WO2021087663A1

WO2021087663A1 - Systems and methods for determining name for boarding point

Info

Publication number: WO2021087663A1
Application number: PCT/CN2019/115323
Authority: WO
Inventors: Mingquan CHEN; Zhibin Wu; Bolong LIU; Li Ma; Wangting CHEN
Original assignee: Beijing Didi Infinity Technology And Development Co., Ltd.
Priority date: 2019-11-04
Filing date: 2019-11-04
Publication date: 2021-05-14
Also published as: CN114041129A

Abstract

A system and method for determining a name for a boarding point are disclosed. The method includes obtaining a current location of a subject (510). The method includes determining at least one boarding point based on the current location of the subject (520). The method includes selecting a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point (530). The method includes determining a score of each of the plurality of candidate POIs by using a trained POI model (540). The method includes determining a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs (550). The method includes designating a name of the target POI as a name of the corresponding boarding point (560).

Description

SYSTEMS AND METHODS FOR DETERMINING NAME FOR BOARDING POINT

TECHNICAL FIELD

This disclosure generally relates to an online service platform, and more particularly, relates to systems and methods for determining a name for a boarding point.

BACKGROUND

With the development of Internet technology, online services, such as online taxi hailing services, are starting to play a significant role in people’s daily lives. When a subject (e.g., a passenger) initiates a service request, a system providing the online taxi hailing services may suggest one or more boarding points on a map for the subject. However, in some cases, the subject may not like to select the boarding point based on graphic display in the map, but prefer to make the selection based on text, which includes a name of the boarding point. It will improve user experience to provide a suitable name for the boarding point for user direction and guidance. Thus, it is desirable to provide systems and methods to determine a name for a boarding point to improve user experience for an online service platform.

SUMMARY

According to an aspect of the present disclosure, a method may include one or more of the following operations performed by at least one processor. The method may include obtaining a current location of a subject. The method may also include determining at least one boarding point based on the current location of the subject. The method may also include selecting a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs. At least part of the feature information associated with the plurality of POIs may be determined based on a plurality of historical orders. The method may also include determining, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. The method may further include determining a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs. The method may still further include designating a name of the target POI as a name of the corresponding boarding point.

In some embodiments, the feature information associated with the plurality of POIs may include at least one of a global feature or a local feature associated with a link.

In some embodiments, the global feature may include at least one of a name feature, a classification feature, a statistics feature, a brand feature, or a distance between the POI and the boarding point.

In some embodiments, the local feature associated with a link may include at least one of a local statistics feature, a local feature associated with a historical pick-up location, a local feature associated with a historical departure location, or an orientation of the POI relative to the link.

In some embodiments, the brand feature may include a first tier brand, a second tier brand, and a non-brand. The method may also include determining whether the plurality of candidate POIs include a first tier brand POI based on the feature information associated with the plurality of POIs.

In some embodiments, the method may also include determining a first group of candidate POIs, in response to a determination that the plurality of candidate POIs does not include the first tier brand POI.

In some embodiments, the method may also include, in response to a determination that the plurality of candidate POIs include the first tier brand POI, determining a second group of candidate POIs and a third group of candidate POIs. The second group of candidate POIs may include the first tier brand POI. The third group of candidate POIs may include at least one of the first tier brand POI, the second tier brand POI, or the non-brand POI.

In some embodiments, the method may also include obtaining a plurality of historical orders. The method may also include associating a POI with at least one historical order of the plurality of historical orders. The method may also include associating the POI with at least one link based on link information and the at least one historical order associated with the POI. The method may also include determining a local feature, associated with each link of the at least one link, of the POI based on the at least one historical order associated with the POI. The method may also include determining a global feature of the POI based on the local feature, associated with each link of the at least one link, of the POI.

In some embodiments, the trained POI model may be determined according to a training process. The training process may include obtaining a preliminary POI model. The training process may include obtaining a plurality of training samples, wherein the plurality of training samples includes historical information associated with a plurality of historical orders. The training process may include extracting sample features of each of the plurality of training samples. The training process may include determining the trained POI model by training the preliminary POI model based on the sample features.

In some embodiments, the method may also include modifying the score of at least one candidate POI of the plurality of candidate POIs based on the feature information of the plurality of candidate POIs.

In some embodiments, the method may also include obtaining voice data of the subject. The method may also include determining a mentioned POI by analyzing the voice data of the subject. The method may also include determining a confidence level of the mentioned POI based on the location of the subject.

In some embodiments, the method may also include transmitting signals to a terminal associated with the subject to instruct the terminal to display the name of the corresponding boarding point, thereby directing the subject to the corresponding boarding point.

According to another aspect of the present disclosure, a method may include one or more of the following operations performed by at least one processor. The method may include obtaining a current location of a subject. The method may include displaying at least one boarding point. The method may include displaying a name of the at least one boarding point.

According to another aspect of the present disclosure, a system may include at least one storage medium storing a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the stored set of instructions, the at least one processor causes the system to obtain a current location of a subject. The at least one processor may also cause the system to determine at least one boarding point based on the current location of the subject. The at least one processor may also cause the system to select a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs. At least part of the feature information associated with the plurality of POIs may be determined based on a plurality of historical orders. The at least one processor may also cause the system to determine, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. The at least one processor may also cause the system to determine a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs. The at least one processor may also cause the system to designate a name of the target POI as a name of the corresponding boarding point.

According to another aspect of the present disclosure, a system may include at least one storage medium storing a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the stored set of instructions, the at least one processor causes the system to obtain a current location of a subject. The at least one processor may also cause the system to display at least one boarding point. The at least one processor may also cause the system to display a name of the at least one boarding point.

According to still another aspect of the present disclosure, a non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method. The method may include obtaining a current location of a subject. The method may also include determining at least one boarding point based on the current location of the subject. The method may also include selecting a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs. At least part of the feature information associated with the plurality of POIs may be determined based on a plurality of historical orders. The method may also include determining, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. The method may further include determining a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs. The method may still further include designating a name of the target POI as a name of the corresponding boarding point.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary online service system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determining a name of a boarding point according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determining at least part of feature information associated with a POI according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determining a confidence level of a mentioned POI according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for determining a trained POI model according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary process for determining at least part of feature information associated with a POI according to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary first group of candidate POIs according to some embodiments of the present disclosure;

FIG. 11 is a schematic diagram illustrating an exemplary second group of candidate POIs according to some embodiments of the present disclosure; and

FIG. 12 is a schematic diagram illustrating an exemplary third group of candidate POIs according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the present disclosure and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure, as well as the methods of operation, various components of the stated system, functions of the related elements of structure, and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

The flowcharts used in the present disclosure illustrate operations that systems implemented according to some embodiments of the present disclosure. It is to be expressly understood that the operations of the flowchart may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the present disclosure are described primarily regarding online transportation service, it should also be understood that this is only one exemplary embodiment. The systems and methods of the present disclosure may be applied to any other kind of on demand service. For example, the systems and methods of the present disclosure may be applied to transportation systems of different environments including land (e.g. roads or off-road) , water (e.g. river, lake, or ocean) , air, aerospace, or the like, or any combination thereof. The vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a boat, a vessel, an aircraft, a spaceship, a hot-air balloon, a driverless vehicle, or the like, or any combination thereof. The transportation systems may also include any transportation system for management and/or distribution, for example, a system for sending and/or receiving an express. The application of the systems and methods of the present disclosure may include a mobile device (e.g. smart phone or pad) application, a webpage, a plug-in of a browser, a client terminal, a custom system, an internal analysis system, an artificial intelligence robot, or the like, or any combination thereof.

The terms “passenger, ” “requester, ” “requestor, ” “service requester, ” “service requestor, ” and “customer” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may request or order a service. Also, the terms “driver, ” “provider, ” “service provider, ” and “supplier” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may provide a service or facilitate the providing of the service. The term “user” in the present disclosure is used to refer to an individual, an entity or a tool that may request a service, order a service, provide a service, or facilitate the providing of the service. In the present disclosure, terms “requester” and “requester terminal” may be used interchangeably, and terms “provider” and “provider terminal” may be used interchangeably.

The terms “request, ” “service, ” “service request, ” and “order” in the present disclosure are used interchangeably to refer to a request that may be initiated by a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, a supplier, or the like, or any combination thereof. Depending on context, the service request may be accepted by any one of a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, or a supplier. In some embodiments, the service request is accepted by a driver, a provider, a service provider, or a supplier. The service request may be chargeable or free.

The positioning technology used in the present disclosure may be based on a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a Galileo positioning system, a quasi-zenith satellite system (QZSS) , a wireless fidelity (WiFi) positioning technology, or the like, or any combination thereof. One or more of the above positioning systems may be used interchangeably in the present disclosure.

An aspect of the present disclosure is directed to systems and methods for determining a name for a boarding point. According to some systems and methods of the present disclosure, the processing engine may obtain a current location of a subject (e.g., a user) . The processing engine may determine at least one boarding point based on the current location of the subject. The processing engine may select a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs. At least part of the feature information associated with the plurality of POIs may be determined based on a plurality of historical orders. The processing engine may determine, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. The processing engine may determine a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs. The processing engine may designate a name of the target POI as a name of the corresponding boarding point. According to some systems and methods of the present disclosure, the processing engine may further transmit signals to a terminal (e.g., a requestor terminal) associated with the subject to instruct the terminal to display the name of the corresponding boarding point, thereby directing the subject to the corresponding boarding point. Accordingly, the name of the boarding point may be determined based on historical behaviors associated with subjects, which may improve user experience.

It should be noted that online transportation service, such as online taxi-hailing including taxi hailing combination services, is a new form of service rooted only in post-Internet era. It provides technical solutions to users and service providers that could raise only in post-Internet era. In pre-Internet era, when a passenger hails a taxi on the street, the taxi request and acceptance occur only between the passenger and one taxi driver that sees the passenger. If the passenger hails a taxi through a telephone call, the service request and acceptance may occur only between the passenger and one service provider (e.g., one taxi company or agent) . Online taxi, however, allows a user of the service to automatically distribute a service request in real-time to a vast number of individual service providers (e.g., taxi) distance away from the user. It also allows a plurality of service providers to respond to the service request simultaneously and in real-time. Therefore, through the Internet, the online transportation systems may provide a much more efficient transaction platform for the users and the service providers that may never meet in a traditional pre-Internet transportation service system.

FIG. 1 is a schematic diagram illustrating an exemplary online service system according to some embodiments of the present disclosure. For example, the online service system 100 may be an online transportation service platform for transportation services such as taxi hailing, chauffeur services, delivery vehicles, express car, carpool, bus service, driver hiring, shuttle services, etc.

The online service system 100 may include a server 110, a network 120, one or more client terminals (e.g., one or more requestor terminals 130, one or more provider terminals 140) , and a storage device 150.

In some embodiments, the server 110 may be a single server, or a server group. The server group may be centralized, or distributed (e.g., the server 110 may be a distributed system) . In some embodiments, the server 110 may be local or remote. For example, the server 110 may access information and/or data stored in the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) , and/or the storage device 150 via the network 120. As another example, the server 110 may be directly connected to the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) , and/or the storage device 150 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server 110 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2.

In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data to perform one or more functions described in the present disclosure. For example, the processing engine 112 may obtain a current location of a subject. As another example, the processing engine 112 may determine at least one boarding point based on the current location of the subject. As still another example, the processing engine 112 may select a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs. As still another example, the processing engine 112 may determine, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. As still another example, the processing engine 112 may determine a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs. As still another example, the processing engine 112 may designate a name of the target POI as a name of the corresponding boarding point.

In some embodiments, the processing engine 112 may include one or more processing engines (e.g., signal-core processing engine (s) or multi-core processor (s) ) . Merely by way of example, the processing engine 112 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field-programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction-set computer (RISC) , a microprocessor, or the like, or any combination thereof.

The network 120 may facilitate exchange of information and/or data. In some embodiments, one or more components in the online service system 100 (e.g., the server 110, the one or more requestor terminals 130, the one or more provider terminal 140, or the storage device 150) may send information and/data to other component (s) in the online service system 100 via the network 120. For example, the processing engine 112 may obtain a current location of a subject from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) and/or the storage device 150 via the network 120. As another example, the processing engine 112 may obtain a plurality of historical orders from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) and/or the storage device 150 via the network 120. As another example, the processing engine 112 may obtain a preliminary POI model or a trained POI model from the storage device 150 via the network 120. As another example, the processing engine 112 may obtain voice data of a subject. As still another example, the processing engine 112 may transmit signals to a terminal (e.g., the requestor terminal 130) associated with a subject to instruct the terminal to display a name of a boarding point. In some embodiments, the network 120 may be any type of wired or wireless network, or any combination thereof. Merely by way of example, the network 120 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (PTSN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points such as base stations and/or internet exchange points 120-1, 120-2…through which one or more components of the online service system 100 may be connected to the network 120 to exchange data and/or information.

In some embodiments, a service requester may be a user of the requester terminal 130. In some embodiments, the user of the requester terminal 130 may be someone other than the service requester. For example, a user A of the requester terminal 130 may use the requester terminal 130 to send a service request for a user B or receive a service confirmation and/or information or instructions from the server 110. In some embodiments, a service provider may be a user of the provider terminal 140. In some embodiments, the user of the provider terminal 140 may be someone other than the service provider. For example, a user C of the provider terminal 140 may use the provider terminal 140 to receive a service request for a user D, and/or information or instructions from the server 110.

In some embodiments, the requestor terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a motor vehicle 130-4, or the like, or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, a smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistance (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments, built-in device in the motor vehicle 130-4 may include an onboard computer, an onboard television, etc. In some embodiments, the requestor terminal 130 may be a device with positioning technology for locating the position of the service requester and/or the requestor terminal 130.

In some embodiments, the provider terminal 140 may be similar to, or the same device as the requestor terminal 130. In some embodiments, the provider terminal 140 may be a device with positioning technology for locating the position of the driver and/or the provider terminal 140. In some embodiments, the requestor terminal 130 and/or the provider terminal 140 may communicate with other positioning device to determine the position of the service requester, the requestor terminal 130, the service provider, and/or the provider terminal 140. In some embodiments, the requestor terminal 130 and/or the provider terminal 140 may send positioning information to the server 110.

The storage device 150 may store data and/or instructions. For example, the data may be a trained POI model, one or more training samples, one or more historical orders, or the like, or a combination thereof. In some embodiments, the storage device 150 may store data obtained from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) . For example, the storage device 150 may store a plurality of candidate POIs determined by the processing engine 112. As another example, the storage device 150 may store a trained POI model determined by the processing engine 112. As another example, the storage device 150 may store a target POI determined by the processing engine 112. As still another example, the storage device 150 may store feature information associated with a plurality of POIs determined by the processing engine 112. In some embodiments, the storage device 150 may store data and/or instructions that the server 110 may execute or use to perform exemplary methods described in the present disclosure. For example, the storage device 150 may store instructions that the processing engine 112 may execute or use to select a plurality of candidate POIs from a plurality of POIs based on at least one boarding point and feature information associated with the plurality of POIs. As another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to determine, based on feature information associated with a plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model. As still another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to determine a target POI from a plurality of candidate POIs based on scores of the plurality of candidate POIs. As still another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to determine a trained POI model.

In some embodiments, the storage device 150 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drives, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM) . Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically-erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more components in the online service system 100 (e.g., the server 110, the one or more client terminals) . One or more components in the online service system 100 may access the data and/or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected to or communicate with one or more components in the online service system 100 (e.g., the server 110, the one or more client terminals) . In some embodiments, the storage device 150 may be part of the server 110.

In some embodiments, one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140) of the online service system 100 may have permissions to access the storage device 150. In some embodiments, one or more components of the online service system 100 may read and/or modify information relating to the service requester, the service provider, and/or the public when one or more conditions are met. For example, the server 110 may read and/or modify one or more service requesters’ information after a service is completed. As another example, the provider terminal 140 may access information relating to the service requester when receiving a service request from the requester terminal 130, but the provider terminal 140 may not modify the relevant information of the service requester.

In some embodiments, information exchanging of one or more components of the online service system 100 may be achieved by way of requesting a service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or an immaterial product. The tangible product may include food, medicine, commodity, chemical product, electrical appliance, clothing, car, housing, luxury, or the like, or any combination thereof. The immaterial product may include a servicing product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include an individual host product, a web product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The mobile internet product may be used in a software of a mobile terminal, a program, a system, or the like, or any combination thereof. The mobile terminal may include a tablet computer, a laptop computer, a mobile phone, a personal digital assistance (PDA) , a smart watch, a point of sale (POS) device, an onboard computer, an onboard television, a wearable device, or the like, or any combination thereof. For example, the product may be any software and/or application used in the computer or mobile phone. The software and/or application may relate to socializing, shopping, transporting, entertainment, learning, investment, or the like, or any combination thereof. In some embodiments, the software and/or application relating to transporting may include a traveling software and/or application, a vehicle scheduling software and/or application, a mapping software and/or application, etc. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a rickshaw (e.g., a wheelbarrow, a bike, a tricycle) , a car (e.g., a taxi, a bus, a private car) , a train, a subway, a vessel, an aircraft (e.g., an airplane, a helicopter, a space shuttle, a rocket, a hot-air balloon) , or the like, or any combination thereof.

One of ordinary skill in the art would understand that when an element (or component) of the online service system 100 performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when the requester terminal 130 transmits out a service request to the server 110, a processor of the requester terminal 130 may generate an electrical signal encoding the request. The processor of the requester terminal 130 may then transmit the electrical signal to an output port. If the requester terminal 130 communicates with the server 110 via a wired network, the output port may be physically connected to a cable, which further may transmit the electrical signal to an input port of the server 110. If the requester terminal 130 communicates with the server 110 via a wireless network, the output port of the requester terminal 130 may be one or more antennas, which convert the electrical signal to electromagnetic signal. Similarly, the provider terminal 140 may process a task through operation of logic circuits in its processor, and receive an instruction and/or a service request from the server 110 via electrical signals or electromagnet signals. Within an electronic device, such as the requester terminal 130, the provider terminal 140, and/or the server 110, when a processor thereof processes an instruction, transmits out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., the storage device 150) , it may transmit out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.

It should be noted that the online service system 100 is merely provided for the purposes of illustration, and is not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. For example, the online service system 100 may further include a database, an information source, or the like. As another example, the online service system 100 may be implemented on other devices to realize similar or different functions. However, those variations and modifications do not depart from the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. In some embodiments, the server 110, the requester terminal 130, and/or the provider terminal 140 may be implemented on the computing device 200. For example, the processing device 112 may be implemented on the computing device 200 and configured to perform functions of the processing device 112 disclosed in this disclosure.

The computing device 200 may be used to implement any component of the online service system 100 as described herein. For example, the processing device 112 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the online service as described herein may be implemented in a distributed fashion on a number of similar platforms to distribute the processing load.

The computing device 200 may include COM ports 250 connected to and from a network connected thereto to facilitate data communications. The computing device 200 may also include a processor 220, in the form of one or more, e.g., logic circuits, for executing program instructions. For example, the processor 220 may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.

The computing device 200 may further include program storage and data storage of different forms including, for example, a disk 270, a read only memory (ROM) 230, or a random access memory (RAM) 240, for storing various data files to be processed and/or transmitted by the computing device 200. The computing device 200 may also include program instructions stored in the ROM 230, RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device 200 may also include an I/O component 260, supporting input/output between the computer and other components. The computing device 200 may also receive programming and data via network communications.

Merely for illustration, only one processor is described in FIG. 2. Multiple processors are also contemplated, thus operations and/or steps performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device 200 executes both operation A and operation B, it should be understood that operation A and operation B may also be performed by two different CPUs and/or processors jointly or separately in the computing device 200 (e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B) .

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure. In some embodiments, the requester terminal 130 or the provider terminal 140 may be implemented on the mobile device 300. As illustrated in FIG. 3, the mobile device 300 may include a communication platform 310, a display 320, a graphic processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and a storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.

In some embodiments, the mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to online services or other information from the online service system 100. User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 112 and/or other components of the online service system 100 via the network 120.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure. In some embodiments, the processing engine 112 may include an obtaining module 410, a boarding point determination module 420, a candidate POI selection module 430, a score determination module 440, a target POI determination module 450, a name determination module 460, a transmission module 470, a training module 480, a feature determination module 490, and a confidence level module 495.

The obtaining module 410 may be configured to obtain data and/or information associated with the online service system 100. For example, the obtaining module 410 may obtain a current location of a subject. As another example, the obtaining module 410 may obtain voice data of a subject. As still another example, the obtaining module 410 may obtain a plurality of historical orders. As still another example, the obtaining module 410 may obtain a preliminary POI model.

The boarding point determination module 420 may be configured to determine one or more boarding points. In some embodiments, the boarding point determination module 420 may determine one or more boarding points based on a current location of a subject. For example, the boarding point determination module 420 may determine one or more preset boarding points located in a predetermined range (e.g., 500 meters) with a current location of a subject as the center, as one or more boarding points.

The candidate POI selection module 430 may be configured to determine one or more candidate POIs. In some embodiments, the candidate POI selection module 430 may select one or more candidate POIs from a plurality of POIs based on one or more boarding points and feature information associated with the plurality of POIs. For example, the candidate POI selection module 430 may determine a first group of candidate POIs. As another example, the candidate POI selection module 430 may determine a second group of candidate POIs and a third group of candidate POIs.

The score determination module 440 may be configured to determine a score for a candidate POI. In some embodiments, the score determination module 440 may determine, based on feature information associated with a plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model.

The target POI determination module 450 may be configured to determine a target POI. In some embodiments, the target POI determination module 450 may determine a target POI from a plurality of candidate POIs based on scores of the plurality of candidate POIs. For example, the target POI determination module 450 may rank a plurality of candidate POIs according to their scores in a descending order. The target POI determination module 450 may determine a candidate POI with a highest score as a target POI.

The name determination module 460 may be configured to determine a name for a boarding point. For example, the name determination module 460 may designate a name of a target POI as a name of a corresponding boarding point.

The transmission module 470 may be configured to transmit signals to one or more components of the online service system 100. For example, the transmission module 470 may transmit signals to a terminal associated with a subject (e.g., the requestor terminal 130) to instruct the terminal to display a name of a corresponding boarding point.

The training module 480 may be configured to determine a trained POI module. In some embodiments, the training module 480 may determine a trained POI module by training a preliminary POI model. For example, the training module 480 may obtain a plurality of training samples. As another example, the training module 480 may extract sample features of each of a plurality of training samples. As still another example, the training module 480 may train a preliminary POI model based on sample features. More descriptions of the determination of a trained POI module may be found elsewhere in the present disclosure (e.g., FIG. 8, and descriptions thereof) .

The feature determination module 490 may be configured to determine feature information associated with a POI. For example, the feature determination module 490 may associate a POI with at least one historical order of a plurality of historical orders. As another example, the feature determination module 490 may associate a POI with at least one link based on link information and at least one historical order associated with the POI. As still another example, the feature determination module 490 may determine a local feature associated with a link of a POI based on at least one historical order associated with the POI. As still another example, the feature determination module 490 may determine a global feature of a POI based on a local feature associated with at least one link of the POI.

The confidence level module 495 may be configured to determine a confidence level of a mentioned POI. For example, the confidence level module 495 may determine a mentioned POI by analyzing voice data of a subject. As still another example, the confidence level module 495 may determine a confidence level of a mentioned POI based on a current location of a subject.

The modules in the processing engine 112 may be connected to or communicated with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or any combination thereof. Two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more units. In some embodiments, one or more modules may be added or omitted. For example, the processing engine 112 may further include a storage module (not shown) used to store information and/or data (e.g., the feature information) associated with the plurality of POIs. As another example, the training module 480 may be unnecessary and the trained POI model may be obtained from a storage device (e.g., the storage 150) , such as the ones disclosed elsewhere in the present disclosure. In some embodiments, one or more modules may be combined into a single module. For example, the target POI determination module 450 and the name determination module 460 may be combined as a single module which may both determine the target POI and designate the name of the target POI as the name of the corresponding boarding point.

FIG. 5 is a flowchart illustrating an exemplary process for determining a name of a boarding point according to some embodiments of the present disclosure. In some embodiments, the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing engine 112 (e.g., the obtaining module 410) may obtain a current location of a subject.

In some embodiments, the subject may be a service requestor (e.g., a passenger) using an online service (e.g., an online taxi hailing service) via the online service system 100. In some embodiments, the current location (also referred to as a departure location) of the subject may be a geographic location (e.g., longitudinal and latitudinal coordinates) where the subject locates when the subject initiates a service request. As used herein, a service request may be a request for any location based services. In some embodiments, the service request may be a request for a transportation service (e.g., a taxi service, a delivery service, a vehicle hailing service) .

In some embodiments, the processing engine 112 may obtain the geographic location of the subject from one or more components of the online service system 100. For example, the subject may carry one or more sensors with positioning function, and the processing engine 112 may obtain the geographic coordinates of the subject from the one or more sensors. Specifically, the processing engine 112 may obtain the geographic coordinates of the subject via a GPS device and/or an inertial measurement unit (IMU) sensor mounted on the requestor terminal 130 carried by the subject.

The processing engine 112 may continuously or periodically obtain the geographic coordinates of the subject from the one or more sensors (e.g., the GPS device) . Additionally or alternatively, the sensor with positioning function (e.g., the GPS device) may transmit the geographic coordinates of the subject to a storage device (e.g., the storage device 115) of the online service system 100 via the network 120 continuously or periodically. The processing engine 112 may access the storage device and retrieve one or more geographic coordinates of the subject.

In 520, the processing engine 112 (e.g., the boarding point determination module 420) may determine at least one boarding point based on the current location of the subject.

As used herein, a boarding point may refer to a location where a service provider (e.g., a driver) may pick up a service requestor (e.g., a passenger) . In some embodiments, the processing engine 112 may determine the one or more boarding points based on the current location (e.g., geographic coordinates) of the subject and locations of a plurality of preset boarding points. For example, a preset boarding point may be set every 100 meters along a road. The processing engine 112 may determine one or more preset boarding points located in a predetermined range (e.g., 500 meters) with the current location of the subject as the center, as the one or more boarding points.

In some embodiments, the plurality of preset boarding points and corresponding geographic coordinates may be stored in a storage device (e.g., the storage device 150) of the online service system 100 or an external database (e.g., a boarding point database 902) . The processing engine 112 may access the storage device or the external database and retrieve the one or more boarding point based on the geographic coordinates of the subject and the geographic coordinates of the plurality of preset boarding points.

In 530, the processing engine 112 (e.g., the candidate POI selection module 430) may select a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs.

As used herein, a POI may refer to a name of a location or a business name. In some embodiments, the feature information associated with the POI may include a global feature, a link feature associated with a link, or the like, or any combination thereof. The global feature may include a name feature, a classification feature, a statistics feature, a brand feature, a distance between the POI and the boarding point, an addition feature, or the like, or any combination thereof.

The name feature of the POI may include a gate, an exit/entrance port, a station (e.g., a bus station, a subway station) , an intersection, or the like, or any combination thereof. The classification feature of the POI may include a community, a hospital, a school, a hotel, a shop, a bank, or the like, or any combination thereof. The brand feature of the POI may include a brand POI, a non-brand POI, or the like. The brand POI may include a first tier brand POI and a second tier brand POI. As used herein, “a brand POI” may refer to that a business name associated with the POI is related to a trademark. For example, the brand POI may include a KFC, a Safeway supermarket, a Macy’s mall, or the like. The non-brand POI may include a Haidian bus station, a Luyuan community, or the like. As used herein, “a first tier brand POI” may refer to that the POI has a relatively high hot degree, and “a second tier brand POI” may refer to that the POI has a relatively low hot degree. For example, the hot degree of the first tier brand POI may be greater than a hot degree threshold, and the hot degree of the second tier brand POI may be lower than the hot degree threshold.

In some embodiments, the distance between the POI and the boarding point may be a straight-line distance or a travel distance from the POI to the boarding point. For example, the processing engine 112 may determine a route from the POI to the boarding point, and determine the distance between the POI and the boarding point by determining the length of the route from the POI to the boarding point.

The statistics feature of the POI may include a hot degree of the POI, a number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period, a number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period, or the like, or any combination thereof. In some embodiments, the processing engine 112 may recommend one or more default POIs as the starting location or the destination when the subject initiate a service request, the subject may modify the one or more default POIs to determine a specific POI as the starting location or the destination. As used herein, a starting location may refer to a location that a subject inputs/selects to start a service (e.g., an online taxi hailing service) via a terminal device (e.g., the requestor terminal 130) when the subject initiates a service request. As used herein, a destination may refer to a location that a subject inputs/selects to end a service (e.g., an online taxi hailing service) via a terminal device (e.g., the requestor terminal 130) when the subject initiates a service request.

As used herein, the hot degree of the POI may indicate the popularity of the POI. In some embodiments, the hot degree of the POI may be associated with the number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period, the number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period, or the like. For example, the processing engine 112 may determine a first weight (e.g., W1) corresponding to a first number of times (e.g., N1) that the POI is retrieved or selected as a starting location by subjects in a certain time period, a second weight (e.g., W2) corresponding to a second number of times (e.g., N2) that the POI is retrieved or selected as a destination by subjects in a certain time period, a third weight (e.g., W3) corresponding to a third number of times (e.g., N3) that the POI is modified from a default POI as a starting location by subjects in a certain time period, a fourth weight (e.g., W4) corresponding to a fourth number of times (e.g., N4) that the POI is modified from a default POI as a destination by subjects in a certain time period. The processing engine 112 may determine the hot degree of the POI based on the first weight, the second weight, the third weight, the fourth weight, the first number, the second number, the third number, and the fourth number. The processing engine 112 may determine that the hot degree of the POI may be W1×N1+ W2×N2+ W3×N3+ W4×N4 (W1+W2+W3+W4=1) .

In some embodiments, the addition feature of the POI may be associated with historical drop-off locations of the subjects and historical destinations of the subjects. As used herein, a drop-off location may refer to a location where a subject actually ends a service. For example, the drop-off location may be the location where a passenger actually gets off a vehicle. In some embodiments, the drop-off location may be the same as or different from the destination. In some embodiments, when the subject inputs/selects a POI as a destination and ends the service (e.g., gets off a vehicle) at a specific drop-off location that is different from the destination, the processing engine 112 may determine the specific drop-off location as the addition feature of the POI. That is, the POI may be used as a name of the specific drop-off location.

The local feature associated with a link may include a local statistics feature, a local feature associated with a historical pick-up location, a local feature associated with a historical departure location, an orientation of the POI relative to the link, or the like, or any combination thereof.

As used herein, a “link” may be an element of road or street in a map. A link may correspond to a segment of a road or a street on the map. In some embodiments, a road may include one or more links. The one or more links may be connected one by one via one or more nodes. For example, Changan Street may be mapped to five links on the map by, e.g., manually annotated mapping. The five links may be connected one by one via its nodes to constitute Changan Street. In some embodiments, a region (e.g., Chaoyang district, Beijing city) may include a plurality of roads. Thus, a road network of the region may be represented as an aggregation of links. In some embodiments, the link may correspond to one or two driving directions. As used herein, “a driving direction of a link” may refer to a direction in which an object (e.g., a vehicle) may travel on a road corresponding to the link. For example, the links corresponding to a one-way road may include one driving direction, and the links corresponding to a two-way road may include two driving directions.

In some embodiments, the processing engine 112 may store a plurality of links, one or more nodes corresponding to each link of the plurality of links, and one or two driving directions corresponding to the each link of the plurality of links, in a storage device (e.g., the storage device 150) of the online service system 100 or an external database (e.g., a link database 905, a node-link table 906) . The processing engine 112 may access the storage device or the external database and retrieve information associated with the plurality of the links.

In some embodiments, a POI may correspond to one or more links. In some embodiments, the processing engine 112 may determine the one or more links corresponding to the POI based on information associated with a plurality of historical orders. More descriptions of the determination of the one or more links corresponding to the POI may be found elsewhere in the present disclosure (e.g., FIG. 6, and descriptions thereof) .

The orientation of the POI relative to the link may indicate which side (e.g., a right side, a left side) of the link the POI is located on. In some embodiments, the processing engine 112 may determine the orientation of the POI relative to the link based on the location of the POI and the driving direction corresponding to the link.

The local statistics feature associated with a link of the POI may include a hot degree of the POI associated with the link, a similarity weighted hot degree of the POI associated with the link, a hot degree of the POI associated with the link with a similarity greater than a similarity threshold (e.g., 0.8) , a weighted hot degree of a POI associated with the link with a similarity greater than a similarity threshold (e.g., 0.8) , or the like, or any combination thereof.

The local feature of the POI associated with a historical pick-up location may include a hot degree of the POI associated with the historical pick-up location, a number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, a number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, or the like, or any combination thereof. As used herein, a pick-up location may refer to a location where a subject actually starts a service. For example, the pick-up location may be the location where the subject actually gets on a vehicle. In some embodiments, the pick-up location may be the same as or different from the starting location.

The local feature associated with a historical departure location may include a hot degree of the POI associated with the historical departure location, a weighted hot degree of the POI associated with the historical departure location, a number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical departure location, a number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical departure location, or the like, or any combination thereof. As used herein, a departure location (also referred to as the current location of the subject) may refer to a location where a subject locates when the subject initiates a service request via a terminal device (e.g., the requestor terminal 130) . In some embodiments, the departure location may be the same as the pick-up location (or the starting location) or different from the pick-up location (or the starting location) .

In some embodiments, the processing engine 112 may determine at least part of the feature information associated with the POI (e.g., the statistics feature, the local statistics feature, the local feature associated with a historical pick-up location, the local feature associated with a historical departure location) based on a plurality of historical orders. More descriptions of the at least part of the feature information associated with the POI and the determination of the at least part of the feature information associated with the POI may be found elsewhere in the present disclosure (e.g., FIG. 6, and descriptions thereof) .

In some embodiments, the processing engine 112 may select the plurality of candidate POIs from the plurality of POIs based on the location of the one or more boarding points and the feature information associated with the plurality of POIs. In some embodiments, for each boarding point of the at least one boarding point, the processing engine 112 may determine one or more first POIs located within a certain range (e.g., 50 meters, 100 meters) with the location of the each boarding point as a center, according to a spatial index method. Exemplary spatial index method may include Geohash, HHCode, Grid, Z-order, Quadtree, Octree, UB-tree, R-tree, or the like. For example, the processing engine 112 may determine one or more first POIs according to an R-tree spatial index method. As used herein, R-trees may refer to tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. As another example, the processing engine 112 may determine one or more first POIs according to a grid spatial index method. As used herein, a grid or mesh may refer to a regular tessellation of a manifold or 2-D surface that divides it into a series of contiguous cells, which can then be assigned unique identifiers and used for spatial indexing purposes.

The processing engine 112 may determine the plurality of candidate POIs from the plurality of first POIs based on the name feature, the classification feature, and/or the statistics feature of each of the plurality of first POIs. For example, the processing engine 112 may determine the first POI with the hot degree greater than a hot degree threshold as the candidate POI. As another example, the processing engine 112 may determine the first POI whose name feature or classification feature is not a village, a street, a toilet, or the like, as the candidate POI. As still another example, the processing engine 112 may determine the first POI with a relatively high location visibility as the candidate POI. As used herein, “a relatively high location visibility of a POI” may refer to that it is easy for a subject (e.g., a passenger) to see the POI when the subject is located at a boarding point. In some embodiments, a POI located at a basement floor, or a 10th floor of a building may have a relatively low location visibility.

In some embodiments, the plurality of POIs and corresponding feature information may be stored in a storage device (e.g., the storage device 150) of the online service system 100 or an external database (e.g., a POI database 901) . The processing engine 112 may access the storage device or the external database and select the plurality of candidate POIs from the plurality of POI based on the locations of the one or more boarding points and the feature information associated with the plurality of POIs.

In some embodiments, the processing engine 112 may determine whether the plurality of candidate POIs include a first tier brand POI based on the brand features associated with the plurality of candidate POIs. In response to a determination that the plurality of candidate POIs does not include the first tier brand POI, the processing engine 112 may determine a first group of candidate POIs, as illustrated in FIG. 10. In response to a determination that the plurality of candidate POIs include one or more first brand POIs, the processing engine 112 may determine a second group of candidate POIs and a third group of candidate POIs. The second group of candidate POIs may include the one or more first tier brand POIs, as illustrated in FIG. 11. The third group may include the one or more first brand POIs, one or more second brand POIs, and one or more non-brand POIs, as illustrated in FIG. 12.

In some embodiments, the processing engine 112 may perform a normalization operation on the at least part of the feature information of the plurality of candidate POIs.

For example, the processing engine 112 may perform the normalization operation on the global features (e.g., the statistics features) of the plurality of candidate POIs. The normalization result of each candidate POI of the plurality of candidate POIs may reflect importance of the each candidate POI in the plurality of candidate POIs in the determination of the target POI from the plurality of candidate POIs. In some embodiments, the normalization operation may be performed according to Equation (1) :

where POI _global normalized _i refers to a normalization result of an i ^th candidate POI;

refers to a global feature of the i ^th candidate POI; and j refers to a number of the candidate POIs.

As another example, the processing engine 112 may perform the normalization operation on the local features associated with a specific link (e.g., the local statistics features) of the plurality of candidate POIs. The normalization result of each candidate POI of the plurality of candidate POIs may reflect importance of the each candidate POI in the plurality of candidate POIs associated with the specific link in the determination of the target POI from the plurality of candidate POIs. In some embodiments, the normalization operation may be performed according to Equation (2) :

where POI _link normalized _i refers to a normalization result of an i ^th candidate POI;

(<POI _i, link>) refers to a local feature associated with a link of the i ^th candidate POI; and j refers to a number of the candidate POIs.

As still another example, the processing engine 112 may perform the normalization operation on the local features associated with a plurality of links (e.g., the local statistics features) of a specific candidate POI. The normalization result of each link of the plurality of links may reflect importance of the each link in the plurality of links. In some embodiments, the normalization operation may be performed according to Equation (3) :

where Link _POI normalized _i refers to an normalization result of an i ^th link associated with a specific POI;

(<POI, Link _i>) refers to a local feature associated with the i ^th link of the specific POI; and j refers to a number of the links associated with the specific POI.

In 540, the processing engine 112 (e.g., the score determination module 440) may determine, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model.

In some embodiments, the trained POI model may be determined by training a preliminary POI model. The trained POI model may include one or more algorithms used for generating an output result (e.g., the score of the candidate POI) based on input data (e.g., the feature information associated with the candidate POI, the normalization result of the feature information associated with the candidate POI, a confidence level of a mentioned POI as described in connection with FIG. 7) .

In some embodiments, the preliminary POI model may be a supervised learning model. The processing engine 112 may train the preliminary POI model based on a plurality of training samples. In some embodiments, the plurality of training samples may include exemplary inputs for the supervised learning model and labels that indicate desired outputs corresponding to the exemplary inputs. The process for training the supervised learning model may enable the preliminary POI model to learn a general rule that maps inputs to corresponding outputs. Exemplary algorithms that may be used to train the supervised machine learning model may include a gradient boosting decision tree (GBDT) algorithm, a decision tree algorithm, a Random Forest algorithm, a logistic regression algorithm, a support vector machine (SVM) algorithm, a Naive Bayesian algorithm, an AdaBoost algorithm, a K-anearest neighbor (KNN) algorithm, a Markov Chains algorithm, or the like, or any combination thereof.

More descriptions of the determination of the trained POI model may be found elsewhere in the present disclosure (e.g., FIG. 8, and descriptions thereof) .

In 550, the processing engine 112 (e.g., the target POI determination module 450) may determine a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs.

In some embodiments, if the processing engine 112 determines the first group of candidate POIs as described in connection with operation 530, the processing engine 112 may rank the plurality of candidate POIs in the first group according to their scores in a descending order. The processing engine 112 may determine the candidate POI with a highest score as the target POI.

In some embodiments, if the processing engine 112 determines the second group of candidate POIs and the third group of candidate POIs as described in connection with operation 530, the processing engine 112 may rank the plurality of candidate POIs in each of the second group of candidate POIs and the third group of candidate POIs according to their scores in a descending order, respectively. The processing engine 112 may determine a first candidate POI with a highest score in the second group of candidate POIs, and a second candidate POI with a highest score in the third group of candidate POIs. The processing engine 112 may select the target POI from the first candidate POI and the second candidate POI based on the feature information of the first candidate POI and the second candidate POI. For example, the processing engine 112 may select the candidate POI with a greater hot degree from the first candidate POI and the second candidate POI as the target POI.

In 560, the processing engine 112 (e.g., the name determination module 460) may designate a name of the target POI as a name of the corresponding boarding point. For example, if the name of the target POI is “KFC, Haidian store” , the processing engine 112 may determine that the name of the corresponding boarding point is “KFC, Haidian store” .

In 570, the processing engine 112 (e.g., the transmission module 470) may transmit signals to a terminal associated with the subject to instruct the terminal to display the name of the corresponding boarding point, thereby directing the subject to the corresponding boarding point.

In some embodiments, the processing engine 112 may transmit the signals to the requestor terminal 130 to instruct a visual interface of the requestor terminal 130 to display the name of the corresponding boarding point. The subject may go to the boarding point under the guidance of the name of the boarding point.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure.

In some embodiments, the processing engine 112 may modify the score of at least one candidate POI of the plurality of candidate POIs based on the feature information of the plurality of candidate POIs. In some embodiments, the processing engine 112 may modify the scores of one or more candidate POIs (e.g., top 3, top 5, top 1%, top 5%) of the plurality of candidate POIs in the first group of the candidate POIs and the third group of candidate POIs based on the feature information (e.g., the name feature, the classification feature, the distance between the candidate POI and the boarding point, the orientation of the candidate POI relative to the link) of the plurality of candidate POIs.

For example, if the name feature (or the classification feature) of the candidate POI includes a station (e.g., a bus station, a subway station) , an intersection, or a gate, the processing engine 112 may modify the score of the candidate POI. As another example, if the candidate POI is located on the left side of the link, the processing engine 112 may modify the score of the candidate POI. As still another example, if the distance between the candidate POI and the boarding point is greater than a distance threshold, the processing engine 112 may modify the score of the candidate POI.

In some embodiments, the processing engine 112 may modify the scores of the one or more candidate POIs (e.g., top 3, top 5, top 1%, top 5%) of the plurality of candidate POIs based on an inheritance relationship associated with the plurality of candidate POIs. In some embodiments, a POI (also referred to as a son POI) may corresponding to zero, one or more parent POIs. As used herein, “a son POI and a parent POI” may refer to that there is an inheritance relationship (e.g., a geographical location, a spatial location, an affiliation relationship) between the son POI and the parent POI. For example, the parent POI may be “Chaoyang community” , and the son POIs may include “Chaoyang community, north gate, ” “Chaoyang community, south gate, ” “Chaoyang community, east gate, ” “Chaoyang community, west gate” . In some embodiments, if there is the inheritance relationship in the plurality of candidate POIs, the processing engine 112 may modify the scores of the candidate parent POI or the candidate son POI.

In some embodiments, the processing engine 112 may modify the scores of the one or more candidates POI by decreasing the scores of the one or more candidate POI according to a preset rule. The preset rule may be set manually by a subject, or determined by one or more components of the online service system 100.

FIG. 6 is a flowchart illustrating an exemplary process for determining at least part of feature information associated with a POI according to some embodiments of the present disclosure. In some embodiments, the process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 600. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 6 and described below is not intended to be limiting.

In 610, the processing engine 112 (e.g., the obtaining module 410) may obtain a plurality of historical orders.

A historical order may refer to an order that has been fulfilled. In some embodiments, information associated with the historical order may include an order number, a historical starting location, a historical destination, a historical departure location, a historical pick-up location, a historical drop-off location, user’s identity information (e.g., an identification (ID) , a telephone number, a user’s name) , or the like, or any combination thereof.

In some embodiments, the processing device 112 may obtain the information associated with the plurality of historical orders from one or more components of the online service system 100 (e.g., the requester terminal 130, the provider terminal 140, the storage device 150) , or from an external source (e.g., a database) via the network 120.

In 620, the processing engine 112 (e.g., the feature determination module 490) may associate a POI with at least one historical order of the plurality of historical orders.

In some embodiments, the processing engine 112 may determine the one or more historical order associated with the POI based on the information associated with the plurality of historical orders. For example, the processing engine 112 may determine the one or more historical orders with the POI as the historical starting location as the one or more historical orders associated with the POI. As another example, the processing engine 112 may determine the one or more historical orders with the POI as the historical destination as the one or more historical orders associated with the POI. As still another example, the processing engine 112 may determine one or more historical pick-up locations located within a certain range (e.g., 45 meters) with the POI as the center. The processing engine 112 may determine corresponding historical orders as the one or more historical orders associated with the POI. As still another example, the processing engine 112 may determine one or more historical departure locations located within a certain range (e.g., 100 meters) with the POI as the center. The processing engine 112 may determine corresponding historical orders as the one or more historical orders associated with the POI.

In 630, the processing engine 112 (e.g., the feature determination module 490) may associate the POI with at least one link based on link information and the at least one historical order associated with the POI.

In some embodiments, the link information may include information associated with a plurality of links in a certain area. For example, the link information may include one or more nodes associated with each link of the plurality of links, one or two driving directions associated with the each link of the plurality of links, or the like, or any combination thereof. In some embodiments, the link information may be stored in a storage device (e.g., the storage device 150) of the online service system 100 or an external database (e.g., a link database 905, a node-link table 906) . The processing engine 112 may access the storage device or the external database and retrieve the link information.

In some embodiments, the processing engine 112 may determine the one or more links associated with the POI based on the location of the POI and the link information. For example, the processing engine 112 may determine a plurality of nodes located within a certain range (e.g., 100 meters) with the location of the POI as a center. The processing engine 112 may determine a plurality of first links associated with the plurality of nodes. The processing engine 112 may determine one or more second links from the plurality of first links based on the one or more historical orders associated with the POI. For example, the processing engine 112 may determine the one or more second links associated with the historical pick-up locations of the subjects. As another example, the processing engine 112 may determine the one or more second links associated with the historical drop-off locations of the subjects. As used herein, “a link associated with a historical pick-up location (or a historical drop-off location) ” may refer to that the historical pick-up location (or the historical drop-off location) is located on the link. The processing engine 112 may determine the one or more second links as the one or more links associated with the POI.

In 640, the processing engine 112 (e.g., the feature determination module 490) may determine a local feature, associated with each link of the at least one link, of the POI based on the at least one historical order associated with the POI.

The local feature associated with a link may include a local statistics feature, a local feature associated with a historical pick-up location, a local feature associated with a historical departure, or the like, or any combination thereof.

The local statistics feature associated with a link of the POI may include a hot degree of the POI associated with the link, a similarity weighted hot degree of the POI associated with the link, a hot degree of the POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) , a weighted hot degree of a POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) , or the like, or any combination thereof.

In some embodiments, the processing engine 112 may determine the local statistics feature associated with a link based on the one or more historical orders associated with the POI. For example, the processing engine 112 may determine a first set of historical orders from the one or more historical orders associated with the POI. The historical departure locations of the first set of historical orders may be located on the link. The processing engine 112 may determine the number of times that the POI is retrieved or selected as the historical staring location (or the historical destination) by subjects in a certain time period, the number of times that the POI is modified from a default POI as the historical starting location (or the historical destination) by subjects in a certain time period, by analyzing the first set of historical orders. The processing engine 112 may determine the hot degree of the POI associated with the link based on the number of times that the POI is retrieved or selected as the historical staring location (or the historical destination) by subjects in a certain time period, the number of times that the POI is modified from a default POI as the historical starting location (or the historical destination) by subjects in a certain time period.

The processing engine 112 may determine the similarity weighted hot degree of a specific POI associated with the link, a hot degree of the specific POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) , a weighted hot degree of the specific POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) , based on a similarity between the specific POI and each of a plurality of POIs associated with the link, the hot degree of the specific POI associated with the link, and the hot degrees of the plurality of POIs associated with the link. As used herein, “a similarity between a first POI and a second POI” may refer to a relevance between the first POI and the second POI. In some embodiments, the processing engine 112 may determine the similarity between the first POI and the second POI by matching each character of the first POI and the second POI. A higher similarity between the characters of the first POI and the characters of the second POI may correspond to a higher relevance between the first POI and the second POI.

For example, assuming that a first similarity between a first POI and a specific POI is 0.6, a second similarity between a second POI and the specific POI is 0.9, the hot degree of the first POI associated with the link is 100, the hot degree of the second POI associated with the link is 200, and the hot degree of the specific POI associated with the link is 50, the processing engine 112 may determine that the similarity weighted hot degree of the specific POI associated with the link may be 290 (i.e., 100×0.6+200×0.9+50=290) . Similarly, the processing engine 112 may determine that the weighted hot degree of the specific POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) may be 230 (i.e., 200× 0.9+50=230) . The processing engine 112 may determine that the hot degree of the specific POI associated with the link with a similarity greater than a preset threshold (e.g., 0.8) may be 250 (i.e., 200+50=250) .

The local feature of the POI associated with a historical pick-up location may include a hot degree of the POI associated with the historical pick-up location, a number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, a number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, or the like, or any combination thereof.

In some embodiments, the processing engine 112 may determine one or more historical pick-up locations located within a certain range (e.g., 45 meters) with the POI as the center. The processing engine 112 may determine corresponding historical orders as the one or more historical orders associated with the POI as described in connection with operation 620. In some embodiments, the processing engine 112 may determine a second set of historical orders from the one or more historical orders associated with the POI. The historical departure locations of the second set of historical orders may be located on the link. The processing engine 112 may determine the number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, the number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, by analyzing the second set of historical orders. The processing engine 112 may determine the hot degree of the POI associated with the historical pick-up location based on the number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location, the number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical pick-up location.

The local feature associated with a historical departure location may include a hot degree of the POI associated with the historical departure location, a weighted hot degree of the POI associated with the historical departure location, a number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical departure location, a number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical departure location, or the like, or any combination thereof.

In some embodiments, the processing engine 112 may determine one or more historical departure locations located within a certain range (e.g., 100 meters) with the POI as the center. The processing engine 112 may determine corresponding historical orders as the one or more historical orders associated with the POI, as described in connection with operation 620. In some embodiments, the processing engine 112 may determine a third set of historical orders from the one or more historical orders associated with the POI. The historical departure locations of the third set of historical orders may be located on the link. The processing engine 112 may determine the number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical departure location, the number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical departure location, by analyzing the third set of historical orders. The processing engine 112 may determine the hot degree of the POI associated with the historical departure location, the weighted hot degree of the POI associated with the historical departure location, based on the number of times that the POI is retrieved or selected as a starting location or a destination by subjects in a certain time period associated with the historical departure location, the number of times that the POI is modified from a default POI as a starting location or a destination by subjects in a certain time period associated with the historical departure location.

In 650, the processing engine 112 (e.g., the feature determination module 490) may determine a global feature of the POI based on the local feature, associated with each link of the at least one link, of the POI.

In some embodiments, the processing engine 112 may determine a sum of the local feature, associated with the each link of the one or more links, of the POI, as the global feature of the POI. For example, assuming that the POI is associated with five links, and the hot degree of the POI associated with a first link, a second link, a third link, a fourth link, and a fifth link are 50, 80, 100, 120, 60, respectively, the processing engine 112 may determine that the hot degree of the POI is 410 (i.e., 50+80+100+120+60=410) .

FIG. 7 is a flowchart illustrating an exemplary process for determining a confidence level of a mentioned POI according to some embodiments of the present disclosure. In some embodiments, the process 700 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 700 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 7 and described below is not intended to be limiting.

In 710, the processing engine 112 (e.g., the obtaining module 410) may obtain voice data of a subject.

In some embodiments, the processing engine 112 may obtain the voice data of the subject (e.g., a passenger) and/or voice data of a service provider (e.g., a driver) from a storage device (e.g., the storage device 150) . In some embodiments, the processing engine 112 may obtain the voice data from a device (e.g., the requestor terminal 130, the provider terminal 140) . For example, the device may obtain the voice data of the passenger and/or the voice data of the driver via an I/O port, for example, a microphone of the requestor terminal 130 and/or the provider terminal 140.

In 720, the processing engine 112 (e.g., the confidence level module 495) may determine a mentioned POI by analyzing the voice data of the subject.

In some embodiments, the processing engine 112 may determine the mentioned POI by analyzing the voice data of the subject and/or the voice data of the service provider based on a speech recognition model (e.g., an n-gram model) . Exemplary n-gram model may include a class-based n-gram model, a topic-based n-gram model, a cache-based n-gram model, a skipping n-gram model, or the like. The n-gram may refer to a contiguous sequence of n items (e.g., phonemes, syllables, letters, and words) from a given sample of text or speech. The N-gram model may be a type of probabilistic language model for predicting the occurrence of a word based on the occurrence of its N–1 previous words.

In some embodiments, the processing engine 112 may determine a POI having a highest probability by analyzing the voice data according to the n-gram model, as the mentioned POI. In some embodiments, the processing engine 112 may determine a plurality of POIs having the probabilities higher than a probability threshold by analyzing the voice information according to the n-gram model. The processing engine 112 may determine the mentioned POI from the plurality of POIs based on feature information of the plurality of POIs. For example, the processing engine 112 may determine the POI having a shortest distance between a current location of the subject and the POI as the mentioned POI. As another example, the processing engine 112 may determine the POI having a greatest hot degree as the mentioned POI. As still another example, the processing engine 112 may determine the POI included in a plurality of candidate POIs as described in connection with operation 530, as the mentioned POI.

Merely by way of example, assuming that the voice data of the subject (e.g., a passenger) and the service provider (e.g., a driver) is: “I am in the KFC, emm, do you know the KFC here, I do not know the KFC, you can locate in the KFC, OK, I am in the KFC in the shopping mall, but I am……” , the processing engine 112 may determine that the mentioned POI may be “KFC (Jinke) ” .

In 730, the processing engine 112 (e.g., the confidence level module 495) may determine a confidence level of the mentioned POI based on a current location of the subject.

As used herein, the confidence level of the mentioned POI may indicate an importance of the mentioned POI in the determination of a target POI. A higher confidence level of the mentioned POI may correspond to a higher importance of the mentioned POI in the determination of the target POI. In some embodiments, the processing engine 112 may determine the confidence level of the mentioned POI based on a location of the mentioned POI, the current location of the subject, and a location of the service provider. For example, the processing engine 112 may determine a route from the service provider to the subject. The processing engine 112 may determine the confidence level of the mentioned POI based on a distance between the mentioned POI and the route from the service provider to the subject. For example, a shorter distance between the mentioned POI and the route from the service provider to the subject may correspond to a higher confidence level of the mentioned POI. In some embodiments, the distance between the mentioned POI and the route from the service provider to the subject may be a shortest distance between the mentioned POI and the route from the service provider to the subject.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, one or more operations may be added in process 700. For example, an operation for obtaining voice data of the service provider may be added before operation 720. As another example, an operation for obtaining the location of the service provider may be added before operation 730. In some embodiments, the location of the service provider may change over time. In some embodiments, a plurality of locations of the service provider and a time point corresponding to each location of the plurality of locations may be stored in a storage device (e.g., the storage device 150) of the online service system 100. The processing engine 112 may access the storage device and retrieve the location of the service provider based on the time point when the voice data of the service provider is obtained.

FIG. 8 is a flowchart illustrating an exemplary process for determining a trained POI model according to some embodiments of the present disclosure. In some embodiments, the process 800 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 800. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 800 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 8 and described below is not intended to be limiting.

In 810, the processing engine 112 (e.g., the obtaining module 410) may obtain a preliminary POI model.

In some embodiments, the preliminary POI model may be a supervised learning model. For example, the preliminary POI model may include a preliminary Convolutional Neural Network (CNN) model, a preliminary Recurrent Neural Network (RNN) model, or the like. The preliminary POI model may include one or more preliminary parameters which may be default settings of the online service system 100 or may be adjustable in different situations.

The processing engine 112 may obtain the preliminary POI model from a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure and/or an external data source (not shown) via the network 120.

In 820, the processing engine 112 (e.g., the training module 480) may obtain a plurality of training samples. The plurality of training samples may include information associated with a plurality of historical orders.

A historical order may refer to an order that has been fulfilled. In some embodiments, information associated with the historical order may include an order number, a historical starting location, a historical destination, a historical departure location, a historical pick-up location, a historical drop-off location, user’s identity information (e.g., an identification (ID) , a telephone number, a user’s name) , one or more historical boarding points associated with the historical departure location, a plurality of historical candidate POIs associated with the one or more historical boarding points, a historical target POI associated with a corresponding historical boarding point, a confidence level of a historical mentioned POI, or the like, or any combination thereof.

In 830, the processing engine 112 (e.g., the training module 480) may extract sample features of each of the plurality of training samples.

In some embodiments, the sample features may include historical feature information of the plurality of historical candidate POIs, the confidence level of the historical mentioned POI, a normalization result of the historical feature information of the plurality of historical candidate POIs, or the like, or any combination thereof. The historical feature information of the plurality of historical candidate POIs may include a historical global feature, a historical local feature associated with a link, as described in connection with operation 530.

In some embodiments, the processing engine 112 may determine a sample label for each of the plurality of training samples. As used herein, a sample label is a value within a predetermined range (e.g., 0～1) and may be associated with one or more features of the training sample, for example, a distance between the historical boarding point and the historical pick-up location of the subject. In some embodiments, the longer the distance between the historical boarding point and the historical pick-up location of the subject, the lower the sample label may be.

In 840, the processing engine 112 (e.g., the training module 480) may train the preliminary POI model based on the sample features.

In some embodiments, the processing engine 112 may input the sample features and the sample label of each of the plurality of training samples into the preliminary POI model to update the preliminary parameters of the preliminary POI model.

In 850, the processing engine 112 (e.g., the training module 480) may determine whether a preset condition is satisfied.

For example, the processing engine 112 may determine a loss function of the preliminary POI model and determine a value of the loss function based on the plurality of sample features and the plurality of sample labels. Further, the processing engine 112 may determine whether the value of the loss function is less than a loss threshold. In response to the determination that the value of the loss function is less than the loss threshold, it may be determined that the preset condition is satisfied.

As another example, the processing engine 112 may determine whether an accuracy rate of the preliminary POI model is larger than an accuracy rate threshold. In response to the determination that the accuracy rate is less than the accuracy rate threshold, it may be determined that the preset condition is satisfied.

As still another example, the processing engine 112 may determine whether a number count of iterations is larger than a count threshold. In response to the determination that the number count of iterations is larger than the count threshold, it may be determined that the preset condition is satisfied.

In response to the determination that the preset condition is satisfied, the processing engine 112 may designate the preliminary POI model as the trained POI model in 860, which means that the training process has been completed.

In response to the determination that the preset condition is not satisfied, the processing engine 112 may execute the process 800 to return to operation 810 to update the plurality of preliminary parameters (i.e., to update the preliminary POI model) until the condition is satisfied.

FIG. 9 is a schematic diagram illustrating an exemplary process for determining at least part of feature information associated with a POI according to some embodiments of the present disclosure. In some embodiments, process 900 may illustrate the process for determining the at least part of feature information (e.g., a statistics feature) associated with the POI (e.g., a candidate POI) in combination with process 500 in FIG. 5, process 600 in FIG. 6, and process 700 in FIG. 7.

As shown in FIG. 9, in 911, the processing engine 112 may obtain at least part of feature information (e.g., a name feature, a classification feature, a brand feature) of a plurality of POIs, as described in connection with operation 530. For example, the processing engine 112 may obtain the at least part of feature information (e.g., a name feature, a classification feature, a brand feature) of the plurality of POIs from a POI database 901 and or a brand POI database 903. In 912, the processing engine 112 may determine a plurality of first POIs based on boarding point information stored in a boarding point database 902, as described in connection with operation 530. For example, the processing engine 112 may determine whether there is a boarding point locates within a certain range (e.g., 100 meters) with the location of the POI as a center. In response to a determination that there is one or more boarding points locate within the certain range (e.g., 100 meters) with the location of the POI as the center, the processing engine 112 may determine the POI as the first POI. In 913, the processing engine 112 may determine a plurality of candidate POIs based on the at least part of the feature information (e.g., a name feature, a classification feature, a brand feature) of the plurality of first POIs, as described in connection with operation 530. In 910, the processing engine 112 may grid the plurality of candidate POIs, as described in connection with operation 530. For example, the processing engine 112 may grid the plurality of candidate POIs according to a grid spatial index method.

In 921, the processing engine 112 may obtain information associated with a plurality of historical orders as described in connection with operation 610. In 922, the processing engine 112 may associate each of the plurality of historical orders with one or more new links. In some embodiments, link information associated with a plurality of links may be updated. For example, a link may be deleted or divided into two or more new links. The relationships between old links and corresponding new links may be stored in a new link-old link table 904. The processing engine 112 may associate the each of the plurality of historical orders with the one or more new links based on information associated with the plurality of historical orders, and the new link-old link table 904. In 923, the processing engine 112 may determine a statistics feature associated with a historical departure location. In 920, the processing engine 112 may associate each candidate POI of the plurality of candidate POIs with at least one historical order of the plurality of historical orders, as described in connection with operation 620.

In 930, the processing engine 112 may associate the each candidate POI of the plurality of candidate POIs with at least one link based on link information stored in a link database 905, as described in connection with operation 630. For example, in 931, the processing engine 112 may grid a plurality of nodes associated with a plurality of links based on the link information according to the grid spatial index method. One or more nodes located in a same grid as the candidate POI may be designated as the one or more nodes associated with the candidate POI. The processing engine 112 may determine the one or more links corresponding to the one or more nodes associated with the candidate POI as the one or more links associated with the candidate POI based on a node-link table 906.

In 940, the processing engine 112 may determine a local feature, associated with each link of the at least one link, of the each candidate POI of the plurality of candidate POIs, as described in connection with operation 640. In 950, the processing engine 112 may determine a global feature of the each candidate POI of the plurality of candidate POIs, as described in connection with operation 650.

FIG. 10 is a schematic diagram illustrating an exemplary first group of candidate POIs according to some embodiments of the present disclosure. FIG. 11 is a schematic diagram illustrating an exemplary second group of candidate POIs according to some embodiments of the present disclosure. FIG. 12 is a schematic diagram illustrating an exemplary third group of candidate POIs according to some embodiments of the present disclosure.

In some embodiments, the processing engine 112 may determine whether a plurality of candidate POIs include a first tier brand POI based on brand features associated with the plurality of candidate POIs as described in connection with operation 530. In response to a determination that the plurality of candidate POIs does not include the first tier brand POI, the processing engine 112 may determine a first group of candidate POIs. As shown in FIG. 10, the first group of candidate POIs may include one or more second tier brand POIs and one or more non-brand POIs, for example, a second tier brand POI-1, a second tier brand POI-1, a non-brand POI-1, ....., a second tier brand POI-N, and a non-brand POI-N.

In response to a determination that the plurality of candidate POIs include the first tier brand POI, the processing engine 112 may determine a second group of candidate POIs and a third group of candidate POIs. As shown in FIG. 11, the second group of candidate POIs may include a plurality of first tier brand POIs, for example, a first tier brand POI-1, a first tier brand POI-2, a first tier brand POI-3, ....., and a first tier brand POI-N. As shown in FIG. 12, the third group of candidate POIs may include the plurality of first tier brand POIs, one or more second tier brand POIs, and one or more non-brand POIs, for example, a first tier brand POI-1, a second tier brand POI-1, a non-brand POI-1, a first tier brand POI-2, ....., a first tier brand POI-N, a second tier brand POI-N, and a non-brand POI-N.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure, and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment, ” “an embodiment, ” and “some embodiments” mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to “an embodiment” or “one embodiment” or “an alternative embodiment” in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “module, ” “unit, ” “component, ” “device, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code 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. In the latter scenario, 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) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, it is to be understood that such detail is solely for that purpose, and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claim subject matter lie in less than all features of a single foregoing disclosed embodiment.

Claims

A method implemented on a computing device having at least one processor and at least one storage device, the method comprising:

obtaining a current location of a subject;

determining at least one boarding point based on the current location of the subject;

selecting a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs, wherein at least part of the feature information associated with the plurality of POIs is determined based on a plurality of historical orders;

determining, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model;

determining a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs; and

designating a name of the target POI as a name of the corresponding boarding point.
The method of claim 1, wherein the feature information associated with the plurality of POIs includes at least one of a global feature or a local feature associated with a link.
The method of claim 2, wherein the global feature includes at least one of a name feature, a classification feature, a statistics feature, a brand feature, or a distance between the POI and the boarding point.
The method of any one of claims 2-3, wherein the local feature associated with a link includes at least one of a local statistics feature, a local feature associated with a historical pick-up location, a local feature associated with a historical departure location, or an orientation of the POI relative to the link.
The method of claim 3, wherein the brand feature includes a first tier brand, a second tier brand, and a non-brand, and determining a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs comprises:

determining whether the plurality of candidate POIs include a first tier brand POI based on the feature information associated with the plurality of POIs.
The method of claim 5, wherein determining a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs further comprises:

in response to a determination that the plurality of candidate POIs does not include the first tier brand POI,

determining a first group of candidate POIs.
The method of claim 5, wherein determining a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs further comprises:

in response to a determination that the plurality of candidate POIs include the first tier brand POI,

determining a second group of candidate POIs and a third group of candidate POIs, wherein the second group of candidate POIs includes the first tier brand POI, and the third group of candidate POIs includes at least one of the first tier brand POI, the second tier brand POI, or the non-brand POI.
The method of any one of claims 1-7, wherein at least part of the feature information associated with a POI is determined according to a method, and the method comprising:

obtaining a plurality of historical orders;

associating a POI with at least one historical order of the plurality of historical orders;

associating the POI with at least one link based on link information and the at least one historical order associated with the POI;

determining a local feature, associated with each link of the at least one link, of the POI based on the at least one historical order associated with the POI; and

determining a global feature of the POI based on the local feature, associated with each link of the at least one link, of the POI.
The method of any one of claims 1-8, wherein the trained POI model is determined according to a training process, and the training process comprises:

obtaining a preliminary POI model;

obtaining a plurality of training samples, wherein the plurality of training samples includes historical information associated with a plurality of historical orders;

extracting sample features of each of the plurality of training samples; and

determining the trained POI model by training the preliminary POI model based on the sample features.
The method of any one of claims 1-9, further comprises:

modifying the score of at least one candidate POI of the plurality of candidate POIs based on the feature information of the plurality of candidate POIs.
The method of claim 1, further comprises:

obtaining voice data of the subject;

determining a mentioned POI by analyzing the voice data of the subject; and

determining a confidence level of the mentioned POI based on the location of the subject.
The method of claim 1, further comprises:

transmitting signals to a terminal associated with the subject to instruct the terminal to display the name of the corresponding boarding point, thereby directing the subject to the corresponding boarding point.
A method implemented on a computing device having at least one processor and at least one storage device, the method comprising:

obtaining a current location of a subject;

displaying at least one boarding point; and

displaying a name of the at least one boarding point.
A system, comprising:

at least one storage medium storing a set of instructions;

at least one processor in communication with the at least one storage medium, when executing the stored set of instructions, the at least one processor causes the system to:

obtain a current location of a subject;

determine at least one boarding point based on the current location of the subject;

select a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs, wherein at least part of the feature information associated with the plurality of POIs is determined based on a plurality of historical orders;

determine, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model;

determine a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs; and

designate a name of the target POI as a name of the corresponding boarding point.
The system of claim 14, wherein the feature information associated with the plurality of POIs includes at least one of a global feature or a local feature associated with a link.
The system of claim 15, wherein the global feature includes at least one of a name feature, a classification feature, a statistics feature, a brand feature, or a distance between the POI and the boarding point.
The system of any one of claims 15-16, wherein the local feature associated with a link includes at least one of a local statistics feature, a local feature associated with a historical pick-up location, a local feature associated with a historical departure location, or an orientation of the POI relative to the link.
The system of claim 16, wherein the brand feature includes a first tier brand, a second tier brand, and a non-brand, and to determine a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs, the at least one processor causes the system to:

determine whether the plurality of candidate POIs include a first tier brand POI based on the feature information associated with the plurality of POIs.
The system of claim 18, wherein to determine a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs, the at least one processor causes the system to:

in response to a determination that the plurality of candidate POIs does not include the first tier brand POI,

determine a first group of candidate POIs.
The system of claim 18, wherein to determine a plurality of candidate POIs based on the at least one boarding point and feature information associated with a plurality of POIs, the at least one processor causes the system to:

in response to a determination that the plurality of candidate POIs include the first tier brand POI,

determine a second group of candidate POIs and a third group of candidate POIs, wherein the second group of candidate POIs includes the first tier brand POI, and the third group of candidate POIs includes at least one of the first tier brand POI, the second tier brand POI, or the non-brand POI.
The system of any one of claims 14-20, wherein at least part of the feature information associated with a POI is determined according to a method, and the method comprising:

obtaining a plurality of historical orders;

associating a POI with at least one historical order of the plurality of historical orders;

associating the POI with at least one link based on link information and the at least one historical order associated with the POI;

determining a local feature, associated with each link of the at least one link, of the POI based on the at least one historical order associated with the POI; and

determining a global feature of the POI based on the local feature, associated with each link of the at least one link, of the POI.
The system of any one of claims 14-21, wherein the trained POI model is determined according to a training process, and the training process comprises:

obtaining a preliminary POI model;

obtaining a plurality of training samples, wherein the plurality of training samples includes historical information associated with a plurality of historical orders;

extracting sample features of each of the plurality of training samples; and

determining the trained POI model by training the preliminary POI model based on the sample features.
The system of any one of claims 14-22, the at least one processor causes the system to:

modify the score of at least one candidate POI of the plurality of candidate POIs based on the feature information of the plurality of candidate POIs.
The system of claim 14, the at least one processor causes the system to:

obtain voice data of the subject;

determine a mentioned POI by analyzing the voice data of the subject; and

determine a confidence level of the mentioned POI based on the location of the subject.
The system of claim 14, the at least one processor causes the system to:

transmit signals to a terminal associated with the subject to instruct the terminal to display the name of the corresponding boarding point, thereby directing the subject to the corresponding boarding point.
A system, comprising:

at least one processor, wherein when executing a set of device instructions, the at least one processor is directed to:

obtain a current location of a subject;

display at least one boarding point; and

display a name of the at least one boarding point.
A non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising:

obtaining a current location of a subject;

determining at least one boarding point based on the current location of the subject;

selecting a plurality of candidate POIs from a plurality of POIs based on the at least one boarding point and feature information associated with the plurality of POIs, wherein at least part of the feature information associated with the plurality of POIs is determined based on a plurality of historical orders;

determining, based on feature information associated with the plurality of candidate POIs, a score of each of the plurality of candidate POIs by using a trained POI model;

determining a target POI from the plurality of candidate POIs based on the scores of the plurality of candidate POIs; and

designating a name of the target POI as a name of the corresponding boarding point.