# US 11,079,243 B2 · Continuous delivery > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,079,243 B2 - Document ID: US11079243B2 - Status: Granted - Kind: B2 - Application number: 16/228,908 - Filed: 2018-12-21 - Published: 2020-04-16 - Granted: 2021-08-03 - Earliest claimed: 2018-10-15 - Assignees: Accenture Global Solutions Limited - Applicants: Accenture Global Solutions Limited - Inventors: Marzieh Dashti; Carlos Miguel Garcia Mejias; Henry Frederick Cartwright; Konstantinos Mammas; Shane O. Meachair; Daniel Gradovich; David E. Hubbard - Web page: https://patentgrep.com/patent/US11079243B2 ## CPC - G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents} - G06Q 10/047: Optimisation of routes or paths, e.g. travelling salesman problem - G01C 21/3484: {Personalized, e.g. from learned user behaviour or user-defined profiles} - G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)} - G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling} - G06Q 10/08355: {Routing methods} - G06Q 10/0838: {Historical data} - G06Q 10/0843: {using forecasting or optimisation} - G06Q 10/0841: {Compliance with shipping requirements} - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - 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 ## Abstract Implementations include determining a set of routes for delivery of assets within a delivery network based on realized and forecasted demand for a period, during at least partial execution of the set of routes, receiving real-time data representative of delivery of assets, and updated demand forecasts, determining, by an optimization controller, a time-to-trigger, and a cut-off time, and an updated demand forecast, the time-to-trigger defining an interval, at which the set of routes for delivery of assets is to be reoptimized, in response to the time-to-trigger being achieved, re-optimizing, by a route optimization module of the continuous delivery platform, the set of routes to provide a revised set of routes, and conveying at least one route of the revised set of routes to a delivery personnel, the at least one route being different from any route in the set of routes, and being executed during the period. ## Claims ### Claim 1 A computer-implemented method executed by one or more processors for selective optimization of delivery routes using a continuous delivery platform, the method comprising: determining a set of routes for delivery of assets within a delivery network based on demand forecast and realized demand for a period, the demand forecast being indicative of prediction of demand for delivery of assets within the period; during at least partial execution of the set of routes, receiving real-time data representative of delivery of assets, and updated demand forecast; determining, by an optimization controller of the continuous delivery platform, one or more of a time-to-trigger, and a cut-off time at least partially based on the real-time data, and the updated demand forecast, the time-to-trigger defining an interval, at which the set of routes for delivery of assets is to be reoptimized; in response to the time-to-trigger being achieved, re-optimizing, by a route optimization module of the continuous delivery platform, the set of routes to provide a revised set of routes; and conveying at least one route of the revised set of routes to a delivery personnel, the at least one route being different from any route in the set of routes, and being executed during the period. ### Claim 2 (depends on 1) The method of claim 1, wherein routes are defined as a plurality of dynamic nodes, at least one node changing between a route in the set of routes, and a route in the revised set of routes. ### Claim 3 (depends on 1) The method of claim 1, wherein the time-to-trigger is intermittently updated during the period, and at least one subsequent re-optimization of routes is executed based on an updated time-to-trigger. ### Claim 4 (depends on 1) The method of claim 1, wherein the time-to-trigger is at least partially determined by on incremental learning based on the demand forecast, and actual conditions within the delivery network. ### Claim 5 (depends on 4) The method of claim 4, wherein the incremental learning at least partially comprises one of retraining a predictive model based on a most-recent dataset available, and retraining the predictive model based on a full dataset including historical data, and the most-recent dataset. ### Claim 6 (depends on 1) The method of claim 1, wherein at least a portion of the real-time data is received from a mobile computing device of the deliver personnel, and the at least one route of the revised set of routes is conveyed to the mobile computing device. ### Claim 7 (depends on 1) The method of claim 1, wherein the cut-off time defines a time during the period, after which re-optimization of the routes is not executed. ### Claim 8 (depends on 1) The method of claim 1, wherein the at least one route comprises one of a new route, and a modified route. ### Claim 9 (depends on 1) The method of claim 1, wherein the set of routes is determined based on historical asset demand data. ### Claim 10 (depends on 1) The method of claim 1, wherein determining the set of routes includes predicting one or more route destinations. ### Claim 11 (depends on 1) The method of claim 1, wherein the real-time data is representative of a location of a delivery vehicle and/or of a location of an asset. ### Claim 12 (depends on 1) The method of claim 1, further comprising optimizing a time for delivery of an asset on one or more routes of the set of routes. ### Claim 13 A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for selective optimization of delivery routes using a continuous delivery platform, the operations comprising: determining a set of routes for delivery of assets within a delivery network based on demand forecast and realized demand for a period, the demand forecast being indicative of prediction of demand for delivery of assets within the period; during at least partial execution of the set of routes, receiving real-time data representative of delivery of assets, and updated demand forecast; determining, by an optimization controller of the continuous delivery platform, one or more of a time-to-trigger, and a cut-off time at least partially based on the real-time data, and the updated demand forecast, the time-to-trigger defining an interval, at which the set of routes for delivery of assets is to be reoptimized; in response to the time-to-trigger being achieved, re-optimizing, by a route optimization module of the continuous delivery platform, the set of routes to provide a revised set of routes; and conveying at least one route of the revised set of routes to a delivery personnel, the at least one route being different from any route in the set of routes, and being executed during the period. ### Claim 14 (depends on 13) The computer-readable storage medium of claim 13, wherein routes are defined as a plurality of dynamic nodes, at least one node changing between a route in the set of routes, and a route in the revised set of routes. ### Claim 15 (depends on 13) The computer-readable storage medium of claim 13, wherein the time-to-trigger is intermittently updated during the period, and at least one subsequent re-optimization of routes is executed based on an updated time-to-trigger. ### Claim 16 (depends on 13) The computer-readable storage medium of claim 13, wherein the time-to-trigger is at least partially determined by on incremental learning based on the demand forecast, and actual conditions within the delivery network. ### Claim 17 (depends on 16) The computer-readable storage medium of claim 16, wherein the incremental learning at least partially comprises one of retraining a predictive model based on a most-recent dataset available, and retraining the predictive model based on a full dataset including historical data, and the most-recent dataset. ### Claim 18 (depends on 13) The computer-readable storage medium of claim 13, wherein at least a portion of the real-time data is received from a mobile computing device of the deliver personnel, and the at least one route of the revised set of routes is conveyed to the mobile computing device. ### Claim 19 (depends on 13) The computer-readable storage medium of claim 13, wherein the cut-off time defines a time during the period, after which re-optimization of the routes is not executed. ### Claim 20 (depends on 13) The computer-readable storage medium of claim 13, wherein the at least one route comprises one of a new route, and a modified route. ### Claim 21 (depends on 13) The computer-readable storage medium of claim 13, wherein the set of routes is determined based on historical asset demand data. ### Claim 22 (depends on 13) The computer-readable storage medium of claim 13, wherein determining the set of routes includes predicting one or more route destinations. ### Claim 23 (depends on 13) The computer-readable storage medium of claim 13, wherein the real-time data is representative of a location of a delivery vehicle and/or of a location of an asset. ### Claim 24 (depends on 13) The computer-readable storage medium of claim 13, wherein operations further comprise optimizing a time for delivery of an asset on one or more routes of the set of routes. ### Claim 25 A system, comprising: one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for selective optimization of delivery routes using a continuous delivery platform, the operations comprising: determining a set of routes for delivery of assets within a delivery network based on demand forecast and realized demand for a period, the demand forecast being indicative of prediction of demand for delivery of assets within the period; during at least partial execution of the set of routes, receiving real-time data representative of delivery of assets, and updated demand forecast; determining, by an optimization controller of the continuous delivery platform, one or more of a time-to-trigger, and a cut-off time at least partially based on the real-time data, and the updated demand forecast, the time-to-trigger defining an interval, at which the set of routes for delivery of assets is to be reoptimized; in response to the time-to-trigger being achieved, re-optimizing, by a route optimization module of the continuous delivery platform, the set of routes to provide a revised set of routes; and conveying at least one route of the revised set of routes to a delivery personnel, the at least one route being different from any route in the set of routes, and being executed during the period. ### Claim 26 (depends on 25) The system of claim 25, wherein routes are defined as a plurality of dynamic nodes, at least one node changing between a route in the set of routes, and a route in the revised set of routes. ### Claim 27 (depends on 25) The system of claim 25, wherein the time-to-trigger is intermittently updated during the period, and at least one subsequent re-optimization of routes is executed based on an updated time-to-trigger. ### Claim 28 (depends on 25) The system of claim 25, wherein the time-to-trigger is at least partially determined by on incremental learning based on the demand forecast, and actual conditions within the delivery network. ### Claim 29 (depends on 28) The system of claim 28, wherein the incremental learning at least partially comprises one of retraining a predictive model based on a most-recent dataset available, and retraining the predictive model based on a full dataset including historical data, and the most-recent dataset. ### Claim 30 (depends on 25) The system of claim 25, wherein at least a portion of the real-time data is received from a mobile computing device of the deliver personnel, and the at least one route of the revised set of routes is conveyed to the mobile computing device. ### Claim 31 (depends on 25) The system of claim 25, wherein the cut-off time defines a time during the period, after which re-optimization of the routes is not executed. ### Claim 32 (depends on 25) The system of claim 25, wherein the at least one route comprises one of a new route, and a modified route. ### Claim 33 (depends on 25) The system of claim 25, wherein the set of routes is determined based on historical asset demand data. ### Claim 34 (depends on 25) The system of claim 25, wherein determining the set of routes includes predicting one or more route destinations. ### Claim 35 (depends on 25) The system of claim 25, wherein the real-time data is representative of a location of a delivery vehicle and/or of a location of an asset. ### Claim 36 (depends on 25) The system of claim 25, wherein operations further comprise optimizing a time for delivery of an asset on one or more routes of the set of routes.