WO2018205561A1

WO2018205561A1 - Systems and methods for processing an abnormal order

Info

Publication number: WO2018205561A1
Application number: PCT/CN2017/113573
Authority: WO
Inventors: Zhangxun LIU; Xiaolin DENG; Chao Dong; Bing Han; Wenyi BAO
Original assignee: Beijing Didi Infinity Technology And Development Co., Ltd.
Priority date: 2017-05-09
Filing date: 2017-11-29
Publication date: 2018-11-15

Abstract

Systems and methods for processing an abnormal order are provides. The system includes at least one storage medium including a set of instructions for determining responsibility between a service requester and a service provider who are associated with a cancelled order; and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may receive an order of an on-demand service from a service requester; receive a cancelling request of the order from the service requester before the service provider completes the on-demand service; conduct at least one logic judgement to conclude a first result that cancellation of the order is associated with a fault from a party of the service requester and the service provider based on the order and a first predetermined rule; and send electronic signal including punishment information to the party.

Description

SYSTEMS AND METHODS FOR PROCESSING AN ABNORMAL ORDER

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No. 201710322027.6 filed on May 9, 2017, and Chinese Patent Application No. 201710322041.6 filed on May 9, 2017, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods for processing an abnormal order, and in particular, systems and methods for determining responsibility between a service requester and a service provider in an abnormal order.

BACKGROUND

With the development and popularization of Internet technology, online on-demand services (e.g., calling a taxi, food delivery service, etc. ) have become more and more popular. However, there are often some abnormal orders in the online on-demand services. For example, an abnormal order may include an order cancelled by either a service provider or a service requester, an order complained by either a service provider or a service requester, an uncompleted order, etc. The lack of a suitable processing mechanism to process the abnormal orders may bring some troubles for service providers or service requesters. Accordingly, it is desirable to provide systems and methods for processing abnormal orders in online on-demand services.

SUMMARY

According to an aspect of the present disclosure, a system is provided. The system may include at least one storage medium, and at least one processor in communication with the at least one computer-readable storage medium. The at least one storage medium may include a set of instructions for determining responsibility between a service requester and a service provider who are associated with a cancelled order. When the at least one processor executes the set of instructions, the at least one processor may be directed to: receive an order of an on-demand service from a service requester; receive a cancelling request of the order from the service requester before the service provider completes the on-demand service; conduct at least one logic judgement to conclude a first result that cancellation of the order is associated with a fault from a party of the service requester and the service provider based on the order and a first predetermined rule; and send electronic signal including punishment information to the party.

In some embodiments, to conclude the first result, the at least one processor may be further directed to: obtain at least one feature from the order; determine a probability that cancellation of the order is caused by the party based on a responsibility model and the at least one feature; and determine that the probability is greater than a probability threshold.

In some embodiments, the at least one processor may be further directed to: obtain a plurality of historical early-terminated orders; obtain a plurality of historical features from a plurality of historical early-terminated orders, and determine the responsibility model based on the plurality of historical features and a Logistic Regression algorithm.

In some embodiments, the at least one feature may include: time of receiving the order, time of cancelling the order, a GPS trace of the order, a historical order-cancellation record of the service requester or the service provider, a distance between the service provider and the service requester, picking-up time of the order, an estimated distance of the order, a start location of the order, a destination location of the order, or an estimated duration of the order.

In some embodiments, the at least one processor may be further directed to determine a profile of the service provider or a profile of the service requester based on the determined first result, each profile including at least one of a service score or a service completion success rate.

In some embodiments, the first predetermined rule may include that the service requester cancels the order during the service provider is on the way to the service requester.

In some embodiments, the first predetermined rule may include that the service provider cancels the order after a predetermined period of time since the service provider reaching a start location of the order.

In some embodiments, the at least one processor may be further directed to: receive, from the party, a complaint associated with the first result; send the complaint to the reviewer terminal; and receive a second result from the reviewer terminal, the second result being determined by the reviewer terminal based on the first result, the complaint and the order.

In some embodiments, the at least one processor may be further directed to: determine a complaint type of the complaint; determine a priority of the complaint based on the complaint type; and send the complaint to the reviewer based on the priority.

In some embodiments, the system may further include at least one base station in wireless communication with the processor; a service requester mobile device associated with the service requester and in wireless communication with the at least one base station to send a first portion of the at least one feature to the at least one processor; and a service provider mobile device associated with the service provider and in wireless communication with the at least one base station to send a second portion of the at least one feature to the at least one processor.

According to another aspect of the present disclosure, a method for processing an abnormal order is provided. The method may include: receiving, by at least one processor, an order of an on-demand service from a service requester; receiving, by the at least one processor, a cancelling request of the order from the service requester before the service provider completes the on-demand service; conducting, by the at least one processor, at least one logic judgement to conclude a first result that cancellation of the order is associated with a fault from a party of the service requester and the service provider based on the order and a first predetermined rule; and sending, by the at least one processor, electronic signal including punishment information to the party.

In some embodiments, the conducting at least one logic judgement to conclude the first result may include: obtaining at least one feature from the order; determining a probability that cancellation of the order is caused by the party based on a responsibility model and the at least one feature; and determining that the probability is greater than a probability threshold.

In some embodiments, the method may further include: obtaining, by the at least one processor, a plurality of historical early-terminated orders; obtaining, by the at least one processor, a plurality of historical features from a plurality of historical early-terminated orders, and determining, by the at least one processor, the responsibility model based on the plurality of historical features and a Logistic Regression algorithm.

In some embodiments, the method may further include: determining, by the at least one processor, a profile of the service provider or a profile of the service requester based on the determined first result, each profile including at least one of a service score or a success rate of completing the on-demand service.

In some embodiments, the method may further include: receiving, from the party, a complaint associated with the first result; sending the complaint to the reviewer terminal; and receiving a second result from the reviewer terminal, the second result being determined by the reviewer terminal based on the first result, the complaint and the order.

In some embodiments, the method may further include: determining, by the at least one processor, a complaint type of the complaint; determining, by the at least one processor, a priority of the complaint based on the complaint type; and sending, by the at least one processor, the complaint to the reviewer based on the priority.

In some embodiments, the method may further include: sending, by a service requester mobile device associated with the service requester, a first portion of the at least one feature to at least one processor; and sending, by a service provider mobile device associated with the service provider, a second portion of the at least one feature to the at least one processor; wherein the service requester mobile device and the service provider mobile device are in wireless communication with the at least one base station, and the at least one base station is wireless communication with the at least one processor.

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. 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 system for processing an abnormal order according to some embodiments of the present disclosure;

FIG. 2A 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. 2B 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. 3 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary first result module according to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary complaint module according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for processing an abnormal order according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for concluding a first result according to some embodiments of the present disclosure;

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

FIG. 9 is a flowchart illustrating an exemplary process for sending a complaint to a reviewer 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.

Generally, the word “module” or “unit” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions. A module or a unit described herein may be implemented as software and/or hardware and may be stored in any type of non-transitory computer-readable medium or other storage device. In some embodiments, a software module/unit may be compiled and linked into an executable program. It will be appreciated that software modules can be callable from other modules/units or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules/units configured for execution on computing devices (e.g., processor 210 as illustrated in FIG. 2) may be provided on a computer-readable medium, such as a compact disc, a digital video disc, a flash drive, a magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that needs installation, decompression, or decryption prior to execution) . Such software code may be stored, partially or fully, on a storage device of the executing computing device, for execution by the computing device. Software instructions may be embedded in a firmware, such as an EPROM. It will be further appreciated that hardware modules/units may be included in connected logic components, such as gates and flip-flops, and/or can be included of programmable units, such as programmable gate arrays or processors. The modules/units or computing device functionality described herein may be implemented as software modules/units, but may be represented in hardware or firmware. In general, the modules/units described herein refer to logical modules/units that may be combined with other modules/units or divided into sub-modules/sub-units despite their physical organization or storage. The description may be applicable to a system, an engine, or a portion thereof.

These and other features, and characteristics of the present disclosure, as well as the methods of operation and 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 a 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 implement according to some embodiments of the present disclosure. It is to be expressly understood, 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 in the present disclosure are described primarily regarding processing abnormal orders in online transportation services, 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 online on-demand services. For example, the systems and methods of the present disclosure may be applied to food delivery services, online life services, express delivery services, or the like, or any combination thereof. The vehicle of the transportation services may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, a driverless vehicle, or the like, or any combination thereof. The application scenarios of the system or method of the present disclosure may include 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 position and/or distance in the present disclosure may be acquired by positioning technology embedded in a user terminal. The positioning technology used in the present disclosure may include 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 (Wi-Fi) positioning technology, or the like, or any combination thereof. One or more of the above positioning technologies may be used interchangeably in the present disclosure.

An aspect of the present disclosure relates to systems and methods for processing an abnormal order. According to the present disclosure, the systems and methods may determine whether the abnormal order satisfies a predetermined rule, and determine that the abnormality of the abnormal order is caused by who (e.g., the service requester or the service provider’s ) . The systems and method may also introduce a human worker to review the result if the service requester or the service provider disagrees with the result, and obtain a final result determined by the human worker.

It should be noted that the determining responsibility in an abnormal order is a newly emerged service rooted in post-Internet era. It provides the technical solutions to service requesters, service providers, and the server of the services that could rise in post-Internet era. In pre-Internet era, it is impossible to determine responsibility in the current abnormal order according to analyzing a plurality of historical early-terminated orders and features of the current abnormal order. Therefore, the present solution is deeply rooted in and aimed to solve a problem only occurred in post-Internet era.

FIG. 1 is a schematic diagram illustrating an exemplary system 100 for processing abnormal orders according to some embodiments of the present disclosure. For example, the system 100 may be an online on-demand service platform for transportation services such as car hailing services, chauffeur services, vehicle delivery services, carpooling services, bus services, driver hiring services, and shuttle services, etc. The system 100 may include a server 110, a network 120, a service requester terminal 130, a service provider terminal 140 and a storage 150. The server 110 may include a processing engine 112.

The server 110 may process data and/or information relating to an abnormal order. For example, the server 110 may receive an abnormal order, and process the abnormal order to determine a responsibility of the abnormal order. 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 service requester terminal 130, service provider terminal 140 and/or the storage 150 via the network 120. As another example, the server 110 may be directly connected to the service requester terminal 130, service provider terminal 140 and/or the storage 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 having one or more components illustrated in FIG. 2 in the present disclosure.

In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data relating to the abnormal order to perform one or more functions described in the present disclosure. For example, the processing engine 112 may obtain an abnormal order and process the abnormal order to determine a responsibility of the abnormal order. In some embodiments, the processing engine 112 may include one or more processing engines (e.g., single-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 the exchange of information and/or data. In some embodiments, one or more components in the system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, and the storage 150, etc. ) may send information and/or data to other component (s) in the system 100 via the network 120. For example, the server 110 may obtain complaint from the service requester terminal 130 and/or the service provider terminal 140 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or a 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 (PSTN) , 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 data uploading system 100 may be connected to the network 120 to exchange data and/or information.

The service requester terminal 130 may be referred to a mobile terminal that is used by a service requester to send information related to the on-demand service. For example, the service requester terminal 130 may request or order a service, send a complaint associated with a responsibility result to the server, and/or send information of an order (e.g., a first portion of at least one feature of the order) to the server 110. For example, the service requester terminal 130 may be a smart phone, a PDA, a tablet computer, etc. The service provider terminal 140 may be referred to a mobile terminal that is used by a service provider to send information related to the on-demand service. For example, the service provider terminal 140 may receive information associated with the service requester, send a complaint associated with a responsibility result to the server, and/or send information of the order (e.g., a second portion of at least one feature of the order) to the server 110. In some embodiments, the service provider terminal 140 may be the same or similar type as the service requester terminal 130. For example, the service provider may use a smart phone, a tablet computer, a built-in device in a motor vehicle, a laptop computer, a desktop computer etc., as the provider terminal to facilitate the providing of the service. As another example, the service provider terminal 140 may include a taxi, a shuttle bus, a limousine, a bus, a shared-bicycle, a shared-scooter, etc., that provide the service to the requester. The service requester terminal 130 and service provider terminal 140 may collect and store information related to a plurality of abnormal orders which may be requested by the users or provided by the providers. In some embodiments, “requester” and “requester terminal” may be used interchangeably, and “provider” and “provider terminal” may be used interchangeably.

In some embodiments, the service requester terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, and 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 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 any combination thereof. In some embodiments, the wearable device may include a bracelet, footgear, glasses, a helmet, a watch, clothing, a backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a personal digital assistance (PDA) , a gaming device, a navigation device, a point of sale (POS) device, a laptop, a desktop, or the like, or any combination thereof. 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, augmented reality glasses, 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^TM, an Oculus Rift^TM, a Hololens^TM, a Gear VR^TM, etc. In some embodiments, a built-in device in the motor vehicle 130-4 may include an onboard computer, an onboard television, etc. In some embodiments, the service requester terminal 130 may be a device with positioning technology for locating the position of the requester and/or the service requester terminal 130.

The storage 150 may store data and/or instructions relating to the abnormal order. In some embodiments, the storage 150 may store data obtained from the service requester terminal 130 or service provider terminal 140. In some embodiments, the storage 150 may store data and/or instructions that the server 110 may execute or use to perform exemplary methods described in the present disclosure. In some embodiments, the storage 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 drive, 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 thyrisor 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 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 150 may be connected to the network 120 to communicate with one or more components in the system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc. ) . One or more components in the system 100 may access the data or instructions stored in the storage 150 via the network 120. In some embodiments, the storage 150 may be directly connected to or communicate with one or more components in the system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc. ) . In some embodiments, the storage 150 may be part of the server 110.

In some embodiments, one or more components in the system 100 (e.g., the server 110, the service requester terminal 130, the service provider terminal 140, etc. ) may have permission to access the storage 150. In some embodiments, one or more components in the system 100 may read and/or modify information relating to the requester, 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 users’profile (e.g., service scores, levels, etc. ) after determining responsibility of abnormal orders.

In some embodiments, information exchanging of one or more components in the system 100 may be achieved by way of receiving an abnormal order. The object relating to the abnormal order 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, etc. ) , a car (e.g., a taxi, a bus, a private car, etc. ) , a train, a subway, a vessel, an aircraft (e.g., an airplane, a helicopter, a space shuttle, a rocket, a hot-air balloon, etc. ) , or the like, or any combination thereof.

FIG. 2 is a schematic diagram illustrating exemplary hardware and software components of an exemplary computing device on which the server 110, the service requester terminal 130, the service provider terminal 140, and/or the storage 150 may be implemented according to some embodiments of the present disclosure. For example, the server 110 may be implemented on the computing device 200 and configured to perform functions of the server 110 disclosed in this disclosure.

The computing device 200 may be a general-purpose computer or a special-purpose computer; both may be used to implement an abnormal orders processing system in the present disclosure. The computing device 200 may be used to implement one or more functions disclosed in the present disclosure. For example, the server 110 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 processing abnormal orders 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, for example, may include COM ports 240 connected to and from a network connected thereto to facilitate data communications. The computing device 200 may also include a processor 210, for executing program instructions. The computing device 200 may also include a battery 220, for managing the power for the system. The computing device 200 may also include program instructions stored in the storage 230. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device 200 may execute the operation system stored in the storage 230, for example, Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like. The computing device 200 also includes an I/O component 250, supporting input/output between the computing device and other components therein. The computing device 200 may also receive programming and data via network communications.

Merely for illustration, only one processor 210 is illustrated in the computing device 200. However, it should be noted that the computing device 200 in the present disclosure may also include multiple processors, thus operations and/or method steps that are 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 step A and step B, it should be understood that step A and step B may also be performed by two different processors jointly or separately in the computing device 200 (e.g., the first processor executes step A and the second processor executes step B, or the first and second processors jointly execute steps A and B) .

Those skilled in the art will recognize that the present teachings are amenable to a variety of modifications and/or enhancements. For example, although the implementation of various components described herein may be embodied in a hardware device, it may also be implemented as a software only solution, for example, an installation on an existing server. In addition, the computing device 200 as disclosed herein may be implemented as a firmware, firmware/software combination, firmware/hardware combination, or a hardware/firmware/software combination.

FIG. 2B is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device 200B on which the service requester terminal 130 or the service provider terminal 140 may be implemented according to some embodiments of the present disclosure. As illustrated in FIG. 2B, the mobile device 200B may include a communication platform 201, a display 202, a graphic processing unit (GPU) 203, a central processing unit (CPU) 204, an I/O 205, a memory 206, and a storage 209. The CPU 204 may include interface circuits and processing circuits similar to the processor 220. 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 200B. In some embodiments, a mobile operating system 207 (e.g., iOS^TM, Android^TM, Windows Phone^TM, etc. ) and one or more applications 208 may be loaded into the memory 206 from the storage 209 in order to be executed by the CPU 204. The applications 280 may include a browser or any other suitable mobile apps for receiving and rendering information relating to a service request or other information from the location based service providing system on the mobile device 200B. User interactions with the information stream may be achieved via the I/O devices 205 and provided to the processing engine 112 and/or other components of the system 100 via the network 120.

In order to implement various modules, units and their functions described above, a computer hardware platform may be used as hardware platforms of one or more elements (e.g., a module of the sever 110 described in FIG. 2A) . Since these hardware elements, operating systems, and program languages are common, it may be assumed that persons skilled in the art may be familiar with these techniques and they may be able to provide information required in the route planning according to the techniques described in the present disclosure. A computer with user interface may be used as a personal computer (PC) , or other types of workstations or terminal devices. After being properly programmed, a computer with user interface may be used as a server. It may be considered that those skilled in the art may also be familiar with such structures, programs, or general operations of this type of computer device. Thus, extra explanations are not described for the figures.

FIG. 3 is a block diagram illustrating an exemplary processing engine 112 according to some embodiments of the present disclosure. The processing engine 112 may include a communication module 310, a first result module 320, a complaint module 330, a second result module 340, a training module 350, and an attribute modification module 360. The modules in processing engine 112 may be hardware circuits of all or part of the processing engine 112. The modules in processing engine 112 may also be implemented as an application or set of instructions read and executed by the processing engine 112. Further, the modules in processing engine 112 may be any combination of the hardware circuits and the application/instructions. For example, the modules may be the part of the processing engine 112 when the processing engine 112 is executing the application/set of instructions. In some embodiments, there may be interconnections between these modules. For example, the first result module 320 may receive information from the communication module 310, and send information to the complaint module 330.

The communication module 310 may be configured to obtain or transmit data in processing an abnormal order. For example, the abnormal order may be an online on-demand service request, such as an online taxi service request or online goods delivery request, which is cancelled before the service is formally completed. The communication module 310 may receive an abnormal order from the service requester terminal 130, the service provider terminal 140, or the storage 150 via the network 120. For example, the communication module 310 may first receive the order for the on-demand service from the service requester via Internet, assign the on-demand service to a service provider, and then before the service provider actually starts serving the service requester, the communication module 310 may receive a cancellation request from the service requester to cancel the on-demand service, thereby making the order abnormal. As another example, the communication module 310 may receive a complaint associated with the first result, and send the complaint to a reviewer. The reviewer may determine a second result based on the complaint. The communication module 310 may also receive the second result from the reviewer. In some embodiments, the communication module 310 may execute obtaining or transmitting data in connection with processing the abnormal order in a form of electronic current or electrical signals. Merely by way of example, the communication module 310 may receive electrical signals encoding the abnormal order, receiving electrical signals encoding the complaint associated with the first result, sending electrical signals encoding the complaint to the reviewer, or receiving electrical signals encoding the second result from the reviewer, or the like, or any combination thereof.

The first result module 320 may be configured to process the abnormal order to determine a first result with respect to the responsibility of the abnormal order. In some embodiments, the first result module 320 may determine the first result based on the abnormal order and a predetermined rule. For example, the first result module 320 may obtain at least one feature from the abnormal order, determine a probability associated with the abnormal order based on a responsibility model and the at least one feature, determine whether the probability is larger than a probability threshold, and determine the first result that the abnormality of the abnormal order is caused by the service requester or the service provider.

In some embodiments, the communication module 310 may send out a decision associated with the first result determined by the first result module 320. For example, after determining the party at fault (i.e., the service provider or the service requester who should be responsible to the early cancellation of the order) , the communication module 310 may send out electronic signals directing the party at fault or the innocent party. The electronic signals may include the first result (e.g., a conclusion that which party is at fault) and/or a punishment as a consequence of the abnormal order (e.g., a punitive charge to the party at fault or a reduction of credit score to the party at fault) .

The complaint module 330 may be configured to process the complaint information in processing the abnormal order. For example, the complaint module 330 may determine a complaint type of the complaint. As another example, the complaint module 330 may determine a priority of the complaint based on the complaint type. As still another example, the complaint module 330 may send the complaint to the reviewer based on the priority.

The second result module 340 may be configured to store a second result in processing the abnormal order. For example, after the reviewer sending the second result via the communication module 340, the second result module 340 may store the second result. As another example, the communication module 310 may access the second result module 340 to obtain the second result, and send the second result to the service requester and/or the service provider.

The training module 350 may be configured to train the historical features of the historical early-terminated orders to obtain a responsibility model for predicting a probability that who is responsible for the abnormality of the abnormal order in processing the abnormal order. For example, the training module 350 may obtain a plurality of historical early-terminated orders from a database or a server, extract a plurality of historical features from the plurality of historical early-terminated orders, and train the plurality of historical features with the logistic regression (LR) algorithm to obtain the responsibility model. As another example, the training module 350 may update the responsibility model based on the second result.

The attribute modification module 360 may be configured to modify an attribute of the service requester and/or the service provider. For example, the attribute modification module 360 may modify a profile of the service provider or a profile of the service requester based on the second result. The profile may include a service score, a success rate, a credit score, a service level, a count of successful orders, or the like, or any combination thereof.

The communication module 310, the first result module 320, the complaint module 330, the second result module 340, the training module 350, and the attribute modification module 360 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 as a single module, and any one of the modules may be divided into two or more units. For example, the second result module 340 may be integrated into the first result module 320 as a single module which may both process the abnormal orders to obtain a result of a responsibility of the abnormality of the abnormal order. As another example, the processing engine 112 may include a storage module (not shown in FIG. 3) which may be configured to store the data temporarily or permanently. As still another example, the communication module 310 may be divided into two units of an obtaining unit and a sending unit to implement the functions of the communication module 310, respectively.

FIG. 4 is a block diagram illustrating an exemplary first result module 320 according to some embodiments of the present disclosure. The first result module 320 may include a feature obtaining unit 410, a probability determination unit 420, and a responsibility determination unit 430. The units in the first result module 320 may be hardware circuits of all or part of the first result module 320. The units in the first result module 320 may also be implemented as an application or set of instructions read and executed by the processing engine 112. Further, the units in the first result module 320 may be any combination of the hardware circuits and the application/instructions. In some embodiments, there may be interconnections between these units. For example, the probability determination unit 420 may receive information from feature obtaining unit 410, and send information to responsibility determination unit 430.

The feature obtaining unit 410 may be configured to obtain at least one feature from the abnormal order in processing the abnormal order. For example, the feature obtaining unit 410 may extract at least one feature from the abnormal order. The at least one feature of the abnormal orders may include time of receiving the abnormal order, time of cancelling the abnormal order, a GPS trace of the abnormal order, a historical order-cancellation record of the service requester or the service provider, a distance between the service provider and the service requester when the abnormal order is generated, picking-up time of the abnormal order, an estimated distance of the abnormal order, a start location of the abnormal order, a destination location of the abnormal order, an estimated duration of the abnormal order, or the like, or any combinations thereof.

The probability determination unit 420 may be configured to determine a probability predicting a likelihood that the abnormality of the abnormal order is caused by who (e.g., the service requester, the service provider) in processing the abnormal order. For example, the probability determination unit 420 may determine the probability that the abnormality of the abnormal order is caused by the service requester. As another example, the probability determination unit 420 may determine the probability that the abnormality of the abnormal order is caused by the service provider.

The responsibility determination unit 430 may be configured to determine the responsibility of the abnormality of the abnormal order. For example, the determination unit 430 may determine the service requester’s responsibility (the abnormality of the abnormal order is caused by the service requester) in response to a determination that the probability is greater than a probability threshold. As another example, the determination unit 430 may determine the service provider’s responsibility (the abnormality of the abnormal order is caused by the service provider) in response to a determination that the probability is not greater than the probability threshold

The feature obtaining unit 410, the probability determination unit 420, and the responsibility determination unit 430 in the first result module 320 may be connected to or communicated with each other via a wired connection or a wireless connection. Two or more of the units may be combined as a single unit, and any one of the units may be divided into two or more sub-units. For example, the feature obtaining unit 410 may be integrated into the probability determination unit 420 as a single module which may both obtain the at least one feature from the abnormal order and determine the probability. As another example, the first result module 320 may include a storage unit (not shown in FIG. 4) which may be configured to store the data temporarily or permanently.

FIG. 5 is a block diagram illustrating an exemplary complaint module 330 according to some embodiments of the present disclosure. The complaint module 330 may include a complaint receiving unit 510, a type determination unit 520, and a complaint sending unit 530. The units in the complaint module 330 may be hardware circuits of all or part of the complaint module 330. The units in the complaint module 330 may also be implemented as an application or set of instructions read and executed by the processing engine 112. Further, the units in the complaint module 330 may be any combination of the hardware circuits and the application/instructions. In some embodiments, there may be interconnections between these units. For example, the type determination unit 520 may receive information from the complaint receiving unit 510, and send information to the complaint sending unit 530.

The complaint receiving unit 510 may be configured to receive a complaint associated with the first result in processing the abnormal order. For example, the complaint unit 510 may receive the complaint from the service requester or the service provider.

The type determination unit 520 may be configured to determine a type of a complaint according to a predetermined type rule in processing the abnormal order. For example, the type determination unit 520 may determine a complaint type of the abnormal order according to the way how the service requester and/or the service provider sent the complaint. As another example, the type determination unit 520 may determine a complaint type of the abnormal order according to a service type of the abnormal order.

The complaint sending unit 530 may be configured to send the complaint to a reviewer in processing the abnormal order. For example, the complaint unit 530 may send the complaint to the reviewer based on a priority of the abnormal order.

The complaint receiving unit 510, the type determination unit 520, and the complaint sending unit 530 in the complaint module 330 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 units may be combined as a single unit, and any one of the modules may be divided into two or more sub-units.

FIG. 6 is a flowchart illustrating an exemplary process 600 for processing an abnormal order according to some embodiments of the present disclosure. In some embodiments, one or more steps in the process 600 may be executed by the system 100 as illustrated in FIG. 1. For example, one or more steps in the process 600 may be implemented as a set of instructions (e.g., an application program) stored in the storage (e.g., the storage 150, the storage 230, etc. ) , and invoked and/or executed by the server 110 (e.g., the processing engine 112, the processor 210 of the processing engine 112) . The operations of the illustrated process 600 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 discussed. Additionally, the order in which the operations of the process 600 as illustrated in FIG. 6 and described below is not intended to be limiting.

In 610, the processing engine 112 (e.g., the processor 210, the communication module 310) may receive an abnormal order.

In come embodiments, the abnormal order may be an online on-demand service request that is cancelled before the service is formally completed. Cancellation of the on-demand service may be conducted by a service requestor or a service provider. For example, the abnormal order may be an online taxi service request or online goods delivery request that is early-terminated and/or cancelled when the service provider is still on the way to the service requester, or when the service requester is on his/her way to the pick-up location or goods-delivery location. Accordingly, in this step, the processing engine 112 (e.g., the processor 210, the communication module 310) may first receive the order for the on-demand service from the service requester via Internet, assign the on-demand service to a service provider, and then before the service provider actually starts serving the service requester, the on-demand service order is cancelled, thereby making the order abnormal.

In some embodiments, the processing engine 112 may receive a real-time abnormal order from the service requester terminal 130 or the service provider terminal 140 via the network 120. In some embodiments, the processing engine 112 may receive a historical early-terminated order stored in the storage 150.

In some embodiments, the abnormal order may include an order which is abnormal during or after a service providing process. For example, the abnormal order may include an order that is abnormally operated by a service requester and/or a service provider (e.g., a cancelled order caused by a service requester and/or a service provider, an abandoned order caused by a service requester and/or a service provider, a delayed order caused by a service requester and/or a service provider, etc. ) , an order that is complained by a service requester and/or a service provider, an uncompleted order, or the like, or any combination thereof.

In some embodiments, the abnormal order may include a plurality of features. For example, the features may include time of receiving the abnormal order, time of cancelling the abnormal order, a GPS trace of the abnormal order, a historical cancelling record (e.g., historical order early- cancellation record) of the service requester or the service provider relating to the abnormal order, a distance between the service provider and the service requester when the abnormal order is generated, picking-up time, an estimated distance of the abnormal order, a start location of the abnormal order, a destination location of the abnormal order, an estimated duration of the abnormal order, a distance between the service provider when accepting the abnormal order and the start location of the abnormal order, an estimated duration that the service provider travels to the start location, or the like, or any combination thereof.

In 620, the processing engine 112 (e.g., the processor 210, the first result module 320) may conclude a first result based on the abnormal order and a first predetermined rule.

In some embodiments, the first result may indicate that who is responsible for abnormality of the abnormal order. For example, after the order is early cancelled, the processing engine (e.g., the processor 210, the first result module 320) may operate logic circuits therein to conduct a logic judgement whether the requester is at fault to the cancellation or the service provider is at fault to the cancellation. Whoever is at fault to the early cancellation of the order would be responsible for the consequence of the cancellation. Accordingly, the first result may include a service requester’s responsibility (i.e., the service requester is responsible for the abnormality of the abnormal order) and/or a service provider’s responsibility (i.e., the service provider is responsible for the abnormality of the abnormal order) .

In some embodiments, the first predetermined rule may be determined by the system 100 according to different applications scenarios, or may be stored in a storage (e.g., the storage 150, the storage 230, etc. ) of the system 100. The predetermined rule may include a service requester’s responsibility rule, a service provider’s responsibility rule, etc.

For example, the service requester’s responsibility rule may include that the service requester cancels the abnormal order during the service provider is on the way to the service requester, the service requester cancels the abnormal order after a first predetermined period of time since the service provider reaching a start location of the abnormal order, the service requester cancels the abnormal order after a second predetermined period of time since the abnormal order is generated, a probability that the abnormality of the abnormal order is caused by the service requester is greater than a first probability threshold (or a probability that the abnormality of the abnormal order is caused by the service provider is less than a second probability threshold) , or the like, or any combination thereof.

As another example, the service provider’s responsibility rule may include that the service provider cancels the abnormal order after a third predetermined period of time since the abnormal order is generated, the service provider is late for a fourth predetermined period of time for picking-up the service requester, the service provider travels a predetermined distance greater than a normal distance of the abnormal order, the service provider charges a predetermined money greater than a normal price of the abnormal order, a probability that the abnormality of the abnormal order is caused by the service requester is less than a third probability threshold (or a probability that the abnormality of the abnormal order is caused by the service provider is greater than a fourth probability threshold) , or the like, or any combination thereof.

In some embodiments, the processing engine 112 may determine that the service requester is responsible for the abnormality of abnormal order if the abnormal order satisfies a first predetermined rule. For example, if the abnormal order satisfies that the service requester cancels the abnormal order during the service provider is on the way to the service requester, the processing engine 112 may determine that the service requester is responsible for the abnormality of abnormal order. As another example, if the abnormal order satisfies that the service requester cancels the abnormal order after a first predetermined period of time since the service provider reaching a start location of the abnormal order, the processing engine 112 may determine that the service requester is responsible for the abnormality of abnormal order. In some embodiments, if the abnormal order does not satisfy the first predetermined rule, the processing engine 112 may determine that the service provider is responsible for the abnormality of abnormal order. In some embodiments, detailed description of determining the first result may be found in connection with FIG. 7 in the present disclosure.

In some embodiments, the processing engine 112 (e.g., the processor 220, the attribute modification module 360) may modify a profile of the service requester and/or the service provider after determining the first result. The profile may include a service score, a service completion success rate, a credit score, a count of successful orders, a service level, or the like, or any combination thereof. For example, if the first result indicates that the service requester is responsible for the abnormality of the abnormal order, the processing engine 112 may modify a service score, a service completion success rate, a credit score, etc. in the profile of the service requester.

In 630, the processing engine 112 (e.g., the processor 210, the communication module 310, the complaint receiving unit 510) may send out a decision associated with the first result. For example, after determining the party at fault (i.e., the service provider or the service requester who should be responsible to the early cancellation of the order) , the processing engine 112 (e.g., the processor 210, the communication module 310, the complaint receiving unit 510) may send out electronic signals directing the party at fault or the innocent party. The electronic signals may include the first result (e.g., a conclusion that which party is at fault) and/or a punishment as a consequence of the abnormal order (e.g., a punitive charge to the party at fault or a reduction of credit score to the party at fault) .

In some embodiments, the electronic signal including punishment information may refer to a notification, which may be sent to the party’s terminal device. For example, the notification may be a refund or a compensation of the abnormal order, which may be displayed by an application associated with the abnormal order on the user interface of the terminal device.

In 640, the processing engine 112 (e.g., the processor 210, the communication module 310, the complaint receiving unit 510) may receive a complaint associated with the first result.

In some embodiments, the complaint may refer to a disagreement with the first result of the abnormal order. For example, if the service provider of the abnormal order receives the first result that the abnormality of the abnormal order is caused by the service provider, the service provider disagrees with the first result, then the service provider may send a compliant associated with the first result to the processing engine 112. In some embodiments, the complaint may include an abnormal order that the service requester or the service provider complained, at least one feature (e.g., the time of receiving the abnormal order, time of cancelling the abnormal order, a GPS trace of the abnormal order, a historical cancelling record (e.g., historical order early-cancellation record) of the service requester or the service provider, a distance between the service provider and the service requester when the abnormal order is generated, picking-up time, an estimated distance of the abnormal order, a start location of the abnormal order, a destination location of the abnormal order, an estimated duration of the abnormal order, etc. ) of the abnormal order, details of the complaint (e.g., why the service requester or the service provider complains about the first result of the abnormal order, a proof provided by the service requester or the service provider) , or the like, or any combination thereof.

In some embodiments, the processing engine 112 may receive complaint from the party of the service requester and the service provider via the service requester terminal 130 and/or the service provider terminal 140. For example, after obtaining the first result of the abnormal order determined by the processing engine 112, the service requester or the service provider may agree or disagree with the determined first result. If the service requester or the service provider disagrees with first result, he/she may send a complaint about the first result to the processing engine 112. The service requester or the service provider may send the complaint via an application in the service requester terminal 130 or the service provider terminal 140, a telephone call, a short massage, an e-mail, a social platform, an instant message, or the like, or any combination thereof. In some embodiments, the processing engine 112 may classify the complaint into a complaint type according to the way that the service requester or the service provider sends the complaint. For example, complaint type may include a call complaint (e.g., the complaint is sent via a telephone call) , an application complaint (e.g., the complaint is sent via an application) , a message complaint (e.g., the complaint is sent via a short message) , or the like, or any combination thereof.

Merely by way of example, the service requester or the service provider may send the complaint via an application (e.g., a car hailing application, a food delivery application, etc. ) installed in the service requester terminal 130 or the service provider terminal 140. The application may include a complaint interface. The complaint interface may include an icon, a button on the user interface of the application. The service requester or the service provider may activate the corresponding complaint interface to send the complaint to the processing engine 112.

In 650, the processing engine 112 (e.g., the processor 210, the communication module 310) may send the complaint to a reviewer.

In some embodiment, the reviewer may be a reviewer terminal in communication with the processing engine 112 via wired, wireless network. The reviewer device may automatically process the complaint and draw a conclusion. Alternatively, the reviewer may also include a person manually operate the reviewer device and draw the conclusion. In some embodiments, the reviewer may review the first result determined based on the first predetermined rule.

In some embodiments, the processing engine 112 may receive a plurality of complaints at same time or during a same time period. The processing engine 112 may assign priorities for the plurality of complaints based on the complaint types thereof. The processing engine 112 may send the plurality of complaints based on the priorities. In some embodiments, detailed description of sending the complaint to the reviewer based on priority may be found in connection with FIG. 9 in the present disclosure.

In 660, the processing engine 112 (e.g., the processor 210, the communication module 310) may receive a second result from the reviewer. In some embodiments, the processing engine 112 (e.g., the processor 210, the communication module 310) may receive the second result from the reviewer via the reviewer terminal.

In some embodiments, the reviewer may determine the second result based on the first result and the complaint (e.g., the details of the complaint, the features of the abnormal order, etc. ) . For example, after a service provider receiving a first result that the service provider is responsible for the abnormality of the abnormal order, the service provider may send a complaint including a complaint reason why he/she thinks the abnormality of the abnormal order is not his/her responsibility, and/or provide a proof to the processing engine 112. The processing engine 112 may send the complaint (e.g., the reason and/or the proof) to the reviewer via the reviewer terminal. The reviewer may determine the second result based on the first result, the complaint reason, and/or the proof. For example, the reviewer may review the features of the abnormal order stored in a storage (e.g., the storage 150, the storage 230, etc. ) , and take the complaint into consideration to determine a responsibility that who (the service requester or the service provider) is response for the abnormality of the abnormal order. As another example, if the complaint reason, the proof, and/or the features of the abnormal order that the service provider provided indicate that the abnormality is caused by the service requester rather than the service provider, the reviewer may determine that the second result is the service requester’s responsibility. As still another example, the reviewer may contact the service requester and/or the service provider of the abnormal order to learn details of the abnormal order to make a decision that who is responsible for the abnormality of the abnormal order.

In some embodiments, after the reviewer sending the second result to the processing engine 112, the processing engine 112 may receive the second result from the reviewer via the reviewer terminal. In some embodiments, the second result may be stored in the second result module 340. In some embodiments, the processing engine 112 may send the second result to the service provider and/or the service requester associated with the abnormal order form the second result module 340. In some embodiments, the processing engine 112 may modify a profile of the service requester and/or the service provider. For example, if the second result indicates that the service provider is responsible for the abnormality of the abnormal order, the processing engine 112 may modify a service score, a success rate of completing the on-demand service, and a credit score, in the profile of the service provider based on the second result.

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. For example, one or more other optional steps (e.g., a storing step, a preprocessing step) may be added elsewhere in the exemplary process 600. As another example, one or more steps (e.g., steps 630-660) may be deleted in the exemplary process 600. As still another example, all the steps in the exemplary process 600 may be implemented in a computer-readable medium including a set of instructions. The instructions may be transmitted in a form of electronic current or electrical signals.

FIG. 7 is a flowchart illustrating an exemplary process 700 for concluding the first result according to some embodiments of the present disclosure. In some embodiments, one or more steps in the process 700 may be executed by the system 100 as illustrated in FIG. 1. For example, one or more steps the process 700 may be implemented as a set of instructions (e.g., an application) stored in the storage (e.g., the storage 150, the storage 230, etc. ) , and invoked and/or executed by the server 110 (e.g., the processing engine 112, the processor 210 of the processing engine 112) . The operations of the illustrated process 700 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 discussed. Additionally, the order in which the operations of the process 700 as illustrated in FIG. 7 and described below is not intended to be limiting.

In 710, the processing engine 112 (e.g., the processor 210, the first result module 320, the feature obtaining unit 410) may obtain at least one feature from the abnormal order.

In some embodiments, the abnormal order may be an on-demand service order requested by the service requester, and being cancelled by the service requester before the service provider completes the on-demand service. In some embodiments, the abnormal order may refer to an abnormal order that need to be processed. For example, the abnormal order may include a real-time abnormal order, an abnormal order stored in a storage (e.g., the storage 150, the storage 230, etc. ) , etc. The at least one feature may include time of receiving the abnormal order, time of cancelling the abnormal order, a GPS trace of the abnormal order, a historical order-cancellation record of the service requester or the service provider, a distance between the service provider and the service requester when the abnormal order is generated, picking-up time, an estimated distance of the abnormal order, a start location of the abnormal order, a destination location of the abnormal order, an estimated duration of the abnormal order, or the like, or any combination thereof.

In some embodiments, the service requester terminal 130 may send a first portion of the at least one feature to the processing engine 112 (e.g., the processor 210, the first result module 320, the feature obtaining unit 410) via the network 120. For example, the service requester terminal 130 may send the first portion of the at least one feature to the processing engine 112 in wireless communication with the base station 120-1 and/or the base station 120-2. The first portion of the at least one feature may include the time of cancelling the abnormal order, the GPS trace of the abnormal order, the historical order-cancellation record of the service requester, a location of the service requester when the abnormal order is generated, the picking-up time, the estimated distance of the abnormal order, the start location of the abnormal order, the destination location of the abnormal order, or the like, or any combination thereof. In some embodiments, the service provider terminal 140 may send a second portion of the at least one feature to the processing engine 112 (e.g., the processor 210, the first result module 320, the feature obtaining unit 410) via the network 120. For example, the service provider terminal 140 may send the first portion of the at least one feature to the processing engine 112 in wireless communication with the base station 120-1 and/or the base station 120-2. The second portion of the at least one feature may include the time of receiving the abnormal order, the time of cancelling the abnormal order, the GPS trace of the abnormal order, the historical order-cancellation record of the service provider, the location of the service requester when the abnormal order is generated, the picking-up time, or the like, or any combination thereof.

In 720, the processing engine 112 (e.g., the processor 210, the first result module 320, the probability determination unit 420) may determine a probability that cancellation of the order is caused by the party based on a responsibility model and the at least one feature.

In some embodiments, the processing engine 112 may calculate each of the at least one feature as a vectorization value. The processing engine 112 may input the at least one feature into the responsibility model. The output of the responsibility model may include a probability that the abnormality of the abnormal order is caused by the service requester and/or the service provider. In some embodiments, the responsibility model may refer to a prediction model for predicting a probability that the abnormality of the abnormal order is caused by the service requester and/or the service provider. For example, the responsibility model may include a formula, an algorithm (e.g., a logistic regression algorithm, a linear regression, etc. ) , a program, a criterion, or the like, or any combination thereof. As another example, the responsibility model may include a decision tree learning model, an association rule learning model, an artificial neural network model, a deep learning model, an inductive logic programming model, a support vector machine model, a Bayesian network model, a reinforcement learning model, a representation learning model, a similarity and metric learning model, a logistic regression (LR) model, a linear regression model, or the like, or any combination thereof. In some embodiments, descriptions of the determining of the probability model may be found in connection with FIG. 8 in the present disclosure.

Merely by way of example, the responsibility model may include an algorithm as Formula 1,

wherein θ^T= [θ₁, θ₂, …, θ_n] , x= [x₁, x₂, …, x_n] , x_i denotes the vectorization value of a feature i, θ_i denotes a weighted value of the feature i. h_θ (x) denotes a probability value that the abnormality of the abnormal order is caused by the service requester.

In some embodiments, the vectorization value x_i may include a predetermined integer value. For example, if the feature i is included in the at least one feature in the predetermined responsibility model, the vectorization value x_i may be 1; if the feature i is not included in the at least one feature in the predetermined responsibility model, the vectorization value x_i may be 0. The weighted values θ^T may include a plurality of predetermined values less than 1.

In some embodiments, the processing engine 112 may input at least one feature (including vectorization value x_i of each feature i, and corresponding weighted value of each feature i) obtained from an abnormal order into Formula 1, and calculate a probability value h_θ (x) that the abnormality of the abnormal order is caused by the service requester.

In 730, the processing engine (e.g., the processor 210, the first result module 320, the responsibility determination unit 430) may determine whether the probability is larger than a probability threshold.

In some embodiments, the probability may represent a relative responsibility degree of the service requester in determination process of the responsibility of the abnormality of the abnormal order. The higher the relative responsibility degree of the service requester, the higher the probability. In some embodiments, the probability threshold may be a positive numerical value between 0 and 1. For example, the probability threshold may be a positive numerical value between 0.5 and 1. As another example, the probability threshold may be 0.7. In some embodiments, the probability threshold may be a predetermined value stored in a storage (e.g., the storage 150) of the system 100, or may be determined according to different applications scenarios.

In response to the determination that the probability is greater than the probability threshold, in 740, the processing engine (e.g., the processor 210, the first result module 320, the responsibility determination unit 430) may determine a first result that the abnormal order is the service requester’s responsibility (the abnormality of the abnormal order is caused by the service requester) .

In response to the determination that the probability is not greater than the probability threshold, in 750, the processing engine (e.g., the processor 210, the first result module 320, the responsibility determination unit 430) may determine a first result that the abnormal order is the service requester’s responsibility (the abnormality of the abnormal order is caused by the service provider) .

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. For example, one or more other optional steps (e.g., a storing step, a preprocessing step) may be added elsewhere in the exemplary process 700. As another example, all the steps in the exemplary process 700 may be implemented in a computer-readable medium including a set of instructions. The instructions may be transmitted in a form of electronic current or electrical signals.

FIG. 8 is a flowchart illustrating an exemplary process 800 for determining a responsibility model according to some embodiments of the present disclosure. In some embodiments, one or more steps in the process 800 may be executed by the system 100 as illustrated in FIG. 1. For example, one or more steps in the process 800 may be implemented as a set of instructions (e.g., an application program) stored in the storage (e.g., the storage 150, the storage 230, etc. ) , and invoked and/or executed by the server 110 (e.g., the processing engine 112, the processor 210 of the processing engine 112) . The operations of the illustrated process 800 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 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 processor 210, the training module 350) may obtain a plurality of historical early-terminated orders.

The historical early-terminated orders may refer to the abnormal orders with accurate predetermined responsibility. The historical early-terminated orders may be stored in a storage (e.g., the storage 150, the storage 230, etc. ) . In some embodiments, the historical early-terminated orders may include a plurality of historical abnormal orders having second results determined by reviewers in connection with process 600 in FIG. 6.

In 820, the processing engine 112 (e.g., the processor 210, the training module 350) may obtain a plurality of historical features from the plurality of historical early-terminated orders.

In some embodiments, the processing engine 112 may extract at least one historical feature from each historical early-terminated order. The plurality of historical features may include time of receiving the historical early-terminated order, time of cancelling the historical early-terminated order, a GPS trace of the historical early-terminated order, a historical order-cancellation record of the service requester or the service provider of the historical early-terminated order, a distance between the service provider and the service requester when the historical early-terminated order is generated, picking-up time, an estimated distance of the historical early-terminated order, a start location of the historical early-terminated order, a destination location of the historical early-terminated order, an estimated duration of the historical early-terminated order, or the like, or any combination thereof. In some embodiments, the processing engine 112 may calculate each of the plurality historical features as a vectorization value.

In 830, the processing engine 112 (e.g., the processor 210, the training module 350) may determine the responsibility model based on the plurality of historical features and a Logistic Regression algorithm. For example, the processing engine 112 may train the plurality of historical features using the LR algorithm to generate the responsibility model.

In some embodiments, the LR algorithm may include a logistic function (also called Sigmoid function) , the curve of the LR function is a S type curve, the LR function may be represented as Formula 2:

the boundary of the function is linear, and the boundary may be represented as Formula 3:

the responsibility model may be calculated based on Formula 2 and Formula 3 as Formula 4:

In some embodiments, the processing engine 112 may input the vectorization values x of the plurality of historical features extracted from the plurality of historical early-terminated orders with accurate responsibilities into Formula 4 to obtain a plurality of functions. The processing engine 112 may calculate the weighted values θ^T= [θ₁, θ₂, …, θ_n] by solving the plurality of functions using an iteration method. The processing engine 112 may put θ^T= [θ₁, θ₂, …, θ_n] into Formula 4 to obtain the responsibility model.

In some embodiments, the processing engine 112 (e.g., the processor 210, the training module 350) may update the responsibility model based on the second result determined by the reviewer and the at least one feature of the abnormal order in connection with the process 600 in FIG. 6. In some embodiments, the processing engine 112 may modify the weighted values θ^T= [θ₁, θ₂, …, θ_n] to update the responsibility model under a predetermined rule (e.g., after a process of processing an abnormal order, every predetermined time period, or the like, or any combination thereof) .

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. For example, one or more other optional steps (e.g., a storing step, a preprocessing step) may be added elsewhere in the exemplary process 800. As another example, all the steps in the exemplary process 800 may be implemented in a computer-readable medium including a set of instructions. The instructions may be transmitted in a form of electronic current or electrical signals

FIG. 9 is a flowchart illustrating an exemplary process 900 for sending a complaint to a reviewer according to some embodiments of the present disclosure. In some embodiments, one or more steps in the process 900 may be executed by the system 100 as illustrated in FIG. 1. For example, one or more steps in the process 900 may be implemented as a set of instructions (e.g., an application) stored in the storage (e.g., the storage 150, the storage 230, etc. ) , and invoked and/or executed by the server 110 (e.g., the processing engine 112, the processor 210 of the processing engine 112) . The operations of the illustrated process 900 presented below are intended to be illustrative. In some embodiments, the process 900 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 9 and described below is not intended to be limiting.

In 910, the processing engine 112 (e.g., the processor 210, the complaint module 330, the type determination unit 520) may determine a complaint type of the complaint.

In some embodiments, the processing engine 112 may determine the complaint type of a complaint according to the way how the service requester and/or the service provider sent the complaint. For example, the complaint type may include a call complaint (e.g., the complaint is sent via a telephone call) , an application complaint (e.g., the complaint is sent via an application) , a message complaint (e.g., the complaint is sent via a short message) , or the like, or any combination thereof.

In some embodiments, the processing engine 112 may determine the complaint type of a complaint according to time of receiving the complaint from the service requester and/or the service provider. For example, the complaint type may include an urgency complaint, a common complaint, etc.

In some embodiments, the processing engine 112 may determine the complaint type of a complaint according to a service type of the abnormal order. For example, in a transportation online services, the complaint type may include a taxi complaint, a private car complaint, a carpooling complaint, or the like, or any combination thereof.

In 920, the processing engine 112 (e.g., the processor 210, the complaint module 330, the type determination unit 520) may determine a priority of the complaint based on the complaint type.

The priority of the complaint may refer to a relative importance degree of the complaint in the sending process of the complaint to the reviewer. In some embodiments, the processing engine 112 may assign a priority to the complaint based on the complaint type. For example, if the complaint type is based on the way how the service requester and/or the service provider sent the complaint, the processing engine 112 may assign a highest priority to the call plaint. Other complaint types such as the application complaint, the message complaint may be assigned priority based the time of receiving the complaint. The earlier the time of receiving the complaint is, the higher priority of the complaint may be. As another example, if the complaint type is based on the service type of the abnormal order, the processing engine 112 may assign a highest priority to the private car complaint. Other complaint types such as the taxi complaint, the carpooling complaint may be assigned priority based the time of receiving the complaint.

In 930, the processing engine 112 (e.g., the processor 210, the complaint module 330, the complaint sending unit 530) may send the complaint to a reviewer based on the priority.

In some embodiments, the processing engine 112 (e.g., the processor 210, the complaint module 330, the complaint sending unit 530) may send the complaint to the reviewer via the reviewer terminal. For example, the processing engine 112 may first send a call complaint to the reviewer terminal based on the highest priority of the call complaint among a plurality of message complains and application complaints.

In some embodiments, the reviewer may include a human customer services staff who may review the first result determined based on the predetermined rule. After receiving the complaint from the service requester or the service provider, the reviewer may review the first result to determine a second result based on the at least one feature of the abnormal order, the complaint (e.g., the complaint reason, the proof, etc. ) . For example, the reviewer may disagree with the first result, and make an opposite determination of the first result (the second result is opposite to the first result) . As another example, the reviewer may agree with the first result, and make a same determination as the first result (the second result is same as the first result) .

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. For example, one or more other optional steps (e.g., a storing step, a preprocessing step) may be added elsewhere in the exemplary process 900. As another example, all the steps in the exemplary process 900 may be implemented in a computer-readable medium including a set of instructions. The instructions may be transmitted in a form of electronic current or electrical signals.

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/or “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 “unit, ” “module, ” 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, claimed subject matter may lie in less than all features of a single foregoing disclosed embodiment.

Claims

A system for processing an abnormal order, comprising:

at least one storage medium including a set of instructions for determining responsibility between a service requester and a service provider who are associated with a cancelled order; and

at least one processor in communication with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to:

receive an order of an on-demand service from a service requester;

receive a cancelling request of the order from the service requester before the service provider completes the on-demand service;

conduct at least one logic judgement to conclude a first result that cancellation of the order is associated with a fault from a party of the service requester and the service provider based on the order and a first predetermined rule; and

send electronic signal including punishment information to the party.
The system of claim 1, wherein to conclude the first result, the at least one processor is further directed to:

obtain at least one feature from the order;

determine a probability that cancellation of the order is caused by the party based on a responsibility model and the at least one feature; and

determine that the probability is greater than a probability threshold.
The system of claim 2, wherein the at least one processor is further directed to:

obtain a plurality of historical early-terminated orders;

obtain a plurality of historical features from the plurality of historical early-terminated orders; and

determine the responsibility model based on the plurality of historical features and a Logistic Regression algorithm.
The system of claim 2, wherein the at least one feature includes:

time of receiving the order,

time of cancelling the order,

a GPS trace of the order,

a historical order-cancellation record of the service requester or the service provider,

a distance between the service provider and the service requester,

picking-up time of the order,

an estimated distance of the order,

a start location of the order,

a destination location of the order, or

an estimated duration of the order.
The system of claim 1, wherein the at least one processor is further directed to:

determine a profile of the service provider or a profile of the service requester based on the determined first result, each profile including at least one of a service score or a service completion success rate.
The system of claim 1, wherein the first predetermined rule includes that the service requester cancels the order during the service provider is on the way to the service requester.
The system of claim 1, wherein the first predetermined rule includes that the service provider cancels the order after a predetermined period of time since the service provider reaching a start location of the order.
The system of claim 1, further comprising a reviewer terminal in communication with the processor, wherein the at least one processor is further directed to:

receive, from the party, a complaint associated with the first result;

send the complaint to the reviewer terminal; and

receive a second result from the reviewer terminal, the second result being determined by the reviewer terminal based on the first result, the complaint and the order.
The system of claim 8, wherein to send the complaint to the reviewer terminal, the at least one processor is further directed to:

determine a complaint type of the complaint;

determine a priority of the complaint type; and

send the complaint to the reviewer terminal based on the priority.
The system of claim 1, further comprising:

at least one base station in wireless communication with the at least one processor;

a service requester mobile device associated with the service requester and in wireless communication with the at least one base station to send a first portion of at least one feature to the at least one processor; and

a service provider mobile device associated with the service provider and in wireless communication with the at least one base station to send a second portion of the at least one feature to the at least one processor.
A method for processing an abnormal order comprising:

receiving, by at least one processor, an order of an on-demand service from a service requester;

receiving, by the at least one processor, a cancelling request of the order from the service requester before the service provider completes the on-demand service;

conducting, by the at least one processor, at least one logic judgement to conclude a first result that cancellation of the order is associated with a fault from a party of the service requester and the service provider based on the order and a first predetermined rule; and

sending, by the at least one processor, electronic signal including punishment information to the party.
The method of claim 11, wherein the conducting at least one logic judgement to conclude the first result, includes:

obtaining at least one feature from the order;

determining a probability that cancellation of the order is caused by the party based on a responsibility model and the at least one feature;

determining that the probability is greater than a probability threshold.
The method of claim 12, further comprising:

obtaining, by the at least one processor, a plurality of historical early-terminated orders;

obtaining, by the at least one processor, a plurality of historical features from the plurality of historical early-terminated orders; and

determining, by the at least one processor, the responsibility model based on the plurality of historical features and a Logistic Regression algorithm.
The method of claim 12, wherein the at least one feature includes:

time of receiving the order,

time of cancelling the order,

a GPS trace of the order,

a historical order-cancellation record of the service requester or the service provider,

a distance between the service provider and the service requester,

picking-up time of the order,

an estimated distance of the order,

a start location of the order,

a destination location of the order, or

an estimated duration of the order.
The method of claim 11, further comprising:

determining, by the at least one processor, a profile of the service provider or a profile of the service requester based on the determined first result, each profile including at least one of a service score or a service completion success rate.
The method of claim 11, wherein the predetermined rule includes that the service requester cancels the order during the service provider is on the way to the service requester.
The method of claim 11, wherein the predetermined rule includes that the service provider cancels the order after a predetermined period of time since the service provider reaching a start location of the order.
The method of claim 11, further comprising:

receiving, by the at least one processor, from the party, a complaint associated with the first result;

sending, by the at least one processor, the complaint to a reviewer terminal, the reviewer terminal being in communication with the processor; and

receiving, by the at least one processor, a second result from the reviewer terminal, the second result being determined by the reviewer terminal based on the first result, the complaint and the order.
The method of claim 18, wherein the sending the complaint to the reviewer terminal, comprising:

determining a complaint type of the complaint;

determining a priority of the complaint type; and

sending the complaint to the reviewer terminal based on the priority.
The method of claim 11, further comprising:

sending, by a service requester mobile device associated with the service requester, a first portion of at least one feature to the at least one processor; and

sending, by a service provider mobile device associated with the service provider, a second portion of the at least one feature to the at least one processor; wherein

the service requester mobile device and the service provider mobile device are in wireless communication with at least one base station, and

the at least one base station is in wireless communication with the at least one processor.