System, method, and apparatus for robust policy creation and implemenation

Abstract

A system including a policy builder circuit structured to implement a Graphical User Interface (GUI) for creating a policy, where the policy includes at least one of an action definition for a controller of a vehicle or a data collection definition for a controller of a vehicle; determining an initial policy of the policy in response to user interactions with the GUI; and providing the initial policy to a policy implementation manager.

Description

Attorney Docket No. SONA-0033-WOSYSTEM, METHOD, AND APPARATUS FOR ROBUST POLICY CREATION AND IMPLEMENATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of U.S. Provisional Patent Application No. 63 / 726,954, filed on 2 DEC 2024, and entitled “SYSTEM, METHOD, AND APPARATUS FOR ROBUST POLICY CREATION AND IMPLEMENTATION’’ (SONA-0033-P01). The present application claims the benefit of U.S. Provisional Patent Application No. 63 / 742,823, filed on 7 JAN 2025, and entitled “ARTIFICAL INTELLIGENCE APPLICATIONS ON A VEHICLE” (SONA- 0036-P01).

[0002] Each one of the foregoing applications is incorporated herein by reference in the entirety for all purposes.SUMMARY

[0003] In some aspects, the techniques described herein relate to a system including: a policy builder circuit structured to implement a GUI for creating a policy, the policy including at least one of: an action definition for a controller of a vehicle; or a data collection definition for a controller of a vehicle; determining an initial policy of the policy in response to user interactions with the GUI; and providing the initial policy to a policy implementation manager.

[0004] In some aspects, the techniques described herein relate to a system, further including: wherein the initial policy includes standardized data values; and wherein the policy implementation manager is structured to generate a refined policy in response to the initial policy, wherein the refined policy includes vehicle specific data values.

[0005] In some aspects, the techniques described herein relate to a system, wherein the policy implementation manager is further structured to communicate the refined policy to the vehicle, and to confirm the vehicle utilizes the refined policy.

[0006] In some aspects, the techniques described herein relate to a system, wherein the policy implementation manager is further structured to store the initial policy as a template in a policy library, and wherein the policy library is accessible to the policy builder circuit.

[0007] In some aspects, the techniques described herein relate to a system, wherein the policy builder circuit is further structured to provide at least one policy of the policy library to the GUI in response to the user interactions with the GUI.

[0008] In some aspects, the techniques described herein relate to a system, wherein the initial policy includes a policy compatible with a selected group of vehicles.

[0009] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a make and model.Attorney Docket No. SONA-0033-WO

[0010] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a make, model, and year.

[0011] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles including a fleet of vehicles.

[0012] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a common application.

[0013] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a common flow.

[0014] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a common software version for an end point of each vehicle.

[0015] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles associated with a common hardware component.

[0016] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles associated with a common original equipment manufacturer.

[0017] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles associated with a common dealer.

[0018] In some aspects, the techniques described herein relate to a system, wherein the selected group of vehicles includes a group of vehicles sharing a common mission.

[0019] In some aspects, the techniques described herein relate to a system, wherein the common mission includes at least one mission selected from: an emergency vehicle mission; a delivery mission; or a rental vehicle mission.

[0020] In some aspects, the techniques described herein relate to a system, wherein the selected group of the vehicle includes a group of vehicles having a common vehicle operating condition.

[0021] In some aspects, the techniques described herein relate to a system, wherein the common vehicle operating condition includes at least one condition selected from: a fault code condition; a diagnostic condition: a duty cycle condition; or a recall condition.

[0022] In some aspects, the techniques described herein relate to a system, wherein the policy implementation manager is further structured to detect an anomalous event, and to provide an alert to a user in response to the anomalous event.

[0023] In some aspects, the techniques described herein relate to a system, wherein the anomalous event includes an event detected by operation of the refined policy on the vehicle.Attorney Docket No. SONA-0033-WO

[0024] In some aspects, the techniques described herein relate to a system, wherein the user includes at least one of: a service entity; a fleet entity: a user providing the user interactions with the GUI; a manufacturer of the vehicle; or an owner of the vehicle.

[0025] In some aspects, the techniques described herein relate to a system, wherein the anomalous event includes a detected failure of the vehicle to install the refined policy.

[0026] In some aspects, the techniques described herein relate to a system, wherein the anomalous event includes a detected failure of the vehicle to execute the refined policy.

[0027] In some aspects, the techniques described herein relate to a method, including: implementing a GUI for building a policy, the policy including at least one of an action definition for a controller of a vehicle, or a data collection definition for the controller of the vehicle: interpreting user communications on the GUI to determine an initial policy, the initial policy compatible with a selected group of vehicles; adjusting the initial policy to a refined policy compatible with a specific vehicle; communicating the refined policy to the specific vehicle; and confirming the utilization of the refined policy by the specific vehicle.

[0028] In some aspects, the techniques described herein relate to a method, wherein at least a portion of the user communications include natural language communications from the user.

[0029] In some aspects, the techniques described herein relate to a method, further including determining at least one appropriate template policy from a policy library in response to the natural language communications, and providing the at least one appropriate template policy to the GUI.

[0030] In some aspects, the techniques described herein relate to a method, further including using one of the appropriate template policies as a starting point for the user in response to a user selection of one of the at least one appropriate template policies.

[0031] In some aspects, the techniques described herein relate to a method, further including providing the user with a list of available data values in response to the natural language communications.

[0032] In some aspects, the techniques described herein relate to a method, further including providing the user with a list of alternative data values in response to the natural language communicarions, and further in response to determining that user indicated data is not available to the user.

[0033] In some aspects, the techniques described herein relate to a method, further including storing the initial policy in a policy library.BRIEF DESCRIPTION OF THE FIGURES

[0034] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:Attorney Docket No. SONA-0033-WO

[0035] Fig. 1 depicts an example system schematically representing aspects of a vehicle having various components.

[0036] Fig. 2 depicts an example embodiment of an Al model component for a vehicle.

[0037] Fig. 3 depicts an example of a vehicle system having an Al model component for optimizing vehicle diagnostics.

[0038] Fig. 4 depicts a non-limiting outline of supporting operations for Al applications, Al pipeline, and Al at the edge.

[0039] Fig. 5 depicts an example system for supporting a natural language interface for any aspect of a cloud platform.

[0040] Fig. 6 depicts an example system for operations of a model improvement engine.

[0041] Fig. 7 depicts an example cloud platform including a policy builder that facilitates the building and implementation of a policy for use on a vehicle.

[0042] Fig. 8 depicts an example system for building, creating, implementing, monitoring, and / or adjusting policies, technician assistance, and / or models or algorithms on a vehicle or other mobile application.

[0043] Fig. 9 depicts an example system including a policy builder circuit.

[0044] Fig. 10 depicts an example system for a policy implementation manager to generate a refined policy and publish the refined policy to the vehicle.

[0045] Fig. 11 depicts an example system for a policy implementation manager configured to send an alert upon detection of an anomalous event.

[0046] Fig. 12 depicts an example system including a number of functions available to deploy, execute, and / or monitor operations using a policy, to collect data, to perform actions, to perform diagnostic operations on a vehicle.

[0047] Fig. 13 depicts an embodiment for control interfaces for communication with the vehicle and / or to the vehicle through a cloud.

[0048] Fig. 14 depicts an example interface for allowing a user to select desired operations related to building and deploying a policy, a technician, and / or a model / algorithm.

[0049] Fig. 15 depicts an example interface for a user selecting to build an on demand policy that allows for data collection and / or other tasks to be performed.

[0050] Fig. 16 depicts an example interface for a user selecting a new task based on a status.

[0051] In Figs. 17-18 depict example interfaces for a user selecting data to capture when a condition is met.

[0052] Fig. 19 depicts an example interface including an Al assistant to help build a policy.Attorney Docket No. SONA-0033-WO

[0053] Fig. 20 depicts an example interface for a user naming a policy and start conditions for determining the value of a sensor on the vehicle.

[0054] Fig. 21 depicts an example interface for a user invoking the Al assistant.DETAILED DESCRIPTION

[0055] For the purposes of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiments illustrated in the drawings and described in the following written specification. It is understood that no limitation to the scope of the disclosure is thereby intended. It is further understood that the present disclosure includes any alterations and modifications to the illustrated embodiments and includes further applications of the principles disclosed herein as would normally occur to one skilled in the art to which this disclosure pertains.

[0056] Certain aspects of the present disclosure include adjusting the routing of communications on a network, whether between separate devices on the network, or between a device on the network and an external device. Certain aspects of the present disclosure include applying configurations and / or policies to controllers of the vehicle, facilitating communication between end points of the vehicle, including between end points on different networks or different network zones, and between end points that utilize distinct data formatting, data rates, communication protocols, or the like. Without limitation to any aspect of the present disclosure, some tools that can be utilized to tactically implement certain operations herein, in combination with the present disclosure, and descriptions that can enhance understanding of some of the terminology used herein (e.g., policy, end point, external device, network protocol, network type, etc.) can be found in one or more of the following U.S. Patents or Patent Applications: US application 17 / 027,167, filed 21 SEP 2020, and entitled SYSTEM, METHOD, AND APPARATUS TO SUPPORT MIXED NETWORK COMMUNICATIONS ON A VEHICLE (SONA-0006-U01); US application 17 / 027,187, filed 21 SEP 2020, and entitled SYSTEM, METHOD, AND APPARATUS TO EXTRA VEHICLE COMMUNICATIONS CONTROL (SONA-0007-U01); US application 17 / 195,589, filed 8 MAR 2021, and entitled SYSTEM, METHOD, AND APPARATUS FOR MANAGING VEHICLE DATA COLLECTION (SONA-0010-U01); US application 17 / 833,614, filed 6 JUN 2022, and entitled SYSTEM, METHOD, AND APPARATUS FOR MANAGING VEHICLE DATA COLLECTION (SONA-0012-U01); and / or US application 18 / 244,147, filed 8 SEP 2023, and entitled SYSTEM, METHOD, AND APPARATUS TO EXECUTE VEHICLE COMMUNICATIONS USING A ZONAL ARCHITECTURE (SONA-0015-U01), each of which is incorporated herein by reference in the entirety for all purposes.

[0057] A policy, as utilized herein, includes a description of data to be collected, such as data parameters, collection rates, resolution information, priority values (e.g., ordering data collectionAttorney Docket No. SONA-0033-WO values for selection in response to off-nominal conditions where not all data collection parameters can be serviced, etc.). In certain embodiments, a policy further includes event information, which may be stipulated as parameter or quantitative based events (e.g., a given data value exceeds a threshold, etc.), and / or categorical events (e.g., a particular fault code, operational condition or state, or vehicle location / jurisdiction occurs). In certain embodiments, a policy further includes an event response, such as data values to be captured in response to the occurrence of the event, and / or other changes in the data collection scheme such as increased or reduced data collection rates, changes in collected resolution, or the like. In certain embodiments, an event response further includes a time frame associated with the event occurrence, for example a time period after the event occurrence to utilize the adjusted data collection scheme, and / or a time period preceding the event occurrence (e.g., utilizing a rolling buffer or other data collection operation, providing temporary information that can subsequently be captured if the event occurs). In certain embodiments, changes to the data collection scheme for an event can include multiple changes - for example changes over a period of time, further changes based upon the progression of the event (e.g., if the event severity gets worse), and / or criteria to determine that an event is cleared. In certain embodiments, changes to a data collection scheme may be implemented based on event related clearance of the same or another event, for example implementing a data collection change until a next shutdown event of the vehicle, until a service technician clears the event, for a selected number of shutdown events occurs, or the like.

[0058] The utilization of a policy herein may reference a partial policy, for example the implied policy that would be implemented in response to a single data collection scheme from a single user, wherein the full policy is prepared, verified, and communicated to the vehicle after one or more partial policies are aggregated. The utilization of a policy herein may reference an unverified policy, for example after a policy responsive to a number of users is aggregated, but verification operations of the policy are not yet completed (e.g., before it is determined if the data collection implied by the policy can be performed). The utilization of a policy herein may reference a previously applied policy (e.g., a policy present on a vehicle before an updated version of the policy is communicated to the vehicle and / or implemented on the vehicle). The utilization of a policy herein may reference an updated policy, for example a verified policy that is pending for communication to the vehicle and / or confirmed by the vehicle (e.g., from the data collection controller). The utilization of a policy herein may reference an initial policy or a refined policy.

[0059] Example embodiments herein provide for systems and methods that facilitate rapid creation of a policy for a user to be implemented on a vehicle, where the policy can be utilized to collect selected data, perform automated operations on the vehicle, control communication and / or dataAttorney Docket No. SONA-0033-WO permissions between applications, end points, flows, or the like on the vehicle, perform diagnostics, or the like. In certain embodiments, operations herein to perform policy creation can readily be completed within a few minutes, compared to hours or days for previously known systems. Additionally, systems and / or operations to create a policy herein allow for a user of any type, including non-technical users, to create and deploy a fully functioning policy for the vehicle without requiring that the user have specific knowledge about the vehicle, such as available end points and / or end point locations on the vehicle, the specific naming and properties of data values on the vehicle, or the like. Embodiments herein allow for the system to generate recommendations for the user, and / or to extend initial thoughts from the user, into a fully formed policy to achieve the user’s goals. Embodiments herein allow for the user to build a policy in one operation that is usable on any vehicle in scope for the policy, including across vehicles that have varying network configurations, end points, applications on the vehicle, different hardware and / or versions of hardware, or the like, without requiring that the user configure the policy for each vehicle variation or configuration. Example embodiments may be implemented using a configured interface, and / or by exposing capabilities of the system to an application programming interface (API).

[0060] Artificial intelligence (Al) systems are utilized to connect, control, and interpret data from sensors, processors, controllers, and communication devices throughout a vehicle and systems outside of a vehicle. Recent trends have been increasing the burden on the Al models, with more data availability and more controllable elements.

[0061] Traditional vehicle systems suffer from a number of drawbacks and challenges and are not able to efficiently integrate Al models and their functions. These systems have been developed to meet the specific challenges of diverse vehicle requirements and have accordingly developed separately from Al systems.

[0062] As the number of devices (sensors, actuators, interfaces, etc.) and the data rate demand from these devices increases, traditional vehicle systems may not be able to adapt and adequately to enable new features.

[0063] Al systems comprise various interconnected components and elements that work together to perform intelligent tasks in a vehicle setting. Al systems may include multiple data sources (e.g., sensors, radar, cameras), data processing units (e.g., on board GPUs), communication networks (e.g., CAN bus, Wifi), algorithms / models (e.g, computer vision models, language models), user interfaces, training infrastructure, security and privacy mechanisms, optimization tools (e.g., resource management systems, tools to optimize parameters), and the like. By integrating these elements effectively, an Al system can perform complex tasks, adapt to new data, and provide valuable insights or actions related to vehicle control, maintenance, customization, and the like.Attorney Docket No. SONA-0033-WO

[0064] Integrating Al into a vehicle presents several challenges due to the complexity and demands of the automotive environment. The requirements of the components of a vehicle Al system are often different from the requirements of the components of a traditional vehicle system. Likewise, performance and reliability requirements of vehicle applications require different Al system techniques and components than Al systems in other consumer applications. The use of Al systems in vehicles requires special consideration for vehicle systems and special considerations for Al systems.

[0065] For example, the computational power required for Al system to process data is substantial and requires advanced hardware that can operate efficiently within the limited space and power constraints of a vehicle. Additionally, vehicles operate in diverse and often unpredictable environments. Al systems must be robust enough to handle a wide range of scenarios, including adverse weather conditions, high temperatures, and dynamic road situations.

[0066] In another example, the use of Al systems includes significant challenges in the integration with existing systems. Vehicles already contain complex electronic and communication systems, such as CAN and LIN networks. Integrating Al systems with these legacy systems can be challenging due to differences in data protocols, speeds, and architectures. Furthermore, Al systems must comply with numerous existing vehicle regulations, which can vary significantly between regions and countries.

[0067] In another example, the Integration of Al into vehicles also raises data privacy and security concerns. Vehicles equipped with Al systems collect and process large amounts of data, some of which may be sensitive or personally identifiable. The Al models and many of the peripheral vehicle systems require additional security measures to protect against hacking and unauthorized access when Al systems are used.

[0068] In another example, unlike traditional vehicle systems, Al models may need to be frequently updated with new data to improve their performance and adapt to changing environments.Implementing a reliable and secure mechanism while preventing new vulnerabilities or affecting the stability of the system requires careful system design.

[0069] Integrating Al into vehicles, therefore, requires addressing many technical challenges with respect to integration with existing vehicle systems, Al model deployment, and adaptations of Al models to vehicle applications.

[0070] Referencing Fig. 1, an example system schematically depicts aspects of embodiments of the present disclosure. The example system includes a vehicle 102 having various components that may include control elements 104 (e.g., controllers, computing devices, actuators, etc.) and sensors 108. The vehicle 102 may include Al model components 106. The Al model components 106, controlAttorney Docket No. SONA-0033-WO elements 104, and sensors 108 interconnect via one or more networks. A network, as utilized herein, should be understood broadly and may include one or more aspects such as wired or wireless networks and may include a Controller Area Network (CAN), a Media Oriented Systems Transport (MOST) network, a Local Interconnect Network (LIN), a FlexRay network, Wifi, and the like.

[0071] Although the figure depicts control elements 104, sensors 108, and Al model components 106 as isolated and distinct elements, a vehicle may include a plurality of different control elements 104, sensors 108, and Al model components 106 that are distributed throughout the vehicle 102. Control elements 104, sensors 108, and / or Al model components 106 may include shared or separate hardware and may include joint functionality. In one example, a sensor may include actuating functionality. In another example, the sensor may include integrated Al models to identify and classify sensor readings before they are distributed to other systems of the vehicle.

[0072] The control elements 104, sensors 108, and Al model components 106 may interact, exchange data, share computing tasks, and the like to enable various vehicle sensing, control, and / or communication applications. An application, as utilized herein, should be understood broadly. An example application includes a group of related vehicle functions or operations, for example speed control (e.g., of the vehicle, or a sub-component of the vehicle such as an engine or a driveline), anti- lock brake system (ABS) operations, an advanced driver-assistance system (ADAS), performance control (e.g., achieving a torque request, speed request, or other performance request from an operator), or other function of the vehicle. An example application includes a group of related functions apart from the vehicle, such as an application to support geolocation and / or navigation, to request and / or process service information about the vehicle, and / or a third-party application interacting with the operator (e.g., to find the nearest hotel, selected event, etc.). Applications may be implemented by the vehicle manufacturer, a supplier, an original equipment manufacturer, a body builder, a third party, the operator, service personnel, or the like. Applications, as used herein, provide an organizing concept that may be utilized to relate certain data, certain end points, and / or related functions of the vehicle.

[0073] The control elements 104, sensors 108, and Al model components 106 may interact, exchange data, share computing tasks, and the like to enable a group of applications or a service group. A service group, as utilized herein, should be understood broadly. An example service group includes a related group of applications for the vehicle. The related group of applications may be entirely positioned on the vehicle (e.g., one or more vehicle systems, functions, or other applications of the vehicle), and / or may include aspects that are positioned on external devices (e.g., with supporting processing, data collection or storage, externally sourced data used by the service group, etc.) which may be a web application, web tool, cloud application, service application, or the like.Attorney Docket No. SONA-0033-WO

[0074] The Al model components 106, control elements 104, and / or sensors 108 may interact in a coordinated manner to perform intelligent tasks efficiently. The interactions may include the flow of data. Sensors and data sources may collect data from the environment, vehicle status, operation characteristics, user devices, external systems, and the like, which is then transmitted via networks to Al components (e.g., data processing units, Al models). These components, may perform initial data cleaning and preprocessing to ensure the data is usable for further analysis. The cleaned data may then be fed into machine learning algorithms / models of the Al components, which analyze the data, identify patterns, make predictions, initiate or control vehicle applications, and the like. In autonomous vehicles, for instance, these interactions enable the vehicle to navigate safely and efficiently. Sensors such as Lidar and cameras continuously collect data about the vehicle's surroundings. This data is processed in real-time by on-board GPUs and analyzed by computer vision algorithms to detect obstacles and plan the vehicle's path. The outputs of the Al models may be post-processed by Al components to translate the outputs into communications or commands that are used to control or alert systems of the vehicle (e.g., braking, steering).

[0075] In certain embodiments, Al models may be implemented as a model, for example to determine set points, perform virtual sensing operations, and / or to determine any one or more of various vehicle states and / or environmental states for the vehicle (e.g., traffic, following distance, the presence of objects, a speed limit, an unusual traffic pattern, determining a likely destination for the operator, etc.). In certain embodiments, Al models may not utilize Al in the model at runtime, for example a model that benefits from Al determination of effective parameters and / or values for those parameters, relevant ranges for inputs and / or outputs, or the like. In certain embodiments, for example where the model on the vehicle does not utilize Al at runtime, the model may nevertheless be updated from operations of an Al application (e.g., reference Fig. 4) and / or operations of a model improvement engine 606 (e.g., reference Fig. 6), that may utilize Al for one or more aspects of the improvement operations.

[0076] An example system may further include elements for deploying and managing Al models on a vehicle. The system may include one or more remote systems 114 (e.g., servers, cloud services) and / or user control interfaces 110 (e.g., implemented on a personal computing device, laptop, and / or mobile device) that may connect to the vehicle via one or more wired or wireless networks 112. The remote systems 114 and control interfaces 110 may include components for configuring Al models for deployment for specific vehicles and / or vehicle applications. The Al model components 106 on vehicle 102 may include components for testing, staging, and / or modifying Al models for deployment. The example control interfaces 110 may communicate directly with the vehicle 102, and / or may communicate to the vehicle 102 through a cloud server (e.g., remote system 114) thatAttorney Docket No. SONA-0033-WO may host one or more aspects of a policy builder (e.g., Al policy creator of Fig. 13), a model builder (e.g., Al model trainer of Fig. 13), and / or an Al technician builder (e.g., reference Fig. 13), and / or implementation and / or monitoring devices for managing policies, Al models, and / or Al technicians as set forth throughout the present disclosure.

[0077] The remote systems 114 and user control interfaces 110 may facilitate a training infrastructure. Large datasets may be used to train the models. Once trained, the models can be deployed to vehicle 102 where they can make real-time decisions based on new data inputs.

[0078] Security and privacy mechanisms may be enabled on the vehicle and / or remote systems to ensure that the data and models are protected. Security may include encryption methods, access control systems, feedback loops to monitor the system's activity, and the like. Monitoring tools may be deployed to detect anomalies from errors or intrusions into the system.

[0079] Some of the vehicle components, applications, and / or service groups may be regulated. Regulated components, applications, and / or service groups, as utilized herein, and without limitation to any other aspect of the present disclosure, include any components of a system that are regulated with respect to data processing, communications, including data collection, subscriptions, data requests, access to external devices and / or addresses, access to network zones, access to endpoints, utilization of communication resources (e.g., network zone bandwidth, external communication portals, total data limits or quantities, etc.). In one example, regulation may be required to ensure the security, reliability, and / or responsiveness of components, applications, and / or service groups. Regulation may entail different regulation levels or types based on the type of application, the criticality of the application, and the like. For example, climate control applications may be regulated differently than braking control applications. A braking control application is a safety- critical application that may be regulated to a higher level than a climate control application. In one example, the maximum data rate or packet size of a braking control application may be lower than that of a climate control application to ensure reliability and responsiveness. Regulation may affect and / or control the Al model interaction, monitoring, settings, prompts for the models, and the like. An Al model may be configured differently for components, applications, and / or service groups that are regulated and the configuration may depend on the level and / or type of regulation. Regulation, as utilized herein, may represent regulatory requirements, certification requirements, industry standards, safety and / or reliability policies for an entity related to the vehicle (e.g., an owner, fleet owner, manufacturer, OEM, body builder, vehicle dealer, or the like).

[0080] Referencing Fig. 1, an example system may include an external device 116. In the example of Fig. 1, an external device 116 is depicted as communicatively coupled to the vehicle 102. The external device 116 is directly coupled to the vehicle 102, which may include a directed wiredAttorney Docket No. SONA-0033-WO connection (e.g., to a service port, OBD port, or other available connection) and / or a wireless connection (e.g., a WiFi connection such as an IEEE 801.11 compatible connection, and / or a Bluetooth connection). The external device 1 16 may connect to a specific network (e.g., the network 112), and / or may connect to another device. In certain embodiments, the external device 116 may be a service tool, an original equipment manufacturer’s (OEM’s) tool, a manufacturer’s tool, and / or an application (e.g., an application communicating through a computing device such as a laptop, desktop, mobile device, and / or mobile phone; e.g., an application operated by an owner, service personnel, fleet manager, or the like). The external device 116 may include one or more Al components that may include an Al model to facilitate interactions with the vehicle. In on example, the Al model may be a large language model allowing natural language interactions to implement policies, models, and / or Al technicians for the vehicle.

[0081] An Al model, as utilized herein, should be understood broadly. An Al model may include any number of different models whose structure and function may be more suitable for the desired task or may have desirable qualities for specific applications (e.g., size, performance, memory footprint for constrained implementations). Al models may include algorithms and / or complex neural networks. In one example, models may include linear regression and logistic regression models and may be used for predictive analysis and binary classification, respectively. In another example models may include decision trees and random forests for classification and regression tasks. In another example feedforward neural networks, or multilayer perceptrons (MLPs) can be used for various simple predictive tasks. In another example, models may include convolutional neural networks (CNNs), which may be used for image and video recognition tasks. In another example, generative models like Generative Adversarial Networks (GANs), transformer models, T5 (Text-to-Text Transfer Transformer), may be used for natural language processing tasks. The methods described herein are adaptable to include any type of model.

[0082] Referencing Fig. 2, an example embodiment of an Al model component 106 for a vehicle 102 is schematically depicted, illustrating certain further details that may be present in certain embodiments. The component includes hardware components and software components. The component 106 includes stored models 202 to be operated, that are available to be operated, and / or that form at least a part of a model library. The Al model components 106 may include processing units such as embedded CPUs / GPUs and storage components 208. The component 106 includes software components 204 which may include an operating system such as a real-time operating system, automotive-grade operating systems like Automotive Grade Linux. The software may further include Al frameworks and libraries to facilitate the deployment and execution of Al modelsAttorney Docket No. SONA-0033-WO on the vehicle’s hardware. In some cases, the software may include middleware to facilitate communication between different components of the vehicle.

[0083] Component 106 of Fig. 2 may further include a security component 206, such as encryption modules to secure data communication between the Al models and vehicle systems (e.g., sensors and control elements). Security components may include authentication systems that control and enforce authorized policies that determine which systems and users can interact with the Al model and vehicle systems.

[0084] Component 106 of Fig. 2 may further include monitoring element 210. Monitoring elements 210 may include diagnostic tools to monitor the health and performance of the Al model and the interactions of the model with vehicle system. In one example, monitoring may include monitoring memory usage, CPU load, volume / frequency of data transfer and the like. Monitoring element 210 may include a logging functionality to keep track of the operations and identify issues.

[0085] Component 106 of Fig. 2 may further include an integration element 214. Integration element 214 may include Application Programming Interfaces and Software Development Kits to facilitate the integration of the Al model with vehicle systems. Integration element 214 may include data preprocessing and postprocessing modules for normalizing and preparing sensor data before feeding the data to an Al model and / or preparing Al model outputs for other vehicle systems.

[0086] Component 106 of Fig. 2 may further include the storage of user data 216. User data 216 may include user profiles. Users can create and manage user profiles which may include preferences for Al-driven features and applications (e.g., seat positioning, mirror adjustments, climate control settings, and preferred routes). The user data 216 may be automatically loaded when a specific user profile is activated and the Al system can automatically adjust to the stored preferences. User data 216 may include customizations of the sensitivity and behavior of driver assistance features like adaptive cruise control, lane-keeping assist, and collision warnings. For example, a driver may prefer earlier warnings for potential collisions or a more conservative lane-keeping system. User data 216 may include infotainment personalization. User data 216 may include voice recognition settings that provide data to improve user voice recognition accuracy and responsiveness. User data 216 may include behavioral data of the user. User actions and behaviors may be tracked over time to determine preferred settings behaviors in some settings. For instance, user data may include the driver’s acceleration patterns for merging or passing traffic.

[0087] Component 106 of Fig. 2 may further include the storage of vehicle data 218. Vehicle data, such as the age of the vehicle, mileage, and maintenance history, can influence how Al models are configured and used. Al functionalities may be tailored to the specific needs and conditions of the vehicle. Vehicle data may include age, mileage, transmission shift patterns, battery cycles, voltageAttorney Docket No. SONA-0033-WO fluctuations, temperature variations, engine and transmission tuning, fuel efficiency, vehicle features, maintenance history, manufacturer data, recall and service data, and the like.

[0088] Component 106 of Fig. 2 may further include an interface element 212. An interface may include elements for generating data or elements for display to a user. Elements may be provided to a user on a display inside the vehicle, remote devices, portable devices, and the like. A user interface may include a dashboard display to provide the driver with real-time feedback and system status.

[0089] Interface elements may be used to provide interaction and control with Al module components. A user interface (UI) may provide various input methods for the driver and passengers to interact with the vehicle’s Al systems and may include touchscreens, voice commands, physical buttons, and gesture controls. The UI may be configured to provide settings and to customize Al features according to their preferences. This can include adjusting the sensitivity of driver assistance systems and selecting preferred driving modes, for example. The UI may provide real-time visual feedback about the vehicle’s status and Al system operations. This can include displaying the current settings, versions, status of systems and the like. The UI may provide alerts and notifications to the driver generated by the Al models. This can include warnings about potential collisions, maintenance reminders, and system status updates. The UI can be customized based on recorded behavior and adapted using Al modules.

[0090] The arrangement of Fig. 2 is a non-limiting example. Additionally or alternatively, elements of the component 106 may be distributed within a vehicle or external to a vehicle (e.g., cloud servers).

[0091] Referencing Fig. 3, in embodiments provided herein is an example of a vehicle system having an Al model component for optimizing vehicle diagnostics. In embodiments, the Al model components 106 operate to determine the state of the vehicle 102 and generate diagnostics 306 that may be displayed in the vehicle, relayed to an external device 116, and / or used to adjust control elements 104 of the vehicle.

[0092] Al components can be used to analyze historical data and current vehicle conditions to predict potential failures before they occur, determine current failures, and / or provide predictions regarding the health of the systems. In one example, Al component may include large language models which may be used to process complex data sets and generate and query language descriptions of diagnostics. In one example, UUMs of Al model components 106 generate detailed and comprehensible diagnostic reports. These reports can translate complex technical data into plain language, making it easier for vehicle owners to understand the issues and necessary repairs. In applications, plain language descriptions of diagnostic information may be used to query languagebased diagnostics libraries 302. In certain embodiments, aspects of the present disclosure allow forAttorney Docket No. SONA-0033-WOAl model components 106 operating to readily obtain data from any vehicle, regardless of where on the vehicle an end point provides the data (e.g., on a CAN interface), and the data collected from the vehicle can be configured as desired by the user setting up the Al model component 106 (e.g., data rates, resolution, unit selection, etc.). Further, the data can be readily collected on behalf of a user that does not require any knowledge of the specific data location or configuration on the vehicle, which allows the user to focus on the expertise represented by the Al model component 106 (e.g., diagnostic expertise, reliability expertise, purchasing expertise, etc.), greatly expanding the available utilization of Al model components 106 and increasing the speed at which Al model components 106 can be implemented and / or updated.

[0093] Referencing Fig. 3, data from a vehicle may be collected and processed to determine diagnostics 306. Data may include real-time data and / or historical data. Data may be collected from sensors, memory storage such as vehicle data 218 and / or user data 216. The types of data and the locations of data collected may depend on the diagnostics selections. Types of data collected may depend on the type of diagnostics. For example, drivetrain diagnostics may use data from engine and / or transmission systems and may omit data from climate control systems. The collected data may be processed by one or more models to identify trends, patterns, anomalies, and the like. The processing of data may include tokenizing data and processing using one or more Al models. One or more Al models may be used to classify and / or identify patterns in the data. One or more Al models may be trained model to identify patterns in data that are indicative of one or more failures, maintenance needs, and / or imminent failures and / or maintenance needs. The output of the Al models may provide an indication of the type of failure and / or maintenance need and indicate a confidence level of the output.

[0094] In some cases, diagnostic data may include language data descriptions of the vehicle's behavior, symptoms, user-predicted issues, and the like. Al models may be configured to receive the language data descriptions and the vehicle data (e.g., sensor data) and identify trends, patterns, and / or anomalies indicative of failures, maintenance issues, and the like.

[0095] In some configurations, Al models may be configured to generate a natural language description of identified trends, patterns, and / or anomalies in the data. In one implementation, data from sensors may be processed by one or more Al models to generate a natural language description of the behavior or trends of sensor readings and / or timing of behaviors in relation to other events and the like. For example, drive train sensor data and state data of a vehicle may be processed by an Al model (e.g., a fine-tuned LLM) to generate descriptions such as “coolant temperature exceeds nominal value after 10 minutes of idling’’ or “transmission does not shift into fourth gear until the engine reaches 6,000RPMs and the transmission temperature is below 100C.” In certainAttorney Docket No. SONA-0033-WO embodiments, models that are built and / or utilized by users of the system may be stored in a model library 304 for future use and / or to facilitate building new models by users by operating as a starting point for the user. In certain embodiments, the model library 204 provides a corpus of available models that can be recommended to the user based on user natural language inputs, problem statements, capability requests, or the like.

[0096] Natural language descriptions of vehicle data, raw vehicle data, and / or user descriptions may be used to query a diagnostic library 302. In some cases, entries in the diagnostic library may include natural language data generated by technicians that describe symptoms and resolutions in natural language. The query of the language-based diagnostics libraries 302 may be improved with the use of natural language descriptions generated by the Al models.

[0097] In embodiments, natural language descriptions may be used to provide interactive troubleshooting guides. Vehicle owners can describe symptoms in natural language, and the LLM can interpret this input, ask clarifying questions, and guide the user through a series of diagnostic steps.

[0098] Referencing Fig. 3, diagnostics 306 in a vehicle may be used to change operating behavior or the vehicle by providing control signals to control elements 104. In embodiments, one or more Al models may interpret diagnostic 306 to generate new or revised control instructions to the control elements 104. Al models may process the diagnostics and may limit one or more features, derate performance, change operating range, and the like.

[0099] In embodiments, personalization functions (e.g., cruise control parameters, vehicle suspension settings, climate control settings, wiper settings) may be provided according to user profiles. A vehicle can support multiple user profiles, storing individual preferences for different drivers. Personalization may be loaded for an active user (i.e., driver) based on automatic detection of a user profile. In embodiments, a user may be detected from user behaviors. In one example, the way a person interacts with the vehicle's controls may be used to identify the user. Each user has unique habits when it comes to adjusting mirrors, seats, and steering wheel positions. The way a driver handles the steering wheel, the frequency and intensity of acceleration and braking, and their overall driving style, such as speed preferences and reaction times, provide data points. The timing and manner of these adjustments and interactions can act as a signature, distinguishing one user from another.[000100] Al models can be used to identify a user based on behavior through the analysis of patterns and data collected from various sources within a vehicle. Al models may receive the timing and manner of adjustments and interactions to identify a user. Al models may be trained from dataAttorney Docket No. SONA-0033-WO gathered from monitoring user interactions. Gathered data may be labeled with an active user profile and used to train a model, which can then be used to detect a user from future interactions.[000101] In embodiments, Al models may be trained to determine a class of a type of user. A user may not be associated with a stored profile in the vehicle. User behavior and interactions may be used to determine a user type from a set of predetermined profile types. Al models can be used to identify a profile type through analysis of patterns such as timing and manner of adjustments. For example, an Al model may be trained based on correlations that a user who often engages in hard breaking may prefer a responsive accelerator pedal. An Al model may be trained to identify hard braking patterns and adjust the throttle response without determining who is the driver of the vehicle. [000102] In some embodiments, vehicle parameter adjustments may be discretized such that there are a fixed number of possible setting variations (e.g., 10, 100, or 1000). In some embodiments, vehicle parameter adjustments may be continuous or semi-continuous, such that there may be millions or more possible different setting variations. In embodiments, an Al model may be configured to analyze patterns of behavior and determine adjustments to parameters. In one example, the intensity of braking may be used to proportionally adjust vehicle accelerator pedal responsiveness in non-discretized steps.[000103] Referencing Fig. 4, a non-limiting outline of various Al applications for vehicles and / or supporting systems for vehicles (e.g., in a cloud server and / or tool) are schematically depicted. Any one or more, or all, of the applications may be present in a given system, and / or may be implemented by any systems, devices, controllers, engines, procedures, or the like as set forth throughout the present disclosure.[000104] An example application includes determining, recommending, and / or providing personalized recommendations 402 for vehicles and / or for users of a cloud platform configured to communicate with vehicles, and / or personalized vehicle settings 404. The example personalized recommendations may be of any type, including a configuration of any aspect of a vehicle, control routines to implement user preferences and / or user comfort features, data collection routines that will be useful to a user of a cloud platform, and / or configurations for a campaign related to a group of vehicles (including campaign initiation, implementation, monitoring, and / or analysis). In certain embodiments, personalized recommendations may include recommendations to configure interfaces for accessing the cloud platform, such as display settings, arrangement and display of data, default settings, or the like.[000105] An example application includes a natural language user interface 406, supporting natural language options for a user to perform various operations with a cloud platform, including at least operations to build and deploy a policy, to create, implement, monitor, and / or analyze a campaign, toAttorney Docket No. SONA-0033-WO create, implement, tune, and / or iteratively improve a model for the vehicle, to build and deploy an automated operation for vehicle(s), to deploy an Al technician for a group of vehicles, to perform diagnostic and / or troubleshooting operations, to perform service operations, and / or to build predictive maintenance routines and / or to act on the outcomes of those routines. An example application includes a guided diagnostic 408, for example instructing a user on tests, service events, maintenance events, or the like, that should be utilized to respond to a diagnostic or fault code, and / or to determine a root cause of a failure or other anomalous event for the vehicle. An example application includes a predictive maintenance 410 (and / or predictive service) application that determines imminent failures or other conditions in the vehicle, and implements a predictive maintenance operation to avoid the failure and / or reduce an impact of the failure. Predictive maintenance 410 operations may include notifying a vehicle operator, notifying service personnel, notifying a fleet manager or service provider, ordering parts related to the predicted failure or condition, scheduling a service event for the vehicle, or the like.[000106] Fig. 4 additionally includes a non-limiting outline of supporting operations (the Al pipeline in the example of Fig. 4) to improve implementation of applications for vehicles and / or supporting systems, for example supporting data collection 412 (e.g., determining the data collection to support applications, and / or optimizing the implementation of data collection), data preparation 414 (e.g., processing, grouping, summarizing, adjusting formats and / or data properties, converting data to a selected format, units, or resolution, and / or performing up-sampling or down-sampling operations on the data, etc., to support applications), model development 416 (e.g., building a model to be utilized on the vehicle and / or on behalf of the vehicle, for example a virtual sensor, wear model, aging model, predictive model, etc.), model deployment 418 (e.g., deploying models to vehicles, including management of the rollout and / or confirmation of validity and / or receipt), and / or model integration 420 (e.g., ensuring the model is installed and able to run, able to collect the appropriate inputs, and to provide the outputs to selected locations, etc.).[000107] Fig. 4 additionally includes a non-limiting outline of real time improvement operations for models (Al at the Edge, in the example of Fig. 4), including supporting the creation, operations, and / or life cycle management of small specialized models 422, evaluation of model performance 424 (e.g., mission performance such as whether the model is working for the intended purpose, as well as resource utilization by the model), model tuning 426 (e.g., adjusting configurable aspects of the model, for example to ensure the model works as well as possible in the specific vehicle and / or at selected vehicle operating conditions), model miniaturization 428 (e.g., reducing the footprint of the model, such as memory utilization, processing support operations, network communication support operations, execution time or delay, etc., while still maintaining acceptable performance), and / orAttorney Docket No. SONA-0033-WO hardware optimization 430 (e.g.. configuring the model and / or execution of the model to leverage the specific hardware on the vehicle, such as availability of co-processors, fit for specific operating systems, chip command sets, or the like). In certain embodiments, model miniaturization 428 may include making adjustments to the model that do not significantly degrade the performance of the model, at least for the purpose of the model on the vehicle, and may include operations such as linearizing at least a portion of the model, limiting the model to certain operating conditions of interest, removing parameters from the model that do not appear to be effective (e.g., at least as experienced by the vehicle in real operating conditions), reducing the resolution and / or bit depth of parameters of the model, and / or reducing the execution rate and / or data update rate utilized by the model.[000108] Referencing Fig. 5, an example system for supporting a natural language interface for any aspect of a cloud platform 114 is schematically depicted. The system includes a user interface circuit 506 that implements a user interface that can be populated according to the operations being performed by the user (e.g., via user communications 502), and / or by the operations of any aspect of the cloud platform. The system includes a vehicle interface circuit 512 that performs interactions with vehicle(s) to send commands, policies, adjust configurations, and / or to receive data of any kind to support data collection operations (e.g., via vehicle communications 504), monitor any part of the vehicle and / or response to any actions from the cloud platform, and / or to receive confirmations of any actions of interest by the vehicle. The example system includes a natural language engine 508 to support natural language operations, including receiving natural language queries and / or instructions from a user, and translating vehicle data summaries or other responses to the user into a natural language output, as desired by the user. The example system includes a recommendation engine 510 to determine and provide recommendations, which may relate to any operations being performed by the user, including recommendations for building a policy, for building an Al technician, for building, implementing, and / or improving a model, and / or to determine a root cause of a fault, diagnostic operation, or other anomaly related to the vehicle. The example system includes a knowledge base 514, which may be varied according to the operations being performed, to support any other operations of the system including at least operations of the natural language engine. The example knowledge base 514 includes diagnostics 516 (e.g., the definitions of diagnostics and / or faults determined on the vehicle, best practices developed for any diagnostics, etc.), service 518 (e.g., service procedures relevant to the vehicle, part lists, and / or the service history of the vehicle), vehicles 520 (e.g., allowing the user to define the vehicles in scope, such as a particular vehicle, a related group of vehicles, vehicles having specific features, vehicles that have experienced certain events, a model year for a vehicle, vehicles that are members of a fleet, etc.), vehicle data 522 (e.g.,Attorney Docket No. SONA-0033-WO relevant vehicle data that was previously collected, and / or vehicle data that is collected to support ongoing user operations on the cloud platform, which may be collected at the instruction of the user and / or based upon implicit requests by the user by virtue of the user utilizing a function or component that utilizes the specific data, and / or that is collected as a part of determining an automatic recommendation for the user), curated documents 524 (e.g., specific documents that are configured to behave well with the LLM, and / or that can serve as a verified source of truth, representation of expert opinion for relevant topics, etc.), and / or libraries 526 (e.g., data libraries such as parameter definitions for vehicle data, example / previous / template policies, example / previous / template models, and / or example / previous / template Al technicians). The knowledge base 514 may be utilized as additional training data, for example where a general LLM is trained specifically for a group of vehicles and / or for a specific cloud platform, and / or the knowledge base 514 may have relevant portions utilized in the prompt to ensure that recommendations and / or communications from the cloud platform are compliant with the goals of the relevant stakeholder (e.g., a manufacturer, vehicle owner, cloud platform administrator, etc.), and to minimize the risk of hallucinations.[000109] Referencing Fig. 6, an example cloud platform 114 includes a model builder 602 that facilitates the building and implementation of a model for use on a vehicle. The example model builder 602 provides an interface to allow the user to state a modeling goal and / or select from a list of modeling goals, to build out the model, to operate an Al component to verify and / or improve the model, and to roll out the model for implementation on selected vehicles. The example cloud platform 1 14 include a model implementation engine 604 that performs roll out operations of the model to a vehicle, including translating model parameters into vehicle data values, determining where the model should be installed and executed on the vehicle, ensuring that data utilized by the model is available (and / or commanding end points on the vehicle to provide the data), and confirming that the model is installed and operating on the vehicle. The example cloud platform 114 includes a model improvement engine 606 that performs a number of operations to iteratively improve implemented models on the vehicle, for example determining (and / or implementing) miniaturization aspects for the model and confirming that miniaturization adjustments do not degrade the operation of the model within limits such that the miniaturized model still provide the desired benefits of the model (e.g., a virtual sensor still provides a compliant detection value, that a detected condition is still detected and within a specified time frame, that efficiency improvements still achieve benefits above a threshold, etc.). In certain embodiments, the model improvement engine 606 can check model utilization, resource utilization, operating frequency, or the like, and provide the relevant data to the cloud platform to support longer cycle improvements that may go beyondAttorney Docket No. SONA-0033-WO ensuring that the specific model on the specific vehicle is performing as desired (e.g., to plan areas to improve the model generally, to distinguish between multiple model options, etc.).[000110] Referencing Fig. 7, an example cloud platform 114 includes a policy builder 702 that facilitates the building and implementation of a policy for use on a vehicle. A policy may be utilized to collect vehicle data, to detect events on the vehicle and respond to those events (e.g., collecting relevant data, adjusting a vehicle configuration, provide alerts or notifications, etc.), and / or to perform automated vehicle operations (e.g., detecting a first condition such as a person sitting in the driver’s seat, and performing a second operation such as adjusting the seat for the person). Policies support performing a single operation on a vehicle (e.g., detect an event, perform the response, and then the policy discontinues), an ongoing operation (e.g., continuously monitor and / or perform operations until the policy is removed), and / or may be performed any selected number of times and / or until certain conditions are present on the vehicle. The example policy builder 702 provides an interface to allow the user to state a policy goal and / or select from a list of policy goals, to build out the policy, to operate an Al component to verify the policy (e.g., ensuring that the policy can be operated on selected vehicles), and to roll out the policy for implementation on selected vehicles.The example cloud platform 1 14 include a policy implementation engine 704 that performs roll out operations of the policy to a vehicle, including translating policy parameters into vehicle data values, determining where the policy should be installed and executed on the vehicle, ensuring that data utilized by the policy is available (and / or commanding end points on the vehicle to provide the data), and confirming that the policy is installed and operating on the vehicle. The example cloud platform 1 14 includes a policy improvement engine 706 that performs a number of operations to iteratively improve implemented policies on the vehicle. In certain embodiments, the policy improvement engine 606 can check policy utilization, resource utilization, operating frequency, or the like, and provide the relevant data to the cloud platform to support longer cycle improvements that may go beyond ensuring that the specific policy on the specific vehicle is performing as desired (e.g., to plan areas to improve the policy generally, to distinguish between multiple policy options, etc.). [000111] Referencing Fig. 8, an example system is disclosed for building, creating, implementing, monitoring, and / or adjusting policies, technician assistance, and / or models or algorithms on a vehicle or other mobile application. The example system depicts a number of controllers, computing devices, servers, engines, modules, and / or other operational elements configured to perform operations of the present disclosure. Such operational elements (“computing devices”) may be embodied in any manner, including for example as a computing device; as instructions on a computer readable medium that, when executed by a processor, cause the processor to perform one or more operations of the present disclosure; a sensor; an actuator; an input / output device; and / or aAttorney Docket No. SONA-0033-WO display device. The computing devices may be depicted on a single device for clarity of the present description, but a given computing device may be distributed, in whole or part, among several devices, and / or combined in whole or part with other computing devices set forth in the present disclosure. For example, a given computing device may be positioned on a controller of the vehicle, on a cloud server, on a tool, and / or a combination of these. Additionally or alternatively, a given computing device may be distributed on different devices of the system at different times and / or at different operating conditions, and / or depending upon which actions are being performed, and / or how a user is accessing the system (e.g., using a mobile device such as a laptop, phone, or pad; using a tool connected to the vehicle, cloud server, an engine, and / or a mini cloud device; connecting through a mobile application; connecting with a web portal, etc.).[000112] The example system of Fig. 8 includes a cloud server 812 configured to facilitate communications between other system components and the vehicle 802, and / or to communicate policies or other data or messages with the vehicle 802. In certain embodiments, the cloud server 812 includes, and / or is communicatively coupled to, a data store (not shown) having any data throughout the present disclosure, for example data collected from the vehicle 802, stored policies, models, and / or technicians, knowledge base documents, offset vehicle data, preferences, goals, and / or priorities provided by various users interacting with the system, or the like. The example system of Fig. 8 includes a builder engine 814, for example configured to implement a user interface and / or exercise an API to allow users to perform build operations with the system, including building policies, technicians, and / or models / algorithms (e.g., reference Figs. 6-7 and the related description). The example system of Fig. 8 includes an implementation engine 816, for example configured to deploy built aspects such as a policy, technician, and / or model / algorithm, and executions operations such as confirming validity of the aspect, confirming permissions associated with the aspect, collecting and / or storing data from the vehicle, performing monitoring operations during deployment, and the like (e.g., reference Figs. 6-7 and the related description). The example system of Fig. 8 depicts a tool 818 communicatively coupled to the system, which may include coupling to the vehicle 802 (e.g., using a direct hardware link such as a USB or OBD port, and / or using WiFi, Bluetooth, or a similar wireless connection), to the cloud server, to one or more controllers (e.g., including the builder engine 814 and / or implementation engine 816), and / or to a mini cloud 820 (e.g., a computing device hosting selected cloud applications, for example allowing the vehicle to have cloud services when a cloud connection is not available, and / or where a cloud connection is not desirable such as to sandbox test features of the vehicle without connecting to uncontrolled devices or connecting over the internet).Attorney Docket No. SONA-0033-WO[000113] The example system of Fig. 8 includes a vehicle having controller(s) 804 to perform vehicle control operations, to communicate with the cloud server 812 and / or a connected tool 818, or the like. In the example of Fig. 8, the vehicle includes a policy manager 806 that interprets a policy (e.g., provided by the cloud server 812, the mini cloud 820, and / or by a tool 818), determines the validity of the policy, and implements the policy (e.g., providing commands, setting register values or calibrations, enforcing permissions, etc.). In certain embodiments, the controller 804 performs aspects such as installing and / or operating a model, providing requested data to a technician, or the like, which may be at the direction of the policy manager 806. The example vehicle includes end points 810 which may be distributed across network zones of the vehicle, and a data storage 808 to support operations of the system and / or to support vehicle operations. Any one or more, or all, aspects of the vehicle may be distributed, although they are depicted as single devices for clarity of illustration.[000114] Referencing Fig. 9, an example system 900 includes a policy builder circuit 902. The policy builder circuit 902 may be structured to implement a graphical user interface (GUI) 904 for creating a policy 906, and / or to implement a policy creation builder (e.g., 702, 814) by exposing an API to another computing device (e.g., 110) that implements the GUI allowing users to build and deploy policies as set forth herein. The policy 906 may comprise at least one of an action definition 908 for a controller 804 of a vehicle 802 or a data collection definition 912 for a controller 804 of a vehicle 802. The policy builder circuit 902 may be further structured to determine an initial policy 914, for example utilized to build out a specific policy that will actually be implemented on the vehicle 802, in response to user interactions 915 with the GUI 904. The policy builder 902 may be further structured to provide the initial policy 914 to a policy implementation manager 916. In the example of Fig. 9, the policy implementation manager 916 is depicted separate from the vehicle 802, but the policy implementation manager 916 may additionally or alternatively be provided on the vehicle (e.g., to validate the policy and enforce permissions related to the policy) and / or in the cloud or other connected device (e.g., to screen the policy, and to create a refined policy from the initial policy that is configured for the target vehicle). In certain embodiments, the initial policy 914 may include standardized data values or other general aspects, that are translated by the policy implementation manager 916 for implementation on specific vehicles 802.[000115] Referencing Fig. 10, the policy implementation manager 916 may generate a refined policy 1002 and publish the refined policy 1002 to the vehicle 802. The policy implementation manager 916 may further provide the initial policy 914 as a template 1004 for future use (e.g., to be stored policy library and / or knowledge base). As used herein, the initial policy 914 includes a policy that utilizes non-ambiguous terminology for data parameters therein, although the data parameters may not beAttorney Docket No. SONA-0033-WO suitable for direct implementation on the vehicle. For example, the non-ambiguous terminology may utilize terms for data values that are industry standard terms, terms selected for normalization by an organization (e.g., the car manufacturer), or the like, which defines a normally accepted value for the term (e.g., Ambient Air Temperature), but which may not match the terminology utilized on the target vehicle for the policy (e.g., the vehicle ambient air temperature may have a software name or identifier, such as AmbAirTMPTR). Further, the initial policy 914 may not have vehicle specific information, such as which end points have the data that is described by the terminology. However, the initial policy 914 includes a non-ambiguous kernel of the information sought under the policy 906 (and / or the parameters to be commanded, calibrations to be set, etc.), and the policy implementation manager 916 can generate the refined policy 1002 for direct implementation on a vehicle, including generating a number of refined policies 1002 for implementing the initial policy 914 on a group of vehicles having a configuration that varies in some relevant manner (e.g., location of end points, parameter names, parameter units, parameter sampling rates, permissions scheme differences, etc.). The utilization of the initial policy 914 and refined policy 1002 scheme allows for the creation of a single policy that can be utilized on any or all vehicles in scope, and allows for the reutilization of a policy (e.g., as a template 1004, and / or as a starting point for a new policy), while preserving the isolation of the user from the vehicle specifics for each vehicle in scope.[000116] Referencing Fig. 11, the policy implementation manager 916 may be configured to send an alert 1102 upon detection of an anomalous event 1104. The alert 1102 may comprise an SMS message 1106 or a popup message 1108 displayed on a dashboard 1110 or infotainment system 1112 of a vehicle 802. For example, a policy may include data to be collected, and an action definition 908 to respond to an anomaly in the collected data (e.g., a value above a threshold value, a change in value above a threshold change or rate of change, an unexpected or out of range value, etc.). In certain embodiments, an alert 1102 may be generated in response to the anomalous event 1104, for example sending a text message to a user or designated location, and / or providing a message on a user interface 110, 212 (e.g., on the policy builder interface, a policy management interface, etc.). The detection of an anomaly and related alert message are a non-limiting example of an action performed based on the policy, and any data collection or actions available to the system that can be exercised using communications on the vehicle are contemplated herein. In certain embodiments, the anomalous event 1104 relates to the policy itself - for example alerting a user to a failure or delay in deploying the policy, a failure for the policy to execute on the vehicle, etc. In certain embodiments, alerts 1102 may be provided to any user of the cloud platform (e.g., a vehicle operator, owner, manufacturer, service personnel, fleet owner, dealer, cloud platform administrator, etc.), utilizing any communication scheme (e.g., text, interface display, messaging system for the platform,Attorney Docket No. SONA-0033-WO phone communication, etc.). The alert 1102 target and communication scheme may be selected as a part of the policy.[000117] Referencing Fig. 12, an example system includes a number of functions available to deploy, execute, and / or monitor operations using a policy, to collect data, to perform actions, to perform diagnostic operations, etc., on a vehicle. The example of Fig. 12 nominally includes the top layer as operating in the cloud (and / or on a tool, in a mini cloud, or the like), and the lower layers as operating on the vehicle. The position of elements in each layer is non-limiting, and may instead be on the vehicle or in the cloud, respectively, and / or distributed at least in part in both locations. The example cloud functions include an infrastructure management component (e.g., controlling data locations, setting up communications, setting up storage locations, etc.), a collector cloud (e.g., controlling operations to collect data from the vehicle and related policies), an automator cloud (e.g., controlling operations to perform automated actions on the vehicle and implement related policies), a guard cloud (e.g., performing data security operations, commanding encryption, detecting intrusion events or unauthorized communications, etc.), and an updater cloud (e.g., controlling operations to update software, firmware, calibrations, etc. on the vehicle, including on the various controllers distributed within the vehicle). The example vehicle functions include a collector service (e.g., controlling operations to collect data from the vehicle and communicate that data to the cloud), an automator service (e.g., controlling operations to respond to action definitions in one or more policies), a guard service (e.g., performing vehicle side security functions), and an updater service (e.g., implementing updates commanded by the cloud, including ensuring the vehicle is in an acceptable operating condition for the update being performed, providing updates to end points on the vehicle, and ensuring that updates are applied and functioning correctly). The example functions and organization of components depicted in the example of Fig. 12 are non-limiting and illustrative. Operations to support the functions set forth in Fig. 12 may be performed by any systems, engines, controllers, managers, computing devices, and / or any other aspect of the present disclosure. In the example of Fig. 12, functions and services performed within the system are organized into network services, data services, and application services, to provide a convenient example of how the system may be organized from the perspective of a user of the system and / or cloud platform of the present disclosure.[000118] Referencing Fig. 13, an example system adds an Al layer to the cloud side of the system of Fig. 12, including an Al technician builder that allows a user to build a technician for diagnosis, service, maintenance, troubleshooting, or the like; an Al policy creator allowing a user to create and deploy policies to selected vehicles, and an Al model trainer allowing a user to build, deploy, and iteratively improve models and / or algorithms on the vehicle. The example of Fig. 13 includes an AlAttorney Docket No. SONA-0033-WO director cloud performing cloud side operations in the model / algorithm build and implementation loop, and an Al director service on the vehicle side that installs, executes, collects data for, and responds to improvement operations and commands from the Al director cloud for models / algorithms on the vehicle. In the example of Fig. 13, Al supported functions and services performed within the system are organized into the Al technician builder, Al policy creator, and Al model trainer, to provide a convenient example of how the system may be organizes from the perspective of a user of the system and / or cloud platform of the present disclosure.[000119] Referencing Fig. 14, an example interface 212 is depicted allowing a user to select desired operations related to building and deploying a policy, a technician, and / or a model / algorithm, for example on a system having a unified interface to perform all of these. Additionally or alternatively, one or more of these may be implemented on separate interface, and / or may be implemented using an API, where the interface can be presented in any desired manner. The example interface includes an Al technician selection 1402, an Al policy generator selection 1404, and / or an Al on demand selection 1406, for example to implement (respectively), operations to build and / or implement an Al technician, an Al policy builder, and / or an Al model builder.[000120] Referencing Fig. 15, an example interface 212 is depicted with the user selecting to build an on demand policy that allows for data collection and / or other tasks to be performed, with simple selections made by the user, automated support from the system to help the user, and without the user needing any detailed information about the target vehicle beyond being able to sufficiently describe the target vehicle or class of vehicles (e.g., using brand, model, make, year, an identifier for the vehicle, etc.). In the example of Fig. 15, the user has started a new untitled task, and selected the start conditions which will define when the task should be performed (e.g., time based, event based, data based, etc.). Referencing Fig. 16, the user has selected a new task based on “Ignition status”, which in the example is the normalized term, and determines whether the parameter “Vehicle_ADAS_ABS_IsEngaged” has a value of “42” (e.g., indicating a particular state for the parameter). In Figs. 17-18, the user selects data to capture when the start condition is met, which includes certain specified CAN messages in the example.[000121] In certain embodiments, the stop conditions may be set, and / or the stop conditions may be inherent (e.g., a policy that executes a single time). In certain embodiments, the policy may be an on demand policy (e.g., perform once when conditions are met), a persistent policy (e.g., perform continuously until a stop condition is met), and / or a policy that is performed a number of times (e.g., perform the data collection six times). The policy can include any number of start and / or stop conditions, and can include intermediate conditions (e.g., collect a first set of data after a first trigger condition, then collect a second set of data after a second trigger condition, etc.).Attorney Docket No. SONA-0033-WO[000122] Referencing Fig. 19, an example system includes an Al assistant to help build the policy. The example Al assistant may include one or more elements such as a large language model (LLM) to interpret and respond to natural language inputs from the user; a neural network, machine learning component, heuristic algorithm, and / or expert system to classify user inputs, determine suggestions for the user, and / or prioritize between suggestions for the user. In certain embodiments, the Al assistant may include one or more elements such as a fuzzy logic and / or Bayesian analysis component to make decisions and / or prioritize between suggestions for the user, to analyze success or cost functions, or the like.[000123] The example Al assistant provides suggestions to the user about policy elements and / or full policies that support the user’s intentions, based on the user inputs. In the example of Fig. 19, the user has started a new policy but has not directly invoked the Al assistant, and the Al assistant provides high level guidance to the user for some general tasks that may be of interest - for example collecting various data elements. The high level suggestions may be defined by an administrator, based on history by that user and / or other similar users, or the like. In the example of Fig. 20, the user has named the policy “Collect sunroof status changes’’ and entered a start condition for determining the value of the day / night sensor on the vehicle. In the example of Fig. 21, the user has invoked the Al assistant (e.g., by selecting an icon from the interface, using a menu, hitting a hotkey, etc., although in certain embodiments the Al assistant may operate independently). The Al assistant has provided a recommendation for parameters that will help the user determine and understand sun roof usage in the selected vehicles, for example selecting a number of parameters that have been utilized in the past to determine sun roof usage, surveying the data for correlating parameters with sun roof usage (e.g., including anti-correlation parameters), and suggested monitoring windshield wipers, sun roof state, etc., to help the user perform the inferred task of understanding sun roof usage within the selected vehicles. In the example of Fig. 21, the Al assistant has built a fully capable policy that can be implemented on vehicles directly by the user, and / or which the user can use as a basis for further development of the policy. The example Al assistant can parse the user intent from any information provided by the user, including entered questions, titles of the policy, any information already put into the policy by the user, or the like. In certain embodiments, the user can implement the policy to vehicles with a simple activation such as using an “apply” or “continue” button, which may advance to a next stage such as allowing review of the policy, hitting a direct implement button, or the like. In certain embodiments, the policy created by the Al assistant is an initial policy 914, which is updated into a refined policy 1002 during implementation onto specific vehicles of the selected vehicles. In certain embodiments, the system may display that refined policy to the user, place the refined policy in a selected location for review by the user at a later time, and / orAttorney Docket No. SONA-0033-WO the refined policy may be hidden completely from the user (e.g., including hiding the existence of the refined policy from the user). The specific treatment of the refined policy may be selected by an administrator, according to user preferences, or the like.[000124] In certain embodiments, the system tracks the implementation, including for example tracking which vehicles have installed the policy, confirmed proper operation, provided data back on the policy, or the like. In certain embodiments, the system provides a policy implementation browser to the user allowing the user to track policy implementation and response, as well as highlight and investigate anomalies such as vehicles that have not received the policy, that have not implemented the policy (e.g., due to an incompatibility, fault condition, invalid aspect of the policy, etc.), and / or that have not successfully performed the data collection and / or actions defined in the policy. In certain embodiments, the system allows the user to drill down into policy results for specific vehicles or defined groups of vehicles, for example getting statistical data on the implementation of the policy, checking error codes or logs related to specific vehicles, or the like.[000125] In one embodiment, a system may comprise a policy builder circuit structured to implement a GUI for creating a policy. The policy may comprise at least one of an action definition or a data collection definition for a vehicle controller. The policy builder circuit may determine an initial policy of the policy in response to user interactions with the GUI. The initial policy may be provided to a policy implementation manager.[000126] The policy implementation manager may also generate a refined policy from the initial policy, publish the refined policy to the vehicle, and provide the initial policy as a template for further use. The initial policy and / or the refined policy may further comprise collection of data independent of variable names that may not be the same across vehicles.[000127] In some embodiments, the initial policy and the refined policy may be configured to run on a plurality of vehicles independent of model, year of manufacture, software version, or hardware components.[000128] In some embodiments, the user input into the system may be structured as natural language. The user input may also be general or specific to vehicles independent of model, year of manufacture, software version, or hardware components.[000129] In some embodiments, generation of input into the system may be performed at least partially autonomously or by the policy builder circuit.[000130] In another embodiment, the policy implementation manager may be configured to send an alert upon detection of an anomalous event. The alert may be an SMS message or a popup message displayed on a dashboard or an infotainment system. The alert may be sent to a user, a driver, orAttorney Docket No. SONA-0033-WO both. The threshold for detecting the anomalous event may be adjusted autonomously, manually by the user, or manually by the driver.[000131] Certain non-limiting operations are set forth following, which may be implemented in whole or part by any systems, devices, platforms, engines, builders, or the like of the present disclosure.[000132] An example operation allows a user to create a test suite, and to deploy the test suite to any selected vehicle(s) based on the needs of the vehicle. Operations additionally or alternatively include determining criteria for when a test suite should be implemented on a vehicle, and / or implementation details for the test suite.[000133] An example operation exercises a guided diagnostic for a user, either upon request by the user (e.g., “here is a problem I am seeing. . .’’) and / or as a recommendation (e.g., based on detected anomalies, outliers in a data set, issues detected in offset vehicles, or the like) to the user. The guided diagnostic can lead a user through an analysis and / or decision tree, automatically collecting and / or presenting relevant data for the user, and may be based on an expert system (e.g., a pre-built diagnostic tree) and / or built in real time (e.g., utilizing a knowledge base to determine a root cause analysis progression for a given issue).[000134] An example operation includes sharing any aspect from one user to a selected group of other users, for example sharing a model, policy, diagnostic result, recommendation, or the like, and which may include an interface to allow the user to select a group of users to share the aspect with. In certain embodiments, the sharing further includes adjusting aspects to be shared for the target user, for example allowing an engineering user to share a simplified version of an analysis with a business user.[000135] An example operation includes support for a reconfiguration operation, for example determining a set of configurations accounting for different factors such as driving conditions, driver behaviors, operating conditions, fault conditions, or the like, and implementing the reconfiguration options to vehicles at selected times (e.g., a configuration utilized for responding to a vehicle condition, which may be activated on vehicles having that vehicle condition). The example reconfiguration operations may be latent on the vehicle (e.g., a routine on the vehicle that detects the condition and implements the reconfiguration) or in the cloud and implemented to the vehicle when monitoring operations in the cloud detect the condition on the vehicle.[000136] An example operation includes operations to apply any maintenance, monitoring, diagnostic, and / or testing (MTDM) operation to a vehicle, including operations built by systems herein and / or defined by a user. The operations may include any aspects of building, implementing, activating, monitoring, iteratively improving, and / or analyzing MTDM operations.Attorney Docket No. SONA-0033-WO[000137] An example operation includes monitoring the vehicle to determine user preferences for any aspect of the vehicle, and adjusting a configuration of the vehicle in response to those user preferences. In a further example, the adjustment may be automatic, and / or provided to the operator for confirmation. User preferences may be explicit, inferred, defined by other users, and / or provided by a fleet, an owner, and / or a manufacturer.[000138] An example operation includes monitoring of software on the vehicle for optimizations and / or sensitivities. For example, such operations can include determining how the software responds to loss of cloud connectivity (and / or whether that response is working as intended), is the software running in the correct location on the vehicle, are there redundant operations being performed, and / or whether equivalent simplifications are available. In certain embodiments, the operations may be utilized on software that supports the vehicle but is running off vehicle, and / or may further include monitoring such software to determine how it responds to loss of vehicle connectivity (e.g., loss of the ability to receive vehicle data and / or send commands to the vehicle) and / or whether that response is working as intended.[000139] An example operation includes providing an interface to allow a user to build models and / or to deploy the models to the vehicle. The models may be of any type and for any purpose, and can include models built by and / or leveraging the use of Al and / or machine learning operations. The operations may include supporting data collection, providing a catalog of available and / or recommended data from vehicles (which may be customized to selected vehicles) based on the operations in the model and / or natural language or structured goals entered by the user, to prepare data for ease of use by the model (and / or to add tags, metadata, format the data, etc.), and / or to develop the model and improve the model over time.[000140] An example operation utilizes a tool to deploy the model to selected vehicles, and to manage confirmations and / or monitoring related to the deployment. The operations may include building the data funnel from a vehicle source to the model, and configuring the data in the way that it is needed by the model. Operations may include routing the output of the model to the cloud and / or to a service that utilizes the model. Operations may include iteratively improving the model, for example adjusting the model for the hardware on the vehicle, and / or allowing the user building the model to create the model in the cloud in a manner that is independent of the hardware and / or specific data on the selected vehicle(s), but allowing the model to respond to the specific hardware and / or data on the selected vehicle(s) while implemented on the vehicle.[000141] An example operation includes a guided diagnostic that utilizes a knowledge base about the vehicle and / or conditions on the vehicle, to support diagnostic operations to determine root causes of issues detected on the vehicle. In certain embodiments, the guided diagnostic may includeAttorney Docket No. SONA-0033-WO operations of a large language model, with appropriate guard rails to ensure that answers are relevant and responsive (e.g., training the LLM on the knowledge base, and / or managing utilization of the knowledge base through prompt management and / or enforcing citation rules, quotations and / or depictions directly from the knowledge base, etc.). In certain embodiments, the diagnostic operations include converting responses into actionable routines, and / or building a routine library for certain diagnostic operations and leveraging the routine library in guided diagnostic operations. Example operations include taking an output of a model and / or other data element indicating an issue, finding a routine that is related to the requested answer (e.g., providing a number of routines for the user to try), and / or creating policies or other elements that perform at least a portion of a routine allowing the user to implement those with just a confirmation and / or implementing them automatically. Determination of relevant routines (e.g., from a routine library) may include aspects such as: utilizing an expert system to identify relevant routines; observing the commands sent as a part of the determined issue; using a table and / or root cause list related to the problem and / or inputs utilized to determine the problem is present; based on which end points are touched by the problem and / or that provided inputs utilized to determine the problem is present; and / or utilizing a classification and nearest neighbor(s) system to determine likely relevant routines. In certain embodiments, operations for a guided diagnostic may be leveraged to perform a predictive and / or automatic diagnostic, where one or more, or all, of the operations and / or routines are performed based on the detection of an issue (e.g., a fault code, performance issue, inferred issue such as determined from repetitive inputs from an operator, and / or based on an output of a model). In certain embodiments, operations for a guided diagnostic may be leveraged to perform a predictive maintenance operation and / or a predictive service operation, for example utilizing a maintenance and / or service library (e.g., instead of or in addition to the routine library), and determining relevant maintenance and / or service routines based on actual, imminent, or possible issues detected on the vehicle. In certain embodiments, mitigation actions may be performed in a similar manner, for example actions that, given the presence of an issue, may be more likely to prevent long term damage, extensive damage, and / or that prevent the operator from getting into a situation that may be more likely to result in an undesirable outcome.[000142] Any of the foregoing operations may be customized by an owner, operator, manufacturer, cloud support personnel, etc. Any of the foregoing operations may utilize historical data for vehicles, data from offset vehicles, and / or data from combined groups of vehicles. Any of the foregoing operations may utilize a digital twin and / or simulation operation, for example to repeat issues on a vehicle and / or predict upcoming issues for the vehicle, and / or to further illuminate theAttorney Docket No. SONA-0033-WO issue (e.g., creating a set of likely data that was present on the vehicle during an issue where such data was not directly collected, but is consistent with the actual collected data).[000143] The methods and systems described herein may be deployed in part or in whole through a machine having a computer, computing device, processor, circuit, and / or server that executes computer readable instructions, program codes, instructions, and / or includes hardware configured to functionally execute one or more operations of the methods and systems disclosed herein. The terms computer, computing device, processor, circuit, and / or server, as utilized herein, should be understood broadly.[000144] Any one or more of the terms computer, computing device, processor, circuit, and / or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and / or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.[000145] Network and / or communication resources include, without limitation, local area network, wide area network, wireless, internet, or any other known communication resources and protocols. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers include, without limitation, a general purpose computer, a server, an embedded computer, a mobile device, a virtual machine, and / or an emulated version of one or more of these. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be: a distributed resource included as an aspect of several devices; and / or included as an interoperable set of resources to perform described functions of the computer, computing device, processor, circuit, and / or server, such that the distributed resources function together to perform the operations of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of more than one computer, computing device, processor, circuit, and / or server, for example as separately executable instructions stored on the hardware device, and / or as logically partitioned aspects of a set of executable instructions, with someAttorney Docket No. SONA-0033-WO aspects of the hardware device comprising a part of a first computer, computing device, processor, circuit, and / or server, and some aspects of the hardware device comprising a part of a second computer, computing device, processor, circuit, and / or server.[000146] A computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.[000147] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).[000148] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer readable instructions on a server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The computer readable instructions may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server and the like. The server may include one or more of memories, processors, computer readable transitory and / or non -transitory media, storage media, ports (physical and virtual), communication devices, andAttorney Docket No. SONA-0033-WO interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.[000149] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of instructions across the network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.[000150] The methods, program code, instructions, and / or programs may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, program code, instructions, and / or programs as described herein and elsewhere may be executed by the client. In addition, other devices utilized for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.[000151] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across the network. The networking of some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In thisAttorney Docket No. SONA-0033-WO implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.[000152] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules, and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like. The methods, program code, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructural elements.[000153] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.[000154] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players, and the like. These mobile devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and / or programs stored thereon.Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute methods, program code, instructions, and / or programs. The mobile devices may communicate on a peer to peer network, mesh network, or other communications network. The methods, program code, instructions, and / or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program code, instructions, and / or programs executed by the computing devices associated with the base station.[000155] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine readable transitory and / or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time;Attorney Docket No. SONA-0033-WO semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.[000156] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and / or determining any value parameter, input, data, and / or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and / or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and / or determine the data value may be performed.[000157] Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations described herein are schematically described and / or depicted, and operations may be combined, divided, reordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and / or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operational step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitlyAttorney Docket No. SONA-0033-WO contemplated herein, and in certain embodiments re-ordering, subdivision, and / or different grouping of operations is explicitly contemplated herein.[000158] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.[000159] The elements described and depicted herein, including in flow charts, block diagrams, and / or operational descriptions, depict and / or describe specific example arrangements of elements for purposes of illustration. However, the depicted and / or described elements, the functions thereof, and / or arrangements of these, may be implemented on machines, such as through computer executable transitory and / or non-transitory media having a processor capable of executing program instructions stored thereon, and / or as logical circuits or hardware arrangements. Example arrangements of programming instructions include at least: monolithic structure of instructions; standalone modules of instructions for elements or portions thereof; and / or as modules of instructions that employ external routines, code, services, and so forth; and / or any combination of these, and all such implementations are contemplated to be within the scope of embodiments of the present disclosure Examples of such machines include, without limitation, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements described and / or depicted herein, and / or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing flow charts, block diagrams, and / or operational descriptions set forth functional aspects of the disclosed systems, any arrangement of program instructions implementing these functional aspects are contemplated herein. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. Additionally, any steps or operations may be divided and / or combined in any manner providing similar functionality to the described operations. All such variations and modifications are contemplated in the present disclosure. The methods and / or processes described above, and steps thereof, may be implemented in hardware, program code, instructions, and / or programs or any combination of hardware and methods, program code, instructions, and / or programs suitable for a particular application. Example hardware includes a dedicated computing device or specific computing device, a particular aspect or component of a specific computing device, and / or an arrangement of hardware components and / or logical circuits to perform one or more of theAttorney Docket No. SONA-0033-WO operations of a method and / or system. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.[000160] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low- level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer readable instructions, or any other machine capable of executing program instructions.[000161] Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such permutations and combinations are contemplated in embodiments of the present disclosure.[000162] While the disclosure has been disclosed in connection with certain embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples but is to be understood in the broadest sense allowable by law.

Claims

Attorney Docket No. SONA-0033-WOWhat is claimed is:1 . A system comprising: a policy builder circuit structured to implement a GUI for creating a policy, the policy comprising at least one of: an action definition for a controller of a vehicle; or a data collection definition for a controller of a vehicle; determining an initial policy of the policy in response to user interactions with the GUI; and providing the initial policy to a policy implementation manager.

2. The system of claim 1, further comprising: wherein the initial policy comprises standardized data values; and wherein the policy implementation manager is structured to generate a refined policy in response to the initial policy, wherein the refined policy comprises vehicle specific data values.

3. The system of claim 2, wherein the policy implementation manager is further structured to communicate the refined policy to the vehicle, and to confirm the vehicle utilizes the refined policy.

4. The system of claim 1, wherein the policy implementation manager is further structured to store the initial policy as a template in a policy library, and wherein the policy library is accessible to the policy builder circuit.

5. The system of claim 4, wherein the policy builder circuit is further structured to provide at least one policy of the policy library to the GUI in response to the user interactions with the GUI.

6. The system of claim 4, wherein the initial policy comprises a policy compatible with a selected group of vehicles.

7. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a make and model.

8. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a make, model, and year.

9. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles comprising a fleet of vehicles.

10. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a common application.

11. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a common flow.

12. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a common software version for an end point of each vehicle.Attorney Docket No. SONA-0033-WO13. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles associated with a common hardware component.

14. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles associated with a common original equipment manufacturer.

15. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles associated with a common dealer.

16. The system of claim 6, wherein the selected group of vehicles comprises a group of vehicles sharing a common mission.

17. The system of claim 16, wherein the common mission comprises at least one mission selected from: an emergency vehicle mission; a delivery mission; or a rental vehicle mission.

18. The system of claim 6, wherein the selected group of the vehicle comprises a group of vehicles having a common vehicle operating condition.

19. The system of claim 18, wherein the common vehicle operating condition comprises at least one condition selected from: a fault code condition; a diagnostic condition; a duty cycle condition; or a recall condition.

20. The system of claim 3, wherein the policy implementation manager is further structured to detect an anomalous event, and to provide an alert to a user in response to the anomalous event.

21. The system of claim 20, wherein the anomalous event comprises an event detected by operation of the refined policy on the vehicle.

22. The system of claim 21, wherein the user comprises at least one of: a service entity; a fleet entity; a user providing the user interactions with the GUI; a manufacturer of the vehicle; or an owner of the vehicle.

23. The system of claim 20, wherein the anomalous event comprises a detected failure of the vehicle to install the refined policy.Attorney Docket No. SONA-0033-WO24. The system of claim 20, wherein the anomalous event comprises a detected failure of the vehicle to execute the refined policy.

25. A method, comprising: implementing a GUI for building a policy, the policy comprising at least one of an action definition for a controller of a vehicle, or a data collection definition for the controller of the vehicle; interpreting user communications on the GUI to determine an initial policy, the initial policy compatible with a selected group of vehicles; adjusting the initial policy to a refined policy compatible with a specific vehicle; communicating the refined policy to the specific vehicle; and confirming the utilization of the refined policy by the specific vehicle.

26. The method of claim 25, wherein at least a portion of the user communications comprise natural language communications from the user.

27. The method of claim 26, further comprising determining at least one appropriate template policy from a policy library in response to the natural language communications, and providing the at least one appropriate template policy to the GUI.

28. The method of claim 27, further comprising using one of the appropriate template policies as a starting point for the user in response to a user selection of one of the at least one appropriate template policies.

29. The method of claim 26, further comprising providing the user with a list of available data values in response to the natural language communications.

30. The method of claim 29, further comprising providing the user with a list of alternative data values in response to the natural language communications, and further in response to determining that user indicated data is not available to the user.

31. The method of claim 25, further comprising storing the initial policy in a policy library.

Images