JP7516759B2 - Processing device, processing method, and program - Google Patents

Description

The present invention relates to a processing device, a processing method, and a program.

Patent Document 1 discloses a system that determines whether a customer is a VIP customer based on IC tag information obtained from the IC tag held by the customer, and when it detects that a VIP customer has visited the store, displays information indicating that fact to store staff.

Patent document 2 discloses a system that determines whether a customer is a good customer or not based on customer identification information obtained from a terminal held by the customer and a customer database that indicates whether each customer is a good customer or not, and notifies a store clerk when it detects that a good customer has visited the store.

JP 2019-185715 A JP 2018-45430 A

Providing services suited to each customer (VIP service, support for the elderly and disabled, etc.) is expected to increase sales at the store. Based on the technology disclosed in Patent Documents 1 and 2, it is possible to detect when a VIP customer or a valued customer visits the store. As a result, the store staff will be able to provide appropriate customer service to that customer.

However, the technology disclosed in Patent Documents 1 and 2 is based on the premise that VIP customers and valued customers are determined in advance based on each customer's purchase history, etc., and information indicating the determination is registered in a database or on each customer's IC tag. With this technology, it is not possible to detect "customers who need to be provided with services suited to them" among customers who visit the store for the first time.

The objective of the present invention is to detect "customers who need to be provided with services suited to them" among customers who visit a store for the first time.

According to the present invention,
An acquisition means for acquiring images captured inside the store;
image analysis means for generating possession-related information regarding the possessions of the customer based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
A processing apparatus is provided having:

Further, according to the present invention,
The computer
Acquire images taken inside the store,
generating item-related information about the customer's items based on the images;
determining whether a service appropriate for the customer is required based on the belongings-related information;
A processing method is provided for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer.

Further, according to the present invention,
Computer,
An acquisition means for acquiring images taken inside the store;
image analysis means for generating possession related information regarding the customer's possessions based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
A program is provided to function as a

According to the present invention, it is possible to detect "customers who need to be provided with services suited to them" among customers who visit a store for the first time.

FIG. 2 is a diagram for explaining an example of use of the processing apparatus of the present embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of a processing device according to the present embodiment. FIG. 2 is a functional block diagram of a processing apparatus according to an embodiment of the present invention; FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. 4 is a flowchart showing an example of a processing flow of the processing device of the present embodiment. FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. 4 is a flowchart showing an example of a processing flow of the processing device of the present embodiment. FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. 4 is a flowchart showing an example of a processing flow of the processing device of the present embodiment. FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. 4 is a flowchart showing an example of a processing flow of the processing device of the present embodiment. FIG. 2 is a diagram illustrating an example of information processed by the processing device of the present embodiment. 4 is a flowchart showing an example of a processing flow of the processing device of the present embodiment. FIG. 2 is a diagram for explaining an example of use of the processing apparatus of the present embodiment.

First Embodiment
The processing device of this embodiment generates information about the belongings of a visiting customer based on the image, and judges whether the customer needs appropriate services (VIP treatment, support for elderly people with disabilities, etc.) based on the information. The details will be described below.

First, an overview of the processing device 10 will be described using Figure 1. The processing device 10 is a server (e.g., a cloud server) and communicates with the store system 20 of each store via a communication network 30 such as the Internet. The store system 20 transmits images generated by cameras installed in or around the store to the processing device 10. The processing device 10 performs a predetermined calculation process based on the images received from each store system 20, and returns the calculation results to each store system 20. The store system 20 displays the received calculation results on a display or transmits them to a predetermined mobile device.

Next, an example of the hardware configuration of the processing device 10 will be described. The functional parts of the processing device 10 of this embodiment are realized by any combination of hardware and software, centered on the CPU (Central Processing Unit) of any computer, memory, programs loaded into the memory, a storage unit such as a hard disk that stores the programs (which can store programs that are stored before the device is shipped, as well as programs downloaded from storage media such as CDs (Compact Discs) or servers on the Internet), and a network connection interface. Those skilled in the art will understand that there are many variations in the methods and devices for implementing these.

Figure 2 is a block diagram illustrating the hardware configuration of the processing device 10 of this embodiment. As shown in Figure 2, the processing device 10 has a processor 1A, a memory 2A, an input/output interface 3A, a peripheral circuit 4A, and a bus 5A. The peripheral circuit 4A includes various modules. Note that the peripheral circuit 4A is not required. Note that the processing device 10 may be configured as a single device that is physically and/or logically integrated, or may be configured as multiple devices that are physically and/or logically separated. When configured as multiple devices that are physically and/or logically separated, each of the multiple devices can have the above hardware configuration.

The bus 5A is a data transmission path for the processor 1A, memory 2A, peripheral circuit 4A, and input/output interface 3A to send and receive data to each other. The processor 1A is, for example, a processing device such as a CPU or a GPU (Graphics Processing Unit). The memory 2A is, for example, a memory such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The input/output interface 3A includes an interface for acquiring information from an input device, an external device, an external server, an external sensor, a camera, etc., and an interface for outputting information to an output device, an external device, an external server, etc. Examples of the input device are a keyboard, a mouse, a microphone, a touch panel, a physical button, a camera, etc. Examples of the output device are a display, a speaker, a printer, a mailer, etc. The processor 1A can issue commands to each module and perform calculations based on the results of those calculations.

Next, the functional configuration of the processing device 10 will be described. FIG. 3 shows an example of a functional block diagram of the processing device 10. As shown in the figure, the processing device 10 has an acquisition unit 11, an image analysis unit 12, a judgment unit 13, an output unit 14, and a memory unit 15. Note that the processing device 10 does not need to have the memory unit 15. In this case, an external device configured to be able to communicate with the processing device 10 has the memory unit 15.

The acquisition unit 11 acquires images taken inside the store. The acquisition unit 11 acquires images sent by each store system 20. The acquisition unit 11 also acquires information that identifies the store system 20 that sent each image.

Each store is equipped with one or more cameras. The cameras are installed at any location inside the store or in its surroundings (such as the parking lot) and capture video images. The store system 20 acquires the images generated by the cameras through real-time processing and transmits them to the processing device 10 through real-time processing.

In this specification, "acquisition" may include "the device itself going to retrieve data stored in another device or storage medium (active acquisition)" based on user input or program instructions, such as receiving data by making a request or inquiry to another device, or accessing and reading out another device or storage medium. Also, "acquisition" may include "inputting data output from another device to the device itself (passive acquisition)" based on user input or program instructions, such as receiving data that is distributed (or transmitted, push notification, etc.). Also, "acquisition" may include selecting and acquiring data or information received, and "generating new data by editing data (converting to text, rearranging data, extracting some data, changing the file format, etc.), and acquiring the new data."

The image analysis unit 12 analyzes the image (hereinafter, sometimes simply referred to as "image") acquired by the acquisition unit 11. For example, the image analysis unit 12 extracts a customer (person) from the image. Then, the image analysis unit 12 issues in-store person identification information corresponding to the extracted customer, and tracks the customer within the image.

The image analysis unit 12 also generates belongings-related information about the extracted belongings of the customer. The image analysis unit 12 then links the belongings-related information of the customer to the in-store personal identification information of the customer and stores the belongings-related information of the customer in the storage unit 15.

Figure 4 shows a schematic example of personal belongings-related information generated by the image analysis unit 12. The personal belongings-related information shown in the figure includes image analysis results and calculation results.

The results of image analysis are information that can be extracted from the appearance of belongings contained in the image, such as, but not limited to, type (bag, accessories, watch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc.

The calculation result indicates the luxury level of each belonging calculated based on the image analysis result. The more valuable (expensive) the belonging, the higher the luxury level. For example, a database may be created in advance that indicates the luxury level, type, and external characteristics (shape, color, pattern, logo, material, etc.) of each of a number of products. The image analysis unit 12 may then calculate the luxury level of each belonging based on the image analysis result and the database. The luxury level may be indicated by a number of ranks, such as "S, A, B, C (S is the most luxurious, and A, B, C are the less luxurious)," or may be indicated by a binary value indicating whether the item is luxurious or not. Alternatively, the luxury level may be indicated numerically, such as "a value from 0 to 100 (the higher the value, the more luxurious)."

As shown in FIG. 4, the image analysis unit 12 generates personal item-related information for each customer extracted from the image. Also, as shown in FIG. 4, the image analysis unit 12 generates personal item-related information for each customer extracted from the image.

Returning to FIG. 3, the judgment unit 13 judges whether or not a service suitable for the customer is required based on the belongings-related information. For example, the judgment unit 13 may judge a customer who has belongings whose luxury level is equal to or higher than a first reference level as a "customer who needs to be provided with a service suitable for the customer." Alternatively, the judgment unit 13 may judge a customer who has a predetermined number of belongings whose luxury level is equal to or higher than a second reference level as a "customer who needs to be provided with a service suitable for the customer." Alternatively, the judgment unit 13 may calculate a score for each customer using a predetermined calculation formula based on the luxury level of each customer's belongings (for example, the more high-quality belongings a customer has, the higher the score), and judge a customer whose calculated score is equal to or higher than a reference value as a "customer who needs to be provided with a service suitable for the customer."

The judgment unit 13 may judge whether or not a customer needs to be provided with a service suited to the customer based on judgment criteria corresponding to each store. In this way, for example, the criteria for judging a customer as "a customer who needs to be provided with a service suited to the customer" can be different for a store that handles luxury goods (high-priced items) and a store that handles other items.

The output unit 14 outputs customer identification information to identify customers who are determined to require the provision of services suited to them. The output customer identification information is sent to each store system 20. The store system 20 may display the customer identification information via any display viewable by the store clerk, may display the customer identification information on the store clerk's mobile device, or may output the customer identification information via a printer.

The customer identification information includes at least one of the following: characteristics of the customer estimated based on the image, characteristics of the customer's belongings identified based on the image, and an image of the customer.

For example, the output unit 14 may output an image that includes a "customer who needs to be provided with services suited to the customer" from among the images acquired by the acquisition unit 11 as customer identification information. In this case, the store clerk can easily recognize the customer included in the image as a "customer who needs to be provided with services suited to the customer."

However, from the perspective of privacy protection, etc., it may not be desirable to use a customer's image without permission. Therefore, the output unit 14 may output the customer's characteristics (age, sex, build, etc.) estimated based on the image, and the characteristics of the customer's belongings identified based on the image, as customer identification information. In this example, the customer identification information would be, for example, "a woman in her 30s wearing a red coat."

Next, an example of the processing flow of the processing device 10 will be described using the flowchart in FIG.

When the acquisition unit 11 acquires an image sent from a certain store system 20 as a processing target (S100), the image analysis unit 12 performs a process to extract a customer (person) from the processing target image (S101). If a customer is not extracted from the processing target image (No in S101), the process proceeds to S111. On the other hand, if a customer is extracted from the processing target image (Yes in S101), the image analysis unit 12 designates one of the extracted customers as the processing target (S102).

If the specified customer is not a customer currently being tracked (No in S103), the image analysis unit 12 starts tracking (S105). For example, the image analysis unit 12 issues new in-store person identification information corresponding to the customer and stores it in the storage unit 15. Then, the process proceeds to S106.

On the other hand, if the specified customer is a customer being tracked (Yes in S103), the image analysis unit 12 determines whether the customer has been certified as a "customer who needs to be provided with services suited to the customer" (S104). If the customer has been certified (Yes in S104), the process proceeds to S110. On the other hand, if the customer has not been certified (No in S104), the process proceeds to S106, where a belongings determination is performed. If the customer is being tracked, the belongings of the customer have been determined at least once before. By repeatedly performing the belongings determination in this manner, it becomes possible to detect new belongings that have not been detected previously due to the way they are reflected in the image.

In S106, the image analysis unit 12 generates personal item-related information (see FIG. 4) based on the image, links it to the customer's in-store personal identification information, and stores it in the memory unit 15. That is, the image analysis unit 12 analyzes the image to extract the customer's personal items, and extracts information from the appearance of each of the extracted items, such as type (bag, accessories, wristwatch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc. Then, the image analysis unit 12 calculates the luxury level of each item based on the extraction results.

Next, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information (S107).

If the customer is determined to be a "customer who needs to be provided with services suited to the customer" (Yes in S108), the output unit 14 outputs customer identification information for identifying the customer (S109). Specifically, the output unit 14 transmits the customer identification information to the store system 20 that transmitted the image. The store system 20 may display the customer identification information via any display that can be viewed by the store clerk, may display the customer identification information on the store clerk's mobile terminal, or may output the customer identification information via a printer. Note that if the customer is determined to be a "customer who needs to be provided with services suited to the customer," the judgment unit 13 may link the customer to the in-store personal identification information of the customer and store this in the memory unit 15. For example, the image analysis unit 12 may make the judgment of S104 based on the information.

On the other hand, if the customer is not determined to be a "customer who needs to be provided with services suited to the customer" (No in S108), the output unit 14 does not output the customer-specific information of the customer.

In S110, the image analysis unit 12 determines whether there are any customers not specified in S102 among the customers extracted from the image. If there are (Yes in S110), the image analysis unit 12 returns to S102 and repeats the same process. On the other hand, if there are no customers (No in S110) and there is no input to end the process (No in S111), the image analysis unit 12 returns to S100 and repeats the same process.

Next, the effects of this embodiment will be described. The processing device 10 of this embodiment generates information about the belongings of customers who visit the store based on images, and judges whether each customer is a "customer who needs to be provided with services suited to the customer" (a customer who is likely to make a purchase, a customer who is likely to spend a large amount, etc.) based on this information. With this type of processing device 10, it is possible to detect "customers who need to be provided with services suited to the customer" that are included among customers who visit the store for the first time.

The processing device 10 can also output customer identification information to identify customers who are determined to be "customers who need to be provided with services suited to the customer." Based on this customer identification information, store staff can identify "customers who need to be provided with services suited to the customer" among the customers in the store.

The processing device 10 can also output the characteristics of the customer estimated based on the image and the characteristics of the customer's belongings identified based on the image as customer identification information for identifying "customers who need to be provided with services suited to them." In this case, there is no need to use the customer's image as information for identifying "customers who need to be provided with services suited to them," which is preferable from the standpoint of privacy protection, etc.

The processing device 10 can also determine whether a customer is a "customer who needs to be provided with services suited to the customer" based on criteria that correspond to each store. In this way, for example, the criteria for determining whether a customer is a "customer who needs to be provided with services suited to the customer" can be different for a store that handles luxury goods (high-priced items) and a store that handles other items. As a result, it can be appropriately determined for each store whether a customer is a "customer who needs to be provided with services suited to the customer."

Next, a modified example of this embodiment will be described. In this embodiment, as described above, a customer can be tracked within an image, but if tracking is once interrupted and then the customer is detected again within an image, new in-store personal identification information may be issued for the customer as a newly detected customer, and tracking may be continued. In other words, the process may proceed to No in S103, and the process from S105 onwards may be performed.

In addition, the newly detected customer may be determined to be identical to a previously tracked customer based on the customer's appearance features (facial features, features of belongings) and the customer's attributes (age, sex, etc.) estimated from the image. If it is determined that they are identical, tracking may be performed using the information from the previous tracking. That is, in the determination of S103, if the customer specified in S102 is not the customer being tracked at that time, the image analysis unit 12 may determine the identity based on the appearance features, etc. of the customer specified in S102 and the appearance features, etc. of the previously tracked customer. If it is determined that they are identical, tracking of the customer specified in S102 may be resumed by linking the information from the previous tracking. In this case, the process may proceed to Yes in S103 and perform the processes from S104 onwards. Note that when determining identity based on belongings, it may be determined that they are the same customer if a predetermined percentage (90% or more) of the belongings are identical.

Another variation of this embodiment will be described. Up to this point, the judgment unit 13 has judged whether each customer is a "customer who needs to be provided with services suited to the customer" based on the luxury of the customer's belongings. In this way, it has been possible to detect customers who are likely to make purchases, customers who are likely to spend a large amount, etc. as "customers who need to be provided with services suited to the customer."

In a modified example, in addition to or instead of the above-mentioned determination process, the determination unit 13 may determine that "customers whose belongings include a predetermined type of object" are "customers who need to be provided with services suited to the customer." The predetermined type of object is an object that is likely to be possessed by customers who need support, such as the elderly or disabled, and examples of such objects include a cane and a wheelchair. In this way, it becomes possible to detect customers who need support, such as the elderly or disabled, as "customers who need to be provided with services suited to the customer."

Second Embodiment
The processing device 10 of this embodiment generates information on the in-store behavior of customers who visit the store based on the images, and judges whether each customer is a "customer who needs to be provided with services suited to the customer" (a customer who is likely to make a purchase, a customer who is likely to spend a large amount, etc.) based on the generated information and the belongings-related information described in the first embodiment. This will be described in detail below.

The image analysis unit 12 generates behavioral information about the customer's in-store behavior based on the image. The image analysis unit 12 then links the behavioral information of the customer to the in-store personal identification information of the customer and stores the behavioral information of the customer in the storage unit 15.

The behavioral information indicates whether or not a specific behavior that leads to the purchase of a product has been performed. The specific behavior that leads to the purchase of a product may be anything that can be identified from an image, and examples include, but are not limited to, moving around the store along a specific path (such as wandering around the cash register), staying in front of a product for a standard amount of time or more, staying in the store for a standard amount of time or more, staying in front of promotional media (signage, posters, mannequins, etc.) for a standard amount of time or more, stopping in front of promotional media a standard number of times or more, picking up a product, touching a product, putting a product back after picking it up, comparing multiple products, checking price tags, looking around the store while wandering around (looking for a store clerk), operating a smartphone (collecting information on products of interest, consulting family or acquaintances about purchases, etc.), talking to a companion (consulting about a purchase), trying on clothes, etc.

Figure 6 shows a schematic example of behavioral information generated by the image analysis unit 12. As shown in the figure, one or more predetermined behaviors that lead to the purchase of a product are defined in advance, and the image analysis unit 12 may update the behavioral information (register the fact that the behavior has been performed) in response to the detection of each behavior. Furthermore, when the image analysis unit 12 detects a behavior related to a product, such as picking up, comparing, trying on, touching, etc., it may identify the product that was the subject of the behavior based on the external characteristics of the product. The image analysis unit 12 may then register information identifying the product in the behavioral information.

The determination unit 13 determines whether a customer is a "customer who needs to be provided with a service suited to the customer" based on the belongings-related information and the behavioral information. For example, the determination unit 13 may calculate a belongings-related score for each customer using a predetermined calculation formula based on the luxury of each customer's belongings (e.g., the more high-quality belongings a customer has, the higher the score). The determination unit 13 may also calculate a behavior-related score for each customer using a predetermined calculation formula based on whether each customer has performed the above-mentioned predetermined behavior (e.g., the more predetermined behaviors a customer performs, the higher the score). The determination unit 13 may then calculate a total score for each customer using a predetermined calculation formula based on the belongings-related score and the behavior-related score (e.g., the higher the belongings-related score, and the higher the behavior-related score, the higher the score), and determine that a customer whose total score is equal to or higher than a reference value is a "customer who needs to be provided with a service suited to the customer".

A weighting value may be set for each of the above-mentioned predetermined actions that lead to the purchase of a product. The weighting value may then be used to calculate the above-mentioned behavior-related score. In this case, the greater the weighting value of the predetermined action, the higher the behavior-related score will be.

Next, an example of the processing flow of the processing device 10 will be described using the flowchart in FIG.

When the acquisition unit 11 acquires an image sent from a certain store system 20 as a processing target (S200), the image analysis unit 12 performs a process to extract a customer (person) from the processing target image (S201). If a customer is not extracted from the processing target image (No in S201), the process proceeds to S212. On the other hand, if a customer is extracted from the processing target image (Yes in S201), the image analysis unit 12 designates one of the extracted customers as the processing target (S202).

If the specified customer is not a customer currently being tracked (No in S203), the image analysis unit 12 starts tracking (S205). For example, the image analysis unit 12 issues new in-store person identification information corresponding to the customer and stores it in the storage unit 15. Then, the process proceeds to S206.

On the other hand, if the specified customer is a customer being tracked (Yes in S203), the image analysis unit 12 determines whether the customer has been certified as a "customer who needs to be provided with services suited to the customer" (S204). If the customer has been certified (Yes in S204), the process proceeds to S211. On the other hand, if the customer has not been certified (No in S204), the process proceeds to S206, where a belongings determination (S206) and a behavior determination (S207) are performed. If the customer is being tracked, the belongings determination and behavior determination of the customer have been performed at least once before. By repeatedly performing the belongings determination in this way, it becomes possible to newly detect belongings that have not been detected before due to the way they are reflected in the image. Furthermore, by repeatedly performing the behavior determination in this way, it becomes possible to continuously accumulate the behavior details of each customer while they are in the store.

In S206, the image analysis unit 12 generates personal item-related information (see FIG. 4) based on the image, links it to the customer's in-store personal identification information, and stores it in the memory unit 15. That is, the image analysis unit 12 analyzes the image to extract the customer's personal items, and extracts information from the appearance of each of the extracted items, such as type (bag, accessories, wristwatch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc. Then, the image analysis unit 12 calculates the luxury level of each item based on the extraction results.

In S207, the image analysis unit 12 generates behavioral information (see FIG. 6) based on the image, and stores the information in the memory unit 15 in association with the customer's in-store personal identification information. For example, the image analysis unit 12 may detect that a predefined behavior has been performed based on the image, and update the behavioral information (register the performance of the behavior) in response to the detection of each behavior. Furthermore, when the image analysis unit 12 detects a behavior related to a product, such as picking up, comparing, trying on, touching, etc., it may identify the product that was the subject of the behavior based on the external characteristics of the product. The image analysis unit 12 may then register information identifying the product in the behavioral information.

Next, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information and behavioral information (S208).

If the customer is determined to be a "customer who needs to be provided with a service suitable for the customer" (Yes in S209), the output unit 14 outputs customer identification information for identifying the customer (S210). Specifically, the output unit 14 transmits the customer identification information to the store system 20 that transmitted the image. The store system 20 may display the customer identification information via any display that can be viewed by the store clerk, may display the customer identification information on the store clerk's mobile terminal, or may output the customer identification information via a printer. At this time, the output unit 14 may further output details of the predetermined behavior that leads to the purchase performed by the customer. Examples of the outputted details of the predetermined behavior include "picked up product A," "stayed in front of product A for more than five minutes," and "compared product A with product B." If the customer is determined to be a "customer who needs to be provided with a service suitable for the customer," the judgment unit 13 may link the customer to the in-store person identification information of the customer and store that fact in the storage unit 15. For example, the image analysis unit 12 may make the judgment of S204 based on the information.

On the other hand, if the customer is not determined to be a "customer who needs to be provided with services suited to the customer" (No in S209), the output unit 14 does not output the customer-specific information of the customer.

In S211, the image analysis unit 12 determines whether there are any customers not specified in S202 among the customers extracted from the image. If there are (Yes in S211), the image analysis unit 12 returns to S202 and repeats the same process. On the other hand, if there are no customers (No in S211) and there is no input to end the process (No in S212), the image analysis unit 12 returns to S200 and repeats the same process.

The other configurations of the processing device 10 in this embodiment are the same as those in the first embodiment (including modified examples).

As described above, the processing device 10 of this embodiment achieves the same effects as the first embodiment. Furthermore, since it is possible to determine whether each customer is a "customer who needs to be provided with services suited to the customer" based on the customer's behavior within the store in addition to the customer's belongings, the accuracy of the determination is improved.

Here, a modified example of this embodiment will be described. In the flow of FIG. 7, after a customer is recognized as "a customer who needs to be provided with services suited to the customer", the behavior of the customer is not determined (S207). However, even after a customer is recognized as "a customer who needs to be provided with services suited to the customer", the behavior of the customer may be determined (processing similar to S207). Then, the purchase history of the customer at the time of visiting the store may be linked to the behavioral information of the customer and stored in the memory unit 15. The purchase history includes the purchase amount, information on the purchased product (name, price, etc.), etc.

According to this modified example, it is possible to accumulate information indicating the relationship between purchasing history and in-store behavior. Then, the processing device 10 can calculate the relationship between in-store behavior and purchases based on this information. That is, it is possible to calculate which in-store behavior is more likely to lead to purchases (or which leads to higher-value purchases), etc. Then, based on the calculation results, the processing device 10 may set a weighting value for each of the predetermined behaviors that lead to product purchases. The more a predetermined behavior is more likely to lead to purchases (or which leads to higher-value purchases), the higher the weighting value.

Third Embodiment
The processing device 10 of this embodiment stores the past purchase history of each customer in association with the customer identification information. Then, the processing device 10 identifies the customer identification information of the customer who visited the store based on the image. Then, the processing device 10 judges whether each customer is a "customer who needs to be provided with services suited to the customer" (a customer who is likely to make a purchase, a customer who is likely to spend a large amount, etc.) based on the identified customer's past purchase history and the belongings-related information described in the first embodiment. Note that the processing device 10 may further judge whether each customer is a "customer who needs to be provided with services suited to the customer" based on the behavior information described in the second embodiment. This will be described in detail below.

The storage unit 15 stores the past purchase history of each customer in association with the customer identification information. FIG. 8 shows a schematic example of the stored purchase history. In the illustrated example, the purchase history includes the purchase date and time, the purchase amount, and information on the purchased product (name, price, etc.). Furthermore, in the illustrated example, the purchase history includes behavioral information (see the second embodiment) generated at the time of the store visit.

Although not shown, the purchase history may further include statistical results of multiple purchases. The statistical results may include, for example, the number of purchases, the average purchase amount, the maximum purchase amount, and favorite items estimated from past purchases.

Note that the purchase history information shown here is merely an example, and may include other information, or may not include some of the example information.

The image analysis unit 12 identifies the customer identification information of the customer contained in the image based on the image acquired by the acquisition unit 11. For example, the storage unit 15 stores customer information that links at least one of the facial image and appearance features (facial features) of each customer with the customer identification information. The image analysis unit 12 may then identify the customer identification information of the customer contained in the image by matching the facial image of the customer contained in the image or the features extracted from it with the above customer information.

Then, the image analysis unit 12 links at least a portion of the purchase history linked to the identified customer identification information to the in-store identification information of that customer and stores it in the memory unit 15.

Figure 9 shows a schematic example of a purchase history that the image analysis unit 12 has linked to in-store identification information and stored in the storage unit 15. In the example shown, in-store person identification information, customer identification information, and at least a portion of the purchase history (number of purchases, average purchase amount, maximum purchase amount, favorite items, etc.) are registered in a linked manner.

The determination unit 13 determines whether a customer is a "customer who needs to be provided with a service suited to the customer" based on the belongings-related information and the purchase history. For example, the determination unit 13 may calculate a belongings-related score for each customer using a predetermined calculation formula based on the luxury of each customer's belongings (e.g., the more high-quality belongings a customer has, the higher the score). The determination unit 13 may also calculate a purchase history-related score for each customer using a predetermined calculation formula based on the purchase history of each customer (e.g., the more purchases a customer makes, the higher the average purchase amount, and the higher the maximum purchase amount, the higher the score). The determination unit 13 may then calculate a total score for each customer using a predetermined calculation formula based on the belongings-related score and the purchase history-related score (the higher the belongings-related score and the higher the purchase history-related score, the higher the score), and determine that a customer whose total score is equal to or higher than a reference value is a "customer who needs to be provided with a service suited to the customer".

The determination unit 13 may determine whether a customer is a "customer who needs to be provided with a service suited to the customer" based on the belongings-related information, behavioral information, and purchase history. For example, the determination unit 13 may calculate the belongings-related score and the purchase history-related score using the above method. Furthermore, the determination unit 13 may calculate the behavior-related score using the method described in the second embodiment. Then, the determination unit 13 may calculate a total score for each customer using a predetermined calculation formula based on the belongings-related score, the purchase history-related score, and the behavior-related score (the higher the belongings-related score, the higher the purchase history-related score, and the higher the behavior-related score, the higher the score), and determine that a customer whose total score is equal to or higher than a reference value is a "customer who needs to be provided with a service suited to the customer".

Next, an example of the processing flow of the processing device 10 will be described using the flowchart of FIG. 10. Note that, here, an example of processing will be described in which each customer is determined to be a "customer who needs to be provided with services suited to the customer" based on possession-related information, behavioral information, and purchase history, but as described above, the processing device 10 may determine whether each customer is a "customer who needs to be provided with services suited to the customer" based on possession-related information and purchase history without using behavioral information. In this case, the processing of S305 and S309 will be unnecessary.

When the acquisition unit 11 acquires an image sent from a certain store system 20 as a processing target (S300), the image analysis unit 12 performs a process to extract a customer (person) from the processing target image (S301). If a customer is not extracted from the processing target image (No in S301), the process proceeds to S3142. On the other hand, if a customer is extracted from the processing target image (Yes in S301), the image analysis unit 12 designates one of the extracted customers as the processing target (S302).

Then, if the specified customer is not a customer being tracked (No in S303), the image analysis unit 12 starts tracking (S306). For example, the image analysis unit 12 issues new in-store person identification information corresponding to the customer and stores it in the storage unit 15. Next, the image analysis unit 12 identifies the customer identification information of the customer based on the face image of the customer included in the image (S307). Then, the image analysis unit 12 links the identified customer identification information to the in-store person identification information of the customer and stores it in the storage unit 15 (see FIG. 9). In addition, the image analysis unit 12 links at least a part of the purchase history (see FIG. 8) linked to the identified customer identification information to the in-store person identification information of the customer and stores it in the storage unit 15 (see FIG. 9). Note that if the customer identification information is not identified (if the information of the customer is not registered), the image analysis unit 12 does not perform the process of linking the in-store person identification information to the customer identification information and the purchase history and storing them. Then, proceed to S308.

On the other hand, if the specified customer is a customer being tracked (Yes in S304), the image analysis unit 12 determines whether the customer has been certified as a "customer who needs to be provided with services suited to the customer" (S234). If the customer has been certified (Yes in S304), the process proceeds to S305, where a behavioral determination is made, and then the process proceeds to S313. The content of the behavioral determination in S305 is the same as the content of the behavioral determination in S309, which will be described later. In this way, by making a behavioral determination and generating behavioral information even after the customer has been certified as a "customer who needs to be provided with services suited to the customer", it is possible to collect information showing the relationship between the behavioral information and actual purchases.

On the other hand, if the customer has not been certified (No in S304), the process proceeds to S308, where a possessions determination (S308) and behavior determination (S309) are performed. If the customer is being tracked, the possessions determination and behavior determination of that customer have been performed at least once previously. By repeatedly performing possessions determination in this manner, it becomes possible to detect possessions that were not detected previously due to the way they are captured in the image. Furthermore, by repeatedly performing behavior determination in this manner, it becomes possible to continuously accumulate information on the behavior of each customer while they are in the store.

In S308, the image analysis unit 12 generates personal item-related information (see FIG. 4) based on the image, links it to the customer's in-store personal identification information, and stores it in the memory unit 15. That is, the image analysis unit 12 analyzes the image to extract the customer's personal items, and extracts information from the appearance of each of the extracted items, such as type (bag, accessories, wristwatch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc. Then, the image analysis unit 12 calculates the luxury level of each item based on the extraction results.

In S309, the image analysis unit 12 generates behavioral information (see FIG. 6) based on the image, and stores the information in the memory unit 15 in association with the customer's in-store personal identification information. For example, the image analysis unit 12 may detect that a predefined behavior has been performed based on the image, and update the behavioral information (register the performance of the behavior) in response to the detection of each behavior. Furthermore, when the image analysis unit 12 detects a behavior related to a product, such as picking up, comparing, trying on, touching, etc., it may identify the product that was the subject of the behavior based on the external characteristics of the product. The image analysis unit 12 may then register information identifying the product in the behavioral information.

Next, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information, behavioral information, and purchase history (S301). If the purchase history is not stored in association with the in-store person identification information, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information and behavioral information.

If the customer is judged to be a "customer who needs to be provided with a service suited to the customer" (Yes in S311), the output unit 14 outputs customer identification information for identifying the customer (S312). Specifically, the output unit 14 transmits the customer identification information to the store system 20 that transmitted the image. The store system 20 may display the customer identification information via any display that can be viewed by the store clerk, may display the customer identification information on the store clerk's mobile terminal, or may output the customer identification information via a printer. At this time, the output unit 14 may further output details of the predetermined behavior that leads to the purchase performed by the customer. Examples of the outputted details of the predetermined behavior include "picked up product A," "stayed in front of product A for more than 5 minutes," and "compared product A with product B." In addition, the output unit 14 may further output information on the purchase history of the customer (number of purchases, average purchase amount, maximum purchase amount, preference items estimated from past purchases, etc.). If the customer is determined to be a "customer who needs to be provided with services suited to the customer," the determination unit 13 may link the customer's in-store personal identification information and store this information in the storage unit 15. For example, the image analysis unit 12 may make the determination of S304 based on this information.

On the other hand, if the customer is not determined to be a "customer who needs to be provided with services suited to the customer" (No in S312), the output unit 14 does not output the customer-specific information of the customer.

In S313, the image analysis unit 12 determines whether there are any customers not specified in S302 among the customers extracted from the image. If there are (Yes in S313), the image analysis unit 12 returns to S302 and repeats the same process. On the other hand, if there are no customers (No in S313) and there is no input to end the process (No in S314), the image analysis unit 12 returns to S300 and repeats the same process.

The other configurations of the processing device 10 in this embodiment are the same as those in the first and second embodiments (including modified examples).

As described above, the processing device 10 of this embodiment achieves the same effects as the first and second embodiments. Furthermore, when a customer's purchase history is registered, the processing device 10 can determine whether or not each customer is a "customer who needs to be provided with services suited to the customer" based on the customer's purchase history in addition to the customer's belongings, thereby improving the accuracy of the determination. Furthermore, the processing device 10 can determine whether or not each customer is a "customer who needs to be provided with services suited to the customer" based on the customer's in-store behavior in addition to the customer's belongings and purchase history, thereby further improving the accuracy of the determination.

Here, a modified example of this embodiment will be described. Each store may be able to select whether or not to execute the "process of determining whether or not each customer is a 'customer who needs to be provided with a service suited to the customer' using past purchase history" described in this embodiment. In order to use past purchase history, it is necessary to identify the customer using an image or the like, but some stores do not wish to perform such processing from the viewpoint of protecting privacy. On the other hand, some stores wish to perform the "process of determining whether or not each customer is a 'customer who needs to be provided with a service suited to the customer' using past purchase history" after obtaining the customer's consent, in order to increase the accuracy of determining whether or not each customer is a 'customer who needs to be provided with a service suited to the customer'. According to this modified example, in which the processing contents of the processing device 10 can be selected for each store, it becomes possible to provide services that meet the needs of each store.

The selection made by each store is stored in the storage unit 15. For stores that have selected to execute the "process of determining whether or not each customer is a 'customer who needs to be provided with services suited to the customer' using past purchase history", the process described in this embodiment (e.g., the flow in FIG. 10) is executed. On the other hand, for stores that have selected not to execute the "process of determining whether or not each customer is a 'customer who needs to be provided with services suited to the customer' using past purchase history", the process described in the first or second embodiment (e.g., the flow in FIG. 5 or FIG. 7) is executed.

Fourth Embodiment
The processing device 10 of this embodiment has a function of determining which of the staff on duty should be notified of the customer identification information, and notifying the determined staff of the customer identification information. This enables the customer to be served by a staff suited to the customer. This will be described in detail below.

The output unit 14 notifies the customer identification information to a store clerk determined based on at least one of the customer's attributes estimated based on the image, the customer's location within the store, the attributes of each store clerk, and the location of each store clerk within the store.

Inferred customer attributes include, for example, age, gender, etc.

The customer's location within the store is identified based on the image. For example, a customer in an image generated by a camera photographing the precious metals section is identified as being in the precious metals section. In another example, a customer in an image generated by a camera photographing aisle A is identified as being in aisle A.

The attributes of each sales clerk include, for example, age, gender, products in charge, etc. For example, sales clerk information for each of multiple sales clerks belonging to a store may be created in advance and stored in the storage unit 15. Then, based on the sales clerk information, information on the sales clerks currently on duty, such as that shown in FIG. 11, may be created and stored in the storage unit 15.

The position of the sales clerk within the store is identified based on the image. For example, the memory unit 15 may store sales clerk information that links at least one of the facial image and appearance features (facial features) of each sales clerk with sales clerk identification information. The image analysis unit 12 may then identify the sales clerk included in the image by matching the facial image of the person included in the image or features extracted from it with the sales clerk information. The position of the identified sales clerk within the store may then be identified by a process similar to that used to identify the position of the customer within the store.

For example, the output unit 14 may notify a clerk of the same sex on duty as the "customer who needs to be provided with services suited to the customer" of the customer identification information. In addition, the output unit 14 may notify a clerk of the same sex on duty as the "customer who needs to be provided with services suited to the customer" of the customer identification information if the age of the "customer who needs to be provided with services suited to the customer" satisfies a first condition (e.g., teens, twenties), and may notify a clerk of the opposite sex on duty as the "customer who needs to be provided with services suited to the customer" of the customer identification information if the age of the "customer who needs to be provided with services suited to the customer" satisfies a second condition (e.g., 30s or older).

Additionally, the output unit 14 may notify the customer identification information to the store clerk on duty who is closest to the "customer who needs to be provided with service suited to the customer" (e.g., in the same corner and same aisle, or in the corner closest to the customer's location).

In addition, the output unit 14 may notify the customer identification information to the salesperson on duty who is in charge of the products displayed in the area where the "customer who needs the provision of suitable service" is located.

Next, an example of the processing flow of the processing device 10 will be described using the flowchart in FIG. 12. Note that, here, an example of processing will be described in which each customer is determined to be a "customer who needs to be provided with services suited to the customer" based on the belongings-related information, but as described in the second and third embodiments, the processing device 10 may also determine whether each customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information and at least one of the behavioral information and the purchase history.

When the acquisition unit 11 acquires an image sent from a certain store system 20 as a processing target (S400), the image analysis unit 12 performs a process to extract a customer (person) from the processing target image (S401). If a customer is not extracted from the processing target image (No in S401), the process proceeds to S112. On the other hand, if a customer is extracted from the processing target image (Yes in S401), the image analysis unit 12 designates one of the extracted customers as the processing target (S402).

If the specified customer is not a customer currently being tracked (No in S403), the image analysis unit 12 starts tracking (S405). For example, the image analysis unit 12 issues new in-store person identification information corresponding to the customer and stores it in the storage unit 15. Then, the process proceeds to S406.

On the other hand, if the specified customer is a customer being tracked (Yes in S403), the image analysis unit 12 determines whether the customer has been certified as a "customer who needs to be provided with services suited to the customer" (S404). If the customer has been certified (Yes in S404), the process proceeds to S411. On the other hand, if the customer has not been certified (No in S404), the process proceeds to S406, where a belongings determination is performed. If the customer is being tracked, the belongings of the customer have been determined at least once before. By repeatedly performing the belongings determination in this manner, it becomes possible to detect new belongings that have not been detected previously due to the way they are reflected in the image.

In S406, the image analysis unit 12 generates personal item-related information (see FIG. 4) based on the image, links it to the customer's in-store personal identification information, and stores it in the memory unit 15. That is, the image analysis unit 12 analyzes the image to extract the customer's personal items, and extracts information from the appearance of each of the extracted items, such as type (bag, accessories, wristwatch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc. Then, the image analysis unit 12 calculates the luxury level of each item based on the extraction results.

Next, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information (S407).

If the customer is judged to be a "customer who needs to be provided with a service suited to the customer" (Yes in S408), the output unit 14 determines a clerk on duty to notify the customer identification information for identifying the customer based on at least one of the attributes of the customer estimated based on the image, the customer's location in the store, the attributes of each clerk on duty, and the locations of each clerk on duty in the store (S409). Then, the output unit 14 notifies the determined clerk of the customer identification information for identifying the customer (S410). Specifically, the output unit 14 transmits the customer identification information and information identifying the mobile device held by the determined clerk (previously stored in the storage unit 15 in association with each clerk) to the store system 20 that transmitted the image. The store system 20 transmits the received customer identification information to the mobile device identified by the information identifying the received mobile device. Transmission to the mobile device may be achieved by push notification or the like via a specific application installed on the mobile device. If the customer is determined to be a "customer who needs to be provided with services suited to the customer," the determination unit 13 may link the customer's in-store personal identification information and store this information in the storage unit 15. For example, the image analysis unit 12 may make the determination of S404 based on this information.

On the other hand, if the customer is not determined to be a "customer who requires the provision of services suited to the customer" (No in S408), the output unit 14 does not output the customer-specific information of the customer.

In S411, the image analysis unit 12 determines whether there are any customers not specified in S402 among the customers extracted from the image. If there are (Yes in S411), the image analysis unit 12 returns to S402 and repeats the same process. On the other hand, if there are no customers (No in S411) and there is no input to end the process (No in S412), the image analysis unit 12 returns to S400 and repeats the same process.

The rest of the configuration of the processing device 10 of this embodiment is the same as that of the first to third embodiments (including modified examples).

As described above, the processing device 10 of this embodiment achieves the same effects as the first to third embodiments. In addition, the processing device 10 can notify the customer identification information to a sales clerk determined based on at least one of the attributes of the customer, the customer's location in the store, the attributes of each sales clerk on duty, and the location in the store of each sales clerk on duty. As a result, it becomes possible for each customer to be served by a sales clerk suited to that customer.

Fifth embodiment
The processing device 10 of this embodiment has a function of, when determining that a certain customer is "a customer who needs to be provided with a service suited to the customer," determining an appropriate method of serving the customer based on the attributes of the store and the attributes of the customer, and outputting advice on the method of serving the customer together with customer identification information. This will be described in detail below.

The output unit 14 further outputs advice on how to serve "customers who need to be provided with service suited to them." The output unit 14 outputs advice according to at least one of the attributes of the store and the attributes of the "customers who need to be provided with service suited to them."

For example, the memory unit 15 may store information that defines appropriate customer service methods for each store attribute and each customer attribute, as shown in FIG. 13. The output unit 14 may then determine the customer service advice to be output based on the information.

Next, an example of the processing flow of the processing device 10 will be described using the flowchart of FIG. 14. Note that, here, an example of processing will be described in which each customer is determined to be a "customer who needs to be provided with services suited to the customer" based on the belongings-related information. However, as described in the second and third embodiments, the processing device 10 may determine whether each customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information and at least one of the behavioral information and the purchase history. In addition, the processing device 10 may determine the store clerk to notify of the customer identification information and advice, as described in the fourth embodiment, and notify the determined store clerk.

When the acquisition unit 11 acquires an image sent from a certain store system 20 as a processing target (S500), the image analysis unit 12 performs a process to extract a customer (person) from the processing target image (S501). If a customer is not extracted from the processing target image (No in S501), the process proceeds to S512. On the other hand, if a customer is extracted from the processing target image (Yes in S501), the image analysis unit 12 designates one of the extracted customers as the processing target (S502).

If the specified customer is not a customer currently being tracked (No in S503), the image analysis unit 12 starts tracking (S505). For example, the image analysis unit 12 issues new in-store person identification information corresponding to the customer and stores it in the storage unit 15. Then, the process proceeds to S506.

On the other hand, if the specified customer is a customer being tracked (Yes in S503), the image analysis unit 12 determines whether the customer has been certified as a "customer who needs to be provided with services suited to the customer" (S504). If the customer has been certified (Yes in S504), the process proceeds to S511. On the other hand, if the customer has not been certified (No in S504), the process proceeds to S506, where a belongings determination is performed. If the customer is being tracked, the belongings of the customer have been determined at least once before. By repeatedly performing belongings determination in this manner, it becomes possible to detect new belongings that have not been detected previously due to the way they are reflected in the image.

In S506, the image analysis unit 12 generates personal item-related information (see FIG. 4) based on the image, links it to the customer's in-store personal identification information, and stores it in the memory unit 15. That is, the image analysis unit 12 analyzes the image to extract the customer's personal items, and extracts information from the appearance of each of the extracted items, such as type (bag, accessories, wristwatch, glasses, sunglasses, coat, jacket, trousers, skirt, shoes, car, etc.), shape, color, pattern, logo, material, etc. Then, the image analysis unit 12 calculates the luxury level of each item based on the extraction results.

Next, the judgment unit 13 judges whether the customer is a "customer who needs to be provided with services suited to the customer" based on the belongings-related information (S507).

If the customer is determined to be a "customer who needs to be provided with services suited to the customer" (Yes in S508), the output unit 14 determines advice on a customer service method based on at least one of the attributes of the store and the attributes of the customer (S509). Then, the output unit 14 outputs customer identification information for identifying the customer and the determined customer service advice (S510). Specifically, the output unit 14 transmits the customer identification information and the customer service advice to the store system 20 that transmitted the image. The store system 20 may display the customer identification information and the customer service advice via any display that can be viewed by the store clerk, may display the customer identification information and the customer service advice on the store clerk's mobile terminal, or may output the customer identification information and the customer service advice via a printer. Note that if the customer is determined to be a "customer who needs to be provided with services suited to the customer," the judgment unit 13 may link the customer to the in-store person identification information of the customer and store that fact in the storage unit 15. For example, the image analysis unit 12 may make the judgment of S504 based on the information.

On the other hand, if the customer is not determined to be a "customer who needs to be provided with services suited to the customer" (No in S508), the output unit 14 does not output the customer identification information and customer service advice for that customer.

In S511, the image analysis unit 12 determines whether there are any customers not specified in S502 among the customers extracted from the image. If there are (Yes in S511), the image analysis unit 12 returns to S502 and repeats the same process. On the other hand, if there are no customers (No in S511) and there is no input to end the process (No in S512), the image analysis unit 12 returns to S500 and repeats the same process.

The rest of the configuration of the processing device 10 of this embodiment is the same as that of the first to fourth embodiments (including modified examples).

As described above, the processing device 10 of this embodiment achieves the same effects as the first to fourth embodiments. In addition, the processing device 10 can determine and notify appropriate customer service advice based on the attributes of the store and the attributes of "customers who need appropriate service." As a result, it becomes possible to provide customer service that is appropriate for each customer.

Now, we will explain a modified example that can be applied to all the embodiments. Up to this point, it has been assumed that the processing device 10, as shown in FIG. 1, is a server (e.g., a cloud server), which communicates with the store system 20 of each of a plurality of stores via a communication network 30 such as the Internet, and performs processing related to the plurality of stores. As a modified example, as shown in FIG. 15, the processing device 10 may be installed in each store, communicate only with the store system 20 of the store in which it is installed, and perform only processing related to that store.

The present invention has been described above with reference to the embodiments (and examples), but the present invention is not limited to the above-mentioned embodiments (and examples). Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

Below, examples of reference forms are given.
1. An acquisition means for acquiring images taken inside a store;
image analysis means for generating possession-related information regarding the possessions of the customer based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
A processing device having
2. The processing device according to 1, wherein the belongings-related information indicates a luxury level of the customer's belongings.
3. The image analysis means generates behavioral information regarding the in-store behavior of the customer based on the image,
3. The processing device according to claim 1, wherein the determining means determines whether or not it is necessary to provide a service suitable for the customer based on the behavioral information.
4. The processing device according to 3, wherein the behavioral information indicates whether or not a predetermined behavior that leads to the purchase of a product has been performed.
5. The image analysis means identifies customer identification information of the customer based on the image,
5. The processing device according to any one of 1 to 4, wherein the determination means determines whether or not it is necessary to provide a service suitable for the customer based on a purchase history registered in association with the customer identification information.
6. The processing device according to any one of 1 to 5, wherein the output means outputs, as the customer identification information, at least one of the features of the customer estimated based on the image, the features of the customer's belongings identified based on the image, and an image of the customer.
7. The processing device according to any one of 1 to 6, wherein the determination means determines whether or not it is necessary to provide a service suitable for the customer based on a determination criterion corresponding to each of the stores.
8. The processing device according to any one of 1 to 7, wherein the output means further outputs advice on how to serve the customer.
9. The processing device according to 8, wherein the output means outputs the advice according to an attribute of the store.
10. The processing device according to any one of 1 to 9, wherein the output means notifies the customer identification information to a store clerk determined based on at least one of attributes of the customer estimated based on the image, the position of the customer in the store, attributes of each store clerk, and the position of each store clerk in the store.
11. The computer:
Acquire images taken inside the store,
generating item-related information about the customer's items based on the images;
determining whether a service appropriate for the customer is required based on the belongings-related information;
A processing method for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer.
12. The computer
An acquisition means for acquiring images taken inside the store;
image analysis means for generating possession related information regarding the customer's possessions based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
A program that functions as a

1A Processor 2A Memory 3A Input/Output I/F
4A Peripheral circuit 5A Bus 10 Processing device 11 Acquisition unit 12 Image analysis unit 13 Determination unit 14 Output unit 15 Memory unit 20 Store system 30 Communication network

Claims (9)

An acquisition means for acquiring images captured inside the store;
image analysis means for generating possession-related information regarding the possessions of the customer based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
having
The belongings related information indicates the luxury of the customer's belongings. The image analysis means identifies customer identification information of the customer based on the image,
The processing device according to claim 1 , wherein the determining means determines whether or not it is necessary to provide a service suitable for the customer, based on a purchase history registered in association with the customer identification information. The processing device according to claim 1 or 2, wherein the output means outputs at least one of the customer's characteristics estimated based on the image, the characteristics of the customer's belongings identified based on the image, and an image of the customer as the customer identification information. The processing device according to claim 1 , wherein the determining unit determines whether or not it is necessary to provide a service suitable for the customer based on a determination criterion corresponding to each of the stores. The processing device according to claim 1 , wherein the output means further outputs advice on how to serve the customer. The processing device according to claim 5 , wherein the output means outputs the advice according to an attribute of the store. The processing device according to claim 1, wherein the output means notifies the customer identification information to a store clerk determined based on at least one of the attributes of the customer estimated based on the image, the location of the customer in the store, the attributes of each store clerk, and the location of each store clerk in the store. The computer
Acquire images taken inside the store,
generating item-related information about the customer's items based on the images;
determining whether a service appropriate for the customer is required based on the belongings-related information;
outputting customer identification information for identifying the customer for whom it is determined that a service suitable for the customer is required;
The belongings-related information indicates a luxury level of the customer's belongings. Computer,
An acquisition means for acquiring images taken inside the store;
image analysis means for generating possession related information regarding the customer's possessions based on the images;
A determination means for determining whether or not a service suitable for the customer is required based on the belongings-related information;
an output means for outputting customer identification information for identifying the customer who is determined to require provision of a service suitable for the customer;
Function as a
The belongings-related information indicates the luxury of the customer's belongings.

Images