CN112825174A - Litigation prediction method, device, system and computer storage medium - Google Patents
Litigation prediction method, device, system and computer storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device and a system for litigation prejudgment and a computer storage medium. The method comprises the following steps: acquiring transaction dispute information; calculating dispute characteristics of the transaction dispute information; obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics; and obtaining the predicted litigation fact and the litigation prediction information at least according to the predicted dispute reason. According to the embodiment of the invention, the electronic commerce online dispute resolution platform can be connected with the legal litigation prejudging system, so that prejudgment on the electronic commerce dispute litigation can be realized, and effective references can be provided for buyers and sellers.
Description
Technical Field
The present invention relates to the field of electronic commerce technologies, and in particular, to a method and an apparatus for litigation prediction, a system for litigation prediction, and a computer storage medium.
Background
The millions of transactions that are generated by various e-commerce platforms each day involve many disputes, which creates a need for an efficient and effective solution to e-commerce transaction disputes. At present, an electronic commerce online dispute resolution platform exists, and buyers and sellers can resolve some transaction disputes online through the electronic commerce online dispute resolution platform.
In the course of research on the above prior art, the inventor finds that when a transaction dispute cannot be resolved, legal procedures are usually required to be resorted, and therefore, it is necessary to provide a legal action result prediction method based on an e-commerce online dispute resolution platform to predict the judgment result of an e-commerce litigation case and provide an effective reference for buyers and sellers.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a new technical solution for litigation prejudgment.
According to a first aspect of embodiments of the present invention, there is provided a method of litigation anticipation, the method comprising:
acquiring transaction dispute information;
calculating dispute characteristics of the transaction dispute information;
obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics;
and obtaining the predicted litigation fact and the litigation prediction information at least according to the predicted dispute reason.
Optionally, the calculating dispute characteristics of the transaction dispute information includes:
calculating semantic similarity between the transaction dispute information and nodes in the legal knowledge graph;
taking the semantic similarity as an initial value of probability distribution of the transaction dispute information;
calculating a directed acyclic graph of the legal knowledge graph based on the initial probability distribution value to obtain an element distribution probability of the transaction dispute information on the legal knowledge graph;
and taking the element distribution probability as the dispute characteristic of the transaction dispute information.
Optionally, the transaction dispute information includes: transaction information, buyer information and seller information;
the transaction information at least comprises price information, quantity information, commodity information and dispute record information;
the buyer information at least comprises buyer credit information, buyer complaint frequency information, buyer complaint record information and buyer grade information;
the seller information at least comprises seller credit information, seller grade information, seller complaint times information and seller complaint record information.
Optionally, the calculating dispute characteristics of the transaction dispute information includes:
calculating transaction characteristics of the transaction dispute information according to the transaction information;
according to the buyer information, buyer characteristics of the transaction dispute information are calculated;
and calculating the seller characteristics of the transaction dispute information according to the seller information.
Optionally, the obtaining of the reason for the predicted dispute and the result of the predicted dispute at least according to the dispute characteristics includes:
and calculating to obtain the reason of the predicted dispute and the result of the predicted dispute at least according to the transaction characteristics, the buyer characteristics and the seller characteristics.
Optionally, the obtaining of the reason for the predicted dispute and the result of the predicted dispute at least according to the dispute characteristics includes:
taking the transaction characteristics, the buyer characteristics and the seller characteristics as the input of a dispute prediction model, and calculating to obtain the predicted dispute reason;
and taking the transaction characteristics, the buyer characteristics, the seller characteristics and the predicted dispute reason as the input of the dispute prediction model, and calculating to obtain the predicted dispute result.
Optionally, the obtaining of the fact of the predicted litigation and the information of the predicted litigation according to at least the reason of the predicted dispute includes:
taking the reason of the predicted dispute as the input of a litigation prediction model, and calculating to obtain the predicted litigation fact;
and calculating the predicted litigation fact and the predicted dispute reason as the input of the litigation prediction model to obtain the litigation prediction information.
Optionally, the method further includes:
providing the predicted litigation fact and the litigation anticipation information to a user.
According to a second aspect of the embodiments of the present invention, there is also provided a method of litigation anticipation, the method comprising:
the method comprises the steps that a client side obtains transaction dispute information input by a user;
the client side sends a litigation pre-judging request to a server, wherein the litigation pre-judging request at least comprises the transaction dispute information;
the server obtains a predicted litigation fact corresponding to the transaction dispute information and litigation information according to the litigation prediction request;
the server sends the predicted litigation fact and the litigation prejudgment information to the client;
and the client sends the received litigation pre-judging information to the client for displaying.
According to a third aspect of the embodiments of the present invention, there is also provided a device for litigation prejudgment, the device including:
the acquisition module is used for acquiring transaction dispute information;
the calculation module is used for calculating dispute characteristics of the transaction dispute information;
the dispute prediction module is used for obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics;
and the litigation predicting module is used for obtaining the actual litigation prediction and the litigation prediction information at least according to the dispute prediction reason.
Optionally, the dispute prediction module is specifically configured to output a single reason for the predicted dispute and a single result of the predicted dispute;
the litigation prediction module is specifically configured to output a plurality of the predicted litigation facts and a plurality of the litigation prejudgment information.
The transaction dispute information comprises: buyer information, seller information, and transaction information.
According to a fourth aspect of the embodiments of the present invention, there is also provided a litigation-outcome prediction system, the system including:
the system comprises a first module, a second module and a third module, wherein the first module is used for encoding dispute information, and the dispute information comprises buyer information, seller information and transaction information;
a second module, communicatively coupled to the first module, the second module configured to obtain a reason for the predicted dispute and a result of the predicted dispute;
a third module communicatively coupled to the first module and the second module, the third module configured to obtain predicted litigation facts and litigation anticipation information.
Optionally, the encoding, by the first module, the dispute information includes: the legal knowledge graph LKG, discrete information, and textual information are encoded.
Optionally, the second module includes a single-label classifier, and the third module includes a multi-label classifier.
Optionally, the information encoded by the first module is used as input information of the second module and the third module.
Optionally, the output information of the second module is used as the input information of the third module.
According to a fifth aspect of the embodiments of the present invention, there is also provided an apparatus for litigation anticipation, including: a memory and a processor; the memory is configured to store instructions for controlling the processor to operate so as to perform the method of litigation pretreatments as set forth in any one of the first aspect of embodiments of the invention.
According to a fifth aspect of embodiments of the present invention, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of litigation anticipation as described in any one of the first aspects of embodiments of the present invention.
The method, the device, the system and the computer storage medium have the advantages that the electronic commerce online dispute resolution platform and the law litigation prejudging system can be connected, prejudging on the electronic commerce dispute litigation can be realized, and effective references are provided for buyers and sellers.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a block diagram showing a hardware configuration of an electronic apparatus 1000 that can be used to implement an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a method for litigation anticipation according to an embodiment of the invention.
Fig. 3 is a schematic structural diagram of a network implementing the method for litigation anticipation of an embodiment of the present invention.
FIG. 4 is a schematic illustration of a partial legal knowledge graph according to an embodiment of the invention.
Fig. 5 is a schematic structural diagram of a device 3000 for litigation anticipation according to an embodiment of the invention.
Fig. 6 is a schematic diagram of the hardware structure of another litigation prejudging device according to the embodiment of the invention.
Fig. 7 is a schematic structural diagram of the litigation-result prediction system 5000 according to the embodiment of the invention.
Fig. 8 is a flowchart illustrating a method for litigation anticipation according to a second embodiment of the invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Various embodiments and examples according to embodiments of the present invention are described below with reference to the accompanying drawings.
< hardware configuration >
Fig. 1 is a block diagram showing a hardware configuration of an electronic apparatus 1000 that can be used to implement an embodiment of the present invention.
As shown in fig. 1, the electronic device 1000 of the present embodiment may be a laptop, a desktop computer, a mobile phone, a tablet computer, etc.
As shown in fig. 1, the electronic device 1000 may include a processor 1010, a memory 1020, an interface device 1030, a communication device 1040, a display device 1050, an input device 1060, a speaker 1070, a microphone 1080, and the like.
The processor 1010 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 1020 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like.
The interface device 1030 includes, for example, a USB interface, a headphone interface, and the like.
The communication device 1040 can perform wired or wireless communication, for example.
The display device 1050 is, for example, a liquid crystal display panel, a touch panel, or the like.
The input device 1060 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 1070 and the microphone 1080.
In this embodiment, the memory 1020 of the electronic device 1000 is configured to store instructions for controlling the processor 1010 to operate at least to perform the method of litigation anticipation according to any embodiment of the invention.
It should be understood by those skilled in the art that although a plurality of devices of the electronic apparatus 1000 are shown in fig. 1, the present invention may only relate to some of the devices, for example, the electronic apparatus 1000 only relates to the memory 1020, the processor 1010 and the display device 1050.
The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
< method for predicting litigation >
< method embodiment I >
Fig. 2 is a flowchart illustrating a method for litigation anticipation according to an embodiment of the invention. The litigation prediction method may be specifically executed by the electronic device 1000.
As shown in fig. 2, the method for litigation prediction in this embodiment may include steps 2100 to 2400 as follows:
Wherein the transaction dispute information comprises: transaction information, buyer information, and seller information. Specifically, the transaction information at least includes price information, quantity information, commodity information and dispute record information. The buyer information at least comprises buyer credit information, buyer complaint frequency information, buyer complaint record information and buyer grade information. The seller information at least comprises seller credit information, seller grade information, seller complaint times information and seller complaint record information.
When calculating the dispute characteristics of the transaction dispute information, the electronic device 1000 may calculate the dispute characteristics by using a legal knowledge graph. As shown in fig. 3, the electronic device 1000 calculates semantic similarity between semantic information (word embedding) in the transaction dispute information and nodes in the legal knowledge graph; and taking the semantic similarity as the initial value of the probability distribution of the transaction dispute information. Then, the electronic device 1000 calculates a directed acyclic graph of the legal knowledge graph based on the initial value of the probability distribution, and transfers the directed acyclic graph from a source node (source node) to a target node (target node), where the transfer mechanism may be, for example, an access transfer manner of random walk (random walk). The electronic device 1000 obtains the element distribution probability of the transaction dispute information on the legal knowledge base according to the calculation result, and uses the element distribution probability as the dispute characteristic of the transaction dispute information. Thereby obtaining the dispute characteristics of the transaction dispute information.
As shown in fig. 4, is part of a legal knowledge graph. The method comprises the following steps of (1) node 'false price', 'false promotion', 'commodity problem', 'other false behavior' pointing to node 'false behavior (tend to be) and (b) node' in sequence; the node "false behavior (tend to be) and the node" original that the consumer (the platform tend to be a consumer) "both point to the node" tend to be fraudulent/false behavior (tend) ". The electronic device 1000 may, based on the initial value of the probability distribution, transfer the node "false price" to the node "tend fraud/false behavior (tend to be fraud)", and calculate an element distribution probability of the transaction dispute information on the legal knowledge base as a dispute feature of the transaction dispute information.
It should be noted that the transaction dispute information includes transaction information, buyer information and seller information, and correspondingly, as shown in fig. 3, when calculating the dispute feature of the transaction dispute information, the electronic device 1000 calculates the corresponding feature according to the transaction information, the buyer information and the seller information, respectively. Specifically, the electronic device 1000 calculates transaction characteristics (transaction embedding) of the transaction dispute information according to the transaction information; the electronic device 1000 calculates buyer characteristics (buyer embedding) of the transaction dispute information according to the buyer information; the electronic device 1000 calculates the seller characteristics (seller embedding) of the transaction dispute information according to the seller information.
The electronic device 1000 uses the above-mentioned calculation method using the legal knowledge base when calculating the transaction characteristics, the buyer characteristics, and the seller characteristics, which are not described herein again.
2300, obtaining a reason for the predicted dispute and a result of the predicted dispute at least according to the dispute characteristics.
In the step 2200, the transaction characteristic, the buyer characteristic and the seller characteristic calculated by the electronic device 1000 are obtained, and in this step, the electronic device 1000 may specifically calculate the reason for the predicted dispute and the result of the predicted dispute according to the transaction characteristic, the buyer characteristic and the seller characteristic.
In practical application, the dispute reason and the dispute result are predicted at the same time, and the dispute reason is predicted as the input of the dispute result. Specifically, as shown in fig. 3, the electronic device 1000 uses the transaction characteristics, the buyer characteristics, and the seller characteristics as input of a dispute prediction model to calculate the predicted dispute REASON (REASON). The electronic device 1000 calculates the predicted dispute result (real) by using the transaction characteristics, the buyer characteristics, the seller characteristics and the predicted dispute reason as the input of the dispute prediction model.
It should be noted that both the reason for dispute prediction and the dispute result prediction are single-label classification tasks, that is, one dispute reason is predicted from a plurality of dispute reasons as an output reason for dispute prediction; and predicting a dispute result from the dispute results as the output dispute prediction result. For example, the predicted dispute result may be a return, a refund, a rejection, or the like. This is not to be taken as an example.
2400, obtaining the predicted litigation fact and the litigation prediction information at least according to the reason of the predicted dispute.
In this step, the electronic device 1000 calculates the predicted litigation FACT (FACT) by using the predicted dispute cause calculated in the above step as an input of a litigation prediction model; the predicted litigation fact and the cause of the predicted dispute are used as the input of the litigation prediction model, and the litigation prediction information (JudGMENT) is calculated.
In practical application, the predicted litigation fact and the litigation anticipation information are multi-label classification tasks. That is, the predicted litigation fact that predicts, as output, a plurality of litigation facts among a plurality of litigation facts; predicting multiple litigation results from multiple litigation results as the output litigation prediction information.
For example, the predicted litigation fact may be, for example, historical litigation information, litigation evidence, relevant legal regulations, or the like; the litigation prejudgment information can be the success probability of litigation and the responsibility possibly required to be assumed after litigation, such as goods return and refund, triple indemnity, refund only, return fee only or responsibility assumed by a platform. This is not to be taken as an example.
The electronic device 1000 learns more accurate dispute characteristics by a series of multi-task learning and using a large amount of dispute data of the electronic commerce online dispute resolution platform, and then applies the dispute characteristics to litigation predication, so that the litigation predication is more accurate.
Further, for a user (the user may be a buyer or a seller) who is unsatisfied with the solution of the e-commerce dispute and is about to raise the litigation, the electronic device 1000 may provide the predicted litigation fact and the litigation anticipation information to the user after obtaining the predicted litigation fact and the litigation anticipation information, so as to serve as an effective suggestion for the litigation behavior of the user.
In the method for prejudging litigation, the electronic device obtains the transaction dispute information; calculating dispute characteristics of the transaction dispute information; obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics; and obtaining the fact of the predicted litigation and the information of the predicted litigation at least according to the reason of the predicted dispute. Therefore, the electronic commerce online dispute resolution platform and the legal litigation prejudging system can be connected, prejudging of the electronic commerce dispute litigation can be realized, and effective references are provided for buyers and sellers.
< method example two >
Fig. 8 is a flowchart illustrating a method for litigation anticipation according to a second embodiment of the invention.
As shown in fig. 8, the method of this embodiment may include steps 8100 to 8500:
in step 8100, the client obtains the transaction dispute information input by the user.
Specifically, the client may be a seller side, a buyer side, or a third party platform side, which is not limited herein. The user may input transaction dispute information on the client, for example, the transaction dispute information may include dispute information, case information, and the like.
In step 8200, the client sends a litigation pre-judging request to the server, wherein the litigation pre-judging request at least comprises the transaction dispute information.
After receiving the transaction dispute information input by the user, the client requests the server for litigation prejudgment according to the transaction dispute information, that is, sends out a litigation prejudgment request, so that the server can give out litigation prejudgment information obtained by predicting the dispute, such as success probability of litigation and the like, according to the litigation prejudgment request.
In step 8300, the server obtains the predicted litigation fact and the litigation prediction information corresponding to the deal dispute information according to the litigation prediction request.
The server obtains the predicted litigation fact and the litigation prediction information corresponding to the deal dispute information by using the litigation prediction method shown in fig. 2 based on the litigation prediction request. The specific prediction method can be described with reference to the above method embodiments, and is not described herein again.
In step 8400, the server sends the predicted litigation fact and the litigation anticipation information to the client.
In step 8500, the client sends the received litigation anticipation information to the client for display.
Furthermore, the client may also present, to the user, historical litigation information, litigation basis, litigation result, legal regulations under which the historical case is judged, and the like related to the transaction dispute, which are not listed here.
In the litigation pre-judging method of the embodiment, after the user inputs the trading dispute information through the client, the client may initiate a litigation pre-judging request to the server according to the trading dispute information, so that the server returns the predicted litigation fact and the litigation pre-judging information according to the litigation pre-judging request. Therefore, the electronic commerce online dispute resolution platform and the legal litigation prejudging system can be connected, prejudging of the electronic commerce dispute litigation can be realized, and effective references are provided for buyers and sellers.
< apparatus embodiment >
Fig. 5 is a schematic structural diagram of a device 3000 for litigation anticipation according to an embodiment of the invention.
As shown in fig. 5, the litigation predicting device 3000 may include: an acquisition module 3100, a calculation module 3200, a dispute prediction module 3300, and a litigation prediction module 3400.
The obtaining module 3100 is configured to obtain dispute information.
The calculation module 3200 is configured to calculate dispute characteristics of the transaction dispute information.
The dispute prediction module 3300 is configured to obtain a predicted dispute reason and a predicted dispute result at least according to the dispute characteristics.
The litigation prediction module 3400 is configured to obtain a predicted litigation fact and litigation prediction information at least according to the reason for the predicted dispute.
The calculation module 3200 is specifically configured to calculate semantic similarity between the transaction dispute information and a node in the legal knowledge graph; taking the semantic similarity as the initial value of probability distribution of the transaction dispute information; calculating a directed acyclic graph of the legal knowledge graph based on the initial probability distribution value to obtain the element distribution probability of the transaction dispute information on the legal knowledge graph; and taking the element distribution probability as the dispute characteristic of the transaction dispute information.
The dispute prediction module 3300 is specifically configured to output a single reason for the predicted dispute and a single result of the predicted dispute; the litigation prediction module 3400 is specifically configured to output a plurality of the predicted litigation facts and a plurality of the litigation anticipation information.
Specifically, the transaction dispute information includes: transaction information, buyer information and seller information; the transaction information at least comprises price information, quantity information, commodity information and dispute record information; the buyer information at least comprises buyer credit information, buyer complaint frequency information, buyer complaint record information and buyer grade information; the seller information at least comprises seller credit information, seller grade information, seller complaint times information and seller complaint record information.
Correspondingly, the calculating module 3200 is specifically configured to calculate the transaction characteristics of the transaction dispute information according to the transaction information; calculating buyer characteristics of the transaction dispute information according to the buyer information; and calculating the seller characteristics of the transaction dispute information according to the seller information. The dispute prediction module 3300 is configured to calculate the predicted dispute reason and the predicted dispute result according to at least the transaction characteristic, the buyer characteristic, and the seller characteristic.
Specifically, the dispute prediction module 3300 is configured to use the transaction characteristics, the buyer characteristics, and the seller characteristics as inputs of a dispute prediction model, and calculate to obtain the predicted dispute reason; and calculating to obtain the dispute prediction result by taking the transaction characteristics, the buyer characteristics, the seller characteristics and the dispute prediction reason as the input of the dispute prediction model.
The litigation predicting module 3400 is specifically configured to calculate the predicted litigation fact by using the reason for the predicted dispute as an input of a litigation predicting model; and calculating the predicted litigation fact and the reason of the predicted dispute as the input of the litigation prediction model to obtain the litigation prediction information.
Fig. 6 is a schematic diagram of the hardware structure of another litigation prejudging device according to the embodiment of the invention.
As shown in fig. 6, the apparatus 4000 for litigation anticipation of the present embodiment may include a memory 4200 and a processor 4100.
The memory 4200 is used to store instructions for controlling the processor 4100 to operate to perform the method of litigation anticipation of any embodiment of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
The litigation prediction device of the present embodiment may be used to implement the technical solution of the method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.
< litigation prediction System >
Fig. 7 is a schematic structural diagram of the litigation anticipation system 5000 according to the embodiment of the invention.
As shown in fig. 7, the litigation predicting system 5000 may include a first module 5100, a second module 5200, and a third module 5300.
The first module 5100 is configured to encode dispute information, where the dispute information includes buyer information, seller information, and transaction information.
The second module 5200 is communicatively coupled to the first module 5100, and the second module 5200 is configured to obtain a reason for the predicted dispute and a result of the predicted dispute.
The third module 5300 is communicatively coupled to the first module 5100 and the second module 5200, and the third module 5300 is configured to obtain the predicted litigation fact and the litigation anticipation information.
The encoding of the dispute information by the first module 5100 includes: a Legal Knowledge Graph (LKG), discrete information, and textual information are encoded. It should be noted that the discrete information may specifically be discrete information shown in fig. 3, that is, structured data of some buyer and seller in the transaction process, such as buyer credit, seller credit, etc. The text information may specifically be text information shown in fig. 3, such as a product title, a product evaluation, a conversation record during a transaction or in a right-maintaining dispute, and the like.
Specifically, the second module 5200 includes a single label classifier; the third module 5300 includes a multi-label classifier. The single label classifier is used for obtaining a single prediction dispute reason and a single prediction dispute result. The multi-label classifier is specifically configured to obtain a plurality of the predicted litigation facts and a plurality of the litigation prejudgment information.
In practical applications, the information encoded by the first module 5100 is used as input information of the second module 5200 and the third module 5300. The output information of the second module 5200 serves as the input information of the third module 5300.
The litigation pre-judging system can be used for pre-judging the electronic commerce dispute litigation, and provides effective reference for buyers and sellers.
< computer storage Medium >
In this embodiment, a computer storage medium is further provided, on which a computer program is stored, which, when executed by a processor, implements the method for litigation prediction according to any embodiment of the present invention.
Those skilled in the art will understand that, in the field of electronic technology, the above method can be embodied in products by software, hardware and a combination of software and hardware, and those skilled in the art can easily generate an information processing apparatus including modules for performing respective operations in the information processing method according to the above embodiment based on the method of the above embodiment of the invention.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. A software implementation and a hardware implementation are equivalent for the skilled person. The skilled person can choose software or hardware to implement the above described scheme as desired. Therefore, specific software or hardware is not limited herein.
The present invention may be an apparatus, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, 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). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (19)
1. A method of litigation prediction, the method comprising:
acquiring transaction dispute information;
calculating dispute characteristics of the transaction dispute information;
obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics;
and obtaining the predicted litigation fact and the litigation prediction information at least according to the predicted dispute reason.
2. The method as claimed in claim 1, wherein said calculating dispute characteristics of said transaction dispute information comprises:
calculating semantic similarity between the transaction dispute information and nodes in the legal knowledge graph;
taking the semantic similarity as an initial value of probability distribution of the transaction dispute information;
calculating a directed acyclic graph of the legal knowledge graph based on the initial probability distribution value to obtain an element distribution probability of the transaction dispute information on the legal knowledge graph;
and taking the element distribution probability as the dispute characteristic of the transaction dispute information.
3. The method of claim 2, wherein the transaction dispute information comprises: transaction information, buyer information and seller information;
the transaction information at least comprises price information, quantity information, commodity information and dispute record information;
the buyer information at least comprises buyer credit information, buyer complaint frequency information, buyer complaint record information and buyer grade information;
the seller information at least comprises seller credit information, seller grade information, seller complaint times information and seller complaint record information.
4. The method as claimed in claim 3, wherein said calculating dispute characteristics of said transaction dispute information comprises:
calculating transaction characteristics of the transaction dispute information according to the transaction information;
according to the buyer information, buyer characteristics of the transaction dispute information are calculated;
and calculating the seller characteristics of the transaction dispute information according to the seller information.
5. The method as claimed in claim 4, wherein said obtaining a reason for predicted dispute and a result of predicted dispute based on at least said dispute characteristics comprises:
and calculating to obtain the reason of the predicted dispute and the result of the predicted dispute at least according to the transaction characteristics, the buyer characteristics and the seller characteristics.
6. The method as claimed in claim 5, wherein said obtaining a reason for predicted dispute and a result of predicted dispute based on at least said dispute characteristics comprises:
taking the transaction characteristics, the buyer characteristics and the seller characteristics as the input of a dispute prediction model, and calculating to obtain the predicted dispute reason;
and taking the transaction characteristics, the buyer characteristics, the seller characteristics and the predicted dispute reason as the input of the dispute prediction model, and calculating to obtain the predicted dispute result.
7. The method of claim 6, wherein obtaining predicted litigation facts and litigation prediction information based on at least the predicted dispute reason comprises:
taking the reason of the predicted dispute as the input of a litigation prediction model, and calculating to obtain the predicted litigation fact;
and calculating the predicted litigation fact and the predicted dispute reason as the input of the litigation prediction model to obtain the litigation prediction information.
8. The method of claim 1, further comprising:
providing the predicted litigation fact and the litigation anticipation information to a user.
9. A method of litigation prediction, the method comprising:
the method comprises the steps that a client side obtains transaction dispute information input by a user;
the client side sends a litigation pre-judging request to a server, wherein the litigation pre-judging request at least comprises the transaction dispute information;
the server obtains a predicted litigation fact corresponding to the transaction dispute information and litigation information according to the litigation prediction request;
the server sends the predicted litigation fact and the litigation prejudgment information to the client;
and the client sends the received litigation pre-judging information to the client for displaying.
10. An apparatus for litigation, the apparatus comprising:
the acquisition module is used for acquiring transaction dispute information;
the calculation module is used for calculating dispute characteristics of the transaction dispute information;
the dispute prediction module is used for obtaining a dispute prediction reason and a dispute prediction result at least according to the dispute characteristics;
and the litigation predicting module is used for obtaining the actual litigation prediction and the litigation prediction information at least according to the dispute prediction reason.
11. The apparatus according to claim 10, wherein the dispute prediction module is specifically configured to output a single reason for the predicted dispute and a single result of the predicted dispute;
the litigation prediction module is specifically configured to output a plurality of the predicted litigation facts and a plurality of the litigation prejudgment information.
12. The apparatus of claim 10, wherein the transaction dispute information comprises: buyer information, seller information, and transaction information.
13. A litigation predicting system, the system comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for encoding dispute information, and the dispute information comprises buyer information, seller information and transaction information;
a second module, communicatively coupled to the first module, the second module configured to obtain a reason for the predicted dispute and a result of the predicted dispute;
a third module communicatively coupled to the first module and the second module, the third module configured to obtain predicted litigation facts and litigation anticipation information.
14. The litigation-outcome prediction system of claim 13, wherein: the first module encoding the dispute information comprises: the legal knowledge graph LKG, discrete information, and textual information are encoded.
15. The litigation-outcome prediction system of claim 14, wherein the second module comprises a single-label classifier and the third module comprises a multi-label classifier.
16. The litigation-outcome prediction system of claim 14, wherein information encoded by the first module is input to the second and third modules.
17. The litigation-outcome prediction system of claim 16, wherein the output information of the second module is used as input information for the third module.
18. An apparatus for litigation, comprising: a memory and a processor; the memory is configured to store instructions for controlling the processor to operate so as to perform a method of litigation anticipation as claimed in any one of claims 1 to 8.
19. A computer storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of litigation anticipation as recited in any one of claims 1 to 8.
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