JP6867063B1 - Jewel selling price estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate the selling price of a jewel with high accuracy and automatically without relying more on manpower. SOLUTION: In a gem selling price estimation program for estimating a gem selling price, an information acquisition step of acquiring image information of an image of the appearance of a gem to be purchased, and reference image information of an image of the appearance of a gem in the past. , An estimation step that estimates the selling price of the gem to be purchased based on the reference image information according to the image information acquired in the information acquisition step, using the degree of association with the selling price of the gem in three or more stages. Is characterized by having a computer execute the above. [Selection diagram] Fig. 4

Description

The present invention relates to a gem selling price estimation program and system suitable for estimating a gem selling price with high accuracy.

If you want to buy a new gem, you may want to know the proper selling price for the gem you want to buy.

There are many factors that should be referred to in this selling price, such as not only the type and brand of gemstones, but also the place of origin, weight, appearance color and brilliance, and cut condition. Setting an appropriate selling price while referring to each of these factors requires experience and knowledge, which is a difficult task and requires a great deal of labor.

For this reason, a system that can evaluate the selling price of gemstones with high accuracy without relying on human hands has been conventionally desired.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to be able to estimate the selling price of a gemstone with high accuracy and automatically without relying more on manpower. To provide a gem selling price estimation program and system.

The gemstone selling price estimation program according to the present invention is a gemstone selling price estimation program for estimating the selling price of a gemstone, and the color, brilliance, transparency, and processed state of the gemstone extracted by capturing an image of the appearance of the gemstone to be estimated. An information acquisition step for acquiring appearance information consisting of any of the above, and reference appearance information consisting of any of the color, brilliance, transparency, and processing state of the gemstone extracted by taking an image of the appearance of the gemstone in the past. Using the degree of association with the selling price of the gemstone in three or more stages, the estimation step of estimating the selling price of the gemstone to be estimated is based on the appearance information for reference according to the appearance information acquired in the above information acquisition step. In the above-mentioned information acquisition step and the above-mentioned estimation step, machine learning is performed using image data obtained by capturing the appearance of the jewel to be estimated and appearance information consisting of any of the color, brilliance, transparency, and processing state of the jewel as teacher data. Using the predicted model, the input is the image data and the output is the appearance information, so that the computer is made to newly acquire the appearance information.

Anyone can easily estimate the selling price of gemstones with high accuracy without any special skill or experience.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the gem selling price estimation program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a selling price estimation system 1 in which a jewelry selling price estimation program to which the present invention is applied is implemented. The selling price estimation system 1 includes an information acquisition unit 9, an estimation device 2 connected to the information acquisition unit 9, and a database 3 connected to the estimation device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the estimation device 2 described later. The information acquisition unit 9 outputs the detected information to the estimation device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

The database 3 stores various information necessary for estimating the selling price. Information necessary for estimating the selling price includes reference image information that images the appearance of the jewel in the past, and the color, brilliance, transparency, and processing state of the jewel extracted by imaging the appearance of the jewel in the past. When acquiring reference appearance information consisting of any of the above, reference clarity information consisting of any of the inclusions, scratches on the jewel extracted by imaging an image of the appearance of the jewel in the past, and the above reference image information. Reference brand information regarding the imaged jewel brand, reference type information regarding the type of jewel imaged when acquiring the above reference image information, and reference regarding the jewel market price captured when acquiring the above reference image information. For reference regarding market information, reference external environment information regarding the external environment at the time of imaging when the jewel was imaged when acquiring the above reference image information, and market conditions at the time of imaging when the jewel was imaged when acquiring the above reference image information. Reference appraisal information acquired by reading the appraisal content described in the appraisal report of the jewel imaged when acquiring the market information and the above reference image information, and the jewel imaged when acquiring the above reference image information. The data set of the reference production area information regarding the production area of the above, the reference weight information regarding the weight of the jewel imaged when acquiring the above reference image information, and the selling price of the jewel for which the judgment was actually made is stored. Has been done.

That is, in the database 3, in addition to such reference image information, reference appearance information, reference clarity information, reference brand information, reference type information, reference market price information, reference external environment information, and reference One or more of the market condition information, the reference appraisal information, the reference production area information, and the reference weight information and the selling price of the jewel are stored in association with each other.

The estimation device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is realized by any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the estimation device 2.

FIG. 2 shows a specific configuration example of the estimation device 2. The estimation device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire estimation device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the estimation device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the selling price estimation system 1 having the above-described configuration will be described. The gems mentioned below are represented by diamond, ruby, platinum, amethyst, sapphire, emerald and the like.

The selling price estimation system 1 determines whether or not the selling price of the jewel to be sold through over-the-counter or mail-order sales is appropriate for the selling side, or whether the selling price of the jewel actually purchased by the purchasing side of the jewel is appropriate. It is applied when determining. As shown in FIG. 3, for example, the selling price estimation system 1 is premised on the premise that three or more levels of association between the reference image information and the selling price of the jewel are set in advance. The reference image information is obtained from the image information obtained by imaging the jewel with respect to the appearance of the jewel, and can be obtained by analyzing the image information. This image may be a moving image as well as a still image. Further, although this image is composed of visible light, it may be composed of a so-called spectrum image in which the display color is switched according to the spectrum, instead of the image. This reference image information may identify the appearance of the gemstone based on any of the size, shape, and color of the gemstone by analyzing the image captured of the gemstone. Further, the reference image information may be composed of an ultrasonic image taken of the jewel. The appearance of these gems may be determined based on previously trained features. At this time, using artificial intelligence, image data such as the size, shape, and color of the jewel and the selling price of the jewel are learned, and when actually acquiring the reference image information, these are learned. The selling price of the jewel may be determined by comparing it with the image data.

In such a case, a prediction model machine-learned using the image information and the appearance of any of the size, shape, and color of the jewel as teacher data was used, the input was the image information, and the output was determined as the appearance. The appearance may be specified based on the result.

The selling price of the jewel is the actual selling price of the jewel imaged as the reference image information. This selling price may be obtained by accessing a database stored in an operating company such as a store, e-commerce, or a pawn shop, or may be manually entered each time.

A data set consisting of such reference image information and the selling price of a jewel is acquired and learned.

In the example of FIG. 3, it is assumed that the input data is, for example, reference image information P01 to P03. The reference image information P01 to P03 as such input data is linked to the selling price of the jewel as an output. In this output, the selling price of the gem as the output solution is displayed.

The reference image information is associated with each other through three or more levels of association with the selling prices A to D of the jewel as the output solution. Assuming that the jewels are of the same type, the selling price is, for example, the selling price A is 500,000 yen, the selling price B is 400,000 yen, the selling price C is 350,000 yen, and the selling price D. Are arranged such as 300,000 yen. The reference image information is arranged on the left side through this degree of association, and the selling price of each gem is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price of which jewel with respect to the reference image information arranged on the left side. In other words, this degree of association is an index showing what kind of gemstone selling price each reference image information is likely to be associated with, and the most probable gemstone selling price is selected from the reference image information. It shows the accuracy in doing so. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the selling price of the gemstone as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The estimation device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the estimation device 2 accumulates a past data set as to which of the reference image information and the selling price of the jewel in that case is adopted and evaluated in determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference image information is α. It is assumed that the selling price A of the jewel is highly evaluated as the selling price of the jewel for such reference image information. By collecting and analyzing such a data set, the degree of association with the reference image information is strengthened.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01, analysis is performed from various data as a result of evaluating the selling price of the past jewel. In the case of the reference image information P01, if there are many cases of the selling price A of the gemstone, the degree of association leading to the evaluation of the selling price of the gemstone is set higher, and there are many cases of the selling price B of the gemstone. In some cases, the degree of association that leads to the evaluation of the selling price of this gemstone is set higher. For example, in the example of the reference image information P01, the selling price A of the jewel and the selling price C of the jewel are linked. The degree of association of w14, which leads to the selling price C of, is set to 2 points.

Further, as shown in FIG. 4, this degree of association may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of the appearance image of the gemstone to be evaluated before and the selling price of the gemstone actually estimated / evaluated, the actual selling price of the new gemstone will be determined from now on. In doing so, the above-mentioned learned data will be used to search for the selling price of gemstones. In such a case, the image information is newly acquired in the area to be actually discriminated. The newly acquired image information is input by the information acquisition unit 9 described above. The image information is acquired by photographing a jewel whose selling price is to be determined. This determination method may be performed by the same method as the reference image information described above.

Based on the image information newly acquired in this way, the selling price of the jewel is determined. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02, the selling price B of the jewel is associated with w15 and the selling price C of the jewel is associated with w16 through the degree of association. There is. In such a case, the selling price B of the gem with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the selling price C of the gemstone having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable selling price of the jewel from the newly acquired image information and display it on the selling side and the purchasing side. By looking at this search result, the selling side and the purchasing side can mutually determine the selling price of the gem based on the selling price of the searched gem. Moreover, since the selling prices of these gemstones can be predicted through image capture without touching the gemstones, even if the gemstones are valuables or antiques, they will not be damaged.

In FIG. 3, as an alternative to the reference image information, there are three stages: reference clarity information consisting of scratches or inclusions of the jewel extracted by imaging the jewel in the past, and the selling price of the jewel. The above degree of association may be learned in advance. The reference clarity information is composed of information such as the size and amount of scratches and inclusions, position, contrast, and properties. When acquiring reference clarity information, as shown in FIG. 5, teacher data includes image data obtained by capturing the appearance of the gem to be purchased and clarity information consisting of scratches on the gem and the size and amount of inclusions. A machine-learned prediction model may be used as the data. Clarity information may be newly acquired by using the input as the image data and the output as the clarity information. That is, this clarity information may be discriminated based on the previously trained features. The scratches referred to here include not only scratches actually attached to jewels but also attached stains. Inclusions are other minerals, liquid gases, etc. in crystals such as diamond.

The reference clarity information is constructed from the clarity information obtained in this manner, and the actual selling price (selling price) of the imaged gemstone is obtained to obtain a data set in the same manner as described above. The degree of association as shown in 4 is formed. Next, image data is acquired by taking an image of the jewel whose selling price is actually estimated. Clarity information is obtained by actually referring to the degree of association shown in FIG. 5 for this image data. It is possible to search for the selling price through the acquired clarity information by referring to the degree of association between the appearance information for reference and the selling price of the jewel, which has been learned in advance, at three or more levels.

As an alternative to the reference image information, there are three or more levels of association between the reference appearance information, which consists of the color, brilliance, transparency, and processing state of the gemstone extracted by imaging the gemstone in the past, and the selling price of the gemstone. The degree may be learned in advance. The reference appearance information is composed of, for example, information such as the color, brilliance, transparency, and processing state of the jewel. Color, brilliance, and transparency may be displayed in hierarchical ranks according to their level. The processed state indicates the cut state of the gemstone, the shape and size of each cut surface, the finish of friction, etc., and is displayed in a layered rank according to the level. You may. When acquiring the appearance information for reference, as shown in FIG. 6, the image data obtained by capturing the appearance of the jewel to be purchased and the appearance information including the color, brilliance, transparency, processing state, etc. of the jewel are used as teacher data. A machine-learned prediction model may be used as the data. The appearance information may be newly acquired by using the input as the image data and the output as the appearance information. That is, this appearance information may be discriminated based on the feature amount learned in the past.

The appearance information for reference is constructed from the appearance information obtained in this manner, and the actual selling price (selling price) of the imaged gemstone is obtained to obtain a data set in the same manner as described above. The degree of association as shown in 4 is formed. Next, image data is acquired by taking an image of the jewel whose selling price is actually estimated. For this image data, the appearance information is obtained by actually referring to the degree of association shown in FIG. It is possible to search for the selling price through the acquired appearance information by referring to the degree of association between the reference appearance information and the selling price of the jewel, which has been learned in advance.

In the example of FIG. 7, it is premised that a combination of the reference image information and the reference brand information is formed. The reference brand information is information indicating the manufacturer or distributor of the gemstone, or information on the brand of the trademark affixed to the gemstone.

The selling price of a gem depends on the brand of the gem, in addition to the reference image information. In particular, the price of accessories greatly depends on whether or not they are famous brands. Therefore, this reference brand information is also added as an explanatory variable.

In the example of FIG. 7, it is assumed that the input data is, for example, reference image information P01 to P03 and reference brand information P14 to 17. The intermediate node shown in FIG. 7 is a combination of reference brand information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price of the gem as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference brand information is associated with each other through three or more levels of association with the selling price of the gem as this output solution. The reference image information and the reference brand information are arranged on the left side through this degree of association, and the selling price of the gemstone is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price of the jewel with respect to the reference image information and the reference brand information arranged on the left side. In other words, this degree of association is an index showing what kind of gemstone sales price each reference image information and reference brand information is likely to be associated with, and is a reference image information and reference brand information. It shows the accuracy in selecting the most probable selling price of gemstones from. Therefore, the optimum selling price of the jewel will be searched for by combining the reference image information and the reference brand information.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the estimation device 2 accumulates past data as to which of the reference image information, the reference brand information, and the selling price of the jewel in that case is suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

For example, it is assumed that the reference image information in the actual case in the past is α. It is also assumed that the reference brand information is a famous brand W. In such a case, the selling price of the gemstone, which indicates how much the selling price of the gemstone was actually, is learned as a data set and defined in the form of the above-mentioned degree of association. Note that such reference image information and reference brand information may be extracted from a management database managed by a distributor, a distributor, or the like.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference brand information P16, the selling price of the jewel is analyzed from the past data. If there are many cases where the selling price of the jewel is A, the degree of association that leads to this selling price A is set higher, and if there are many cases of the selling price B of the jewel and there are few cases of the selling price A of the jewel. , The degree of association that leads to the selling price B of the jewel is set high, and the degree of association that leads to the selling price A of the jewel is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B of the gemstone. The degree of association of w14 connected to B is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node that is a combination of the reference image information P01 and the reference brand information P14, the jewel selling price C has a relation of w15, and the jewel selling price. The degree of association of E is w16. The node 61c is a node that is a combination of the reference position information P15 and P17 with respect to the reference image information P02, and the degree of association of the jewel selling price B is w17 and the degree of association of the jewel selling price D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the selling price of the jewel from now on, the above-mentioned learned data will be used. In such a case, the image information and the brand information are actually acquired. For brand information, enter or select the selling price of the ring for which the selling price is actually estimated.

Based on the newly acquired image information and brand information in this way, the optimum selling price of the jewel is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the brand information is P17, the node 61d is associated via the degree of association, and this node. In 61d, the selling price C of the jewel is w19, and the selling price D of the jewel is w20. In such a case, the selling price C of the gem with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the selling price D of the gemstone having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In FIG. 8, in addition to the above-mentioned reference image information, a combination with the reference type information instead of the above-mentioned reference brand information and a degree of association of three or more levels with the selling price of the jewel for the combination are set. An example is shown.

This reference type information, which is added as an explanatory variable instead of the reference position information, is related to the type of gemstone, and can be classified by, for example, a model number, a product name, a stone type, or the like. It is already known that the type of gem is related to the selling price of the gem. Therefore, the purpose is to improve the accuracy of determining the selling price of gemstones by forming the degree of association by combining the reference type information.

In the example of FIG. 8, it is assumed that the input data is, for example, reference image information P01 to P03 and reference type information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference type information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price of the gem as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference type information is associated with each other through three or more levels of association with the selling price of the jewel as this output solution. The reference image information and the reference type information are arranged on the left side through this degree of association, and the selling price of the jewel is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price of the jewel with respect to the reference image information and the reference type information arranged on the left side. In other words, this degree of association is an index indicating what kind of gemstone sales price each reference image information and reference type information is likely to be associated with, and is a reference image information and reference type information. It shows the accuracy in selecting the most probable selling price of gemstones from.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the estimation device 2 uses the reference image information, the reference type information of the jewel imaged when acquiring the reference image information, and the selling price of the jewel in that case. Past data is accumulated to determine which is more suitable, and by analyzing and analyzing these, the degree of association shown in FIG. 8 is created. The reference type information may be obtained, for example, by capturing an image of a box or instruction manual on which the type is described or a jewel through a camera or the like and converting the type into text through OCR or the like.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference image information P01 is the reference type information P20, the selling price of the jewel is analyzed from the past data. When there are many cases where the selling price of the jewel is A, the degree of association that the selling price of the jewel leads to A is set higher, and there are many cases where the selling price of the jewel is B and the selling price of the jewel is A. If the price is low, the degree of association in which the selling price of the jewel is connected to B is set high, and the degree of association in which the selling price of the jewel is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A of the gem and the selling price B of the gem. The degree of association of w14, which leads to the selling price B, is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference type information P18 is combined with the reference image information P01, the degree of association of the jewel selling price C is w15, and the jewel selling price E. The degree of association is w16. The node 61c is a node that is a combination of the reference type information P19 and P21 with respect to the reference image information P02, and the degree of association of the jewel selling price B is w17 and the degree of association of the jewel selling price D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the selling price of the gemstone from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price of the jewel and the type information are actually acquired. Here, the type information is newly acquired when actually estimating the selling price of the jewel, and the acquisition method is the same as the above-mentioned reference type information.

Based on the newly acquired image information and the type information in this way, the optimum selling price of the jewel is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the type information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C of the jewel by w19 and the selling price D of the jewel by the degree of association w20. In such a case, the selling price C of the gem with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the selling price D of the gemstone having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 9, in addition to the above-mentioned reference image information, a combination with the reference production area information instead of the above-mentioned reference brand information and a degree of association with the selling price of the jewel for the combination are set at three or more levels. An example is shown.

This reference origin information added as this explanatory variable is any information about the origin of the gem. Reference production area information consists of information about the country or region where it was produced. This reference production area information may be obtained by analyzing an image such as a label or an instruction manual in which the production area is described, which is captured by a camera, by OCR, or may be manually acquired.

In the example of FIG. 9, it is assumed that the input data is, for example, reference image information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 9 is a combination of reference image information and reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the selling price of the gem as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference production area information is associated with each other through three or more levels of association with the selling price of the gem as this output solution. The reference image information and the reference production area information are arranged on the left side through this degree of association, and the selling price of the gemstone is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price of the jewel with respect to the reference image information and the reference production area information arranged on the left side. In other words, this degree of association is an index indicating what kind of gemstone sales price each reference image information and reference production area information is likely to be associated with, and is a reference image information and reference production area information. It shows the accuracy in selecting the most probable selling price of gemstones from.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the estimation device 2 determines which of the reference image information, the reference production area information obtained when acquiring the reference image information, and the selling price of the jewel in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 9 is created.

For example, at the time of evaluating the actual selling price of a gemstone in the past, it is assumed that the reference production area information is a certain area in South Africa for a certain reference image information. In such a case, if there are many cases where the selling price of the gemstone is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference production area information P20, the selling price of the jewel is analyzed from the past data. When there are many cases where the selling price of the jewel is A, the degree of association that the selling price of the jewel leads to A is set higher, and there are many cases where the selling price of the jewel is B and the selling price of the jewel is A. If the price is low, the degree of association in which the selling price of the jewel is connected to B is set high, and the degree of association in which the selling price of the jewel is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A of the gem and the selling price B of the gem. The degree of association of w14, which leads to the selling price B, is set to 2 points.

Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 9, the node 61b is a node in which the reference image information P01 and the reference production area information P18 are combined, the degree of association of the jewel selling price C is w15, and the jewel selling price E. The degree of association is w16. The node 61c is a node that is a combination of the reference production area information P19 and P21 with respect to the reference image information P02, and the degree of association of the jewel selling price B is w17 and the degree of association of the jewel selling price D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the selling price of the gemstone from now on, the above-mentioned learned data will be used. In such a case, the image information of the target for determining the selling price of the gemstone and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the selling price of the gemstone, and the acquisition method is the same as the reference production area information described above.

Based on the newly acquired image information and the production area information in this way, the optimum selling price of the gemstone is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C of the jewel by w19 and the selling price D of the jewel by the degree of association w20. In such a case, the selling price C of the gem with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the selling price D of the gemstone having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 10, in addition to the above-mentioned reference image information, a combination with the reference weight information instead of the above-mentioned reference brand information and a degree of association with the selling price of the jewel for the combination are set at three or more levels. An example is shown.

This reference weight information, which is added as an explanatory variable, is information about the weight of the jewel imaged when the reference image information is acquired. The heavier the weight, the higher the selling price, so the reference weight information is also added to the explanatory variables.

In the example of FIG. 10, it is assumed that the input data is, for example, reference image information P01 to P03 and reference weight information P18 to 21. The intermediate node shown in FIG. 10 is a combination of reference weight information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price of the gem as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference weight information is associated with each other through three or more levels of association with the selling price of the gem as this output solution. The reference image information and the reference weight information are arranged on the left side through this degree of association, and the selling price of the gemstone is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price of the jewel with respect to the reference image information and the reference weight information arranged on the left side. In other words, this degree of association is an index indicating what kind of gemstone sales price each reference image information and reference weight information is likely to be associated with, and is a reference image information and reference weight information. It shows the accuracy in selecting the most probable selling price of gemstones from.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the estimation device 2 determines which of the reference image information, the reference weight information obtained when acquiring the reference image information, and the selling price of the jewel in that case. Whether it was suitable or not, the past data is accumulated, and these are analyzed and analyzed to create the degree of association shown in FIG.

For example, it is assumed that the reference weight information is 4 carats for a certain reference image information at the time of evaluating the actual selling price of the gemstone in the past. In such a case, if there are many cases where the selling price of the gemstone is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference weight information P20, the selling price of the jewel is analyzed from the past data. When there are many cases where the selling price of the jewel is A, there are many cases where the selling price of the jewel is linked to A, and the selling price of the jewel is B, and the selling price of the jewel is A. If it is low, the degree of association in which the selling price of the jewel is connected to B is set high, and the degree of association in which the selling price of the jewel is connected to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A of the gem and the selling price B of the gem. The degree of association of w14, which leads to the selling price B, is set to 2 points.

Further, the degree of association shown in FIG. 10 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 10, the node 61b is a node in which the reference image information P01 and the reference weight information P18 are combined, the degree of association of the jewel selling price C is w15, and the jewel selling price E. The degree of association is w16. The node 61c is a node that is a combination of the reference weight information P19 and P21 with respect to the reference image information P02, and the degree of association of the jewel selling price B is w17 and the degree of association of the jewel selling price D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the selling price of the gemstone from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price of the jewel and the weight information are actually acquired. Here, the weight information is newly acquired when actually estimating the selling price of the jewel, and the acquisition method is the same as the above-mentioned reference weight information.

Based on the newly acquired image information and the weight information in this way, the optimum selling price of the jewel is searched for. In such a case, the degree of association shown in FIG. 10 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the weight information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C of the jewel by w19 and the selling price D of the jewel by the degree of association w20. In such a case, the selling price C of the gem with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the selling price D of the gemstone having the lowest degree of association but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference external environmental information instead of the above-mentioned reference brand information and the selling price of the jewel for the combination have three or more levels of association. May be used.

The reference external environment information and external environment information referred to here are, for example, economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption status survey, household data, one week). Average working hours, savings amount statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, tsubo unit price, rent market price, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation, etc.) It is typified by quantity data, wind direction data, humidity data, etc.). The external environment information includes all external information other than the one that reflects a part or all of these data. The external environment information for reference may be categorized as the external environment itself. For example, the classification may be performed by separating the data in the employment statistics. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of GDP increases rapidly or gradually) or the like. This reference external environment information indicates the external environment during the past sales date or several days before and after that date.

The degree of association between the combination of the reference image information and the reference external environment information and the selling price of the jewel in three or more stages is acquired in advance. Then, the image information regarding the appearance of the jewel to be estimated and the external environment information regarding the external environment at the time of imaging (estimation) are newly acquired. Then, the point of estimating the selling price of the jewel based on the reference image information according to the acquired image information and the reference external environment information according to the external environment information is shown in FIGS. 3 to 10 described above. Same as the description.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference market information instead of the above-mentioned reference brand information and the selling price of the jewel for the combination have three or more levels of association. It may be used.

The reference market information and market information referred to here are various information related to the market conditions. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these objects. In addition, information such as Bollinger band, volume, MACD, moving average line, etc. may be attached. In addition, this market information may be accompanied by information such as a chart of each brand, Bollinger band, MACD, and moving average line. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving average lines may be attached to the exchange rate.

The degree of association between the combination of such reference image information and the reference market condition information and the selling price of the jewel in three or more stages is acquired in advance. Then, the image information regarding the appearance of the jewel to be estimated is newly acquired, and the market condition information regarding the market condition at the time of imaging (estimation) is acquired. The point of estimating the selling price of the jewel based on the reference image information according to the acquired image information and the reference market information according to the market information is described in FIGS. 3 to 10 described above. The same is true.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference appraisal information instead of the above-mentioned reference brand information and the selling price of the jewel for the combination have three or more levels of association. It may be used.

The reference appraisal information and the appraisal information referred to here are information acquired by reading the appraisal contents described in the appraisal report of the gemstone imaged when the reference image information is acquired. The appraisal certificate certifies the grade of the jewel, such as color, clarity, and cut, but the proof of whether the jewel is genuine or fake, and the quality of the product sold by the store that sells the product. The information obtained by reading the contents described in the warranty card for guaranteeing the above may be configured as reference appraisal information and appraisal information. As a method of acquiring the reference appraisal information and the appraisal information, an appraisal report or a document or the like in which the content is described may be imaged with a camera, and the guaranteed content may be read by using OCR technology. It may be input by input.

The degree of association between the combination of the reference image information and the reference appraisal information and the selling price of the jewel in three or more stages is acquired in advance. Then, the image information regarding the appearance of the jewel to be estimated and the appraisal information regarding the appraisal content of the jewel are newly acquired. Then, the point of estimating the selling price of the jewel based on the reference image information according to the acquired image information and the reference appraisal information according to the appraisal information is described in FIGS. 3 to 10 described above. The same is true.

Further, in the present invention, in addition to the above-mentioned reference image information, the combination with the reference market price information instead of the above-mentioned reference brand information and the selling price of the jewel for the combination have three or more levels of association. It may be used.

The reference market price information and market price information referred to here are general market price data for the gemstone, and the data may be obtained from a site that discloses such market price data. Data may be acquired from the site of the institution that discloses the market price, its server, or the like, or may be manually input.

The degree of association between the combination of such reference image information and the reference market price information and the selling price of the jewel in three or more stages is acquired in advance. Then, the image information regarding the appearance of the jewel to be estimated and the market price information regarding the container of the jewel are newly acquired. Then, the point of estimating the selling price of the jewel based on the reference image information according to the acquired image information and the reference market information according to the market price information is described in FIGS. 3 to 10 described above. The same is true.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the selling price of a gemstone without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A dataset may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference image information, the reference appearance information, the reference clarity information, the reference brand information, the reference type information, the reference market price information, the reference external environment information, and the reference market condition information. , The explanation has been given by taking as an example the case where the information is composed of any of the reference appraisal information, the reference production area information, and the reference weight information, but the present invention is not limited to this. In other words, the degree of association is, in addition to the reference image information, the reference appearance information, the reference clarity information, the reference brand information, the reference type information, the reference market price information, the reference external environment information, the reference market information, and the reference. It may be composed of a combination of any two or more of the appraisal information, the reference production area information, and the reference weight information. In addition to the reference image information, the degree of association includes reference appearance information, reference clarity information, reference brand information, reference type information, reference market price information, reference external environment information, reference market information, and reference. In addition to any one or more of the appraisal information, the reference production area information, and the reference weight information, other factors may be added to this combination to form the degree of association.

Further, in the above-mentioned example, the case where the reference image information is used as the keynote and this is combined with other reference information to form the degree of association has been described as an example, but the description is not limited to this, and the reference is not limited to this. The degree of association may be formed by combining with other reference information based on the appearance information or the reference clarity information. That is, in addition to the reference appearance information or the reference clarity information, the reference brand information, the reference type information, the reference market price information, the reference external environment information, the reference market condition information, the reference appraisal information, the reference production area information, The degree of association may be configured by combining with any one or two or more of the reference weight information.

In either case, data is input according to the reference information of the degree of association, and the selling price of the jewel is obtained using the degree of association.

Further, according to the present invention, it is characterized in that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this and can be described by a numerical value of 3 or more steps at any stage. It may be configured.

By determining the most probable selling price of gemstones based on the degree of association expressed by the numerical values of three or more levels, the degree of association can be determined in a situation where there are multiple possible candidates for the search solution. It is also possible to search and display in descending order. If the users can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable that it appears once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition, reference image information, reference appearance information, reference brand information, reference product name information, reference production area information, reference successful bid price information, reference external environment information, reference market information, reference text information, reference When support information, reference guarantee information, and reference market price information are acquired, and knowledge, information, and data regarding the selling price of jewelry and improvement measures for these are acquired, the degree of association is increased or decreased accordingly.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

When the degree of association shown in FIG. 3 is composed of the nodes of the neural network in artificial intelligence, as shown in FIG. 10, reference image information is input as input data, and the jewel is used as output data. The selling price may be output, and at least one hidden layer may be provided between the input node and the output node for machine learning.

Further, as shown in FIG. 11, the present invention determines the selling price of a jewel based on the degree of association between two or more types of information, reference information U and reference information V. This reference image information U is the reference water temperature, and the reference information V is the reference brand information, the reference product name information, the reference production area information, the reference successful bid price information, the reference external environment information, the reference market condition information, It shall be any of reference text information, reference support information, reference warranty information, reference containment information, etc.

At this time, as shown in FIG. 11, the output obtained for the reference information U is used as the input data as it is, and is associated with the output (selling price of the jewel) via the intermediate node 61 in combination with the reference information V. You may. For example, for reference information U (reference image information), after an output solution is output as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. You may want to search for the output (the selling price of the gem).

1 Sales price estimation system 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (15)

In the gem selling price estimation program that estimates the selling price of gems
An information acquisition step of acquiring appearance information consisting of any of the color, brilliance, transparency, and processing state of the gemstone extracted by capturing an image of the appearance of the gemstone to be estimated.
Utilizing the three or more levels of association between the reference appearance information consisting of the color, brilliance, transparency, and processing state of the gemstone extracted by taking an image of the appearance of the gemstone in the past and the selling price of the gemstone. It has an estimation step of estimating the selling price of the gem to be estimated based on the reference appearance information according to the appearance information acquired in the above information acquisition step.
In the above information acquisition step and the above estimation step, it is predicted that the image data obtained by capturing the appearance of the jewel to be estimated and the appearance information consisting of any of the color, brilliance, transparency, and processing state of the jewel are machine-learned as teacher data. A jewelry selling price estimation program characterized in that a computer is made to acquire new appearance information by using a model, input is the above image data, and output is the appearance information. In the gem selling price estimation program that estimates the selling price of gems
An information acquisition step for acquiring clarity information consisting of scratches or inclusions of a gemstone extracted by capturing an image of the appearance of the gemstone to be estimated.
Obtaining the above information by using the reference clarity information consisting of any of the scratches or inclusions of the gemstone extracted by taking an image of the appearance of the gemstone in the past and the selling price of the gemstone at three or more levels. It has an estimation step of estimating the selling price of the gem to be estimated above based on the reference clarity information according to the clarity information acquired in the step.
In the above information acquisition step and the above estimation step, a prediction model machine-learned using image data of the appearance of the jewel to be estimated and clarity information consisting of scratches on the jewel or inclusions as teacher data is used. A jewelry selling price estimation program characterized in that a computer is made to acquire new clarity information by using the above-mentioned image data as an input and the above-mentioned clarity information as an output. In the above information acquisition step, the brand information regarding the brand of the gem to be estimated is acquired, and the brand information is acquired.
In the above estimation step, and the reference appearance information, combined with the reference brand information on jewelry brand was captured in obtaining the appearance information for the reference, three or more stages of linkage of the selling price of the gemstone utilizing degrees, based on the further reference brand information corresponding to the acquired brand information in the information acquisition step, jewelry sales price of claim 1, wherein said estimating the selling price of the estimation target jewelry Estimate program. In the above information acquisition step, type information regarding the type of gem to be estimated is acquired.
In the above estimation step, and the reference appearance information, combined with the reference classification information related to the type of gemstone captured in obtaining the appearance information for the reference, three or more stages of linkage of the selling price of the gemstone utilizing degrees, based on the further reference type information corresponding to the acquired type information in the information acquisition step, jewelry sales price of claim 1, wherein said estimating the selling price of the estimation target jewelry Estimate program. In the above information acquisition step, market information regarding the market price of the gem to be estimated is acquired.
In the above estimation step, and the reference appearance information, combined with the reference market information on market prices gemstone captured when acquiring the reference appearance information, three or more stages of the selling price of the gemstone utilizing association degree, based on the further reference rate information corresponding to the obtained market information in the information acquisition step, jewels sold claim 1, wherein said estimating the selling price of the estimation target jewelry Price estimation program. In the above information acquisition step, the external environment information regarding the external environment at the time of estimation is acquired, and the information is acquired.
In the estimation step, the combination of the reference appearance information , the reference external environment information regarding the external environment at the time of imaging when the jewel is imaged when the reference appearance information is acquired, and the selling price of the jewel are used. 3 using the steps above association degree claim further based on the obtained reference external environment information corresponding to the external environment information in the information acquisition step, and estimates the selling price of the estimation target jewelry The jewelry selling price estimation program described in item 1. In the above information acquisition step, the market information regarding the market conditions at the time of estimation is acquired, and the market conditions are acquired.
In the above estimation step, three stages and the reference appearance information, combined with the reference market information about market conditions at the time of imaging of the captured gems in obtaining the appearance information for the reference, the selling price of the gemstone using the above association degree, further based on the obtained reference market information corresponding to the market information in the information acquiring step, according to claim 1, wherein said estimating the selling price of the estimation target jewelry Jewel selling price estimation program. In the above information acquisition step, the appraisal information is acquired by reading the appraisal content described in the appraisal report of the gem to be estimated.
In the estimation step, a combination of the reference appearance information and the reference appraisal information acquired by reading the appraisal content described in the appraisal report of the jewel imaged when the reference appearance information is acquired. , Estimate the selling price of the jewel to be estimated based on the reference appraisal information according to the appraisal information acquired in the information acquisition step, using the degree of association with the selling price of the jewel in three or more stages. The jewelry selling price estimation program according to claim 1, wherein the jewelry selling price is estimated. In the above information acquisition step, the production area information regarding the production area of the gem to be estimated is acquired.
In the above estimation step, and the reference appearance information, combined with the reference origin information on jewelry Origin captured in obtaining the appearance information for the reference, three or more stages of linkage of the selling price of the gemstone utilizing degrees, based on the further reference origin information corresponding to the acquired origin information in the information acquisition step, jewelry sales price of claim 1, wherein said estimating the selling price of the estimation target jewelry Estimate program. In the above information acquisition step, weight information regarding the weight of the gem to be estimated is acquired, and the weight information is acquired.
In the estimation step, the combination having the reference appearance information and the reference weight information regarding the weight of the jewel imaged when the reference appearance information is acquired, and the selling price of the jewel are associated with each other in three or more stages. utilizing degrees, based on the further reference weight information corresponding to the acquired weight information in the information acquisition step, jewelry sales price of claim 1, wherein said estimating the selling price of the estimation target jewelry Estimate program. In the above information acquisition step, brand information regarding the brand of the gem to be estimated is acquired, and the brand information is acquired.
In the estimation step, the combination having the reference clarity information and the reference brand information regarding the brand of the gem imaged when the reference clarity information is acquired, and the selling price of the gem are associated with each other in three or more stages. Estimate the selling price of the gem to be estimated above based on the reference brand information according to the brand information acquired in the above information acquisition step.
The gem selling price estimation program according to claim 2. In the above information acquisition step, type information regarding the type of gem to be estimated is acquired.
In the estimation step, the combination having the reference clarity information and the reference type information regarding the type of the jewel imaged when the reference clarity information is acquired, and the selling price of the jewel are associated with each other in three or more stages. Estimate the selling price of the gem to be estimated above based on the reference type information according to the type information acquired in the above information acquisition step.
The gem selling price estimation program according to claim 2. In the above information acquisition step, market information regarding the market price of the gem to be estimated is acquired.
In the estimation step, there are three or more stages of the combination having the reference clarity information, the reference market price information regarding the market price of the jewel imaged when the reference clarity information is acquired, and the selling price of the jewel. Estimate the selling price of the gem to be estimated above based on the reference market information according to the market information acquired in the above information acquisition step using the degree of association.
The gem selling price estimation program according to claim 2. In the above information acquisition step, the external environment information regarding the external environment at the time of estimation is acquired, and the information is acquired.
In the estimation step, the combination of the reference clarity information, the reference external environment information regarding the external environment at the time of imaging when the gem is imaged when the gem is acquired, and the selling price of the gem are used. Estimate the selling price of the gem to be estimated above based on the external environment information for reference according to the external environment information acquired in the above information acquisition step, using the degree of association of 3 levels or more.
The gem selling price estimation program according to claim 2. The gem selling price estimation program according to any one of claims 1 to 14, wherein in the estimation step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. ..

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