Evaluation device and evaluation method

Abstract

To provide an evaluation device capable of appropriately recommending disposal of belongings in consideration of user favorability to the belongings.SOLUTION: An evaluation device 100 includes: an image acquisition unit 11 that acquires a belongings image obtained by photographing belongings of a user; a feature extraction unit 12 that extracts features of the belongings from the belongings image; a favorability setting unit 14 that sets favorability of the user for the belongings; a statistical information acquisition unit 15 that acquires statistical information indicating a trend situation of an article of the same type as the belongings; a trend estimation unit 16 that estimates a trend corresponding to the belongings based on features of the belongings and the statistical information; and an evaluation unit 20 that evaluates whether or not the belongings should be disposed of based on favorability and the trend corresponding to the belongings.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an evaluation device and an evaluation method. [Background technology]

[0002] There are known technologies that assist users who own items such as clothes and bags in disposing of those items. As a related technology, Patent Document 1 discloses an information processing device that suggests the appropriate time to dispose of a user's belongings. The information processing device identifies used possessions among the user's possessions based on photographed images of the user's possessions and information about the possessions that has been registered in advance, records the frequency of use of the used possessions, and provides suggested information regarding the disposal of the used possessions based on the frequency of use. The information processing device also obtains secondhand market prices of similar items to the used possessions and provides information regarding the estimated selling price of the used possessions based on the secondhand market prices. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-042472 Summary of the Invention [Problem to be solved by the invention]

[0004] When deciding whether to keep an item or dispose of it, it is desirable to take into consideration the user's preference for the item. For example, when considering disposing of an item that the user has a strong attachment to and is highly preferred, it may be difficult for the user to decide to dispose of the item based only on the frequency of use or estimated selling price of the item.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide an evaluation device and an evaluation method that can appropriately recommend the disposal of belongings by taking into account the user's level of favorability toward the belongings. [Means for solving the problem]

[0006] The evaluation device according to the present disclosure includes: an image acquisition unit that acquires a belongings image of the user; a feature extraction unit that extracts features of the belongings from the belongings image; a likeability setting unit that sets a likeability rating of the user for the belongings; a statistical information acquisition unit that acquires statistical information indicating the popularity of items of the same type as the personal belongings; a popularity estimation unit that estimates a popularity corresponding to the belongings based on the characteristics of the belongings and the statistical information; and an evaluation unit that evaluates whether or not the belongings should be disposed of based on the favorability rating and the popularity corresponding to the belongings.

[0007] The evaluation method according to the present disclosure includes: an image acquisition step of acquiring a belongings image of the user; a feature extraction step of extracting features of the belongings from the belongings image; a preference level setting step of setting a preference level of the user for the belongings; a statistical information acquisition step of acquiring statistical information indicating a popularity status of items of the same type as the belongings; a popularity estimation step of estimating a popularity corresponding to the belongings based on the characteristics of the belongings and the statistical information; and an evaluation step of evaluating whether or not the belongings should be disposed of based on the favorability rating and the popularity corresponding to the belongings. [Effects of the Invention]

[0008] The evaluation device and evaluation method according to the present disclosure can appropriately recommend the disposal of belongings by taking into account the user's favorable impression of the belongings. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating a configuration of an evaluation device according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of capturing images of belongings and a user's face using the rear camera and the front camera according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a favorability rating input screen for setting a favorability rating according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of favorability ratings corresponding to facial expressions of users according to the embodiment. [Figure 5] FIG. 5 is a flowchart illustrating a process in which the evaluation device according to the embodiment acquires the state of the user under normal circumstances. [Figure 6] FIG. 6 is a flowchart illustrating the processing of the evaluation device according to the embodiment. [Figure 7] FIG. 7 is a flowchart illustrating the processing of the evaluation device according to the embodiment. [Figure 8] FIG. 8 is a flowchart showing the process of the evaluation device when adjusting the evaluation result according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are designated by the same reference numerals. For clarity of explanation, duplicated explanations will be omitted as necessary.

[0011] (Configuration of evaluation device 100) An evaluation device 100 according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the evaluation device 100. The evaluation device 100 includes an imaging unit 1, a biosensor 2, a display unit 3, an input unit 4, a communication unit 5, a storage unit 6, and a control unit 10.

[0012] The evaluation device 100 is an information processing device that can support a user in disposing of their belongings. A user can use the evaluation device 100 to consider disposing of their own belongings. A belonging is an item that the user owns and can be the subject of consideration for disposal. "Disposal" means that the user gives up the belonging. Methods of disposal include, for example, discarding, selling, or donating.

[0013] Examples of personal belongings include, but are not limited to, clothing, bags, shoes, ornaments, furniture, electrical appliances, or paintings. Personal belongings may be various items that can be disposed of by the user or a person related to the user. A person related to the user is someone who can dispose of the user's personal belongings on the user's behalf. A person related to the user may be, for example, the user's family.

[0014] The evaluation device 100 may be realized by an information processing device such as a smartphone, a PC, or a tablet, for example. Each function of the evaluation device 100 may be realized by a program installed on such an information processing device. For example, a user can use a user terminal such as a smartphone as the evaluation device 100 by installing a predetermined application on the user terminal. The evaluation device 100 may be realized as an additional function of an existing flea market app, a website for trading unwanted items, an internet auction site, or the like. In the following, an example will be described in which the evaluation device 100 is a user's smartphone.

[0015] The photographing unit 1 photographs a subject. The photographing unit 1 is, for example, a photographing device such as a camera. In this embodiment, the photographing unit 1 includes a rear camera 1a and a front camera 1b. The rear camera 1a photographs a subject on the rear side of the evaluation device 100. The front camera 1b photographs a subject on the front side of the evaluation device 100. The front side of the evaluation device 100 is the surface on which the display unit 3 is provided. The back side of the evaluation device 100 is the surface opposite to the surface on which the display unit 3 is provided.

[0016] Fig. 2 is a diagram showing an example of capturing images of belongings and the user's face using rear camera 1a and front camera 1b. Fig. 2 shows only rear camera 1a, but front camera 1b is provided on the opposite side of rear camera 1a.

[0017] The rear camera 1a photographs the user's belongings in accordance with the user's operation. In the example of FIG. 2, the belongings is a sweater SW. The rear camera 1a outputs the photographed image of the belongings to the control unit 10. The front camera 1b photographs the user. Specifically, the front camera 1b photographs an area including the user's face. The front camera 1b outputs the photographed image of the face to the control unit 10.

[0018] The front camera 1b may capture the user's face at the same time that the rear camera 1a captures the user's belongings. This allows the user to capture both their belongings and their own face at the same time. However, the rear camera 1a and the front camera 1b may capture images at different times.

[0019] Although an example in which the photographing unit 1 includes both the rear camera 1a and the front camera 1b has been described here, the present invention is not limited to this. For example, the photographing unit 1 may include only the rear camera 1a, and the rear camera 1a may photograph the user's belongings and the user's belongings at different times.

[0020] Returning to FIG. 1, the biosensor 2 detects biometric information of the user. The biosensor 2 is, for example, a heart rate sensor that measures the user's heart rate when the user photographs their belongings and their face with the photographing unit 1. The biosensor 2 outputs the measurement result, that is, the heart rate, to the control unit 10.

[0021] The biosensor 2 is not limited to a heart rate sensor and may be any of various sensors that detect other biometric information. For example, the biosensor 2 may be any of various sensors that detect the user's blood pressure, pulse wave, sweat rate, respiration, brain waves, etc. The biometric information may be any information that can identify the user's level of liking for a personal item when the user comes into contact with the item. For example, the biometric information may be any information that can identify the user's level of liking for a personal item when the user looks at the item or picks it up.

[0022] Furthermore, the front camera 1b of the image capturing unit 1 described above may be used as the biometric sensor 2. In this case, the front camera 1b as the biometric sensor 2 outputs a facial image of the user to the control unit 10 as biometric information.

[0023] Here, an example will be described in which the evaluation device 100 includes a biosensor 2, but this is not limiting. The evaluation device 100 may be configured not to include a biosensor 2. For example, the evaluation device 100 may acquire bioinformation from a predetermined sensor device that detects bioinformation of a user. For example, the sensor device may be a wearable device such as a smartwatch that is worn on the user's body.

[0024] The display unit 3 displays various information to the user. The display unit 3 is, for example, a display device such as a liquid crystal display. In the following, an example will be described in which the display unit 3 is a touch panel having the function of the input unit 4 that accepts input from the user.

[0025] The input unit 4 receives input from the user. The input unit 4 is an input device such as a keyboard or a mouse. Here, the input unit 4 will be described as a touch panel having the function of the display unit 3.

[0026] The communication unit 5 communicates with devices other than the evaluation device 100 via a network (not shown). The network is a wired or wireless communication line. The network may be configured using, for example, the Internet, a mobile communication network such as 3G, 4G, or 5G, a public telephone network, or a satellite communication network. The communication unit 5 may be a communication interface for performing wired or wireless communication.

[0027] The storage unit 6 stores various data and programs. At least a part of the storage unit 6 is configured with a non-volatile storage medium so that necessary data is retained even when the evaluation device 100 is turned off. For example, the storage unit 6 stores a computer program in which the processing according to this embodiment is implemented. The storage unit 6 also stores images of belongings and the user's status under normal circumstances. Note that the storage unit 6 may not be included in the evaluation device 100, but may be an external storage device on a network.

[0028] The control unit 10 controls various functions of the evaluation device 100. The control unit 10 includes an image acquisition unit 11, a feature extraction unit 12, a user state acquisition unit 13, a favorability rating setting unit 14, a statistical information acquisition unit 15, a popularity estimation unit 16, a detailed information acquisition unit 17, a price acquisition unit 18, a predicted selling price calculation unit 19, an evaluation unit 20, and a registration unit 21.

[0029] The evaluation device 100 also includes a processor and memory, which are not shown in the figure. The processor can load a computer program from the storage unit 6 into the memory and execute the computer program. In this way, the processor realizes the functions of the image acquisition unit 11, feature extraction unit 12, user status acquisition unit 13, favorability rating setting unit 14, statistical information acquisition unit 15, popularity estimation unit 16, detailed information acquisition unit 17, price acquisition unit 18, expected selling price calculation unit 19, evaluation unit 20, and registration unit 21, which are included in the control unit 10.

[0030] Each function of the control unit 10 may be realized by dedicated hardware. Furthermore, some or all of the components may be realized by general-purpose or dedicated circuits, processors, etc., or a combination of these. These may be configured by a single chip, or by multiple chips connected via a bus. Some or all of the components may be realized by a combination of the above-mentioned circuits, etc., and programs. Furthermore, a CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), quantum processor (quantum computer control chip), etc., may be used as the processor.

[0031] The image acquisition unit 11 acquires belongings images obtained by photographing the user's belongings. Specifically, the image acquisition unit 11 acquires belongings images acquired by the rear camera 1a. The image acquisition unit 11 also acquires face images acquired by the front camera 1b. The image acquisition unit 11 may also acquire a pupil image including the user's pupil region from the face image. The face image and pupil image are examples of the user's biometric information. The images acquired by the image acquisition unit 11 are stored in the storage unit 6.

[0032] The feature extraction unit 12 extracts features of the belongings from the belongings image. The features of the belongings indicate characteristics that may affect the value of the belongings. The features of the belongings may be, for example, features related to the appearance, function, performance, material, or durability of the belongings. For example, if the belongings is a bag, the features of the belongings may be the color, shape, number of pockets, number of zippers, size (number of liters), etc.

[0033] The feature extraction unit 12 may extract the features of the belongings using any image recognition technology. Alternatively, the feature extraction unit 12 may extract the features of the belongings by identifying items included in the belongings image and obtaining details of the identified items from the Internet or the like.

[0034] For example, as shown in FIG. 2, suppose a user photographs a sweater SW using the rear camera 1a. The feature extraction unit 12 identifies the sweater SW included in the belongings image using any image recognition technology. For example, the feature extraction unit 12 identifies the model number of the sweater SW based on the belongings image and searches the Internet using the model number. The feature extraction unit 12 acquires the features of the sweater SW, including the color, shape, size, etc. of the sweater SW, from the Internet. As a result, the feature extraction unit 12 extracts the features of the sweater SW.

[0035] Alternatively, the feature extraction unit 12 may acquire the model number of the sweater SW via the input unit 4 and acquire the features of the sweater SW based on the model number. When the user inputs the model number into the input unit 4, the feature extraction unit 12 can acquire the model number of the sweater SW.

[0036] In addition to the model number, the feature extraction unit 12 may acquire the features of the sweater SW via the Internet, etc., using the brand name, release date, price, color, size, etc. The feature extraction unit 12 may also extract the features of the sweater SW by receiving an input of the features of the sweater SW from the user.

[0037] The user state acquisition unit 13 acquires the user state based on the user's biometric information. The user state is information related to the user's level of favorability for their belongings. For example, as shown in FIG. 2, the user state acquisition unit 13 identifies the user state based on the biometric information detected by the biometric sensor 2 when the user photographs their belongings. In this way, the user state acquisition unit 13 acquires the user state when the photograph of their belongings was taken.

[0038] Here, an example will be described in which the user state acquisition unit 13 acquires the size of the user's pupils, facial expression, and heart rate as the user's state.

[0039] The user state acquisition unit 13 acquires the size of the user's pupil based on the pupil image. For example, the user state acquisition unit 13 acquires a bright pupil image by irradiating the user's face with light that makes the pupil relatively brighter. The user state acquisition unit 13 also acquires a dark pupil image by irradiating the user's face with light that makes the pupil relatively darker. The user state acquisition unit 13 detects the user's pupil by calculating a difference image that is the difference between the bright pupil image and the dark pupil image.

[0040] Furthermore, the user state acquisition unit 13 acquires the user's facial expression in the face image using any emotion recognition technology. For example, the user state acquisition unit 13 identifies the user's emotions in the face image.

[0041] Furthermore, the user state acquisition unit 13 acquires the state of the user's heart rate based on the heart rate detected by a heart rate sensor, which is an example of the biosensor 2.

[0042] The user state acquisition unit 13 may acquire the user's state at regular intervals, thereby acquiring the user's state at normal times. The user's state at normal times may be expressed, for example, using statistical values ​​(for example, average values ​​or median values) of the user's state during a predetermined period before the user's belongings are photographed. The normal times indicate a time when the user's state is stable before the user's belongings are photographed. The time when the user's state is stable is, for example, a time when the user's emotional changes are relatively small.

[0043] The user state acquisition unit 13 stores the user's state in normal times in the storage unit 6. This allows the favorability rating setting unit 14 to compare the user's state in normal times with the user's state at the time when the user's belongings are photographed.

[0044] The likeability setting unit 14 sets the user's likeability for a personal item. The likeability indicates the degree of likeability the user has for the personal item. The likeability is related to the strength of the user's attachment to the personal item. The likeability is also related to the strength of the user's intention to dispose of the personal item. For example, as the likeability increases, the user's intention to dispose of the personal item decreases. Conversely, as the likeability decreases, the user's intention to dispose of the personal item increases. Personal items with a high likeability can be considered to be necessary for the user, and personal items with a low likeability can be considered to be unnecessary for the user.

[0045] For example, the likeability setting unit 14 accepts a user's input from the input unit 4 and sets the likeability. FIG. 3 is a diagram showing an example of a likeability input screen 41 for setting the likeability. The likeability input screen 41 includes, for example, a belongings image area 41a and a likeability input area 41b. The belongings image area 41a is an area for displaying a belongings image. In the example of FIG. 3, the sweater SW shown in FIG. 2 is displayed in the belongings image area 41a. The likeability input area 41b is an area for the user to input the likeability of the belongings.

[0046] In the example of Fig. 3, the likeability input area 41b shows five levels of likeability that can be set. The user can select from five levels whether or not the belongings are necessary. In the example of Fig. 3, "unnecessary" is shown as 1, "necessary" as 5, and "neither" as 3.

[0047] The likeability setting unit 14 sets the likeability rating in accordance with the content input into the likeability rating input area 41b. For example, the likeability setting unit 14 sets the likeability rating with a minimum value of 0 and a maximum value of 100. Here, the likeability setting unit 14 sets the likeability rating from 0 to 100 so as to correspond to a five-level scale of 1 to 5. The likeability rating in the lowest state is 0, and the likeability rating in the highest state is 100.

[0048] Specifically, when the user input is 1, the likeability setting unit 14 sets the likeability to 0. Similarly, when the user input is 2, the likeability setting unit 14 sets the likeability to 30, when 3, 50, when 4, 70, and when 5, 100.

[0049] The example shown in FIG. 3 is just an example, and the likeability setting unit 14 may set the likeability level using other methods. The likeability setting unit 14 may set the likeability level at any level. For example, the likeability setting unit 14 may set the likeability level at four levels or less, or at six levels or more. Alternatively, the likeability setting unit 14 may set the likeability level by, for example, accepting a numerical input.

[0050] The method is not limited to the above, and the likeability rating setting unit 14 may compare the user's state in normal times with the user's state at the time the user's belongings are photographed, and set the likeability rating based on the comparison result. The user's state will be specifically described using the above-mentioned examples of the user's pupil size, facial expression, and heart rate.

[0051] The likability rating setting unit 14 assigns a likability rating with an upper limit of 40 to each of the pupil size and facial expression acquired by the user state acquisition unit 13, and assigns a likability rating with an upper limit of 20 to the heart rate. However, the likability rating setting unit 14 may assign likability ratings using any ratio.

[0052] The likeability rating setting unit 14 compares the size of the pupils in normal times with the size of the pupils at the time the photograph of the belongings is taken. The likeability rating setting unit 14 sets a higher likeability rating the more dilated the pupils are at the time the photograph of the belongings is taken compared to normal times. Specifically, if the pupils are dilated more than normal at the time the photograph of the belongings is taken, the likeability rating setting unit 14 sets the likeability rating to 40. If the size of the pupils at the time the photograph of the belongings is unchanged compared to normal times, the likeability rating setting unit 14 sets the likeability rating to 20. If the pupils are closed at the time the photograph of the belongings is taken, the likeability rating setting unit 14 sets the likeability rating to 0.

[0053] The likeability rating setting unit 14 also compares the user's facial expression in normal times with the user's facial expression at the time the photograph of the belongings was taken. If the facial expression at the time the photograph of the belongings was taken is more positive than normal, the likeability rating setting unit 14 sets a higher likeability rating than if the facial expression was negative.

[0054] FIG. 4 is a diagram showing an example of likeability ratings corresponding to a user's facial expression. As shown in the diagram at the bottom right, if the facial expression at the time the user's belongings is photographed is smiler than usual, the likeability rating setting unit 14 sets the likeability rating to 40. As shown in the diagram at the top left, if the facial expression at the time the user's belongings is photographed is unchanged compared to usual, the likeability rating setting unit 14 sets the likeability rating to 20. As shown in the diagrams at the bottom left and top right, if the facial expression at the time the user's belongings is photographed is sadder or angrier than usual, the likeability rating setting unit 14 sets the likeability rating to 0. The user's facial expression may be a natural expression that changes when the user photographs their belongings, or may be an expression intentionally made by the user to coincide with the photograph of the user's belongings.

[0055] Returning to FIG. 1, the description of the likeability rating setting unit 14 will continue. The likeability rating setting unit 14 compares the heart rate at normal times with the heart rate at the time when the photograph of the belongings is taken. If the heart rate at the time when the photograph of the belongings is taken is higher than normal times, the likeability rating setting unit 14 sets a higher likeability rating than if the heart rate has decreased. Specifically, if the heart rate at the time when the photograph of the belongings is taken is higher than normal times by a predetermined amount or more, the likeability rating setting unit 14 sets the likeability rating to 20. In other cases, the likeability rating setting unit 14 sets the likeability rating to 0.

[0056] The likeability setting unit 14 sums up the likeability ratings of pupil size, facial expression, and heart rate, and sets the total value as the likeability rating at the time the photograph of the belongings is taken. Note that, although a method using three pieces of biometric information, pupil size, facial expression, and heart rate, has been shown as an example here, the method is not limited to this. The likeability setting unit 14 may use one or two of the three pieces of biometric information, or may use other biometric information.

[0057] In this way, the likeability rating setting unit 14 sets the user's likeability rating for a personal item using the user's state based on biometric information. This allows the likeability rating setting unit 14 to appropriately set the likeability rating even when it is difficult for the user to input the likeability rating for the personal item. For example, even when the user is unable to input the likeability rating because the user suffers from dementia or a hand injury, a person related to the user can have the evaluation device 100 acquire the user's state at the time the personal item was photographed. This allows the evaluation device 100 to automatically set the likeability rating.

[0058] The statistical information acquisition unit 15 acquires statistical information indicating the popularity of items of the same type as the belongings. The type of belongings indicates the category of the belongings. The hierarchy of categories is not limited. For example, in the example of the sweater SW mentioned above, the type of sweater SW may be "clothing," "tops," "knit," or "sweater," etc.

[0059] The statistical information acquiring unit 15 may limit the type of belongings based on more detailed characteristics. For example, the statistical information acquiring unit 15 identifies a "men's knit sweater" as a type of belongings. The statistical information acquiring unit 15 acquires statistical information indicating the popularity of "men's knit sweaters" from the Internet or the like.

[0060] The statistical information acquiring unit 15 may acquire the statistical information at any timing. The statistical information acquiring unit 15 may acquire the statistical information periodically, or may acquire the statistical information at a predetermined timing. For example, the statistical information acquiring unit 15 may set the time of year-end general cleaning as the predetermined timing.

[0061] The statistical information includes characteristics of the same type of item as the personal item and the popularity status of the item. The characteristics of the item may be, for example, characteristics related to the appearance, function, performance, material, durability, etc. of the item, similar to the characteristics of the personal items described above.

[0062] The popularity of an item may be expressed, for example, using sales data of the item. However, the popularity of an item may also be expressed, for example, using ratings of the item on social networking services (SNS) or websites.

[0063] The statistical information acquisition unit 15 acquires statistical information for each type of item. For example, suppose the item is a bag. The statistical information acquisition unit 15 acquires features such as color, shape, number of pockets, number of zippers, or size (number of liters). The statistical information acquisition unit 15 acquires images of the bag from, for example, the most recent three years, extracts features of the bag from the acquired images using image analysis, and acquires statistical information. The acquired statistical information is stored in the storage unit 6.

[0064] The popularity estimation unit 16 estimates the popularity corresponding to the belongings based on the characteristics of the belongings and statistical information. Specifically, when the characteristics of the belongings are input, the popularity estimation unit 16 outputs the popularity corresponding to the belongings. The characteristics of the belongings may also be input by inputting an image of the belongings. The popularity estimation unit 16 analyzes the input image of the belongings and extracts the characteristics of the belongings. The popularity estimation unit 16 outputs a higher popularity as the feature of the belongings is ranked higher in the statistical information stored in the memory unit 6. The popularity estimation unit 16 outputs a popularity with a maximum value of 100 based on the number of feature items and the ranking of the feature from the top.

[0065] For example, suppose that the number of characteristics of a bag is five (color, shape, number of pockets, number of zippers, size). Since there are five items, the maximum value for each item is 20. Regarding color, the top five colors in the statistical information are, from top to bottom, black, blue, green, yellow, and red. For example, suppose that the popularity levels are set to 20, 15, 10, 5, and 3, in order from top to bottom. If the item in your possession is a blue bag, blue is second from the top, so the popularity level for the color of your possessions is 15.

[0066] The popularity estimation unit 16 calculates the popularity of other items in the same way, sums up the popularity of all items, and outputs the total value as the popularity of the belongings. Note that the number of feature items and the numerical distribution according to the rank may differ for each object.

[0067] The detailed information acquisition unit 17 acquires detailed information about the belongings. The detailed information includes the characteristics of the belongings. The detailed information includes at least the type of the belongings. The detailed information may include the model number of the belongings. The detailed information may also include, for example, the condition of the belongings (new, used, no noticeable stains, stained, etc.). The user inputs the detailed information via the input unit 4. The detailed information acquisition unit 17 accepts the user's input and acquires the detailed information. The detailed information acquisition unit 17 may identify the detailed information from the belongings image and acquire the detailed information.

[0068] The price acquisition unit 18 acquires the price of an item of the same model or a similar item to the owned item based on the detailed information. For example, the price acquisition unit 18 acquires the new price of an item of the same model or a similar item to the owned item from the Internet based on the model number of the owned item. If the new price cannot be acquired because some time has passed since the item was sold, the price acquisition unit 18 acquires the used price. In this case, the price acquisition unit 18 may acquire the used price including the condition of the item.

[0069] The expected selling price calculation unit 19 calculates the expected price when the belongings are sold based on the detailed information and the prices of items of the same type or similar to the belongings. For example, the expected selling price calculation unit 19 calculates the expected price by multiplying the new price by a predetermined coefficient. For example, the expected selling price calculation unit 19 sets a coefficient of 90% for new items, 80% for used items, 50% for items with no noticeable stains, and 20% for items with stains. For example, suppose the new price of an item of the same type as the belongings is 10,000 yen, and the belongings are stained. Since 10,000 yen x 20% = 2,000 yen, the expected selling price calculation unit 19 calculates the expected price of the belongings to be 2,000 yen.

[0070] If the belongings are rare, the expected selling price calculation unit 19 may set a coefficient several times higher than when the belongings are not rare. Also, if only second-hand prices are available on the Internet and the belongings are new, the expected selling price calculation unit 19 may calculate the expected price by back-calculating the above ratio from the condition of the belongings.

[0071] The evaluation unit 20 evaluates whether or not the belongings should be disposed of based on the favorability rating and the popularity corresponding to the belongings. For example, the evaluation unit 20 outputs a disposal recommendation evaluation value that numerically represents whether or not the belongings should be disposed of as the evaluation result. For example, the disposal recommendation evaluation value has a minimum value of 0 (should be disposed of) and a maximum value of 100 (should be kept).

[0072] Specifically, the evaluation unit 20 first expresses the popularity rating of the belongings set by the popularity rating setting unit 14 as a numerical value. The evaluation unit 20 also expresses the popularity rating corresponding to the belongings estimated by the popularity rating estimation unit 16 as a percentage. The evaluation unit 20 calculates a disposal recommendation evaluation value by multiplying the popularity rating by the popularity rating. For example, assume that the popularity rating is 100 and the popularity rating is 80. Since 100×80[%]=80, the evaluation unit 20 outputs 80 as the disposal recommendation evaluation value. In this way, the evaluation unit 20 can express the value of the belongings using a numerical value based on the popularity rating and popularity rating.

[0073] For example, if the user's favorability rating for a personal item is high and the corresponding popularity rating for the personal item is also high, the evaluation unit 20 outputs a high disposal recommendation evaluation value. In this case, the user can easily decide not to dispose of the personal item. Also, for example, if the user's favorability rating for a personal item is high but the corresponding popularity rating for the personal item is low, the evaluation unit 20 outputs a lower disposal recommendation evaluation value than when both the favorability rating and the popularity rating are high. In this case, the user can easily decide to dispose of the personal item.

[0074] Instead of outputting a disposal recommendation evaluation value, the evaluation unit 20 may output display information indicating whether or not the item should be disposed of. For example, the evaluation unit 20 may output either "should be disposed of" or "should continue to hold" as the evaluation result. The evaluation unit 20 may also output any one of three or more levels of evaluation. For example, the evaluation unit 20 may output any one of five levels of evaluation: "should be disposed of," "better to dispose of," "neither," "better to continue to hold," and "should continue to hold."

[0075] The evaluation unit 20 generates display information recommending the disposal of the belongings when the disposal recommendation evaluation value is less than a predetermined value (for example, 50), and generates display information urging the user to keep the belongings when the disposal recommendation evaluation value is equal to or greater than the predetermined value. The evaluation unit 20 outputs the generated display information.

[0076] For example, the evaluation unit 20 displays the evaluation result on the display unit 3. This allows the user to understand the evaluation result, and therefore the user can use the evaluation result to decide whether or not to dispose of the belongings.

[0077] The evaluation unit 20 may store the evaluation results in the storage unit 6. This allows the evaluation unit 20 to compare evaluation results obtained at different times. The evaluation unit 20 may output changes in the evaluation results over time using past evaluation results.

[0078] The evaluation unit 20 may also transmit the evaluation result to a device other than the evaluation device 100. For example, the evaluation unit 20 may transmit the evaluation result to a related party terminal used by a related party of the user. In this way, the related party can understand the evaluation result. As a result, when the user is unable to decide how to dispose of his / her belongings, the related party can make a decision on behalf of the user.

[0079] Furthermore, the evaluation unit 20 may adjust the evaluation result when there is a difference of a predetermined value or more between the price of an item of the same type or similar to the owned item acquired by the price acquisition unit 18 and the estimated price obtained by the estimated selling price calculation unit 19. For example, the evaluation unit 20 adjusts the evaluation result by increasing or decreasing the disposal recommendation evaluation value.

[0080] For example, if the expected price of the belongings is equal to or higher than the price of new items, it is assumed that the user would benefit more from selling the belongings. In such a case, the evaluation unit 20 adjusts the disposal recommendation evaluation value so that the disposal recommendation evaluation value becomes smaller than before the adjustment. For example, the evaluation unit 20 adjusts the disposal recommendation evaluation value so that the disposal recommendation evaluation value becomes half.

[0081] Furthermore, if the estimated price of the belongings is less than a predetermined percentage of the new price, it is assumed that the value of the belongings is small, and therefore the user will not gain much benefit from keeping the belongings. In such cases, the evaluation unit 20 also adjusts the disposal recommendation evaluation value so that the disposal recommendation evaluation value becomes smaller than before the adjustment. For example, if the estimated price is less than one-third of the new price, the evaluation unit 20 adjusts the disposal recommendation evaluation value so that the disposal recommendation evaluation is halved.

[0082] These adjustment methods are merely examples, and the evaluation unit 20 may adjust the disposal recommendation evaluation value using other methods. For example, even if the profit from selling the belongings is large, whether or not to sell the belongings ultimately depends on the user's thoughts. Therefore, the evaluation unit 20 may be able to arbitrarily adjust the disposal recommendation evaluation value.

[0083] The registration unit 21 registers the belongings that have been evaluated by the evaluation unit 20 as items to be disposed of in a viewing list that can be viewed by people other than the user. The viewing list is provided on a screen that can be viewed by other users, for example, in a flea market app, a website for trading unwanted items, or an internet auction site. The viewing list is provided on a sales screen for selling belongings in, for example, a flea market app.

[0084] The registration unit 21 may use the disposal recommendation evaluation value to determine whether to register the belongings in the viewing list. For example, the registration unit 21 registers the belongings in the viewing list when the disposal recommendation evaluation value is less than a predetermined value. The registration unit 21 may also generate an explanatory text for selling the belongings. The registration unit 21 generates an explanatory text including characteristics of the belongings, such as the type of the belongings. The registration unit 21 registers the explanatory text and an image of the belongings in the viewing list. In this way, the user can easily perform the process of selling the belongings.

[0085] The registration unit 21 may generate a proposed description to be registered in the viewing list and display the proposed description on a display screen that can be confirmed by the user. In this way, the user can confirm the proposed description, modify it as necessary, and register it in the viewing list. Also, even when input of other items (e.g., desired price, etc.) is required, the burden on the user can be reduced by having the registration unit 21 generate a proposed description.

[0086] The configuration of the evaluation device 100 has been described above. The configuration of the evaluation device 100 described above is merely an example and may be modified as appropriate. For example, when some or all of the components of the evaluation device 100 are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, in a form in which each is connected via a communication network. For example, the functions of the control unit 10 may be provided in a SaaS (Software as a Service) format.

[0087] For example, in the above description, a user terminal such as a smartphone is used as an example of the evaluation device 100, but this is not limiting. The evaluation device 100 may be realized by, for example, a predetermined server. For example, the server has the functions of the control unit 10 described above. The server acquires biometric information, images of belongings, detailed information about the belongings, etc. from the user terminal via a network. The server performs the processing of the control unit 10 described above using the acquired data, and transmits an evaluation result indicating whether the belongings should be disposed of to the user terminal. In this way, the processing load on the user terminal can be reduced.

[0088] Furthermore, for example, in the above description, an example has been described in which the evaluation device 100 includes the photographing unit 1 and the biosensor 2, but the photographing unit 1 and the biosensor 2 may be provided as devices separate from the evaluation device 100.

[0089] (Processing of evaluation device 100) Next, the processing performed by the evaluation device 100 will be described with reference to Fig. 5 to Fig. 8. Fig. 5 is a flowchart showing the processing by the evaluation device 100 to acquire the user's state under normal circumstances. Figs. 6 and 7 are flowcharts showing the processing by the evaluation device 100. Fig. 8 is a flowchart showing the processing by the evaluation device 100 when adjusting the evaluation result.

[0090] First, the process of acquiring the user's state under normal circumstances will be described with reference to Fig. 5. The user performs this process at a time when their emotional changes are relatively small.

[0091] First, when a user photographs their belongings using the rear camera 1a, the image acquisition unit 11 acquires the image of the belongings captured by the rear camera 1a (S1). Next, the user state acquisition unit 13 acquires the user's biometric information (S2). For example, the user state acquisition unit 13 acquires a facial image of the user photographed by the front camera 1b as biometric information. The user state acquisition unit 13 may acquire a pupil image from the facial image. The user state acquisition unit 13 may also acquire the heart rate detected by the biometric sensor 2 as biometric information. Alternatively, the user state acquisition unit 13 may acquire biometric information from a wearable device or the like that communicates with the evaluation device 100.

[0092] Next, the user state acquisition unit 13 acquires the user's state based on the biometric information (S3). For example, the user state acquisition unit 13 identifies the user's state in normal times based on the user's pupil size, facial expression, and heart rate. The user state acquisition unit 13 stores the acquired user state in the storage unit 6 as the user's state in normal times (S4). The user state acquisition unit 13 may associate the user's belongings image with the user's state and store this information.

[0093] In this process, the detailed information acquiring unit 17 may acquire detailed information about the belongings. For example, the detailed information acquiring unit 17 acquires detailed information such as a model number from the user via the input unit 4, and stores the detailed information in the storage unit 6 in association with the belongings image. This reduces the burden on the user of inputting detailed information at a later time.

[0094] In addition, although an example has been described in which an image of belongings is acquired and stored in the storage unit 6 together with the user's normal state, the present invention is not limited to this. The user state acquisition unit 13 may simply store the user's normal state.

[0095] Next, the processing of the evaluation device 100 will be described with reference to Fig. 6 and Fig. 7. For example, the user performs this processing after a predetermined period of time has elapsed since performing the processing shown in Fig. 5. The user performs this processing when he or she wants to consider disposing of his or her belongings, for example, during a general cleaning.

[0096] First, the image acquisition unit 11 acquires an image of belongings captured by the rear camera 1a (S11). Next, the user status acquisition unit 13 acquires biometric information of the user (S12). For example, the user status acquisition unit 13 acquires an image of the user's face captured by the front camera 1b at the same time as the image captured by the rear camera 1a. The user status acquisition unit 13 may also acquire a pupil image from the face image. The user status acquisition unit 13 may also acquire biometric information detected by the biometric sensor 2.

[0097] Next, the user state acquisition unit 13 acquires the user's state based on the biometric information (S13). The user state acquisition unit 13 identifies the user's state based on the acquired biometric information. The user state acquisition unit 13 identifies, for example, pupil size, facial expression, and heart rate, as in normal times.

[0098] Next, the likeability setting unit 14 sets the user's likeability rating for the belongings (S14). The likeability setting unit 14 compares the user's state in normal times with the user's state at the time the belongings were photographed, and sets the likeability rating based on the comparison result.

[0099] For example, the more dilated the pupils are at the time when the photograph of the belongings is taken compared to normal, the higher the likeability setting unit 14 sets the likeability. Also, if the facial expression at the time when the photograph of the belongings is taken is more positive than normal, the likeability setting unit 14 sets a higher likeability than if the facial expression is negative. Furthermore, if the heart rate at the time when the photograph of the belongings is taken compared to normal, the likeability setting unit 14 sets a higher likeability than if the heart rate is lower.

[0100] The likeability rating setting unit 14 sets likeability ratings for pupil size, facial expression, and heart rate, and calculates the total value of each likeability rating. The likeability rating setting unit 14 sets the total value as the likeability rating at the time the photograph of the belongings is taken. Note that the likeability rating setting unit 14 may accept input from the user via the input unit 4 and set the likeability rating according to the input content.

[0101] Next, the feature extraction unit 12 extracts features of the belongings from the belongings image (S15). For example, the feature extraction unit 12 extracts features related to the appearance, function, performance, material, durability, etc. of the belongings.

[0102] Next, the statistical information acquisition unit 15 acquires statistical information indicating the popularity of items of the same type as the personal item (S16). For example, if the personal item is a bag, the statistical information acquisition unit 15 acquires statistical information indicating the popularity of bags from the Internet or the like. The statistical information acquisition unit 15 acquires images of the bag from, for example, the most recent three years, extracts features of the bag from the acquired images using image analysis, and acquires statistical information. The acquired statistical information is stored in the storage unit 6.

[0103] Next, the popularity estimation unit 16 estimates the popularity of the belongings based on the characteristics of the belongings and the statistical information (S17). For example, the popularity estimation unit 16 analyzes the input image of the belongings and extracts the characteristics of the belongings. The popularity estimation unit 16 outputs a higher popularity as the characteristics of the belongings are higher in the statistical information stored in the memory unit 6.

[0104] Next, the evaluation unit 20 evaluates whether or not the belongings should be disposed of based on the favorability rating and the popularity level corresponding to the belongings (S18). For example, the evaluation unit 20 outputs a disposal recommendation evaluation value that numerically represents whether or not the belongings should be disposed of as the evaluation result. For example, the evaluation unit 20 outputs the disposal recommendation evaluation value with 0 as the minimum value (should be disposed of) and 100 as the maximum value (should be kept). The evaluation unit 20 may display the disposal recommendation evaluation value on the display unit 3. The evaluation unit 20 adjusts the evaluation result as necessary (S19). The adjustment of the evaluation result will be described later.

[0105] The explanation will be continued with reference to Fig. 7. The evaluation unit 20 determines whether or not the user himself / herself will make the decision on whether or not to dispose of the belongings (S20). For example, if the user himself / herself is able to make the decision, the user himself / herself makes the decision, but if the user himself / herself has difficulty making the decision due to illness, injury, etc., someone other than the user (for example, a family member) makes the decision.

[0106] The evaluation unit 20 displays a message such as "Are you the owner?" on the display unit 3, for example, and receives input from the person operating the evaluation device 100. When a person other than the user makes the decision (NO in S20), the evaluation unit 20 determines whether the user's favorability rating for the belongings is equal to or greater than a predetermined value (S21). When the favorability rating is equal to or greater than the predetermined value (YES in S21), the evaluation unit 20 evaluates that the user should continue to own the belongings (S24).

[0107] In this way, the evaluation unit 20 can avoid recommending the disposal of belongings that the user has a high preference for, regardless of their popularity or expected price, when the user has difficulty making a decision, etc. In this way, the user's family and others can continue to hold the user's belongings as mementos.

[0108] If it is determined that the favorability rating is less than a predetermined value (NO in S21) or if the user himself / herself makes the decision (YES in S20), the evaluation unit 20 determines whether the disposal recommendation evaluation value is less than a predetermined value (S22).

[0109] If it is determined that the disposal recommendation evaluation value is less than a predetermined value (YES in S22), the evaluation unit 20 evaluates that the belongings should be disposed of (S23). If it is determined that the disposal recommendation evaluation value is equal to or greater than a predetermined value (NO in S22), the evaluation unit 20 evaluates that the belongings should be continued to be held (S24). When the user himself / herself makes the decision, the user can decide to dispose of the belongings of his / her own will, taking into account the evaluation result of the evaluation device 100. Therefore, the evaluation unit 20 may recommend disposing of the belongings based on the popularity or expected price, for example, even if the user himself / herself has the highest favorability rating for the belongings.

[0110] In this way, the evaluation device 100 can make different evaluations depending on whether the person making the decision on disposal is the user himself or herself or someone other than the user. This makes it possible to avoid disposing of belongings against the user's will even when the user has difficulty making a decision.

[0111] Next, the registration unit 21 registers the belongings in a viewing list (S25). The viewing list is provided on a screen that can be viewed by other users, for example, in a flea market app or the like. The registration unit 21 generates an explanation for selling the belongings and registers the explanation in the viewing list. This allows the user to easily sell the belongings.

[0112] The process of step S19 for adjusting the evaluation result will be described in detail with reference to Fig. 8. First, the detailed information acquisition unit 17 acquires detailed information about the belongings (S191). The detailed information includes at least the type of the belongings. The detailed information may also include the model number of the belongings, characteristics of their appearance, or their condition (new, used, no noticeable stains, stained, etc.).

[0113] Next, the price acquisition unit 18 acquires the prices of products of the same model or similar to the owned item (S192). The price acquisition unit 18 acquires the new prices of products of the same model or similar to the owned item from the Internet, for example, based on the model number of the owned item.

[0114] Next, the expected selling price calculation unit 19 calculates the expected price when the belongings are sold based on the detailed information and the price of the same type or similar items to the belongings (S193). For example, the expected selling price calculation unit 19 calculates the expected price by multiplying the new item price by a predetermined coefficient.

[0115] Next, the evaluation unit 20 determines whether there is a difference of a predetermined amount or more between the price of an item of the same type or similar to the owned item and the estimated price (S194). If it is determined that there is no difference of a predetermined amount or more (NO in S194), the evaluation unit 20 terminates the processing. If it is determined that there is a difference of a predetermined amount or more (YES in S194), the evaluation unit 20 adjusts the disposal recommendation evaluation value so that it is smaller than the value before adjustment (S195). For example, if the estimated price of the owned item is equal to or higher than the price of a new item, the evaluation unit 20 adjusts the disposal recommendation evaluation value so that it is half the value. Furthermore, if the estimated price is equal to or lower than one-third of the price of a new item, the evaluation unit 20 adjusts the disposal recommendation evaluation value so that it is half the value.

[0116] 5 to 8 are merely examples, and the order of the steps may be changed as appropriate.

[0117] As described above, the evaluation device 100 according to this embodiment acquires a personal item image of a user's personal items and extracts features of the personal items from the personal item image. The evaluation device 100 also sets the user's favorability rating for the personal items.

[0118] The evaluation device 100 estimates the popularity of the belongings based on the characteristics of the belongings and statistical information indicating the popularity of items of the same type as the belongings, and evaluates whether the belongings should be disposed of based on the user's popularity rating and the estimation result. In this way, the evaluation device 100 can appropriately recommend to the user or a person related to the user to dispose of the belongings, taking into account the user's popularity rating for the belongings.

[0119] Furthermore, the evaluation device 100 can acquire the user's state based on the user's biometric information, compare the user's state under normal circumstances with the state of the user at the time the photograph of the user's belongings was taken, and set the likeability rating based on the comparison result. In this way, even if the user has difficulty inputting the likeability rating due to illness, injury, etc., the evaluation device 100 can appropriately set the likeability rating.

[0120] The evaluation device 100 also acquires detailed information about the belongings and, based on the detailed information, acquires the price of an identical or similar item to the belongings. The evaluation device 100 calculates the expected price if the belongings are sold based on the detailed information and the price, and adjusts the evaluation result if there is a difference between the price and the expected price that is greater than a predetermined value. In this way, the evaluation device 100 can appropriately evaluate whether or not to dispose of the belongings based on the expected price, etc.

[0121] Furthermore, the evaluation device 100 registers the belongings that have been evaluated as those to be disposed of in a viewing list that can be viewed by people other than the user. In this way, the evaluation device 100 can reduce the burden on the user when selling the belongings on a flea market app or the like.

[0122] As people get older, it becomes more difficult to quickly decide whether or not to dispose of their belongings, and they may not be able to dispose of their accumulated belongings at the appropriate time. The evaluation device 100 can appropriately support the disposal of belongings, and can therefore support, for example, elderly users in their end-of-life preparations. This allows the evaluation device 100 to improve the user's quality of life (QOL) in their retirement years. Furthermore, even when the user has difficulty making a decision on their own, the evaluation device 100 can be used to provide an objective evaluation, allowing the user's family or other relatives to consider disposing of their belongings on behalf of the user. Furthermore, the evaluation device 100 can appropriately support the organization of belongings of users of various ages, not just the elderly.

[0123] Each functional component of the evaluation device 100 described above may be realized by hardware (e.g., a hardwired electronic circuit, etc.) that realizes each functional component, or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuit and a program that controls it, etc.). For example, any process in the present disclosure can be realized by having a CPU execute a computer program.

[0124] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on various types of non-transitory computer-readable medium or tangible storage medium. By way of example and not limitation, non-transitory computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on various types of transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0125] The present disclosure is not limited to the above-described embodiments, and can be modified as appropriate within the scope of the present disclosure. [Explanation of symbols]

[0126] 1. Filming Department 1a Rear camera 1b Front camera 2. Biometric sensors 3 Display section 4 Input section 5. Communications Department 6 Memory section 10 Control Unit 11 Image acquisition unit 12 Feature Extraction Unit 13 User status acquisition unit 14 Likeability setting section 15 Statistical information acquisition section 16 Prevalence Estimation Department 17 Detailed information acquisition section 18 Price Acquisition Department 19 Estimated Selling Price Calculation Section 20 Evaluation Department 21 Registration Department 41 Likeability input screen 41a Inventory Image Area 41b Likeability input area 100 Evaluation Device SW sweater

Claims

1. an image acquisition unit that acquires a belongings image of the user; a feature extraction unit that extracts features of the belongings from the belongings image; a likeability setting unit that sets a likeability rating of the user for the belongings; a statistical information acquisition unit that acquires statistical information indicating the popularity of items of the same type as the personal belongings; a popularity estimation unit that estimates a popularity corresponding to the belongings based on the characteristics of the belongings and the statistical information; and an evaluation unit that evaluates whether the belongings should be disposed of based on the favorability rating and the popularity corresponding to the belongings. Evaluation equipment.

2. a user status acquisition unit that acquires a status of the user based on biometric information of the user; The likeability rating setting unit compares the user's state in normal times with the user's state at the time when the user's belongings are photographed, and sets the likeability rating based on the comparison result. The evaluation device according to claim 1 .

3. a detailed information acquisition unit that acquires detailed information about the belongings; a price acquisition unit that acquires the price of an item of the same type or similar to the owned item based on the detailed information; and an expected selling price calculation unit that calculates an expected price when the belongings are sold based on the detailed information and the price, The evaluation unit adjusts the evaluation result when there is a difference of a predetermined value or more between the price and the predicted price. The evaluation device according to claim 1 or 2.

4. The personal belongings evaluated by the evaluation unit as being to be disposed of are further included in a registration unit that registers the personal belongings in a viewing list that can be viewed by people other than the user. The evaluation device according to claim 1 or 2.

5. an image acquisition step of acquiring a belongings image of the user; a feature extraction step of extracting features of the belongings from the belongings image; a preference level setting step of setting a preference level of the user for the belongings; a statistical information acquisition step of acquiring statistical information indicating a popularity status of items of the same type as the belongings; a popularity estimation step of estimating a popularity corresponding to the belongings based on the characteristics of the belongings and the statistical information; and an evaluation step of evaluating whether or not the belongings should be disposed of based on the favorability rating and the popularity corresponding to the belongings. Evaluation method.

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