Real-time intelligent and dynamic delivery scheduling

A device may receive first data associated with a delivery of an item or service. The first data may be received from a system in association with an order being placed for the item or service. The device may receive second data associated with scheduling the delivery from another device. A portion of the second data may include natural language text data, or natural language audio data. The device may process the first data and the second data using a processing technique to identify information related to scheduling the delivery. The device may perform an action related to the delivery. The action may include scheduling the delivery based on a result of processing the first data and the second data, monitoring the first data and the second data, or modifying the delivery based on monitoring the first data and the second data.

BACKGROUND

A chatbot (e.g., a talkbot, a chatterbot, a bot, or an artificial conversational entity) may include a computer program that conducts a conversation via auditory or textual methods. In some cases, chatbots are designed to simulate how a human would behave as a conversational partner. A chatbot may be used in a dialog system for various practical purposes including customer service or information acquisition.

SUMMARY

According to some possible implementations, a first device may comprise one or more processors to receive first data associated with a delivery of an item or service. The first data may be received from a system associated with the first device. The one or more processors may receive second data associated with scheduling the delivery. At least a portion of the second data may be received from a second device that is not associated with the system associated with the first device. The second data may include calendar data, weather data, or traffic data. The one or more processors may process the first data and the second data using a processing technique to identify information related to scheduling the delivery. The one or more processors may perform an action related to the delivery. The action may include communicating with a recipient of the delivery to schedule the delivery based on a result of processing the first data and the second data, communicating with a delivery agent to schedule the delivery based on the result of processing the first data and the second data, monitoring the first data or the second data to determine a modification to the first data or the second data, or modifying the delivery based on the modification to the first data or the second data.

According to some possible implementations, a method may comprise receiving, by a device, first data associated with multiple deliveries of multiple items or services. The first data may identify multiple recipients to which the multiple items or services are to be delivered, or multiple types of the multiple items or services to be delivered. The method may include receiving, by the device, second data associated with scheduling the multiple deliveries. A first portion of the second data may be received via a chatbot that can communicate with multiple second devices associated with the multiple recipients. A second portion of the second data may be received from multiple third devices. The method may include processing, by the device, the first data and the second data using multiple processing techniques to identify information related to scheduling the multiple deliveries. The method may include performing, by the device, multiple actions related to the multiple deliveries. The multiple actions may include scheduling at least one of the multiple deliveries based on a result of processing the first data and the second data, monitoring the first data or the second data to determine a modification to the first data or the second data, or modifying at least one of the multiple deliveries based on the modification to the first data or the second data.

According to some possible implementations, a non-transitory computer-readable medium may store one or more instructions that, when executed by one or more processors, cause the one or more processors to receive first data associated with one or more deliveries of one or more items or services. The first data may be received from one or more systems in association with one or more orders being placed for the one or more items or services. The one or more instructions, when executed by the one or more processors, may cause the one or more processors to receive second data associated with scheduling the one or more deliveries from one or more devices. One or more portions of the second data may include natural language text data, or natural language audio data. The one or more instructions, when executed by the one or more processors, may cause the one or more processors to process the first data and the second data using one or more processing techniques to identify information related to scheduling the one or more deliveries. The one or more processing techniques may include natural language processing, or artificial intelligence processing. The one or more instructions, when executed by the one or more processors, may cause the one or more processors to perform one or more actions related to the one or more deliveries. The one or more actions may include scheduling the one or more deliveries based on a result of processing the first data and the second data, monitoring the first data and the second data, or modifying the one or more deliveries based on monitoring the first data and the second data.

DETAILED DESCRIPTION

An organization may deliver an item and/or a service to an individual (or another organization or recipient). While the organization may be capable of coordinating delivery on a particular day and/or location at the time the delivery is scheduled, the organization and/or the individual may lack a computer-based system for managing the “last mile,” or “last hour,” of the delivery. For example, the organization and/or the individual may not be capable of modifying delivery to account for weather, traffic, individual preference, a scheduling conflict, and/or the like once a delivery vehicle is in route to deliver the item and/or the service.

Some implementations, described herein, provide a delivery system that is capable of receiving first data associated with a delivery of an item and/or service, receiving second data associated with scheduling the delivery, processing the first data and/or the second data to intelligently schedule the delivery, and/or performing an action related to the delivery. In this way, the delivery system provides a computer-based system to intelligently manage the “last mile” or “last hour” of a delivery (e.g., based on weather data, calendar data, traffic data, etc.), thereby improving a delivery. In addition, this conserves resources, such as processing resources of a device scheduling a delivery, vehicle-related resources, such as fuel, cost, or wear-and-tear related to performing a delivery, and/or the like, by reducing or eliminating a need for a delivery to be scheduled and/or attempted multiple times. Further, this improves an individual's experience related to a delivery via intelligent and dynamic scheduling of the delivery.

FIG. 1is a diagram of an overview of an example implementation100described herein. As shown inFIG. 1, example implementation100may include a delivery system, a set of server devices (e.g., a calendar server device, a weather server device, etc.), and/or a set of client devices (e.g., client devices CD1through CD3).

As shown inFIG. 1, and by reference number110, the delivery system may receive first data associated with a delivery. For example, the delivery system may receive a set of instructions to schedule a delivery for an item and/or a service (e.g., at a particular location and/or on a particular date). When receiving first data, the delivery system may receive millions, billions, billions, etc. of data elements associated with millions, billions, trillions, etc. of deliveries. In this way, the delivery system may receive a data set that cannot be processed manually or objectively by human operators.

As shown by reference numbers120-1and120-2, the delivery system may receive second data associated with scheduling the delivery. For example, the delivery system may receive calendar data from the calendar server device (e.g., data related to a calendar of an individual that is to receive the delivery), weather data from the weather server device, and/or the like. Additionally, or alternatively, and as another example, the delivery system may receive data from client device CD2related to an individual's availability, delivery preferences, modifications to a previously scheduled delivery, and/or the like. When receiving the second data, the delivery system may receive millions, billions, trillions, etc. of data elements associated with millions, billions, trillions, etc. of deliveries. In this way, the delivery system may receive a data set that cannot be processed manually or objectively by human operators.

The delivery system may receive the second data from client device CD2via a chatbot. For example, the delivery system may use a chatbot to intelligently communicate with a user of user device CD2to schedule a delivery, to modify a delivery (e.g., based on an unavailability of the user, inclement weather, etc.) in a conversational manner. This improves communications between the delivery system and a user of client device CD2.

As shown by reference number130, the delivery system may process the first data and/or the second data to schedule the delivery. For example, the delivery system may process calendar data associated with an individual to identify a convenient time for the individual to receive the delivery, weather data to identify a day and/or time when weather is acceptable for the delivery or when to modify the delivery, traffic data to reschedule or re-route the delivery, chatbot communications to identify a preference of the individual, and/or the like.

When processing the first data and/or the second data, the delivery system may use a technique. For example, the delivery system may use machine learning to identify an individual's preference based on prior deliveries of the same individual or a different individual. Additionally, or alternatively, and as another example, the delivery system may use a text and/or audio processing technique, such as natural language processing and/or speech-to-text, to process communications with client device CD2(e.g., chatbot communications, a call, etc.). The delivery system may process the first data and/or the second data using a technique to identify a term and/or phrase related to scheduling the delivery, such as a preferred date and/or time, a modification to a previously scheduled delivery, and/or the like. This permits the delivery system to intelligently schedule a delivery, thereby improving a delivery of an item and/or a service.

As shown by reference number140, the delivery system may provide a message regarding the delivery to client device CD3. For example, the message may include information regarding a date and/or time for a delivery or re-scheduled delivery, a location or an updated location for a delivery, and/or the like. In some cases, client device CD3may be associated with a driver of a delivery vehicle (e.g., a mobile phone of a driver). Additionally, or alternatively, client device CD3may include a navigation system of an autonomous vehicle, and the message may include a set of instructions related to a delivery.

The delivery system may perform the functions described above in real-time. For example, the delivery system may receive a modification to a delivery location from client device CD2after an item has left a facility for delivery. In this case, the delivery system may update information identifying the delivery location and may provide the update to client device CD3and/or a delivery vehicle automatically and in real-time. This conserves resources by reducing or eliminating a failed delivery, such as due to an individual not being present at an originally scheduled delivery location and improves the “last mile” and/or “last hour” of a delivery.

In this way, a delivery system provides a computer-based system to intelligently manage the “last mile” or “last hour” of a delivery (e.g., based on weather data, calendar data, etc.), thereby improving a delivery. In addition, this conserves resources (e.g., processing resources of a device scheduling deliveries, vehicle-related resources, such as fuel or wear-and-tear, etc.) related to performing a delivery, and/or the like, by reducing or eliminating a need for deliveries to be scheduled and/or attempted multiple times. Further, this improves an individual's experience related to a delivery via intelligent and dynamic scheduling of the delivery.

FIG. 2is a diagram of an example environment200in which systems and/or methods, described herein, may be implemented. As shown inFIG. 2, environment200may include client device210, server device220, delivery system230, cloud computing environment232, and a set of computing resources234. Devices of environment200may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

Client device210includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with a delivery. For example, client device210may include a desktop computer, a mobile phone (e.g., a smart phone or a radiotelephone), a laptop computer, a tablet computer, a gaming device, a wearable communication device (e.g., a smart wristwatch or a pair of smart eyeglasses), a navigation device (e.g., a global positioning system (GPS) navigation device), or a similar type of device. In some implementations, client device210may receive data associated with a delivery, as described elsewhere herein. Additionally, or alternatively, client device210may provide data (e.g., via a chatbot) to schedule or to modify a delivery, as described elsewhere herein. In some implementations, client device210may be associated with an autonomous vehicle (e.g., an unmanned aerial vehicle (UAV)) or a robot, as described elsewhere herein. While a single client device210is shown inFIG. 2, in practice, there can be hundreds, thousands, millions, etc. of client devices210in communication with delivery system230.

Server device220includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with a delivery. For example, server device220may include a server (e.g., in a data center or a cloud computing environment), a data center (e.g., a multi-server micro data center), a workstation computer, a virtual machine (VM) provided in a cloud computing environment, or a similar type of device. In some implementations, server device220may provide, to delivery system230, information related to a delivery, as described elsewhere herein. Additionally, or alternatively, server device220may store information related to a delivery (e.g., to facilitate machine learning), as described elsewhere herein. While a single server device220is shown inFIG. 2, in practice, there can be hundreds, thousands, millions, etc. of server devices220in communication with delivery system230.

Delivery system230includes one or more devices capable of intelligently analyzing data related to a delivery and scheduling the delivery. For example, delivery system230may include a cloud server or a group of cloud servers. In some implementations, delivery system230may be designed to be modular such that certain software components can be swapped in or out depending on a particular need. As such, delivery system230may be easily and/or quickly reconfigured for different uses.

In some implementations, as shown, delivery system230may be hosted in cloud comporting environment232. Notably, while implementations described herein describe delivery system230as being hosted in cloud computing environment232, in some implementations, delivery system230may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

Cloud computing environment232includes an environment that hosts delivery system230. Cloud computing environment232may provide computation, software, data access, storage, and/or other services that do not require end-user knowledge of a physical location and configuration of a system and/or a device that hosts delivery system230. As shown, cloud computing environment232may include a group of computing resources234(referred to collectively as “computing resources234” and individually as “computing resource234”).

Computing resource234includes one or more personal computers, workstation computers, server devices, or another type of computation and/or communication device. In some implementations, computing resource234may host delivery system230. The cloud resources may include compute instances executing in computing resource234, storage devices provided in computing resource234, data transfer devices provided by computing resource234, etc. In some implementations, computing resource234may communicate with other computing resources234via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown inFIG. 2, computing resource234may include a group of cloud resources, such as one or more applications (“APPs”)234-1, one or more virtual machines (“VMs”)234-2, one or more virtualized storages (“VSs”)234-3, or one or more hypervisors (“HYPs”)234-4.

Application234-1includes one or more software applications that may be provided to or accessed by one or more devices of environment200. Application234-1may eliminate a need to install and execute the software applications on devices of environment200. For example, application234-1may include software associated with delivery system230and/or any other software capable of being provided via cloud computing environment232. In some implementations, one application234-1may send/receive information to/from one or more other applications234-1, via virtual machine234-2.

FIG. 3is a diagram of example components of a device300. Device300may correspond to client device210, server device220, and/or delivery system230. In some implementations, client device210, server device220, and/or delivery system230may include one or more devices300and/or one or more components of device300. As shown inFIG. 3, device300may include a bus310, a processor320, a memory330, a storage component340, an input component350, an output component360, and a communication interface370.

FIG. 4is a flow chart of an example process400for real-time intelligent and dynamic delivery scheduling. In some implementations, one or more process blocks ofFIG. 4may be performed by delivery system230. In some implementations, one or more process blocks ofFIG. 4may be performed by another device or a group of devices separate from or including delivery system230, such as client device210and server device220.

As shown inFIG. 4, process400may include receiving first data associated with a delivery of an item and/or service (block410). For example, delivery system230may receive, from one or more server devices220, first data associated with a delivery of an item and/or service. In sonic implementations, when receiving first data, delivery system230may receive millions, billions, trillions, etc. of data elements associated with millions, billions, trillions, etc. of deliveries. In this way, delivery system230may receive a data set that cannot be processed manually or objectively by human operators.

In some implementations, delivery system230may receive first data periodically, according to a schedule, based on requesting the first data, based on input from a user of client device210, in real-time, and/or the like. For example, a delivery manager may access a customer relationship management (CRM) system an internal or third party CRM system or another type of electronic commerce system integrated with an organization's front-office and/or back office systems that utilizes technology like hypertext markup language (HTML), cascading style sheets (CSS), BootStrap, and/or Javascript) via client device210to create a new delivery. Continuing with the previous example, the manager may input first data related to the delivery to create the delivery. In some cases, an individual and/or organization (e.g., a recipient) placing an order may provide the first data. In this way, delivery system230may receive first data via a system associated with delivery system230and/or an organization with which delivery system230is associated. In addition, in this way, delivery system230may receive first data in association with an order for an item and/or service being placed.

In some implementations, the first data may relate to a delivery. For example, the first data may include data elements that identify an item and/or service to be delivered, an individual and/or organization to which the item and/or service is to be delivered, a type of the item and/or service (e.g., perishable, valuable, fragile, etc.), a location to which the item and/or service is to be delivered, a quantity of items and/or services to be delivered, a value of the items and/or services, and/or the like. In some implementations, delivery system230may receive the first data when an order is placed for an item and/or a service (e.g., via an electronic commerce system).

In some implementations, an item and/or service may include anything that can be offered to a market. For example, an item may include a commodity (refined/unrefined, processed/unprocessed, etc.), a manufactured item, equipment, and/or the like. In some implementations, and for example, a service may include an at-home cable installation, a plumbing service, a lawn care service (e.g., for an individual's residence or an organization's premises), an at-home care service, an equipment service, a food delivery service to an individual or a business), a transportation service (e.g., for an individual or for a good from one business to another business), and/or the like.

In this way, delivery system230may receive first data associated with a delivery of an item and/or service.

As further shown inFIG. 4, process400may include receiving second data associated with scheduling the delivery (block420). For example, delivery system230may receive, from one or more server devices220, second data associated with scheduling the delivery. In some implementations, when receiving second data, delivery system230may receive millions, billions, trillions, etc. of data elements associated with millions, billions, trillions, etc. of deliveries. In this way, delivery system230may receive a data set that cannot be processed manually or objectively by human operators.

In some implementations, second data may include weather data that identifies weather conditions at a location, calendar data that identifies a set of available and/or unavailable times for an individual, traffic data that identifies traffic conditions at a location or on a route to the location, text of a message (e.g., a short message service (SMS) message or an email from client device210), and/or the like.

In some implementations, delivery system230may receive the second data periodically, according to a schedule, based on requesting the second data, based on input from a user of client device210, in real-time, and/or the like. For example, delivery system230may receive second data, such as calendar data for an individual, from server device220, so as to permit delivery system230to identify an available date and/or time for delivery of the item and/or service. Additionally, or alternatively, delivery system230may receive second data related to traffic conditions on a route to a location of a delivery, so as to permit delivery system230to predict a delivery time for delivery of an item and/or service.

Additionally, or alternatively, and as another example, delivery system230may receive second data related to weather conditions at a location where an item and/or service is to be delivered, so as to permit delivery system230to recommend a modification to a delivery (e.g., to avoid inclement weather, to modify delivery to avoid damage from inclement weather, etc.). Additionally, or alternatively, and as another example, delivery system230may receive second data via a chatbot that delivery system230uses to communicate with a user of client device210regarding scheduling a delivery, modifying a delivery, and/or the like.

In some implementations, using a chatbot may permit delivery system230to communicate with a user of client device210in an intelligent and conversational manner using natural language. Additionally, or alternatively, and as another example, delivery system230may receive second data related to a prior delivery of an individual to which an item and/or service is to be delivered, and/or another individual to which an item and/or service was delivered, such as another individual in the same or similar geolocation as the individual, that has the same or similar characteristics as the individual, that has the same or similar demographics as the individual, that has the same or similar profile as the individual, that has performed the same or similar behavior as the individual, and/or the like (e.g., perform machine learning, to identify a preference, to generate a recommendation, etc., as described in more detail elsewhere herein).

In some implementations, delivery system230may use first data to obtain second data. For example, delivery system230may use first data identifying an individual to which an item and/or service is to be delivered to obtain second data, such as calendar data for the individual (e.g., after receiving permission from the individual to obtain the calendar data). Additionally, or alternatively, delivery system230may use first data that identifies a location to which an item and/or service is to be delivered to obtain second data related to traffic conditions on a route to the location, weather conditions at the location, and/or the like. This permits delivery system230to improve scheduling of a delivery via use of calendar data, weather data, and/or the like. In addition, receiving this type of data in real-time permits delivery system230to generate recommended modifications to a delivery in real-time as calendar data, weather data, traffic data, and/or the like changes. In some implementations, when obtaining second data (e.g., calendar data), delivery system230may obtain authorization from a recipient of a delivery to access the second data (e.g., by sending a message to client device210associated with the recipient).

In this way, delivery system230may receive second data associated with scheduling the delivery.

As further shown inFIG. 4, process400may include processing the first data and/or the second data using a processing technique to identify information related to scheduling the delivery (block430). For example, delivery system230may process the first data and/or the second data using a processing technique to identify information related to scheduling the delivery.

In some implementations, delivery system230may process the first data and/or the second data to identify a date and/or time for delivery of an item and/or service. For example, delivery system230may process calendar data of an individual to which an item and/or service is to be delivered to identify an available date and/or time for the delivery. Additionally, or alternatively, and as another example, delivery system230may process weather data to determine and/or modify a date and/or time for delivery of the item and/or service a date and/or time that avoids inclement weather). Additionally, or alternatively, and as another example, delivery system230may process traffic data to determine a time at which an item and/or service is likely to be delivered, thereby improving the “last mile” and/or “last hour” of a delivery.

In some implementations, delivery system230may process the first and/or second data using a set of rules that relates to scheduling a delivery, a preference of an individual to which an item and/or service is to be delivered, and/or the like. For example, delivery system230may process first data identifying an item as a valuable item (e.g., an item that has a threshold value). Continuing with the previous example, delivery system230may use a set of rules to determine that the item cannot be left at the door of a building to which the item is to be delivered based on the item being a valuable item. In this case, delivery system230may use second data identifying an availability of an individual to which the item is to be delivered to determine a time when the individual may be available to receive the item, and may schedule delivery for the identified time.

Additionally, or alternatively, and as another example, delivery system230may process first and/or second data using natural language processing, computational linguistics, text analysis, and/or the like (e.g., when first data and/or second data is text). For example, delivery system230may use natural language processing to process text of a message from client device210, communications of a chatbot used for communications between delivery system230and a user of client device210, and/or the like. Continuing with the previous example, delivery system230may process the text of a message and/or communications of a chatbot to identify a delivery location, a delivery time, a response to a recommended modification to a delivery, and/or the like.

In some implementations, when processing the first data and/or second data using natural language processing, delivery system230may process the first and/or second data to identify a term and/or phrase included in the data. For example, delivery system230may adjust characters (e.g., add characters, remove characters, etc.), adjust spacing in the first data and/or second data (e.g., add or remove spaces), expand acronyms included in the first data and/or second data (e.g., replace “EPA” with “Environmental Protection Agency”), replace a symbol with a term (e.g., replace an “@” symbol with the term “at”), convert a term included in the first data and/or second data to a root term (e.g., convert “processing,” “processed,” or “processor” to “process”), and/or the like. In this way, delivery system230may process first data and/or second data so that delivery system230can identify a term and/or phrase associated with the first data and/or second data, place the first data and/or second data in a form that delivery system230can use, and/or the like.

In some implementations, delivery system230may process first data and/or second data using automatic speech recognition (ASR), computer speech recognition, speech-to-text, and/or the like (e.g., when first data and/or second data is audio data, such as natural language audio). For example, delivery system230may convert audio from a call to delivery system230to text (e.g., a call to schedule or to reschedule a delivery). In this way, delivery system230may process first data and/or second data so that delivery system230can identify a term and/or phrase associated with the first data and/or second data, place the first data and/or second data in a form that delivery system230can use, and/or the like.

In some implementations, delivery system230may process first data and/or second data using machine learning and/or a big data technique to perform an analysis of the first data and/or the second data. For example, delivery system230may process first data and/or second data associated with a prior delivery for an individual to which an item and/or service is to be delivered, and/or associated with another individual, to identify a preference related a delivery (e.g., to facilitate generation of a recommendation related to scheduling or rescheduling a delivery). Continuing with the previous example, a prior delivery may be associated with second data that indicates a customer satisfaction with the delivery (e.g., customer satisfaction data gathered via a survey, a questionnaire, etc.) and delivery system230may use machine learning and/or a big data technique to identify trends among deliveries that are associated with a threshold level of customer satisfaction (e.g., as indicated by a threshold score on a questionnaire, particular terms and/or phrases in text of a survey, etc.). In this way, delivery system230may generate recommendations that are more likely to result in a positive customer experience relative to, for example, another recommendation or generating a recommendation randomly.

In some implementations, delivery system230may process first data and/or second data in real-time. For example, delivery system230may receive weather data and/or calendar data in real-time and may process the weather data and/or the calendar data to determine whether to recommend a modification to a delivery. Additionally, or alternatively, and as another example, delivery system230may receive a message indicating a modification to a delivery from client device210and may process first data and/or second data associated with the delivery to determine a real-time modification to the delivery based on information included in the message, the first data, and/or the second data. This improves a delivery relative to non-real-time processing of first data and/or second data by permitting delivery system230to quickly and dynamically modify a delivery, thereby improving an efficiency and conserving resources that would otherwise be consumed due to a failed delivery.

In this way, delivery system230may process the first data and/or the second data using a processing technique to schedule the delivery.

As further shown inFIG. 4, process400may include performing an action related to the delivery based on processing the first data and/or the second data (block440). For example, delivery system230may perform an action related to the delivery based on processing the first data and/or the second data.

In some implementations, delivery system230may schedule the delivery. For example, delivery system230may schedule the delivery for a date, time, and/or location based on the first data and/or the second data. Additionally, or alternatively, and as another example, delivery system230may reschedule the delivery to a different date, time, and/or location. In some implementations, delivery system230may use a chatbot to communicate with a user of client device210when scheduling and/or rescheduling a delivery. For example, delivery system230may receive information identifying a requested date and/or time, may receive a request for a modification to a delivery, may confirm a delivery and/or a modification to a delivery, and/or the like based on communications between a user of client device210and delivery system230via a chatbot.

In some implementations, when scheduling a delivery, delivery system230may communicate with a recipient of the delivery to schedule the delivery (e.g., based on a result of processing first data and/or second data). Additionally, or alternatively, when scheduling a delivery, delivery system230may communicate with a delivery agent to schedule the delivery (e.g., based on a result of processing first data and/or second data).

In some implementations, delivery system230may generate a recommendation related to the delivery. For example, delivery system230may generate a recommendation related to a date, time, and/or location of a delivery (e.g., based on calendar data, weather data, traffic data, data associated with prior deliveries, etc.). Additionally, or alternatively, and as another example, delivery system230may generate a recommendation to modify a scheduled delivery (e.g., to a different time, location, date, etc.) based on the first data and/or second data. For example, delivery system230may identify a calendar conflict with a scheduled delivery, a change in weather at a scheduled location of a delivery, heavy traffic associated with a route to a scheduled location of a delivery, and/or the like, and may generate a recommendation to modify a delivery to avoid the calendar conflict, inclement weather, and/or the like.

In some implementations, delivery system230may reorganize multiple deliveries based on a change to first data and/or second data related to a delivery. For example, delivery system230may re-determine a route for multiple deliveries, reschedule the multiple deliveries for different dates and/or times, request that an individual meet a delivery vehicle along a route so that the delivery can be guaranteed, and/or the like, such as to minimize a distance traveled of a delivery vehicle, to minimize a cost of the multiple deliveries, to ensure that all deliveries are completed, and/or to perform a threshold quantity of deliveries during a time period (e.g., to reduce a queue of deliveries by a threshold amount so as to prevent a backlog of deliveries). This increases an efficiency of multiple deliveries, thereby conserving resources related to performing multiple deliveries.

In some implementations, delivery system230may constantly or periodically receive the first data and/or the second data and may constantly or periodically reorganize multiple deliveries. In this way, delivery system230may improve an efficiency of multiple deliveries in real-time.

In some implementations, delivery system230may provide a recommendation to client device210for display. In implementations, delivery system230may provide a recommendation to client device210using a chatbot. This permits delivery system230to communicate with a user of client device210in a conversational manner regarding the recommendation. For example, delivery system230may use a chatbot to communicate with a user of client device210to determine whether the user wants delivery system230to implement a recommendation.

In some implementations, delivery system230may send a message to client device210. For example, delivery system230may send a message to client device210that includes information related to a delivery (e.g., based on a request from client device210for information related to the delivery, based on scheduling or rescheduling the delivery, etc.). Additionally, or alternatively, delivery system230may send a message to client device210associated with a driver of a delivery vehicle. For example, delivery system230may send a message that identifies a modification to a delivery (e.g., a change of time and/or location), that includes a set of instructions to use a different route to the location at which an item and/or service is to be delivered (e.g., to avoid traffic), that causes the driver to leave a facility to deliver an item and/or a service, that causes an item to be loaded on a delivery vehicle, and/or the like.

In some implementations, delivery system230may dispatch an autonomous vehicle to deliver an item and/or a service. For example, delivery system230may send a message to an autonomous vehicle to cause the autonomous vehicle to be dispatched from a facility. Additionally, or alternatively, delivery system230may send a set of instructions to an autonomous vehicle to use a particular route for a delivery and/or to modify a route of the autonomous vehicle based on a modification to a delivery (e.g., by sending a set of instructions to a navigation system associated with the autonomous vehicle).

In some implementations, delivery system230may store the first data and/or the second data related to a delivery. For example, delivery system230may store the first data and/or the second data using memory resources associated with delivery system230. In some implementations, delivery system230may store the first data and/or the second data to permit machine learning, big data analysis, and/or the like.

In some implementations, when storing the first data and/or the second data, delivery system230may aggregate and/or merge the first data and/or the second data with other first data and/or second data, deduplicate the first data and/or the second data, and/or identify missing or corrupted first data and/or second data and obtain replacement first data and/or second data (e.g., using information related to the first data and/or the second data, querying first data and/or second data from server device220, cross-referencing the first data and/or the second data to identify the missing/corrupted first data and/or second data, and/or the like).

This conserves memory resources of delivery system230and/or conserves processing resources of delivery system230by reducing errors in the first data and/or second data, reducing duplicate first data and/or second data, and/or the like. In some implementations, delivery system230may use a big data tool to aggregate and/or merge the first data and/or the second data (e.g., to aggregate and/or merge millions, billions, trillions, etc., of data elements). This permits delivery system230to aggregate and/or merge a data set that cannot be merged manually or objectively by human operators.

In this way, delivery system230may perform an action elated to the delivery based on processing the first data and/or the second data. In this way, delivery system230may use artificial intelligence, natural language processing, application program interface (API) integration, SMS, and/or the like to intelligently and dynamically schedule a delivery in real-time.

AlthoughFIG. 4shows example blocks of process400, in some implementations, process400may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG. 4. Additionally, or alternatively, two or more of the blocks of process400may be performed in parallel. As indicated above,FIG. 4is provided merely as an example. Other examples are possible and may differ from what was described with regard toFIG. 4.

FIG. 5is a diagram of an example implementation500relating to example process400shown inFIG. 4.FIG. 5shows an example system architecture related to the implementations described herein. As shown inFIG. 5, example implementation500may include client devices210-1through210-3, a set of server devices220(e.g., weather server device220and calendar server device220that include corresponding application program interfaces (APIs), such as Google Calendar), delivery system230that includes a CRM system, an operations logic module, a web services module, an artificial intelligence (AI) engine, and a database, and an SMS gateway device.

In some implementations, the elements shown inFIG. 5may be connected via various communication protocols, such as hypertext transfer protocol (HTTP), HTTP Secure (HTIPS), or SMS, various database protocols, such as open database connectivity (ODBC) or object linking and embedding, database (OLE-DB), and/or the like. In some implementations, internal elements of delivery system230, such as the operations logic module and the CRM system may be connected via HTTP. Additionally, or alternatively, the operations logic module may be connected to the database via a database protocol. Additionally, or alternatively, delivery system230may be connected to external elements, such as the SMS gateway device, weather server device220and/or calendar server device220, via HTTPS. This increases security of communications between delivery system230and external elements.

As shown inFIG. 5, and by reference number505, delivery system230may receive first data from client device210-1. For example, delivery system230may receive the first data when a user of client device210-1uses a CRM system to generate an entry for a new delivery. As shown by reference number510, delivery system230may receive the first data from the CRM system and may process the first data using an operations logic model (e.g., that utilizes Java or a similar technology). For example, the operations logic module may identify information related to a delivery, such as a location of a delivery (e.g., when the first data includes information identifying a location of a delivery).

Additionally, or alternatively, and as another example, the operation logic module may identify a set of rules related to the delivery (e.g., a set of rules related to a valuable item when the delivery is for a valuable item). Additionally, or alternatively, and as another example, the operation logic module may identify a set of rules related to the type of item and/or service being delivered, the value of the item and/or service being delivered, the type of customer service level associated with a recipient (e.g., a high, medium, or low service level), and/or the like.

As shown by reference number515, delivery system230may use the operations logic module to obtain second data (e.g., weather data) from weather server device220(e.g., via a weather API). In some implementations, delivery system230may obtain the weather data to permit the operations logic module to use weather data when scheduling the delivery. As shown by reference number520, delivery system230may use the operations logic module to obtain second data (e.g., calendar data) from calendar server device220. In some implementations, the calendar data may identify an availability or an unavailability of an individual to which an item and/or a service is to be delivered. In some implementations, delivery system230may obtain the calendar data to permit the operations logic module to use the calendar data when scheduling the delivery.

As shown by reference number525, delivery system230may use second data stored in a database (e.g., a MySQL database or a MongoDB database) of delivery system230when scheduling the delivery. For example, the operations logic module may use second data related to previous deliveries to the same individual to determine a preference of the individual. Additionally, or alternatively, and as another example, the operations logic module may use second data related to deliveries to other individuals to identify characteristics of a delivery that correlates to a threshold customer satisfaction.

For example, the operations logic module may identify particular customer preferences, such as days of the week, times of the day, rescheduling preferences, inclement weather preferences, and/or the like that correlate to a threshold score for customer satisfaction to permit the operations logic module to predict a preference from an individual and/or organization to which an item and/or service is to be delivered). In some implementations, delivery system230may determine a recommended date, time, and/or location for the delivery based on the first and second data.

As shown by reference number530, the operations logic module may provide information identifying a date, time, and/or location for the delivery to a web services module (e.g. that utilizes Java or a similar technology and provides connectivity between the AI engine and the operations logic module). In some implementations, the web services module may schedule the delivery, add information identifying the delivery to a queue of deliveries, and/or the like. In some implementations, the web services module may be associated with a chatbot. For example, delivery system230may use the chatbot to communicate, in a conversational and natural language manner, with a user of client device210regarding a delivery, as described in more detail below.

As shown by reference number535, the web services module may use the AI engine (e.g., like Watson) to generate messages (e.g., SMS messages) to provide to client device210, to process a message from client device210(e.g., process a natural language response using natural language processing), generate a response message to a message from client device210, and/or the like. For example, the web services module may use the AI engine to generate a message that includes information identifying a delivery that the web services module scheduled. In some implementations, the AI engine may be capable of understanding context and/or semantics of a message based on a term and/or phrase included in natural language text received from client device210. For example, the AI engine may perform a look up of a term and/or phrase in a data structure that includes a set of terms and/or phrases and corresponding context and/or sentiment indicators.

As shown by reference number540, delivery system230may exchange information related to the delivery with the SMS gateway device (e.g., a device that connects internet-based systems, such as delivery system230, and network240, and utilizes MessageBird, Twilio, or a similar technology). For example, the web services module may exchange SMS messages with the SMS gateway device using a chatbot. As shown by reference number545, the SMS gateway device may exchange messages with client device210-2. For example, the SMS gateway device may exchange SMS messages that relate to scheduling a delivery, modifying a delivery, and/or the like. In some implementations, the web services module, the SMS gateway device, and/or client device210-2may exchange messages in real-time.

As shown by reference number550, the web services module and client device210-3may exchange messages. For example, client device210-3may be associated with a driver of a vehicle delivering an item and/or a service and the web services module and client device210-3may exchange messages related to updating a delivery that was modified by a user of client device210-2, updating a route based on a modified delivery, and/or the like. In some implementations, the web services module and client device210-3may exchange messages in real-time.

In this way, delivery system230may use artificial intelligence, natural language processing, API integration, SMS, and/or the like to intelligently and dynamically schedule a delivery in real-time.

As indicated above,FIG. 5is provided merely as an example. Other examples are possible and may differ from what was described with regard toFIG. 5.

FIG. 6is a diagram of an example implementation600relating to example process400shown inFIG. 4.FIG. 6shows an example of a physical and/or logical architecture related to the implementations described herein. As shown inFIG. 6, example implementation600may include client device210, a browse associated with client device210or another client device210), an SMS gateway device, delivery system230that includes proxy server device220, a presentation tier (e.g., including a presentation layer of a CRM system), a service tier (e.g., that includes an application layer, an operations logic layer, and a data access layer), and a data tier, and third party services and software (e.g., provided by, or executing on, third party devices).

As shown by reference number605, a browser, client device210, and/or the SMS gateway device may be connected to proxy server device220. For example, the browser, the SMS gateway, and/or client device210may exchange data and/or messages with delivery system230via proxy server device220. As shown by reference number610, proxy server device220may be connected to the presentation tier of delivery system230(e.g., via a connection to the presentation layer of the CRM system) and/or the service tier of delivery system230(e.g., via a connection to the application layer of delivery system230). It some implementations, the presentation tier and/or the presentation layer may operate on one or more computing resources234and may be associated with providing information for display.

As shown by reference number615, the presentation layer may be connected to the application layer of the service tier of delivery system230. In some implementations, the service tier may operate on one or more computing resources234and may be associated with providing and/or serving third-party services and/or applications (e.g., via the application layer). In some implementations, the application layer may operate on one or more computing resources234and may specify shared protocols and/or interface methods used by hosts in a communication network.

As shown by reference number620, the application layer of the service tier may be connected to third party services and/or software. For example, delivery system230may use the application layer to obtain calendar data, weather data, and/or other types of second data from server devices220, as described elsewhere herein.

As shown by reference number625, the service tier may include an operations logic layer (e.g., associated with the operations logic module described above) that is connected to third party services and/or software. For example, the operations logic layer may operate on one or more computing resources234and may communicate with the third party services and/or software to determine a set of rules, to verify that a delivery satisfies a set of rules, and/or the like. In some implementations, the operations logic layer may be domain specific. For example, an operations logic layer implemented in a retail delivery context may be different than an operations logic layer implemented in a manufacturing delivery context. Continuing with the previous example, the operations logic layer may use different sets of rules for different contexts.

As shown by reference number630, the service tier of delivery system230may be connected to a data tier of delivery system230via a data access layer. In some implementations, the data tier may operate on one or more computing resources234and may be associated with storing and/or retrieving information from a database or a file system. For example, the database shown inFIG. 6may store data related to other deliveries that delivery system230can use to perform machine learning, to identify a preference of an individual to which an item and/or service is to be delivered, and/or the like.

In some implementations, information retrieved from the data tier may be processed by the service tier (e.g., using third party services and/or software) and may be provided for display via the presentation tier. In some implementations, the data access layer may operate on one or more computing resources234and may provide access to data stored in persistent storage (e.g., a database of the data tier).

As indicated above,FIG. 6is provided merely as an example. Other examples are possible and may differ from what was described with regard toFIG. 6.

FIGS. 7A-7Care diagrams of an example implementation700relating to example process400shown inFIG. 4.FIGS. 7A-7Cshow an example of real-time intelligent and dynamic delivery scheduling. As shown inFIG. 7A, example implementation700includes delivery system230and client device210.

As shown inFIG. 7A, and by reference number705, delivery system230may communicate with client device210to schedule a delivery. For example, delivery system230may use a chatbot to communicate with client device210using SMS messages. Continuing with the previous example, delivery system230may use an AI engine that implements natural language processing to process and/or generate the SMS messages. In this way, delivery system230may intelligently communicate with a user of client device210in a conversational and natural language manner.

As shown by reference number710, delivery system230may provide a message to client device210requesting confirmation of a recommended date for delivery of an item and/or service (e.g., the 14thof the month). For example, delivery system230may have processed second data (e.g., calendar data or weather data) to identify the 14thas a day when a user of client device210is available to receive the delivery, as a day when inclement weather is not forecasted, and/or the like.

As shown by reference number715, a user of client device210may input a natural language response to the message from delivery system230. For example, the user may input the text “That works for me.” to confirm the 14thas the date for delivery. In some implementations, delivery system230may receive the response message and may process the response message using natural language processing to determine whether the user has confirmed the recommended date as the date for delivery. As shown by reference number720, delivery system230may use an AI engine to generate the message “Ok. I have scheduled the delivery for the 14th,” to indicate to the user of client device210that the delivery was successfully scheduled for the 14th;

In some implementations, delivery system230may generate a calendar item (e.g., an appointment, an event, an invite, etc.) for the user of client device210that indicates a date, a time, a location, and/or the like for the delivery. In some implementations, delivery system230may provide the calendar item to client device210to cause the calendar item to be added to an electronic calendar associated with the user of client device210. In this way, delivery system230may intelligently and dynamically schedule a delivery.

As shown by reference number725, after confirming the date of delivery, the user of client device210may send a message to delivery system230requesting to reschedule the delivery. For example, the message may include the text “Can I reschedule my delivery for the 15th?” In some implementations, delivery system230may process the text of the message using natural language processing to determine that the user of client device210is requesting to reschedule the delivery from the 14thto the 15th.

In some implementations, when delivery system230receives the message requesting to reschedule the delivery, delivery system230may process second data (e.g., calendar data or weather data) to determine whether the requested date is suitable for delivery. Additionally, or alternatively, delivery system230may determine whether delivery on the requested date satisfies a set of rules. In some implementations, delivery system230may reschedule the delivery for the requested date.

As shown by reference number730, delivery system230may provide a message to client device210indicating that the delivery has been rescheduled for the requested date. For example, the message may include the text “I have updated the delivery for the 15th.” in this way, delivery system230may intelligently and dynamically reschedule a delivery via use of a chatbot and/or natural language processing.

As shown inFIG. 7B, and by reference number735, after rescheduling the delivery, delivery system230may determine that the user has added a calendar appointment that conflicts with the rescheduled delivery, and may provide a message to client device210that indicates the conflict. For example, the message may include the text “I see you have a calendar conflict with a delivery on the 15th. Reschedule?” As shown by reference number740, client device210may provide a response message that the user of client device210input. For example, the message may include the text “Not at all. I'll be available.” in some implementations, delivery system230may process the text of the response message to determine that the user of client device210does not want to reschedule the delivery from the 15th. In this way, delivery system230may monitor second data and intelligently notify a user of client device210when a calendar appointment conflicts with a scheduled delivery.

As shown by reference number745, while monitoring second data (e.g., weather data), delivery system230may determine that inclement weather is forecasted for the same day and/or time as a delivery, and may provide a message to client device210to notify a user of client device210of the inclement weather and to determine whether the user wants to modify the delivery. For example, the message may include the text “I see that there is rain predicted for the 15th. Would you like us to leave the delivery with a neighbor? If so, which one?” Continuing with the previous example, delivery system230may use the message to determine whether the user of client device210wants an item to be delivered to a neighbor, so as to avoid damage due to the inclement weather. In some implementations, delivery system230may generate the recommendation to use a neighbor for delivery based on delivery system230performing machine learning on information related to other deliveries.

As shown by reference number750, client device210may provide a message indicating that the user wants the item to be delivered to a neighbor and may provide an address to which the item is to be delivered. For example, the message may include the text “Yes, 134 Main Street.” where 134 Main Street is the address to which the item is to be delivered. In some implementations, the address may be selected from a map, such that the message provided to delivery system230includes, for example, a set of latitude and longitude coordinates, or another location identifier. In some implementations, when delivery system230receives the message, delivery system230may process the message using natural language processing to determine whether the user wants to modify the delivery and/or a manner in which to modify the delivery (e.g., a modification to delivery address).

As shown by reference number755, delivery system230may provide a message to client device210indicating that the delivery has been modified based on the response message from client device210. For example, the message may include the text “Ok, I've updated delivery to 134 Main Street.” In this way, delivery system230may intelligently notify a user of client device210when inclement weather may interfere with a scheduled delivery.

As shown inFIG. 7C, and by reference number760, on the day of delivery, delivery system230may provide a message to client device210to inform a user of client device210that traffic may delay delivery by a threshold amount of time (e.g., by monitoring second data, such as traffic data). For example, the message may include the text “Traffic is predicted to delay delivery by 15 minutes. Do you want to reschedule?” to determine whether a user of client device210wants to reschedule the delivery based on the delay.

As shown by reference number765, client device210may provide a message to delivery system230indicating that the user does not want to reschedule. For example, the message may include the text “No, thanks.” In some implementations, delivery system230may process the text of the message using natural language processing to determine that the user of client device210does not want to reschedule. In this way, delivery system230may improve the “last mile” and/or “last hour” of a delivery by providing real-time intelligent and dynamic delivery scheduling.

As shown by reference number770, after a delivery has been completed, delivery system230may provide a message to client device210requesting input related to an experience and/or satisfaction of a user of client device210. For example, the message may include the text “Please rate your experience on a scale of 1 (low satisfaction) to 5 (high satisfaction).” to prompt the user to input a score from one to five to indicate a level of satisfaction with the delivery. As shown by reference number775, client device210may provide a message to delivery system230that includes text “5,” indicating that the user's level of satisfaction is a five on a scale of one to five (e.g., indicating a high level of satisfaction).

In some implementations, delivery system230may store information related to the delivery and a corresponding level of satisfaction, and may perform machine learning to determine aspects of a delivery that contribute to positive experience, a negative experience, and/or the like (e.g., in combination with information related to other deliveries). Additionally, or alternatively, when information related a level of satisfaction (e.g., a score) provided to delivery system230satisfies a threshold, delivery system230may provide a message to client device210to prompt a user to input additional text regarding the user's experience and/or level of satisfaction.

In some implementations, when delivery system230receives the additional text, delivery system230may process the text using natural language processing. For example, delivery system230may process the text to identify a term and/or phrase that indicates a particular positive or negative sentiment (e.g., using a data structure that includes terms and/or phrases and corresponding sentiment indicators). In some implementations, delivery system230may perform analytics on information related to the delivery to identify, for example, particular delivery personnel associated with a threshold level of satisfaction, particular customers associated with a threshold level of satisfaction, and/or the like.

In this way, delivery system230may use artificial intelligence, natural language processing, SMS, and/or the like to intelligently and dynamically schedule a delivery in real-time.

As indicated above,FIGS. 7A-7Care provided merely as an example. Other examples are possible and may differ from what was described with regard toFIGS. 7A-7C.

Some implementations, described herein, provide a delivery system that is capable of receiving first data associated with a delivery of an item and/or service, receiving second data associated with scheduling the delivery, processing the first data and/or the second data to intelligently schedule the delivery, and/or performing an action related to the delivery. In this way, the delivery system provides a computer-based system to intelligently manage the “last mile” or “last hour” of a delivery (e.g., based on weather data, calendar data, etc.), thereby improving a delivery. In addition, this conserves resources, such as processing resources of a device scheduling a delivery, vehicle-related resources, such as fuel, cost, or wear-and-tear related to performing a delivery, and/or the like, by reducing or eliminating a need for a delivery to be scheduled and/or attempted multiple times. Further, this improves an individual's experience related to a delivery via intelligent and dynamic scheduling of the delivery.