Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • 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
  • G06Q10/00—Administration; Management
  • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • 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
  • G06Q10/00—Administration; Management
  • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
  • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem

Definitions

• the present disclosure relates to a cold chain system and more specifically, to methods, systems, and computer program products for analyzing and controlling the cold chain system.
• a cold chain is a temperature-controlled supply chain.
• a cold chain is an unbroken and uninterrupted series of storage and distribution activities which maintain a given temperature range for product being moved along the chain.
• a cold chain is used to help extend and ensure the shelf life of products such as fresh agricultural produce, seafood, frozen food, film, fluids, chemicals, pharmaceutical drugs, and other temperature sensitive items.
• Some cold chain system providers offer decision support provided by a software and server implementation for informing inventory and logistics management decisions. These support offering provide more big data tools and automation of the cold chain system control. With more big data tools available, decision making processes are becoming more and more automated. However, these systems focus on completely automating a select portion with no integration of human input with the decision support system.
• a method for risk and decision analysis for a cold chain system includes receiving input parameters at a decision engine, generating, using the decision engine, a recommendation signal that includes a route and a schedule using the input parameters, calculating, using a risk engine, a risk value for the route and the schedule, comparing the risk value versus a risk threshold, generating a notification for requesting user input in response to the risk value being equal to or greater than the risk threshold, transmitting the notification and recommendation signal that includes the route and schedule to a user device used by a user, receiving a user selection from the user device based on at least the notification and recommendation signal, storing the user selection in a network database that includes a computer readable medium storage, instructing cold chain system to execute a logistics event based on the user selection, and storing the logistics event and outcome in the network database.
• the method includes comparing the user selection and the recommendation signal to the stored logistics events and outcome in response to the user selection being on override of the recommendation signal, and generating a report based on time, quality, route logistics, and costs of the outcome as compared to the user selection and the recommendation signal predicted values.
• the method includes categorizing the user that provided the user selection based on the generated report, and granting user access to an expert connect chat room based on the categorization of the user.
• the method includes comparing the risk value versus a high risk threshold, and generating a notification for requesting user input from the user granted access to the expert connect chat room in response to the risk value being equal to or greater than the high risk threshold.
• the method includes wherein the input parameters include at least one of growers information, logistics information, truck drivers and distribution information, retail and wholesalers information, and routing network information, wherein growers' information includes product data, ripening schedules, and sensor data, wherein logistics information includes scheduling data, personnel data, and cost structuring, wherein truck drivers and distribution information includes route information, vehicle maintenance, and sensor data, wherein retail and wholesalers information includes inventory data and delivery schedules, and wherein routing network information includes road closures, traffic patterns, road work, and route feasibility data.
• growers' information includes product data, ripening schedules, and sensor data
• logistics information includes scheduling data, personnel data, and cost structuring
• truck drivers and distribution information includes route information, vehicle maintenance, and sensor data
• retail and wholesalers information includes inventory data and delivery schedules
• routing network information includes road closures, traffic patterns, road work, and route feasibility data.
• the method includes wherein the decision engine is an artificial intelligence (AI) engine.
• AI artificial intelligence
• the method includes wherein generating, using the decision engine, the recommendation signal that includes the route and the schedule using the input parameters includes retrieving logistic events that include routes and schedules from the network database, transmitting logistics events to a routing network, determining, at the routing network, feasibility based on the transmitted logistics events, receiving feasibility data from routing network, recalculating route based on feasibility data in response to at least one route being infeasible, recalculating schedule based on feasibility data in response to the at least one route remaining infeasible, and sending the notification that includes the modified route and schedule to the risk engine if the route is found feasible.
• the method includes, wherein determining feasibility includes comparing a preferred route and preferred schedule with actual routing data in the logistics events, and calculating whether or not the preferred route will be able to attain the preferred schedule.
• the method includes, wherein calculating, using the risk engine, the risk value for the route and the schedule includes receiving the route and the schedule from the recommendation signal, retrieving an original route and schedule from the network database, comparing the original route and schedule with the route and schedule from the recommendation signal, and incrementing risk value in response to comparing the original route and schedule with the route and schedule from the recommendation signal.
• a system for risk and decision analysis that includes a software as a service (SaaS) decision machine and logistics network for a cold chain system.
• the system includes a decision engine that is configured to receive input parameters from at least one of a grower operation, a logistics hub, a semi-trailer truck, a distribution container, a retailer, a wholesaler, a routing network, and a user device, a recommendation signal, generated using the decision engine, that includes a route and a schedule using the input parameters, a risk engine that is configured to calculate a risk value for the route and the schedule, a comparison engine that is configured to compare the risk value versus a risk threshold, a notification generated for requesting user input in response to the risk value being equal to or greater than the risk threshold, a device application programming interface (API) that is configured to transmit the notification and recommendation signal that includes the route and schedule to the user device used by a user, a user selection received from the user device based on at least the notification and recommendation signal,
• API device application programming interface
• the system includes, wherein the SaaS decision machine is further configured to compare the user selection and the recommendation signal to the stored logistics events and outcome in response to the user selection being on override of the recommendation signal, and generate a report based on time, quality, route logistics, and costs of the outcome as compared to the user selection and the recommendation signal predicted values.
• the system includes, wherein the SaaS decision machine is further configured to categorize the user that provided the user selection based on the generated report, and grant user access to an expert connect chat room based on the categorization of the user.
• the system includes, wherein the SaaS decision machine is further configured to compare the risk value versus a high risk threshold, and generate a notification for requesting user input from the user granted access to the expert connect chat room in response to the risk value being equal to or greater than the high risk threshold.
• the system includes, wherein the input parameters include at least one of growers information, logistics information, truck drivers and distribution information, retail and wholesalers information, and routing network information, wherein growers' information includes product data, ripening schedules, and sensor data, wherein logistics information includes scheduling data, personnel data, and cost structuring, wherein truck drivers and distribution information includes route information, vehicle maintenance, and sensor data, wherein retail and wholesalers information includes inventory data and delivery schedules, and wherein routing network information includes road closures, traffic patterns, road work, and route feasibility data.
• growers' information includes product data, ripening schedules, and sensor data
• logistics information includes scheduling data, personnel data, and cost structuring
• truck drivers and distribution information includes route information, vehicle maintenance, and sensor data
• retail and wholesalers information includes inventory data and delivery schedules
• routing network information includes road closures, traffic patterns, road work, and route feasibility data.
• the system includes, wherein the decision engine is an artificial intelligence (AI) engine.
• AI artificial intelligence
• the system further includes logistic events that include routes and schedules and are stored in the network database, a routing network that receives the logistics events and is configured to determine feasibility based on the logistics events and transmit the feasibility data to the decision engine, wherein the decision engine is further configured to recalculate the route based on feasibility data in response to at least one route being infeasible, recalculate the schedule based on feasibility data in response to the at least one route remaining infeasible, and send the notification that includes the modified route and schedule to the risk engine if the route is found feasible.
• the system includes, wherein the routing network is further configured to compare a preferred route and preferred schedule with actual routing data in the logistics events, and calculate whether or not the preferred route will be able to attain the preferred schedule.
• the system includes, wherein the risk engine, when calculating the risk value for the route and the schedule, is further configured to receive the route and the schedule from the recommendation signal, retrieve an original route and schedule from the network database, compare the original route and schedule with the route and schedule from the recommendation signal, and increment risk value in response to comparing the original route and schedule with the route and schedule from the recommendation signal.
• a cold chain control system for analyzing and controlling a cold chain system.
• the system includes one or more processors in communication with one or more types of computer readable storage mediums having program instructions embodied therewith, the program instructions executable by the one or more processors to cause the processors to receive input parameters at an decision engine, generate, using the decision engine, a recommendation signal that includes a route and a schedule using the input parameters, calculate, using a risk engine, a risk value for the route and the schedule, compare the risk value versus a risk threshold, generate a notification for requesting user input in response to the risk value being equal to or greater than the risk threshold, transmit the notification and recommendation signal that includes the route and schedule to a user device used by a user, receive a user selection from the user device based on at least the notification and recommendation signal, store the user selection in a network database that includes a computer readable medium storage, instruct cold chain system to execute a logistics event based on the user selection, and store the logistics event and outcome in the network
• the cold chain control system includes, wherein the one or more types of computer readable storage mediums includes additional program instructions embodied therewith, the additional program instructions executable by the one or more processors to cause the processors to compare the user selection and the recommendation signal to the stored logistics events and outcome in response to the user selection being on override of the recommendation signal, generate a report based on time, quality, route logistics, and costs of the outcome as compared to the user selection and the recommendation signal predicted values, categorize the user that provided the user selection based on the generated report, grant user access to an expert connect chat room based on the categorization of the user, compare the risk value versus a high risk threshold, and generate a notification for requesting user input from the user granted access to the expert connect chat room in response to the risk value being equal to or greater than the high risk threshold.
• FIG. 1 depicts a cloud computing environment according to an embodiment of the present disclosure
• FIG. 2 depicts abstraction model layers according to an embodiment of the present disclosure
• FIG. 3 is a block diagram illustrating one example of a processing system for practice of the teachings herein;
• FIG. 4 depicts a block diagram illustrating an overall system in accordance with an embodiment of the present disclosure
• FIG. 5A depicts a graphical user interface (GUI) of a logistics application input interface on a user device in accordance with an embodiment of the present disclosure
• FIG. 5B depicts another graphical user interface (GUI) of a logistics application showing search results on a user device in accordance with an embodiment of the present disclosure
• FIG. 6A depicts a notification graphical user interface (GUI) on a user device in accordance with an embodiment of the present disclosure
• FIG. 6B depicts a comparison graphical user interface (GUI) on a user device in accordance with an embodiment of the present disclosure
• FIG. 7 depicts a logistics application on user device in accordance with an embodiment of the present disclosure
• FIG. 8 depicts a flow chart of software as a service base software that communicates with a logistic application on a user device in accordance an embodiment of the present disclosure
• FIG. 9 depicts a flow chart of a device API on software as a service AI and human logistics network in accordance with an embodiment of the present disclosure
• FIG. 10 depicts a flow chart for an AI engine on software as a service AI and human logistics network in accordance with an embodiment of the present disclosure
• FIG. 11 depicts a flow chart for a risk engine on software as a service AI and human logistics network in accordance with an embodiment of the present disclosure
• FIG. 12 depicts a flow chart for a human/AI comparison engine on software as a service AI and human logistics network in accordance with an embodiment of the present disclosure
• FIG. 13 is a flow chart of a method for a cold chain system in accordance with an embodiment of the present disclosure.
• Cloud computing is a model of service delivery for enabling convenient, on- demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.
• This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
• On-demand self-service a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
• Resource pooling the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
• Rapid elasticity capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
• Measured service cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
• level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).
• SaaS Software as a Service: the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure.
• the applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web- based e-mail).
• a web browser e.g., web- based e-mail
• the consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
• PaaS Platform as a Service
• the consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
• IaaS Infrastructure as a Service
• the consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
• Private cloud the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off- premises.
• Public cloud the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
• Hybrid cloud the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
• a cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability.
• An infrastructure comprising a network of interconnected nodes.
• cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate.
• Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof.
• This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device.
• FIG. 1 a set of functional abstraction layers provided by cloud computing environment 50 (Fig. 1) is shown. It should be understood in advance that the components, layers, and functions shown in Fig. 2 are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted, the following layers and corresponding functions are provided:
• Hardware and software layer 60 includes hardware and software components.
• hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66.
• software components include network application server software 67 and database software 68.
• Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
• management layer 80 may provide the functions described below.
• Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment.
• Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses.
• Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources.
• User portal 83 provides access to the cloud computing environment for consumers and system administrators.
• Service level management 84 provides cloud computing resource allocation and management such that required service levels are met.
• Service Level Agreement (SLA) planning and fulfillment 85 provides pre- arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
• SLA Service Level Agreement
• Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and processing of messages across multiple communication systems 96. [0066] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for prioritizing delivery of messages across multiple communication systems are provided.
• a messaging system is configured to receive messages for an individual across multiple communication systems utilized by the individual. The messaging system is also configured to determine a priority level associated with each of the messages based on an analysis of the messages and a user profile of the individual.
• the messaging system Based on the determined priority level and the user profile, the messaging system delivers the messages to a desired communication device via a desired messaging system.
• the user profile is updated by the messaging system upon receiving feedback from the individual, wherein the feedback includes message delivery preferences and message priority preferences of the individual.
• processors 101a, 101b, 101c, etc. collectively or generically referred to as processor(s) 101.
• processors 101 may include a reduced instruction set computer (RISC) microprocessor.
• RISC reduced instruction set computer
• processors 101 are coupled to system memory 114 and various other components via a system bus 113.
• ROM Read only memory
• BIOS basic input/output system
• FIG. 3 further depicts an input/output (I/O) adapter 107 and a network adapter 106 coupled to the system bus 113.
• I/O adapter 107 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 103 and/or tape storage drive 105 or any other similar component.
• I/O adapter 107, hard disk 103, and tape storage device 105 are collectively referred to herein as mass storage 104.
• Operating system 120 for execution on the processing system 100 may be stored in mass storage 104.
• a network adapter 106 interconnects bus 113 with an outside network 116 enabling data processing system 100 to communicate with other such systems.
• a screen (e.g., a display monitor) 115 is connected to system bus 113 by display adaptor 112, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller.
• adapters 107, 106, and 112 may be connected to one or more I/O busses that are connected to system bus 113 via an intermediate bus bridge (not shown).
• Suitable I O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI).
• PCI Peripheral Component Interconnect
• Additional input/output devices are shown as connected to system bus 113 via user interface adapter 108 and display adapter 112.
• a keyboard 109, mouse 110, and speaker 111 all interconnected to bus 113 via user interface adapter 108, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.
• the processing system 100 includes a graphics processing unit 130.
• Graphics processing unit 130 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display.
• Graphics processing unit 130 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.
• the system 100 includes processing capability in the form of processors 101, storage capability including system memory 114 and mass storage 104, input means such as keyboard 109 and mouse 110, and output capability including speaker 111 and display 115.
• processing capability in the form of processors 101
• storage capability including system memory 114 and mass storage 104
• input means such as keyboard 109 and mouse 110
• output capability including speaker 111 and display 115.
• a portion of system memory 114 and mass storage 104 collectively store an operating system to coordinate the functions of the various components shown in FIG. 3.
• integrating the user input with the software and system is done by integrating a suggestion algorithm for routes and logistics based upon machine decision and tracking to see if the human over rides the decision. Over time, quality, route logistics, costs are analyzed and results are collected and compared to actual outcomes.
• the comparison over time is used for further recommendations. For example, the more successful users are granted access to connect to an "expert connect chat room.” Other users can connect to the expert connect chat room to ask the user with sound decision making routing and schedule queries. Further the machine may also connected and may query for responses, from and based upon the user that is auto connected to the "expert connect chat room" when the machine decision making determines that the risk involved is more than a high risk tolerance warranting the expert connect chat room user query.
• a cold chain that matches user to machine decision recommendations using a Software as a Service (SaaS) implementation for cold chain specific decisions is provided.
• SaaS Software as a Service
• an "expert connect chat room" that other users and decision engines can connect by the machine decision invoking the expert based upon previous cold chain results can be provided.
• embodiments in accordance with this disclosure can provide immediate value to the cold chain market to enhance learning and grow expertise in machine decision making over time.
• one or more embodiments of the disclosure build intelligence and opens the machine software to leverage expert decisions and also invoke the chat room.
• FIG. 4 depicts a block diagram illustrating an overall system 400 in accordance with an exemplary embodiment.
• the system includes a SaaS logistics network 410.
• the SaaS logistics network 410 may be include hardware and software elements implemented together.
• the SaaS logistics network 410 can be implemented to include AI and Human elements and inputs such that the SaaS logistics network 410 can be called a SaaS AI and Human logistics network 410.
• the logistics network 410 includes SaaS base software 420 as well as a network database 470 and a notification data base 480. Further the logistics network also includes a decision engine 430, a risk engine 440, a comparison engine 450 (also called a human/AI comparison engine), and a device API 460.
• the SaaS base software 420 can be used to trigger the other subcomponents of the logistics network 410.
• the network database 470 can be used to store received data from other elements connected to the logistics network as well as locally generated and processed information.
• the notification data base 480 can be used to store notification that are generated and used by the logistics network 410.
• the logistics network 410 connected to a number of other system elements through network connected such as through a cloud implementation or over the internet 405.
• the system further includes growers 411, logistics 412, truck drivers and distribution 413, retail and wholesalers 414, routing network 415, and a user device 490.
• the growers 411 provide growers information that includes product data, ripening schedules, and sensor data.
• the logistics 412 provide logistics information that includes scheduling data, personnel data, and cost structuring.
• the truck drivers and distribution 413 provide truck drivers and distribution information that includes route information, vehicle maintenance, and sensor data.
• the retail and wholesalers 414 provided retail and wholesalers information that includes inventory data and delivery schedules.
• the routing network 415 provides routing network information that includes road closures, traffic patterns, road work, and route feasibility data.
• the user device provides the ability to receive user input as well and provide processed information and generated signals to a user.
• the user device 490 includes a logistics application as well as a logistics graphical user interface (GUI), a comparison GUI, and a notification GUI.
• GUI logistics graphical user interface
• FIG. 5A depicts a graphical user interface (GUI) 500A of a logistics application input interface on a user device in accordance with an exemplary embodiment.
• the logistics GUI 500A provides interface button, menus, and other elements as shown so that a user can select and input a variety of search terms.
• the logistics GUI is a GUI of the logistics application on the user device that allows the user to select parameters to search the SaaS AI and human logistics network for logistics events that match those parameters.
• a user may select a vehicle or driver in a first step.
• the user may also select a date range in a second step.
• a user may select filters as shown.
• FIG. 5B depicts another graphical user interface (GUI) 500B of a logistics application showing search results on a user device in accordance with an exemplary embodiment.
• the logistics GUI 500B shows one embodiment of search results from such a search, with buttons alongside each event allowing the user to retrieve more details about the event, or compare the event to similar events.
• FIG. 6A depicts a notification graphical user interface (GUI) 600A on a user device in accordance with an exemplary embodiment.
• the notification GUI 600A shows a notification from the AI engine of the SaaS AI and human logistics network that alerts the user that the AI engine encountered a route that was infeasible because of a road closure.
• the notification GUI 600A also shows that the AI engine recommends a new route but with an increased risk level, thus requiring human operator approval.
• the notification GUI 600A may display a statement specifically telling a user what the issue is as well as imagines showing the infeasible route as well as the new route that is recommended.
• the notification GUI 600A can also provide button so that the user can select, edit, or compare the new route that is being suggested.
• FIG. 6B depicts a comparison graphical user interface (GUI) 600B on a user device in accordance with an exemplary embodiment.
• Comparison GUI 600B shows a comparison received from the SaaS AI and human logistics network that compares a selected logistic event to logistic events of the same type and risk level. The comparison may include, for example, the percentage of similar events created by a human operator that were delivered on time versus the percentage of similar events created by the AI engine that were delivered on time.
• the comparison GUI 600B can display a table showing a comparison and/or statements that set out the comparison values as shown.
• FIG. 7 depicts a flow char of a logistics application on a user device in accordance with an exemplary embodiment.
• the logistics application on user device allows the user to access the Logistics GUI 500A, Logistics GUI 500B, Notification GUI 600A, and Comparison GUI 600B as shown in FIGs. 5 and 6 (operation 700) and interact with the SaaS AI and human logistics network by sending and receiving logistics data (operations 705 - 755).
• the logistics application on the user device allows a user to select a vehicle or driver using a logistics GUI 500A (operation 710), allows the user to input a data range using the logistics GUI 500A (operation 715), and allows a user to select filters, such as pick-up, delivery, route change, etc., using the logistics GUI 500A (operation 720). Further, the logistics application sends vehicle/driver selection, data range, and filter selections to the SaaS AI and human logistics network (operation 725), receives search results from the SaaS AU and human logistics network (operation 730), and displays the search results from using a logistics GUI 500B.
• the logistics application checks if there is a notification (operation 735). If there is not a notification, the logistics application goes back to operation 700. If there is a notification, the logistics application receives notification data and opens and populates a notification GUI 600A on a user device (operation 740).
• the logistics application checks if there is a comparison selected (operation 745). If a comparison is not selected, the logistics application goes back to operation 700. If a comparison is selected, the logistics application requests a comparison be processed by the SaaS AI and human logistics network and populates a comparison GUI 600B on the user device (operation 750). The logistics app also receives comparison data from the SaaS AI and human logistics network and displays the comparison data on the comparison GUI 600B on the user device (operation 755).
• FIG. 8 depicts a flow chart of SaaS base software that communicates with a logistic application on a user device in accordance with an exemplary embodiment.
• SaaS base software handshakes with the logistics application on the user device to execute a device API, populate and search a network database, execute an AI engine and a risk engine that includes a risk algorithm if requested, and run a comparison engine if selected.
• the SaaS base software receives information from a user device (operation 800).
• the SaaS base software executes device API to send and receive vehicle or driver search data on the network data base (operation 805), sends search results to user device logistic application from the SaaS AI and human logistics network (operation 810), and receives vehicle/driver selection, data range, and filter selection from the logistic application from SaaS AI and human logistics network (operation 815).
• the SaaS base software also checks is there is a notification requested from the logistics application of the user device (operation 820). If there is not, the SaaS base software returns to operation 800. If there is, the SaaS base software executes the AI engine and the risk engine to get notifications if any (operation 825). The SaaS base software sends the notification data and opens and populates a notification GUI on the issuer device (operation 830).
• the SaaS base software checks is a comparison is selected. If a comparison is not selected the SaaS base software returns to operation 800. If a comparison is selected, the SaaS base software executes the comparison engine that calculates comparison data (operation 840) and sends the comparison data to a comparison GUI on the user device (845).
• FIG. 9 depicts a flow chart of a device API on a SaaS AI and human logistics network in accordance with an exemplary embodiment.
• the device API allows the user device to load new logistics events into the Software as a Service AI and Human Logistics Network (operation 900), whereby it stores the new events in a database (operation 905).
• the device API furthermore allows the user to send parameters to search the network for logistics events (operation 910).
• the API retrieves the events with matching parameters (operation 915) and sends them to the user device (operation 920).
• FIG. 10 depicts a flow chart for an AI engine on a SaaS AI and human logistics network in accordance with an exemplary embodiment.
• An AI engine includes a software protocol on the SaaS AI and Human Logistics Network that allows a machine to operate intelligently based on inputted parameters.
• the inputted parameters may be routing data and scheduling data for a cold chain logistics system retrieved from a data base (operation 1000).
• the AI engine can send a logistics event to a routing network (operation 1005), and network specifically designed to calculate the feasibility of routes based on traffic, weather, and mapping data.
• the AI engine will then receive feasibility data from the routing network (operation 1010).
• the AI engine determines if at least one route is infeasible (operation 1015). If no routes are infeasible then the processes does nothing (1020).
• the AI engine receives notice that at least one route is infeasible as currently schedule, the AI engine first recalculates a new route to attempt to keep the same schedule (operation 1025). If this route is still infeasible (operation 1030), the AI engine recalculates the schedule (operation 1035). If the recalculated schedule is still not feasible (operation 1045), the AI engine retrieves matching notification from the notification database (operation 1050) and notifies the human operator and requests input (operation 1055). If the newly calculated route is feasible, the AI engine sends the feasible route to the risk engine that can include a risk algorithm to calculate risk of the modified route (operation 1040).
• FIG. 11 depicts a flow chart for a risk engine that can include a risk algorithm on software as a service AI and human logistics network in accordance with an exemplary embodiment.
• the Risk engine includes software on the SaaS AI and Human Logistics Network that calculates the risk level associated with a logistics event based on parameters in order to determine if the event has a sufficient level of risk such that a human operator needs to approve the modified route.
• the risk engine retrieves modified route and scheduling data from its own storage (operation 1100).
• the risk engine then retrieves original route and schedule information from the network database (operation 1105) and compares the original and modified route and schedule (operation 1110). If the modified route is greater than 50 miles longer than the original (operation 1115) the risk level is increased by 1 (operation 1120). If the modified route is found to pass through high traffic areas (operation 1125) the risk level is increased by 1 (operation 1130). If the modified route passes through extreme weather areas (operation 1135) the risk level is increased by 1 (operation 1140). If the modified schedule increases delivery by more than four hours (operation 1145), the risk level is increased by 1 (operation 1150).
• the risk level is increased by 1 (operation 1160).
• the risk engine then stores the risk level with route and schedule in the network database (operation 1165). Then the risk level is checked to see if it is greater than or equal to three (operation 1170) and if it is not nothing is done (operation 1185). However, if the risk level is greater than or equal to three then the risk engine retrieves matching notification from the notification database (operation 1175) and notifies the human operation of the risk level and requests input via the user device (operation 1180).
• FIG. 12 depicts a flow chart for a human/AI comparison engine on a SaaS AI and human logistics network in accordance with an exemplary embodiment.
• the human/ AI comparison engine includes software on the SaaS AI and Human Logistics Network that compares the relative success or failure of AI decisions versus comparable human decisions.
• a success may be a simple as the delivery arriving on time
• a failure may be as simple as the delivery not arriving on time.
• the percentage calculation is sent to the user device to be viewed by a user.
• the human/ AI comparison engine receives logistics event user selected to compare success (operation 1200) and retrieves events of the same type and same risk level completed over comparison period (e.g. six months) (operation 1205). Further, the human/AI comparison engine calculates percent of human created events that were on time (operation 1210),calculates the percent of AI created events that were on time (operation 1215), and sends the calculations as comparison data to the user device (operation 1220).
• FIG. 13 is a flow chart of a method for a cold chain system in accordance with an embodiment of the present disclosure.
• the method includes receiving input parameters at an decision engine (operation 1300), generating, using the decision engine, a recommendation signal that includes a route and a schedule using the input parameters (operation 1305), calculating, using a risk engine, a risk value for the route and the schedule (operation 1310), comparing the risk value versus a risk threshold (operation 1315), and generating a notification for requesting user input in response to the risk value being equal to or greater than the risk threshold (1320).
• the method also includes transmitting the notification and recommendation signal that includes the route and schedule to a user device used by a user (operation 1325), receiving a user selection from the user device based on at least the notification and recommendation signal (operation 1330), storing the user selection in a network database that includes a computer readable medium storage (operation 1335), instructing cold chain system to execute a logistics event based on the user selection (operation 1340), and storing the logistics event and outcome in the network database (operation 1345).
• One or more embodiments can be heavily integrated into a cold chain system, and provide for rapid response AI and strategic human decisions in one platform.
• One or more of these embodiments can increases the value of human capital, allowing simpler decisions to be made by AI, increase the effectiveness of human AI cooperation for long term AI integration
• One or more embodiments of the present disclosure may be a system, a method, and/or a computer program product.
• the computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present one or more embodiments.
• the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
• the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
• a non- exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
• RAM random access memory
• ROM read-only memory
• EPROM or Flash memory erasable programmable read-only memory
• SRAM static random access memory
• CD-ROM compact disc read-only memory
• DVD digital versatile disk
• memory stick a floppy disk
• mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
• a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
• Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
• the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
• a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
• Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
• the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
• the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
• electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
• These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
• These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
• the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
• each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
• the functions noted in the block may occur out of the order noted in the figures.
• two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

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• 2017
  • 2017-03-02 CN CN201780018854.4A patent/CN108780537A/zh active Pending
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