portion of a surface of interest having the damage that is exposed to a light from the light source. The portable computing device converts the image to an output image that highlights the damage. If the damage is a dent, the image is converted to a false color image using a saliency algorithm. If the damage is a scratch, the image is converted to a colorspace stretched image using color stretching algorithms. The size of the damage is determined by capturing an image of a ruler placed adjacent to the damage and the portion of surface of interest having the damage. The ruler is then removed from the image. The resulting image is converted to the output image. The ruler is added to the output image.

Abstract: A portable computing device equipped with an image capture device captures an image of a vehicle dashboard of a vehicle. Then, the portable computing device identifies the location of one or more components of the vehicle dashboard in the captured image. Based on the location of the one or more components, the portable computing device segments the captured image to obtain an image of each of the one or more components. Further, the portable computing device processes the images of the one or more components using one or more machine learning models to determine a reading associated with each of the one or more components. An accuracy of the readings is verified and responsively, the portable computing device inputs the readings in respective data fields of an electronic form. The readings associated with the one or more components of the vehicle dashboard represent data associated with the vehicle.