# US 11,897,357 B1 · Scheduling system and method for distributed and unreliable electric vehicle nodes for compute workloads > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,357 B1 - Document ID: US11897357B1 - Status: Granted - Kind: B1 - Application number: 18/194,035 - Filed: 2023-03-31 - Granted: 2024-02-13 - Earliest claimed: 2023-03-31 - Assignees: Mercedes-Benz Group AG - Applicants: Mercedes-Benz Group AG - Inventors: Steffen Hinderer; Natalie Grasser; Stephen Fortner - Web page: https://patentgrep.com/patent/US11897357B1 ## CPC - B60L 53/63: in response to network capacity - B60L 53/66: Data transfer between charging stations and vehicles - B60L 53/62: in response to charging parameters, e.g. current, voltage or electrical charge - B60L 58/13: Maintaining the SoC within a determined range - G06F 9/5044: {considering hardware capabilities} - G06F 9/5072: {Grid computing} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06Q 10/06314: {Calendaring for a resource} - H04L 9/3239: {involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD} - H04L 9/50: {using hash chains, e.g. blockchains or hash trees} - B60L 53/64: Optimising energy costs, e.g. responding to electricity rates - B60L 2260/46: by self learning - B60L 2260/52: drive range estimation, e.g. of estimation of available travel distance - B60L 2260/54: Energy consumption estimation - G06F 2209/5021: Priority - G06F 2209/503: Resource availability - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/088: Non-supervised learning, e.g. competitive learning - H04L 2209/84: Vehicles - B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Methods, computing systems, and technology for scheduling computing tasks to be performed by an unreliable device are presented. For example, a task management system may receive, from a first electric vehicle, vehicle data comprising computing capabilities of the first electric vehicle and an availability calendar of the first electric vehicle. Additionally, the system can generate, based on the computing capabilities of the first electric vehicle, a work package for the first electric vehicle. The work package can include a computing task for the first electric vehicle to complete. Moreover, the system can determine, based on the availability calendar, one or more transmission parameters for communicating the work package to the first electric vehicle. Subsequently, based on the transmission parameters, the system can transmit a request for the first electric vehicle to perform the computing task of the work package. ## Claims ### Claim 1 A computer-implemented method comprising: receiving, from a first electric vehicle, vehicle data comprising computing capabilities of the first electric vehicle and an availability calendar of the first electric vehicle, wherein the computing capabilities of the first electric vehicle are determined onboard the first electric vehicle based on a performance analysis of one or more computing resources onboard the first electric vehicle, and wherein the availability calendar of the first electric vehicle is determined onboard the vehicle and the availability calendar indicates a predicted network connectivity of the first electric vehicle; generating, based on the computing capabilities of the first electric vehicle, a work package for the first electric vehicle, the work package comprising a computing task for the first electric vehicle to complete using at least a portion of the computing resources onboard the first electric vehicle; determining, based on the availability calendar, one or more transmission parameters for communicating the work package to the first electric vehicle, the transmission parameters comprising a time for transmitting the work package to the first electric vehicle; and based on the transmission parameters, transmitting a request for the first electric vehicle to perform the computing task of the work package. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the first electric vehicle is an electric vehicle, and wherein the method further comprises: generating an electronic reward for a user of the first electric vehicle based on a completion of the computing task and a contribution to a reduction in greenhouse gas emissions. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the request comprises a cryptographic signature and a location of the work package. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the work package is stored in a work package storage, the work package storage having compute instructions and data for performing the computing task. ### Claim 5 (depends on 3) The computer-implemented method of claim 3, further comprising: receiving, from a work validator, a confirmation that the computing task has been performed; and generating a smart contract associated with the work package, the smart contract comprising the cryptographic signature and code indicative of an indication by the first electric vehicle that the first electric vehicle has performed the computing task. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising: transmitting the smart contract to a blockchain interface, wherein the blockchain interface maintains a performance status of the computing task. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising: determining that the computing task has not been performed by the first electric vehicle within a predetermined amount of time; and assigning, based on the determination that the computing task has not been performed by the first electric vehicle within the predetermined amount of time, the work package to be performed by a second electric vehicle. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: registering, in an agent inventory, the first electric vehicle as a node agent, the agent inventory having a list of node agents, and wherein each node agent in the list of node agents includes an associated availability calendar. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the availability calendar is determined by the first electric vehicle using one or more machine-learned models based on a network connection vector of the first electric vehicle and a compute mode availability calendar, and wherein the availability calendar is further determined based on user specific data that is only processed onboard the vehicle. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the availability calendar includes a plurality of locations, and wherein the network connection vector of the first electric vehicle is indicative of a network connection of the first electric vehicle in each location of the plurality locations. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the network connection vector of the first electric vehicle is determined by performing a network connectivity speed test with the first electric vehicle. ### Claim 12 (depends on 1) The computer-implemented method of claim 1, wherein the work package is indicative of at least one of: a minimum computing resources requirement, a completion date, and an effort level for performing the work package. ### Claim 13 (depends on 1) The computer-implemented method of claim 1, wherein the availability calendar is a quantized calendar predicting a current state of the first electric vehicle and an operation state at a future period. ### Claim 14 (depends on 13) The computer-implemented method of claim 13, wherein the operation state of the first electric vehicle is either an off state, a download state, or a compute state. ### Claim 15 (depends on 14) The computer-implemented method of claim 14, wherein the computing task is performed by the first electric vehicle only during the compute state. ### Claim 16 (depends on 14) The computer-implemented method of claim 14, wherein the first electric vehicle is restricted from performing the computing task during the download state. ### Claim 17 (depends on 1) The computer-implemented method of claim 1, wherein the availability calendar includes a network availability calendar that determines when the vehicle is able to transmit data over a network. ### Claim 18 (depends on 1) The computer-implemented method of claim 1, wherein the availability calendar includes a compute mode availability calendar that determines a specific time and a time duration that the vehicle is available to perform the computing task of the work package. ### Claim 19 A computing system comprising: a control circuit of a task management system, the control circuit configured to: receive, from a first electric vehicle, vehicle data comprising computing capabilities of the first electric vehicle and an availability calendar of the first electric vehicle, wherein the computing capabilities of the first electric vehicle are determined onboard the vehicle based on a performance analysis of one or more computing resources onboard the first electric vehicle, and wherein the availability calendar of the first electric vehicle is determined onboard the vehicle and the availability calendar indicates a predicted network connectivity of the first electric vehicle; generate, based on the computing capabilities of the first electric vehicle, a work package for the first electric vehicle, the work package comprising a computing task for the first electric vehicle to complete using at least a portion of the computing resources onboard the first electric vehicle; determine, based on the availability calendar, one or more transmission parameters for communicating the work package to the first electric vehicle, the transmission parameters comprising a time for transmitting the work package to the first electric vehicle; and based on the transmission parameters, transmit a request for the first electric vehicle to perform the computing task of the work package. ### Claim 20 One or more non-transitory computer-readable media that store instructions that are executable by a control circuit to: receive, from a first electric vehicle, vehicle data comprising computing capabilities of the first electric vehicle and an availability calendar of the first electric vehicle, wherein the computing capabilities of the first electric vehicle are determined onboard the first electric vehicle based on a performance analysis of one or more computing resources onboard the first electric vehicle, and wherein the availability calendar of the first electric vehicle is determined onboard the vehicle and the availability calendar indicates a predicted network connectivity of the first electric vehicle; generate, based on the computing capabilities of the first electric vehicle, a work package for the first electric vehicle, the work package comprising a computing task for the first electric vehicle to complete using at least a portion of the computing resources onboard the first electric vehicle; determine, based on the availability calendar, one or more transmission parameters for communicating the work package to the first electric vehicle, the transmission parameters comprising a time for transmitting the work package to the first electric vehicle; and based on the transmission parameters, transmit a request for the first electric vehicle to perform the computing task of the work package.