TWI760747B - Liability judgment and claim system and method thereof for traffic accident - Google Patents

Abstract

A liability judgment and claim system for traffic accident is configured for judging the liability and personal injury of the traffic accident, and includes an accident database, a semantic analysis module and a judging module. The accident database includes a liability ratio list including a plurality of liability ratios and each of liability ratios corresponding to an accident feature combination, and a damage level list including a plurality of damage levels and each of damage levels corresponding to a damage feature combination. The semantic analysis module is configured to receive and analyze a traffic accident data and a medical diagnosis data of the traffic accident to generate the accident features and the damage features. The judging module analyzes the traffic accident data and the medical diagnosis data by a judging model and generates the liability ratio and the damage level of the traffic accident.

Description

A system and method for judging liability for traffic accidents and making claims

The present invention relates to a system and method for judging liability for a traffic accident and a claim settlement, and in particular, to a system and method for assisting insurance personnel in judging liability ratio and injury level.

Vehicles have been developed for hundreds of years, and with the continuous advancement of technology, vehicles have become people's means of transportation and an indispensable means of transportation. Due to the popularity of vehicles, more and more people own vehicles. However, in this era of a large number of vehicles, the problem of traffic is also becoming more and more prominent.

Under such factors as poor weather conditions, unfamiliar road conditions, non-compliance with traffic rules, etc., traffic accidents are often caused, especially during the most crowded times (such as commuting and commuting), traffic is more likely to occur ACCIDENT. Generally speaking, when a traffic accident occurs, the driver of the vehicle will call the police and notify the insurance company to go to the scene to assess and deal with the accident. Since a traffic accident can happen anywhere, it still takes some time for the police or insurance company to arrive at the accident scene, so the driver needs to wait at the accident scene for the relevant handlers. In order for the insurance company to clarify and determine the responsibility for the accident, the vehicle needs to stay at the scene of the accident. However, it is often difficult to immediately determine the responsibility of the accident at the accident scene, and it is difficult for the parties involved in the traffic accident to reach an agreement at the accident scene as soon as possible. untie. Further, when a traffic accident occurs in the middle of a road or at an intersection, vehicles staying at the accident site will cause traffic congestion, and if the accident occurs at the time when the traffic is most crowded, the traffic will be seriously affected.

Generally speaking, insurance personnel judge the type of accident according to the preliminary judgment table of traffic accident and the principle of sharing and handling the responsibility of automobile accident, so as to determine the proportion of responsibility for the accident. However, since there are many types of accidents recorded in the principle of sharing responsibility for auto accidents, insurance personnel or the general public need to check the content of the principle of sharing responsibility for vehicle accidents to find out the corresponding type of accident, and then know the proportion of responsibility for accidents. In addition, when driving or personal injury occurs in a traffic accident, insurance personnel also need to judge the type of injury based on the medical diagnosis table. Therefore, the current handling of vehicle accident liability and injuries caused by the accident not only reduces efficiency but also increases costs.

In view of this, one of the scopes of the present invention is to provide a system for judging liability for traffic accidents and for settlement of claims, so as to solve the problems of the prior art.

According to a specific embodiment of the present invention, the traffic accident liability judgment and claim settlement system is used to process and determine the accident liability and personal injury of the traffic accident. The traffic accident liability judgment and claim settlement system includes an accident database, a semantic analysis module and a judgment module. The accident database is used to store the comparison table of the proportion of culprits and the level of injury. Wherein, the accident-responsibility ratio comparison table includes a plurality of accident-responsibility ratios, and each accident-responsibility ratio corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels, and each injury level corresponds to an injury feature combination. The semantic analysis module is used for receiving and analyzing traffic accident data and medical diagnosis data corresponding to the traffic accident to generate at least one accident feature corresponding to the traffic accident data and at least one injury feature corresponding to the medical diagnosis data. The judgment module is connected to the accident database and the semantic analysis module. The judgment module takes a The judgment model analyzes the traffic accident data and medical diagnosis data, and generates the proportion of responsibility and injury level corresponding to the traffic accident. Among them, the judgment model finds the accident feature combination and the injury feature combination corresponding to the traffic accident according to the accident feature and the injury feature respectively.

Among them, the semantic analysis module is Word2Vec.

Further, the system for judging responsibility for traffic accidents and claim settlement of the present invention further includes a learning module connected to the accident database and a semantic analysis module. The learning module generates a judgment model according to the accident feature combination and injury feature combination of the accident database, and returns the accident features and injury features generated by the semantic analysis module to the accident database by means of machine learning and analyzes to update the judgment Model.

Among them, the judgment model is the Bayesian model.

In a specific embodiment, the system for judging liability for a traffic accident and claim settlement of the present invention further includes an image recognition module connection judging module. The traffic accident data contains accident image data and the medical diagnosis data contains diagnostic image data. The image recognition module is used for receiving and analyzing the accident image data and the diagnostic image data to generate at least one accident feature corresponding to the traffic accident data and at least one injury feature corresponding to the medical diagnosis data, and transmit the accident feature and the injury feature to the judgment module.

In a specific embodiment, the traffic accident liability judgment and claim settlement system of the present invention further includes an insurance database and an adjustment module. The adjustment module is connected to the insurance database and the judgment module. The insurance database is used to store traffic accident insurance and medical insurance corresponding to traffic accidents. Among them, the traffic accident insurance corresponds to the full amount of the accident insurance and the medical insurance corresponds to the medical insurance amount. The adjustment module generates the accident compensation amount according to the proportion of liability of the corresponding traffic accident and the amount of accident insurance, and generates medical treatment according to the injury level of the corresponding traffic accident and the amount of medical insurance. compensation amount.

Another scope of the present invention is to provide a method for judging the cause of a traffic accident and settling a claim, so as to solve the problems of the prior art.

According to a specific embodiment of the present invention, a traffic accident liability judgment and claim settlement method is used to process and determine the accident liability and personal injury of the traffic accident. The method for judging and settling a liability for a traffic accident includes the following steps: Step S1: Pre-store a comparison table of the ratio of attribution and a comparison table of injury levels, wherein the comparison table of the ratio of attribution includes a plurality of ratios of attribution and each of the ratios of attribution corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels and each injury level corresponds to a combination of injury characteristics; Step S2: analyze the traffic accident data and medical diagnosis data corresponding to the traffic accident to generate at least one accident feature corresponding to the traffic accident data And at least one injury feature corresponding to the medical diagnosis data; and step S3: analyze the traffic accident data and the medical diagnosis data with a judgment model and generate a liability ratio and an injury level corresponding to the traffic accident, wherein the judgment model finds out respectively according to the accident characteristics and the injury characteristics. The accident feature combination and injury feature combination corresponding to the traffic accident are obtained.

In a specific embodiment, the method for judging liability for a traffic accident and settling a claim further includes the following steps: Step S41 : generating a judgment model according to the accident feature combination and the injury feature combination; and Step S42 : analyzing at least one accident feature and at least one injury feature Machine learning is performed to update the judgment model.

In a specific embodiment, the method for judging the liability of the traffic accident and the claim settlement method further comprises the following steps: Step S6: Pre-store traffic accident insurance and medical insurance corresponding to the traffic accident, the traffic accident insurance corresponds to the accident insurance amount and the medical insurance corresponds to the medical insurance amount ; And step S7: generate the accident compensation amount according to the proportion of responsibility and the accident insurance amount corresponding to the traffic accident, The amount of medical claims is generated according to the injury level of the corresponding traffic accident and the amount of medical insurance.

In a specific embodiment, the method for judging liability for a traffic accident and settling a claim further includes the following steps: Step S8: analyzing the accident image data corresponding to the traffic accident data and the diagnostic image data corresponding to the medical diagnosis data to generate the corresponding traffic accident The accident characteristics of the data and the injury characteristics of the corresponding medical diagnosis data.

To sum up, the traffic accident liability judgment and claim settlement system of the present invention can find out the accident characteristics from the accident documents by means of semantic analysis, so that the system can automatically judge the accident proportion and the injury level of the traffic accident without Human judgment is required. In addition, the system of the present invention also finds the accident characteristics by means of image judgment, and combines machine learning to predict the accident type, so that the system can more accurately and quickly determine the accident rate and injury level of the traffic accident. also. The system of the present invention is combined with an insurance claim settlement system, so that the system can not only judge the accident rate and injury level of traffic accidents, but also pre-estimate the claim settlement amount of vehicles or personnel, so as to improve the efficiency of subsequent claim settlement. Therefore, the traffic accident liability judgment and claim settlement system of the present invention can not only improve the processing efficiency of traffic accidents, but also reduce time and labor costs.

1: Judgment and Claims System for the Cause of Traffic Accidents

11, 21: Accident Database

12, 22: Semantic Analysis Module

13, 23: Judgment module

14, 24: Learning modules

16, 26: Insurance database

17, 27: Adjustment module

25: Image recognition module

S1~S8: Steps

FIG. 1 is a functional block diagram of a traffic accident judging and claim settlement system according to an embodiment of the present invention.

FIG. 2 is a functional block diagram of a traffic accident judging and claim settlement system according to another embodiment of the present invention.

FIG. 3 is a flow chart showing the steps of a method for judging responsibility for a traffic accident and making a claim according to an embodiment of the present invention.

FIG. 4 is a flow chart showing further steps of the method for judging responsibility for a traffic accident and making a claim according to FIG. 3 .

FIG. 5 is a flow chart showing further steps of the method for judging responsibility for a traffic accident and settling claims according to FIG. 3 .

FIG. 6 is a flowchart showing the steps of a method for judging responsibility for a traffic accident and making a claim according to another embodiment of the present invention.

In order for the advantages, spirit and features of the present invention to be more easily and clearly understood, detailed descriptions and discussions will follow with reference to the accompanying drawings by way of specific embodiments. It should be noted that these specific embodiments are only representative specific embodiments of the present invention, and the specific methods, systems, etc. exemplified therein are not intended to limit the present invention or the corresponding specific embodiments.

The terminology used in the various embodiments of the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of the present disclosure. As used herein, the singular is intended to include the plural as well, unless the context clearly dictates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of this disclosure belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized or overly formal meaning, unless explicitly defined in various embodiments of the present disclosure.

In the description of this specification, a description with reference to the terms "one embodiment, some embodiments, examples, specific examples, or some examples" etc. means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is contained in at least one embodiment or illustration of the present invention example. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Furthermore, the indefinite articles "a" and "an" preceding an element or component of the invention are not limiting on the quantitative requirement (ie, the number of occurrences) of the element or component. Thus "a" or "an" should be read to include one or at least one, and elements or components in the singular also include the plural unless the number is clearly intended to be in the singular.

Please refer to Figure 1. FIG. 1 is a functional block diagram of a system 1 for judging the responsibility for a traffic accident and for claim settlement according to an embodiment of the present invention. The system 1 for judging the liability of a traffic accident and claim settlement of the present invention is used for processing and judging the accident liability and personal injury of the traffic accident. As shown in FIG. 1 , the system 1 for judging responsibility for a traffic accident and for claim settlement includes an accident database 11 , a semantic analysis module 12 and a judgment module 13 . The accident database 11 is used to store the comparison table of the proportion of culprits and the comparison table of injury levels. Wherein, the accident-responsibility ratio comparison table includes a plurality of accident-responsibility ratios, and each accident-responsibility ratio corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels, and each injury level corresponds to an injury feature combination. The semantic analysis module 12 is used for receiving and analyzing traffic accident data and medical diagnosis data corresponding to the traffic accident to generate at least one accident feature corresponding to the traffic accident data and at least one injury feature corresponding to the medical diagnosis data. The judgment module 13 is connected to the accident database 11 and the semantic analysis module 12 . The judging module 13 analyzes the traffic accident data and the medical diagnosis data with a judgment model, and generates the responsibility ratio and the injury level corresponding to the traffic accident.

In practice, the accident database 11 can be a storage device (eg, a hard disk), and can store a database corresponding to the types, descriptions, characteristics, responsibility ratios and other related data of various accidents. The liability ratio comparison table and the injury level comparison table can be provided by a third-party notary agency. According to the table, for example: the principle of apportionment of responsibility for accidents caused by automobiles and locomotives, and the disability grade table formulated by the Baofa Center. Please refer to Table 1 and Table 2. Table 1 is a comparison table of the proportion of tolls and responsibilities of a specific embodiment, and Table 2 is a comparison table of injury levels of a specific embodiment. As shown in Table 1 and Table 2, the comparison table of the proportion of accident and responsibility includes a plurality of accident types and proportions of responsibility, and the comparison table of injury grades includes a plurality of degrees of injury and injury levels. Each responsibility ratio corresponds to an accident type, and each accident type corresponds to an accident characteristic combination, and the accident characteristic combination is composed of at least one accident characteristic; each injury level corresponds to an injury degree, and each injury degree is determined by Corresponding to a combination of injury characteristics, and the combination of injury characteristics is composed of at least one injury characteristic; and multiple accident types and multiple injury degrees can respectively correspond to the same liability ratio and injury level. Accident characteristics and injury characteristics can be single words, words, or a sentence that can be used as a judgment by combining words and words. For example, the accident characteristics can be "not maintained" and "safe distance" in the accident type in item 1 in Table 1, or "straight ahead", "left turn", "collision" in item 2 in Table 1. ". The injury characteristics can be "one hand", "five fingers", and "permanent incapacitation" in the degree of injury in the first item in Table 2, or "central nervous system" and "remaining impairment" in the third item in Table 2. , "Hands-free work".

The traffic accident data can be a preliminary traffic accident judgment form provided by a third-party notary agency, and the medical diagnosis data can be a medical certificate provided by a medical institution. After the semantic analysis module 12 receives the traffic accident data and the medical diagnosis data, the semantic analysis module 12 can analyze the traffic accident data and the medical diagnosis data by means of semantic analysis, and extract corresponding accident features and injury features. Further, the semantic analysis module 12 can also be Word2Vec. After the semantic analysis module 12 receives the traffic accident data and the medical diagnosis data, the semantic analysis module 12 converts the text in the traffic accident data and the medical diagnosis data into vectors and performs vector operations to extract accident features and injury features. For example, when the traffic accident data includes "the car A did not notice the car B behind the car A when reversing", the semantic analysis group 12 can determine the accident characteristics of "reversing" according to the traffic accident data, and according to " Not paying attention” to judge the accident characteristics of “not paying attention”. In another example, when the medical diagnosis data includes "the ten fingers of the patient's hands must be amputated", the semantic analysis module 12 can isolate the injury characteristics of the "hands" and the "ten fingers" according to the medical diagnosis data, and The injury characteristic of "permanent incapacitation" is judged from "amputation".

The judging model 131 of the judging module 13 can respectively recombine the accident feature combination and the injury feature combination corresponding to the traffic accident according to the accident feature and the injury feature extracted by the semantic analysis module 12 . After the judgment model 131 combines the combined accident features and injury features into an accident feature combination and an injury feature combination, the judgment module 13 generates a corresponding accident feature combination and injury feature The proportion of attribution and the damage level of the combination. For example, when the accident features captured by the semantic analysis module 12 are "not maintained", "safe distance", and "challenging", at this time, the judgment model 131 combines the first item in Table 1 according to the accident features. The accident type, and then the judging module 13 generates that the responsibility for causing the accident corresponding to the accident type in item 1 in Table 1 is 100%. In another example, when the injury features extracted by the semantic analysis module 12 are “binocular”, “vision”, and “below 0.06”, at this time, the judgment model 131 combines the characteristics of the fourth item in Table 2 according to the accident characteristics. damage type, and then determine that the damage level generated by the module 13 corresponding to the damage type of item 4 in Table 2 is 5.

Further, the system 1 for judging liability for traffic accidents and for settlement of claims includes a learning module 14 connected to the accident database 11 and a semantic analysis module 12 . The learning module 14 generates a judgment model 131 according to the accident feature combination and the injury feature combination of the accident database 11 , and returns the accident feature and injury feature generated by the semantic analysis module 12 to the accident database 11 by means of machine learning. As historical data, the data in the accident database 11 is analyzed to update the judgment model 131 . In practice, the learning module 14 can be integrated into a machine learning chip or program to analyze the accident features and injury features generated by the semantic analysis module 12 . In detail, the learning module 14 can predict the accident feature combination and the injury feature combination according to the occurrence frequency or proportion of the accident feature and the injury feature, and prioritize the accident feature combination and the injury feature combination with a higher occurrence ratio in the judgment. In the model 131, the judgment model 131 is further updated. Therefore, when the judgment module 13 receives the accident feature and injury feature generated by the semantic analysis module 12 next time, the judgment module 13 can preferentially judge the accident feature combination with a higher proportion and the injury feature combination according to the updated judgment model 131 , to reduce analysis time and thus improve efficiency. The judgment model 131 can also be a Bayesian model. Further, the learning module 14 can update the judgment group 13 by means of oversampling. Because one fault-responsibility ratio and injury level can correspond to multiple accident types and multiple injury degrees, and each fault-to-responsibility ratio The number of text ratios of accident types and injury levels differ for example and injury level. Therefore, the judgment model 131 can adjust the number of texts of each responsibility ratio and damage level by means of oversampling, so as to reduce the judgment module 13's preference for analyzing a specific responsibility ratio and damage level, thereby improving the accuracy.

In this specific embodiment, the system 1 for judging the cause of traffic accident and making claims further includes an insurance database 16 and an adjustment module 17 . The adjustment module 17 is connected to the insurance database and the judgment module. The insurance database is used to store traffic accident insurance and medical insurance corresponding to traffic accidents. The traffic accident insurance corresponds to an accident insurance amount, and the medical insurance corresponds to a medical insurance amount. In practice, the traffic accident insurance may be compulsory vehicle insurance, body damage insurance, etc. of the vehicle, and the medical insurance may be medical insurance, accident insurance, etc. for the driver of the vehicle or personnel. The accident insurance amount may be the insurance amount of the vehicle body damage insurance insured for the vehicle, and the medical insurance amount may be the insurance amount of the accident insurance insured by the driver or personnel. After the judging module 12 generates the liability ratio and the injury grade, the adjustment module 17 can calculate the accident claim amount according to the claim ratio corresponding to the liability ratio, and can calculate the medical claim amount according to the claim ratio corresponding to the injury grade . Therefore, the traffic accident liability judgment and claim settlement system of the present invention can not only determine the accident liability, but also quickly calculate the claim settlement amount, so as to reduce the time cost and improve the accident handling efficiency.

In addition to analyzing the accident features and injury features provided by the semantic analysis module 12, the judgment module 13 can also receive accident features and injury features generated in other forms. Please refer to FIG. 2 . FIG. 2 is a functional block diagram of a traffic accident determination and claim settlement system 2 according to another embodiment of the present invention. The difference between this specific embodiment and the above-mentioned specific embodiments is that the system 2 for judging responsibility for traffic accidents and making claims settlement in this specific embodiment further includes an image recognition module 25 . The image recognition module 25 is connected to the judgment module 22 . traffic accident information Contains accident image data, and medical diagnostic data may contain diagnostic image data. The image recognition module is used for receiving and analyzing the accident image data and the diagnostic image data to generate accident features corresponding to the traffic accident data and injury features corresponding to the medical diagnosis data. In practice, the image recognition module 25 can be a smart image recognition chip, and the accident image data can be photos of traffic accidents (such as photos after the collision, photos of the vehicle collision, etc.), and the diagnostic image data can be For X-ray films, magnetic resonance imaging (MRI) and so on. For example, the image recognition module 25 can determine the accident characteristics of "being chased" according to the collision position of the vehicle, and can determine "finger", "thumb", "missing" according to the X-ray of thumb amputation. ' damage characteristics. Further, the image recognition module 25 transmits the accident characteristics and injury characteristics generated after the analysis to the judgment module 23 for the judgment module 23 to judge. In a specific embodiment, the traffic accident liability judgment and claim settlement system of the present invention can simultaneously receive the accident features and injury features generated by the semantic analysis module and the image recognition group, so as to improve the accuracy of the liability ratio and the injury level. , thereby improving efficiency. Please note that the functions of the accident database 21 , the semantic analysis module 22 , the learning module 24 , the judgment module 23 , the insurance database 26 and the adjustment module 27 in this embodiment are the same as those of the previous embodiment. The functions of the components are substantially the same, and will not be repeated here.

Please refer to Figure 3. FIG. 3 is a flow chart showing the steps of a method for judging responsibility for a traffic accident and making a claim according to an embodiment of the present invention. The steps of FIG. 3 can be achieved by the traffic accident culpability judgment and claim settlement system of FIG. 1 . In this specific embodiment, the traffic accident liability judgment and claim settlement method is used to process and determine the accident liability and personal injury of the traffic accident. The method of judging the liability for a traffic accident and setting up a claim includes the following steps: Step S1: Pre-store the comparison table of the ratio of liability and the comparison table of injury levels in the accident database 11, wherein the comparison table of the ratio of liability includes a plurality of ratios of liability and each liability ratio The ratio corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels and each injury level corresponds to a damage feature combination; Step S2: Semantic analysis module 12 points Analyzing the traffic accident data and medical diagnosis data corresponding to the traffic accident to generate accident characteristics corresponding to the traffic accident data and injury characteristics corresponding to the medical diagnosis data; and step S3: the judging module 13 analyzes the traffic accident data and medical diagnosis data with the judgment model 131 Diagnose the data and generate the proportion of responsibility and injury level corresponding to the traffic accident, in which the judgment model finds the accident feature combination and the injury feature combination corresponding to the traffic accident according to the accident characteristics and injury characteristics.

Please refer to Figure 4. FIG. 4 is an advanced flow chart of the method for judging responsibility for a traffic accident and making a claim according to FIG. 3 . In this specific embodiment, the method for judging the cause of a traffic accident and settling a claim further includes the following steps: Step S41 : the learning module 14 generates a judgment model 131 according to the accident feature combination and the injury feature combination in the accident database 11 ; and Step S42 : The learning module 14 performs machine learning on the accident features and injury features to update the judgment model 131 . In practice, after the risk-responsibility ratio comparison table and the injury level comparison table are stored in the accident database 11 , the learning module 14 can generate the judgment model 131 according to the data in the accident database 11 and transmit the judgment model to the judgment module 13 . When the semantic analysis module 12 analyzes the accident characteristics and injury characteristics, and determines that the module 13 generates the proportion of liability and injury level corresponding to the traffic accident, the learning module 14 returns the accident characteristics and injury characteristics to the accident database 11 As historical data, the data in the accident database 11 is then subjected to machine learning to update the judgment module 131 .

Please refer to Figure 5. FIG. 5 is an advanced flow chart of the method for judging responsibility for a traffic accident and making a claim according to FIG. 3 . In this specific embodiment, the method for judging the responsibility of a traffic accident and settling a claim further includes the following steps: Step S6: Pre-store the traffic accident insurance and medical insurance corresponding to the traffic accident in the insurance database 16, and the traffic accident insurance corresponds to an accident insurance amount and The medical insurance corresponds to a medical insurance amount; and Step S7: the adjustment module 17 generates an accident compensation amount according to the proportion of the liability corresponding to the traffic accident and the accident insurance amount, and according to the injury of the corresponding traffic accident. The level of harm and the amount of medical insurance will result in medical claims. In practice, after the judgment module 13 generates the liability ratio and the injury level corresponding to the traffic accident, the adjustment module 17 then calculates the medical insurance amount to generate the medical claim amount according to the liability ratio and the claim settlement ratio of the injury level. The step S1 and the step S6 can be exchanged with each other, and can also be performed simultaneously.

Please refer to Figure 6. FIG. 6 is a flowchart showing the steps of a method for judging responsibility for a traffic accident and making a claim according to another embodiment of the present invention. The steps in FIG. 6 can be achieved by the traffic accident determination and claim system of FIG. 2 . In this specific embodiment, the method for judging the responsibility of a traffic accident and settling a claim further includes the following steps: Step S8: the image recognition module 25 analyzes the accident image data corresponding to the traffic accident data and the diagnostic image data corresponding to the medical diagnosis data , to generate accident characteristics corresponding to traffic accident data and injury characteristics corresponding to medical diagnosis data. In practice, step S2 and step S8 can be interchanged with each other, or can be performed simultaneously.

To sum up, the traffic accident liability judgment and claim settlement system of the present invention can find out the accident characteristics from the accident documents by means of semantic analysis, so that the system can automatically judge the accident proportion and the injury level of the traffic accident without Human judgment is required. In addition, the system of the present invention also finds the accident characteristics by means of image judgment, and combines machine learning to predict the accident type, so that the system can more accurately and quickly determine the accident rate and injury level of the traffic accident. also. The system of the present invention is combined with an insurance claim settlement system, so that the system can not only judge the accident rate and injury level of traffic accidents, but also pre-estimate the claim settlement amount of vehicles or personnel, so as to improve the efficiency of subsequent claim settlement. Therefore, the traffic accident liability judgment and claim settlement system of the present invention can not only improve the processing efficiency of traffic accidents, but also reduce time and labor costs.

Through the detailed description of the preferred embodiments above, it is hoped that the features and spirit of the present invention can be described more clearly, rather than the preferred embodiments disclosed above to describe the present invention. scope is restricted. On the contrary, the intention is to cover various modifications and equivalent arrangements within the scope of the claimed scope of the present invention. Therefore, the scope of the patentable scope for which the present invention is claimed should be construed in the broadest sense in accordance with the above description so as to encompass all possible modifications and equivalent arrangements.

1, 2: Judgment and claim settlement system for traffic accidents

11: Accident Database

12: Semantic Analysis Module

13: Judgment Module

14: Learning Modules

16: Insurance Database

17: Adjustment module

Claims (8)

A traffic accident liability judgment and claim settlement system, which is used to process and determine the accident liability and personal injury of a traffic accident. The traffic accident liability judgment and claim settlement system includes: an accident database for storing a liability ratio A comparison table and an injury level comparison table, wherein the attribution ratio comparison table includes a plurality of attribution ratios and each of the attribution ratios corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels and each The injury levels correspond to an injury feature combination; an insurance database is used to store a traffic accident insurance and a medical insurance corresponding to the traffic accident, the traffic accident insurance corresponds to an accident insurance amount and the medical insurance corresponds to a medical insurance amount ; a semantic analysis module for receiving and analyzing a traffic accident data and a medical diagnosis data corresponding to the traffic accident to generate at least one accident feature corresponding to the traffic accident data and at least one injury feature corresponding to the medical diagnosis data ; a judgment module, connected to the accident database and the semantic analysis module, the judgment module analyzes the traffic accident data and the medical diagnosis data with a judgment model and generates the proportion of attribution and the injury corresponding to the traffic accident level, wherein the judgment model finds out the accident characteristic combination and the injury characteristic combination corresponding to the traffic accident according to the at least one accident characteristic and the at least one injury characteristic, respectively; and an adjustment module, which is connected to the insurance database and the Judging module, the adjustment module generates an accident compensation amount according to the liability ratio corresponding to the traffic accident and the accident insurance amount, and generates a medical compensation amount according to the injury level corresponding to the traffic accident and the medical insurance amount . The system for judging liability and claim settlement for a traffic accident as described in item 1 of the scope of the patent application, wherein the semantic analysis module is Word2Vec. As described in item 1 of the scope of the patent application, the system for judging responsibility for traffic accidents and making claims further includes a learning module, which is connected to the accident database and the semantic analysis module, and the learning module is based on the accident database. The accident feature combination and the injury feature combination generate the judgment model, and the at least one accident feature and the at least one injury feature generated by the semantic analysis module are returned to the accident database by means of machine learning and analyzed to update the judgment model. In the system for judging the cause of traffic accident and claim settlement as described in Item 3 of the scope of the patent application, the judgment model is a Bayesian model. The system for judging liability for a traffic accident and claim settlement as described in item 1 of the scope of the patent application further comprises an image recognition module connected to the judging module, the traffic accident data includes an accident image data and the medical diagnosis data includes a diagnosis image data, the image recognition module is used for receiving and analyzing the accident image data and the diagnosis image data to generate the at least one accident feature corresponding to the traffic accident data and the at least one accident characteristic corresponding to the medical diagnosis data a damage feature, and the at least one accident feature and the at least one damage feature are sent to the judging module. A method for judging the responsibility of a traffic accident and settling claims, which is used to process and determine the accident liability and personal injury of a traffic accident. The method for judging and settling the liability for a traffic accident includes the following steps: an accident database pre-stores a ratio of culpability for comparison and an injury level comparison table, wherein the attributable proportion comparison table includes a plurality of attribution proportions and each of the attributable proportions corresponds to an accident feature combination, and the injury level comparison table includes a plurality of injury levels and each of the The equal injury level corresponds to a combination of injury characteristics; an insurance database pre-stores a traffic accident insurance corresponding to the traffic accident. insurance and a medical insurance, the traffic accident insurance corresponds to an accident insurance amount and the medical insurance corresponds to a medical insurance amount; a semantic analysis module analyzes a traffic accident data and a medical diagnosis data corresponding to the traffic accident to generate corresponding at least one accident feature of the traffic accident data and at least one injury feature corresponding to the medical diagnosis data; a judgment module analyzes the traffic accident data and the medical diagnosis data with a judgment model and generates the attribution ratio corresponding to the traffic accident and the injury level, wherein the judgment model finds out the accident feature combination and the injury feature combination corresponding to the traffic accident according to the at least one accident feature and the at least one injury feature respectively; and an adjustment module according to the traffic accident The liability ratio and the accident insurance amount generate an accident claim amount, and a medical claim amount is generated according to the injury level corresponding to the traffic accident and the medical insurance amount. The method for judging liability for a traffic accident and settling claims as described in item 6 of the scope of the patent application further comprises the following steps: a learning module generates the judgment model according to the combination of the accident characteristics and the combination of the injury characteristics; The at least one accident feature and the at least one injury feature are machine-learned to update the judgment model. The method for judging liability for a traffic accident and settling a claim as described in item 6 of the scope of application for a patent further comprises the following steps: an image recognition module analyzes an accident image data corresponding to the traffic accident data and an image data corresponding to the medical diagnosis data. a diagnostic image data to generate the at least one accident feature corresponding to the traffic accident data and the at least one injury feature corresponding to the medical diagnosis data.

Images