Product proposal device, product proposal method, and product proposal program

Abstract

This enables us to propose products that our clients would want to purchase. [Solution] The input unit 21 provides the new product suggestion model 41, which is a trained model, with information indicating the type of business of the target customer and the target customer's past purchase history of products during a past reference period. The input unit 21 inputs an instruction to the new product suggestion model 41 to generate suggestion information indicating the products to be suggested to the target customer. The output unit 22 outputs the suggestion information generated by the new product suggestion model 41 in accordance with the type of business and purchase history provided by the input unit 21.

Description

Technical Field

[0001] The present disclosure relates to a technology for proposing products to a proposed destination.

Background Art

[0002] When conducting business with a customer, it is necessary to propose appropriate products. By proposing appropriate products, it leads to the customer purchasing the products and increases the transaction record.

[0003] Patent Document 1 describes a technology for extracting recommended products based on conditions.

Prior Art Documents

Patent Documents

[0004] ]>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In Patent Document 1, prioritizing the profit on the store side, products with a high gross profit rate or a short expiration date are extracted as recommended products. Therefore, the recommended products do not propose products that the customer wants to purchase. An object of the present disclosure is to enable the proposal of products that the customer wants to purchase.

Means for Solving the Problems

[0006] The product proposal device according to the present disclosure provides information indicating the business type of the proposed destination and the purchase history of products during the past reference period of the proposed destination to a new product proposal model that is a learned model, and inputs an instruction for generating proposal information indicating the products to be proposed to the proposed destination to the new product proposal model, an input unit, An output unit that outputs proposal information generated by the new product proposal model in correspondence with the business type and purchase history given by the input unit. It is equipped with. [Effects of the Invention]

[0007] This disclosure provides information that identifies the target business type and their product purchase history, and instructs the system to generate proposal information. This makes it possible to generate proposal information that suggests products the target business would like to purchase, and by using this proposal information, it is possible to suggest products that the target business would like to purchase. [Brief explanation of the drawing]

[0008] [Figure 1] Configuration diagram of the product suggestion device 10 according to Embodiment 1. [Figure 2] An explanatory diagram of sales performance data 31 according to Embodiment 1. [Figure 3] An explanatory diagram of customer data 32 according to Embodiment 1. [Figure 4] A flowchart of the processing of the product suggestion device 10 according to Embodiment 1. [Figure 5] A diagram showing an example of a prompt according to Embodiment 1. [Figure 6] A figure showing another example of a prompt according to Embodiment 1. [Figure 7] A diagram showing an example of proposed information related to Embodiment 1. [Figure 8] Configuration diagram of the product suggestion device 10 according to Embodiment 2. [Figure 9] An explanatory diagram of product information 33 relating to Embodiment 2. [Figure 10] A flowchart of the processing of the product suggestion device 10 according to Embodiment 2. [Figure 11] A diagram showing an example of a prompt according to Embodiment 2. [Figure 12] Configuration diagram of the product suggestion device 10 according to Embodiment 3. [Figure 13] An explanatory diagram of popular information 34 according to Embodiment 3. [Figure 14] Flowchart of the processing of the product proposal device 10 according to Embodiment 3. [Figure 15] Diagram showing an example of a prompt according to Embodiment 3. [Figure 16] Flowchart of the processing of the product proposal device 10 according to Embodiment 4. [Figure 17] Diagram showing an example of a prompt according to Embodiment 4. [Figure 18] Diagram showing an example of proposed information according to Embodiment 4. [Figure 19] Configuration diagram of the product proposal device 10 according to Embodiment 5. [Figure 20] Flowchart of the processing of the product proposal device 10 according to Embodiment 5. [Figure 21] Explanatory diagram of the new product proposal assistance screen according to Embodiment 5. [Figure 22] Flowchart of the processing of the product proposal device 10 according to Embodiment 6. [Figure 23] Diagram showing an example of a prompt according to Embodiment 6. [Figure 24] Diagram showing an example of proposed information according to Embodiment 6.

Mode for Carrying Out the Invention

[0009] Embodiment 1. In Embodiment 1, an example in which a liquor store as a seller of liquor proposes new products to a customer such as a restaurant is described. The product is not limited to liquor and may be other types of goods, and the customer is not limited to a restaurant and may be other types of organizations or individuals, etc.

[0010] ***Explanation of Configuration*** Referring to FIG. 1, the configuration of the product proposal device 10 according to Embodiment 1 will be described. The product proposal device 10 is a computer. The product proposal device 10 includes hardware such as a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 is connected to other hardware via signal lines and controls these other hardware.

[0011] Processor 11 is an IC that performs processing. IC stands for Integrated Circuit. Specific examples of processor 11 include CPU, DSP, and GPU. CPU stands for Central Processing Unit. DSP stands for Digital Signal Processor. GPU stands for Graphics Processing Unit.

[0012] Memory 12 is a storage device that temporarily stores data. Specific examples of memory 12 include SRAM and DRAM. SRAM stands for Static Random Access Memory. DRAM stands for Dynamic Random Access Memory.

[0013] Storage 13 is a storage device for storing data. A concrete example of storage 13 is an SSD. SSD stands for Solid State Drive. Alternatively, storage 13 may be a portable recording medium such as an SD® memory card, CompactFlash®, NAND flash, flexible disk, optical disk, compact disk, Blu-ray® disc, or DVD. SD stands for Secure Digital. DVD stands for Digital Versatile Disk.

[0014] Communication interface 14 is an interface for communicating with external devices. Specific examples of communication interface 14 include Ethernet®, USB, and HDMI® ports. USB stands for Universal Serial Bus. HDMI stands for High-Definition Multimedia Interface.

[0015] The product suggestion device 10 comprises an input unit 21, an output unit 22, and a recording unit 23 as functional components. The functions of each functional component of the product suggestion device 10 are realized by software. Storage 13 stores programs that implement the functions of each functional component of the product suggestion device 10. These programs are loaded into memory 12 by the processor 11 and executed by the processor 11. This enables the implementation of the functions of each functional component of the product suggestion device 10.

[0016] The storage device 13 stores sales performance data 31 and customer data 32. However, the sales performance data 31 and customer data 32 may also be stored in an external storage device instead of the storage device 13.

[0017] Referring to Figure 2, the sales performance data 31 related to Embodiment 1 will be explained. Sales performance data 31 is data showing the sales performance of a product. Specifically, sales performance data 31 includes sales information for each customer code and sales date. The customer code is the identification information of the customer. The sales date is the date on which the sale was made. The sales information includes the quantity for each product ID. The product ID is the identification information of the product. The quantity is the number of units sold. ID is an abbreviation for IDentifier.

[0018] Referring to Figure 3, the customer data 32 according to Embodiment 1 will be explained. Customer data 32 contains data about business partners. Specifically, customer data 32 includes the business partner name and business type for each business partner code. The business partner name is the name of the business partner. The business type is the type of business the business partner operates. In this case, since the business partner is a restaurant, the business type is Japanese, Chinese, Asian, Italian, general izakaya, upscale izakaya, etc.

[0019] The product suggestion device 10 is connected to the new product suggestion model 41, which is a trained model, via the communication interface 14. A pre-trained model is a type of generative AI. AI stands for Artificial Intelligence. A pre-trained model may be constructed using algorithms such as BERT or GPT. BERT stands for Bidirectional Encoder Representations from Transformers. GPT stands for Generative Pretrained Transformer. A pre-trained model may be constructed using a combination of multiple algorithms, including these.

[0020] In Figure 1, only one processor 11 was shown. However, there may be multiple processors 11, and multiple processors 11 may work together to execute programs that implement each function.

[0021] ***Explanation of operation*** Referring to Figures 4 to 7, the operation of the product suggestion device 10 according to Embodiment 1 will be explained. The operating procedure of the product suggestion device 10 according to Embodiment 1 corresponds to the product suggestion method according to Embodiment 1. Furthermore, the program that realizes the operation of the product suggestion device 10 according to Embodiment 1 corresponds to the product suggestion program according to Embodiment 1.

[0022] Referring to Figure 4, the processing of the product suggestion device 10 according to Embodiment 1 will be explained. (Step S11: Reception Processing) The input unit 21 receives input from the user's terminal indicating the business partner to whom the new product is proposed. In this case, the input unit 21 receives input of the business partner code of the proposed partner.

[0023] (Step S12: Input Processing) The input unit 21 provides the new product suggestion model 41, which is a trained model, with the type of business of the proposed customer identified from the customer code entered in step S11, and the proposed customer's purchase history of products in the past reference period. The input unit 21 also prompts the new product suggestion model 41 with instructions to generate suggestion information indicating the products to be proposed to the proposed customer. Here, providing information to the new product proposal model 41 may mean entering the information into the prompt and inputting it into the new product proposal model 41. Alternatively, providing information to the new product proposal model 41 may mean making a database or the like containing the necessary information accessible to the new product proposal model 41, and entering key items for extracting the necessary information into the prompt and inputting them into the new product proposal model 41.

[0024] An example of a prompt according to Embodiment 1 will be explained with reference to Figure 5. Figure 5 shows that the instruction document contains instructions for generating proposal information that indicates products to be proposed to the proposed recipient, based on the proposed recipient's business type and the proposed recipient's recent purchase history. The proposed recipient's business type is listed as Italian. The proposed recipient's recent purchase history lists products purchased by the proposed recipient during the past base period. In Figure 5, the type of business of the proposed customer and the customer's past purchase history of products during the past reference period are entered into the new product proposal model 41 by prompting. The input unit 21 can identify the type of business of the proposed customer by searching the customer data 32 using the customer code entered in step S11. The input unit 21 can also identify the customer's past purchase history of products during the past reference period by searching the sales performance data 31 using the customer code entered in step S11.

[0025] Referring to Figure 6, another example of a prompt according to Embodiment 1 will be described. In Figure 6, similar to Figure 5, the instruction document contains instructions for generating proposal information that indicates the products to be proposed to the proposed customer, based on the proposed customer's business type and recent purchase history. The proposed customer identification information contains the proposed customer's trading partner code. Figure 6 assumes that the new product proposal model 41 can access sales performance data 31 and customer data 32. The customer code is entered into the new product proposal model 41 as a prompt, serving as a key item for extracting the target business type and the target's recent purchase history. Furthermore, the prompt may also include instructions to identify the business type of the proposed client from customer data 32 based on the client's business code, and to identify the client's recent purchase history from sales performance data 31.

[0026] When the input unit 21 receives an instruction to generate suggestion information, the new product suggestion model 41 generates product suggestion information corresponding to the business type and purchase history.

[0027] (Step S13: Output processing) The output unit 22 acquires the proposal information generated by the new product proposal model 41 in accordance with the business type and purchase history given in step S12. The output unit 22 outputs the acquired proposal information to the user terminal. The recording unit 23 also writes the acquired proposal information to the storage 13.

[0028] Referring to Figure 7, an example of proposed information related to Embodiment 1 will be explained. The suggestion information includes the suggested product and the reason for the suggestion. Figure 7 shows the suggestion information corresponding to the prompt shown in Figure 5 or Figure 6. In Figure 7, based on the fact that the target business is Italian, Italian whiskey and similar products are extracted as suggested products. Additionally, products not included in the purchase history are extracted as suggested products.

[0029] ***Effects of Embodiment 1*** As described above, the product suggestion device 10 according to Embodiment 1 is instructed to generate suggestion information by being given information that can identify the type of business of the target customer and the purchase history of the product. This makes it possible to generate suggestion information that proposes products that the target customer would like to purchase, and by using this suggestion information, it is possible to propose products that the target customer would like to purchase.

[0030] ***Other configurations*** <Example 1> The customer data 32 may include customer responses for each product that has been proposed in the past. Then, in step S12, the system may be instructed to generate proposal information considering customer responses for each product. This makes it possible to generate proposal information that suggests products similar to those that received a good response. It also prevents the generation of proposal information that suggests products that received a poor response again.

[0031] Embodiment 2. Embodiment 2 differs from Embodiment 1 in that it provides the new product proposal model 41 with product information 33 for each product handled by the retailer. Embodiment 2 explains this difference, while omitting explanations of the same points.

[0032] ***Explanation of the structure*** Referring to Figure 8, the configuration of the product suggestion device 10 according to Embodiment 2 will be described. Embodiment 2 differs from Embodiment 1 in that the product information 33 is stored in the storage 13. However, the product information 33 may be stored in an external storage device instead of the storage 13.

[0033] Referring to Figure 9, the product information 33 relating to Embodiment 2 will be described. Product Information 33 contains information about each product handled by the retailer. Specifically, Product Information 33 stores the type, product name, unit price, and comments for each product ID. The type is the category of the product. In this case, since the products are alcoholic beverages, the categories are beer, wine, sake, whiskey, shochu, cocktails, etc. The unit price is the price per item. The comments are a description of the product by the sales staff or other relevant personnel. For example, the comments might indicate the type of cuisine that the product pairs well with.

[0034] ***Explanation of operation*** Referring to Figure 10, the processing of the product suggestion device 10 according to Embodiment 2 will be explained. The processes in steps S21 and S23 in Figure 10 are the same as the processes in steps S11 and S13 in Figure 4.

[0035] (Step S22: Input Processing) The input unit 21 provides the new product suggestion model 41, which is a trained model, with product information 33, which is information about each of the multiple products handled by the retailer, in addition to the target business type and purchase history of the proposed product. The input unit 21 also prompts the new product suggestion model 41 to generate suggestion information that indicates the specific product to be suggested to the target from among the multiple products handled by the retailer. The input unit 21 may also be instructed to generate suggestion information that indicates the specific product ID or product name of the product to be suggested, rather than the product genre, etc. At this point, product information 33 may be entered into the new product proposal model 41 via a prompt. Alternatively, product information 33 may be made available for reference by the new product proposal model 41.

[0036] An example of a prompt according to Embodiment 2 will be described with reference to Figure 11. In Figure 11, the #Instruction document contains instructions for generating proposal information that indicates specific products to be proposed to the target based on the target's business type and recent purchase history, from among the multiple products handled by the retailer. Figure 11 assumes that the new product proposal model 41 can access product information 33. As mentioned above, product information 33 may also be included in the prompt.

[0037] When the input unit 21 receives an instruction to generate proposal information, the new product proposal model 41 generates proposal information that indicates specific products to be proposed from among the products handled by the retailer, corresponding to the business type, purchase history, and product information 33.

[0038] ***Effects of Embodiment 2*** As described above, the product suggestion device 10 according to Embodiment 2 provides product information 33 of the products handled by the retailer to the new product suggestion model 41 and instructs it to generate suggestion information that indicates specific products to suggest from among the products handled. As a result, products to be suggested are selected from the products handled by the retailer. Consequently, suggestion information indicating products that are not handled is not generated.

[0039] Furthermore, by providing product information 33 to the new product suggestion model 41, it is possible to generate suggestion information that takes into account the comments and other information written for each product. For example, if the comments indicate the type of cuisine that the product is suitable for, the suggestion information generated will propose products whose suggested cuisine type matches the type of cuisine indicated in the comments.

[0040] Embodiment 3. Embodiment 3 differs from Embodiments 1 and 2 in that it provides the new product proposal model 41 with popularity information 34, which indicates the trends of popular products. Embodiment 3 explains this difference, while omitting explanations of the same points. Embodiment 3 describes a case in which a modification has been made to Embodiment 1. However, it is also possible to modify Embodiment 2.

[0041] ***Explanation of the structure*** Referring to Figure 12, the configuration of the product suggestion device 10 according to Embodiment 3 will be described. Embodiment 2 differs from Embodiment 1 in that the popularity information 34 is stored in the storage 13. Note that the popularity information 34 may be stored in an external storage device instead of the storage 13.

[0042] Referring to Figure 13, the popular information 34 according to Embodiment 3 will be described. Popularity Information 34 is information that shows the trends of popular products for each type of business. Specifically, Popularity Information 34 stores one or more popular products for each type of business. The product here may be a product category or a specific product name.

[0043] ***Explanation of operation*** Referring to Figure 14, the processing of the product suggestion device 10 according to Embodiment 3 will be explained. The processes in steps S31 and S33 in Figure 14 are the same as the processes in steps S11 and S13 in Figure 4.

[0044] (Step S32: Input Processing) The input unit 21 provides the new product suggestion model 41 with the target business type and purchase history, as well as popularity information 34 indicating the popularity trends of the partner business type. The input unit 21 also inputs a prompt to the new product suggestion model 41 with instructions to generate suggestion information indicating products to be suggested to the target, taking popularity trends into consideration. Here, the popularity information 34 regarding the business type of the partner may be entered into the new product suggestion model 41 as a prompt. Alternatively, the popularity information 34 may be made available for the new product suggestion model 41 to refer to.

[0045] An example of a prompt according to Embodiment 3 will be described with reference to Figure 15. In Figure 15, the #Instruction document contains instructions for generating suggestion information that indicates products to be suggested to the target based on the target's business type, the target's recent purchase history, and popularity information 34 within the target's business type. In Figure 15, the popularity information 34 within the target's business type is indicated in the prompt. As mentioned above, the new product suggestion model 41 may also be able to access the popularity information 34.

[0046] When the input unit 21 receives an instruction to generate suggestion information, the new product suggestion model 41 generates suggestion information indicating the products to be suggested, corresponding to the business type, purchase history, and popularity information 34.

[0047] ***Effects of Embodiment 3*** As described above, the product suggestion device 10 according to Embodiment 3 provides popularity information 34 to the new product suggestion model 41 and instructs it to generate suggestion information based on the popularity information 34. This makes it possible to generate suggestion information that indicates products that are popular but have not been purchased.

[0048] Embodiment 4. Embodiment 4 differs from Embodiments 1 to 3 in that the conditions for the proposed product are input into the new product proposal model 41. Embodiment 4 explains this difference, while omitting explanations of the same points. Embodiment 4 describes a case in which a modification has been made to Embodiment 1. However, it is also possible to modify Embodiments 2 and 3.

[0049] ***Explanation of operation*** Referring to Figure 16, the processing of the product suggestion device 10 according to Embodiment 4 will be explained.

[0050] (Step S41: Reception Processing) The input unit 21 receives input from the user terminal indicating the trading partner to whom the new product is proposed. The input unit 21 also receives input from the user terminal regarding the conditions of the product to be proposed. The conditions of the product to be proposed include conditions such as the unit price of the product being below a specified amount, the product being handled by the target company's group, the product type being specified, or the product being in stock at the retailer.

[0051] (Step S42: Input Processing) The input unit 21 inputs the new product proposal model 41 into the new product proposal model 41, along with an instruction to generate proposal information indicating the products to be proposed to the recipient, and the conditions for the proposed products received in step S41, which are entered as prompts. Furthermore, if the conditions for the proposed product include a requirement that the unit price of the product be below a specified amount, and this requires input of product information 33, then the product information 33 must be provided to the new product proposal model 41 as described in Embodiment 2. In addition, if there is any other information necessary for the new product proposal model 41 to determine the conditions, it must be provided to the new product proposal model 41. For example, if the conditions for the proposed product include the product being handled by the target company's affiliated group, then information that can identify the trading partners belonging to the target company's affiliated group must be provided to the new product proposal model 41. However, if the information can be obtained through internet searches, etc., it does not need to be provided to the new product proposal model 41.

[0052] An example of a prompt according to Embodiment 4 will be described with reference to Figure 17. Figure 17 shows that the instruction document contains instructions for generating proposal information that indicates products to be offered to the target company based on the target company's business type and recent purchase history, while meeting the specified criteria for the proposed products. The criteria for the proposed products include the condition that expensive alcoholic beverages cannot be offered and that the products must be appealing to young people.

[0053] When the input unit 21 receives an instruction to generate suggestion information, the new product suggestion model 41 generates suggestion information that indicates the products to be suggested from among the products that meet the conditions for the products to be suggested, corresponding to the business type and purchase history.

[0054] (Step S43: Output processing) The output unit 22 acquires the proposal information generated by the new product proposal model 41 in accordance with the business type and purchase history given in step S12. The output unit 22 outputs the acquired proposal information to the user terminal.

[0055] Referring to Figure 18, an example of proposed information related to Embodiment 1 will be described. The suggestion information includes the suggested product and the reason for the suggestion. Figure 18 shows the suggestion information corresponding to the prompt shown in Figure 7. In Figure 18, the suggested product is not an expensive alcoholic beverage, but rather a product that is appealing to young people, meeting the conditions listed in #Conditions for the suggested product.

[0056] (Step S44: Confirmation process) The output unit 22 receives input indicating whether or not to confirm the proposed information output to the user terminal in step S43. If the output unit 22 receives input indicating that the proposed information is to be confirmed, it proceeds to step S45. On the other hand, if the output unit 22 receives input indicating that the proposed information is not to be confirmed, it returns to step S41. This allows for the acceptance of additions or changes to the conditions of the proposed product.

[0057] (Step S45: Recording process) The recording unit 23 writes the proposal information acquired in step S43 to the storage unit 13.

[0058] ***Effects of Embodiment 4*** As described above, the product suggestion device 10 according to Embodiment 4 inputs the conditions of the product to be suggested into the new product suggestion model 41. This makes it possible to prevent the generation of suggestion information that suggests unintended products.

[0059] ***Other configurations*** <Modification 2> If the proposed target is a new store or other entity with which there has been no prior transaction, it is not possible to obtain purchase history. In such cases, the input unit 21 may register sales performance data 31, etc., in the RAG and use the RAG to analyze the trends in the products purchased by the proposed target based on attributes such as the business type or location, and set conditions using the information obtained. For example, conditions such as proposing products that match the trends in purchased products may be set. RAG stands for Retrieval-Augmented Generation.

[0060] Embodiment 5. Embodiment 5 differs from Embodiment 4 in that it sets conditions based on a viewpoint by selecting a viewpoint. Embodiment 5 explains this difference, while omitting explanations of the same points.

[0061] ***Explanation of the structure*** Referring to Figure 19, the configuration of the product suggestion device 10 according to Embodiment 5 will be described. The product suggestion device 10 differs from Embodiment 4 in that it includes a viewpoint receiving unit 24 as a functional component.

[0062] ***Explanation of operation*** Referring to Figure 20, the processing of the product suggestion device 10 according to Embodiment 5 will be explained. The process from step S53 to step S55 in Figure 20 is the same as the process from step S43 to step S45 in Figure 16.

[0063] (Step S51: Reception processing) The input unit 21 receives input from the user terminal used by the user, indicating the business partner to whom the new product is proposed. The perspective receiving unit 24 also receives input from the user terminal, indicating perspective information that shows the viewpoint of the product proposal. In Embodiment 5, the perspective receiving unit 24 allows the user to select one of the following perspectives: product perspective, proposed partner perspective, or retailer perspective. For example, the input unit 21 receives input to the new product proposal support screen shown in Figure 21, thereby receiving information indicating the target customer and perspective information. Specifically, the input unit 21 receives the selection result of the customer code or customer name as information indicating the target customer for the new product proposal. The input unit 21 also receives the selection result of the perspective to be focused on as perspective information.

[0064] In Embodiment 5, conditions are set based on viewpoint, but the input unit 21 may separately accept input of product conditions proposed by the user terminal.

[0065] (Step S52: Input Processing) The input unit 21 inputs the new product proposal model 41 with a prompt, along with an instruction to generate proposal information indicating the product to be proposed to the recipient, and the conditions for the proposed product based on the viewpoint information received in step S51. If the conditions for the proposed product have been entered in step S51, the input unit 21 also includes the entered conditions in the prompt. Here, product-based conditions are conditions that use the product as the basis. For example, product-based conditions include proposing products that take into account the overall sales history, which is not limited to the unit price or the type of business of the target customer, by referring to sales performance data 31 and product information 33, etc. Conditions based on the prospective client's perspective are primarily those that benefit the prospective client. For example, conditions based on the prospective client's perspective include referring to customer data 32, etc., to propose appropriate products based on the prospective client's location, area, and affiliated group, etc. Conditions based on the retailer's perspective are primarily those that benefit the retailer. For example, conditions based on the retailer's perspective include suggesting appropriate products based on inventory, gross profit, and expiration date, referring to product information 133, etc.

[0066] When the input unit 21 receives an instruction to generate suggestion information, the new product suggestion model 41 generates suggestion information that indicates the products to be suggested from among the products that meet the conditions for the products to be suggested, corresponding to the business type and purchase history.

[0067] ***Effects of Embodiment 5*** As described above, the product suggestion device 10 according to Embodiment 5 allows the user to specify a viewpoint for product suggestions and inputs the conditions based on the specified viewpoint into the new product suggestion model 41. This makes it possible to easily set conditions without the user having to specify specific conditions.

[0068] ***Other configurations*** <Variation 3> The input unit 21 may input the input viewpoint information into a pre-trained condition setting model to set the conditions. Specifically, in step S52, the input unit 21 inputs the viewpoint information received in step S41 and the instruction to generate conditions for the proposed product into the condition setting model, causing the condition setting model to generate conditions based on the viewpoint information. At this time, in addition to the viewpoint information, the input unit 21 may also provide the condition setting model with the type of business of the proposed customer identified from the customer code, the purchase history of the proposed customer for products in the past reference period, etc. Then, the input unit 21 inputs the conditions generated by the condition setting model into the prompt, along with the instruction to generate proposal information indicating the product to be proposed to the proposed customer. This allows for the setting of flexible conditions tailored to each perspective, rather than fixed conditions for each perspective. In particular, by providing the target business type and product purchase history, etc., to the condition setting model, it becomes possible to set conditions that are suitable for the target business.

[0069] Embodiment 6. Embodiment 6 differs from Embodiment 2 in that it instructs the system to extract products with the highest sales volume from among the multiple products handled by the retailer, and generates suggestion information based on the extracted results.

[0070] ***Explanation of operation*** Referring to Figure 22, the processing of the product suggestion device 10 according to Embodiment 6 will be explained. The processes in steps S61 and S63 in Figure 22 are the same as the processes in steps S21 and S23 in Figure 10.

[0071] (Step S62: Input Processing) The input unit 21 provides the target business type, purchase history, and product information 33 to the trained new product suggestion model 41. The input unit 21 also inputs an extraction instruction to extract products with the highest sales volume from among the multiple products handled by the retailer. Furthermore, based on the results extracted by the extraction instruction, the input unit 21 prompts the new product suggestion model 41 with an instruction to generate suggestion information indicating which products to propose to the target from among the multiple products handled by the retailer. In this case, you may specify the period for which sales figures will be counted. For example, you could specify the most recent month as the target period. You may also instruct them to limit the types of businesses for which sales figures will be counted to the businesses of the proposed client.

[0072] An example of a prompt according to Embodiment 6 will be described with reference to Figure 23. In Figure 23, the #Instruction sheet contains instructions for generating proposal information that indicates which products to propose to the target based on the target's business type and recent purchase history, from among the multiple products handled by the retailer. The #Output Information sheet also contains instructions to output the top 5 best-selling products and new product suggestions based on the top 5 best-selling products.

[0073] When the input unit 21 receives instructions for extracting and generating suggested information, the new product suggestion model 41 extracts products from among the multiple products handled by the retailer that meet the top sales criteria. Furthermore, taking into account the extracted results, the new product suggestion model 41 generates suggested information that indicates the products to be suggested from among the products handled by the retailer, corresponding to the business type and purchase history.

[0074] Referring to Figure 24, an example of proposed information related to Embodiment 6 will be described. Figure 24 shows the suggestion information corresponding to the prompt shown in Figure 23. The suggestion information shows the top 5 best-selling products. It also shows the suggested product and the reason for the suggestion.

[0075] ***Effects of Embodiment 6*** As described above, the product suggestion device 10 according to Embodiment 6 extracts products with high sales figures and generates suggestion information considering the extraction results. This makes it possible to generate suggestion information that takes best-selling products into consideration.

[0076] ***Other configurations*** <Modification 4> In the embodiments described above, each functional component was implemented in software. However, in the fourth modified example, each functional component may be implemented in hardware.

[0077] When each functional component is implemented in hardware, the product proposal device 10 includes an electronic circuit 15 instead of a processor 11, memory 12, and storage 13. The electronic circuit 15 is a dedicated circuit that implements the functions of each functional component, as well as the functions of the memory 12 and storage 13.

[0078] Electronic circuits 15 can include single circuits, complex circuits, programmed processors, parallel programmed processors, logic ICs, GAs, ASICs, and FPGAs. GA stands for Gate Array. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field-Programmable Gate Array. Each functional component may be implemented in a single electronic circuit 15, or each functional component may be implemented by distributing them across multiple electronic circuits 15.

[0079] <Modification 5> As a fifth variation, some of the functional components may be implemented in hardware, while others may be implemented in software.

[0080] The processor 11, memory 12, storage 13, and electronic circuit 15 are collectively referred to as the processing circuit. In other words, the function of each functional component is realized by the processing circuit.

[0081] Furthermore, the term "part" in the above explanation may be replaced with "circuit," "process," "procedure," "processing," or "processing circuit."

[0082] The various aspects of this disclosure are summarized below as an appendix. (Note 1) An input unit provides a new product proposal model, which is a trained model, with information indicating the type of business of the proposed target and the proposed target's purchase history of products during the past reference period, and also inputs an instruction to generate proposal information indicating the products to be proposed to the proposed target into the new product proposal model. An output unit that outputs proposal information generated by the new product proposal model in correspondence with the business type and purchase history given by the input unit. A product suggestion device equipped with the following features. (Note 2) The input unit provides product information for each of the multiple products to the new product proposal model, and also inputs an instruction to generate proposal information indicating which of the multiple products to propose to the new product proposal model. Product suggestion device as described in Appendix 1. (Note 3) The input unit provides the new product proposal model with popularity information indicating trends in popular products within the aforementioned business sector. Product suggestion device as described in Appendix 1 or 2. (Note 4) The input unit inputs the conditions of the proposed product into the new product proposal model. A product suggestion device as described in any one of the items 1 to 3 in the appendix. (Note 5) The aforementioned product suggestion device further, The Perspective Reception Department accepts perspective information that indicates the viewpoint for product proposals. Equipped with, The input unit inputs the conditions corresponding to the viewpoint indicated by the viewpoint information received by the viewpoint receiving unit. Product suggestion device as described in Appendix 4. (Note 6) When the perspective is product-based, the input unit inputs conditions based on at least one of the following: the unit price of each of the multiple products being proposed, and the sales performance of those multiple products. Product suggestion device as described in Appendix 5. (Note 7) The input unit, when the viewpoint is that of the proposed party, inputs conditions based on at least one of the location of the proposed party and the affiliated group to which the proposed party belongs. Product proposal device as described in Appendix 5 or 6. (Note 8) When the perspective is that of a retailer, the input unit inputs conditions based on at least one of the following for each of the multiple products being proposed: inventory, gross profit, and expiration date. A product suggestion device as described in any one of the items 5 to 7 in the appendix. (Note 9) The input unit receives an extraction instruction to extract products from the plurality of products that meet the highest sales criteria, and an instruction to generate the suggested information based on the results extracted by the extraction instruction. Product suggestion device as described in Appendix 2. (Note 10) The computer provides a new product suggestion model, which is a trained model, with information indicating the type of business of the proposed target and the proposed target's purchase history of products during the past reference period, and also inputs an instruction to the new product suggestion model to generate suggestion information indicating the products to be proposed to the proposed target. A product suggestion method in which a computer outputs suggestion information generated by the new product suggestion model in accordance with the business type and the purchase history. (Note 11) The input process involves providing a trained model, which is a new product proposal model, with information indicating the type of business of the proposed target and the proposed target's purchase history of products during the past reference period, and inputting an instruction to generate proposal information indicating the products to be proposed to the proposed target into the new product proposal model. An output process that outputs proposal information generated by the new product proposal model in response to the business type and purchase history given by the input process. A product suggestion program that makes a computer function as a product suggestion device.

[0083] The embodiments and variations of this disclosure have been described above. Some of these embodiments and variations may be implemented in combination. Alternatively, some or all of them may be implemented in part. However, this disclosure is not limited to the embodiments and variations described above, and various modifications are possible as needed. [Explanation of symbols]

[0084] 10 Product suggestion device, 11 Processor, 12 Memory, 13 Storage, 14 Communication interface, 21 Input unit, 22 Output unit, 23 Recording unit, 24 Viewpoint reception unit, 31 Sales performance data, 32 Customer data, 33 Product information, 34 Popularity information, 41 New product suggestion model.

Claims

1. A perspective receiving unit that displays a screen for inputting perspective information indicating one of the following viewpoints for product proposals: product perspective, proposed to customer perspective, and retailer perspective, and receives the input perspective information. An input unit provides a new product proposal model, which is a trained model, with information indicating the type of business of the proposed target and the purchase history of the proposed target's products during the past reference period, and generates a prompt that includes conditions corresponding to the viewpoint indicated by the viewpoint information received by the viewpoint reception unit, and instructions for generating proposal information indicating products to be proposed to the proposed target based on the type of business of the proposed target and the purchase history, and inputs the prompt into the new product proposal model. An output unit outputs proposal information generated by the new product proposal model in correspondence with the business type and purchase history given by the input unit and the conditions described in the prompt. A product suggestion device equipped with the following features.

2. The input unit provides product information for each of the multiple products to the new product proposal model and generates the prompt containing instructions for generating proposal information indicating which of the multiple products to propose. The product suggestion device according to claim 1.

3. The input unit provides the new product proposal model with popularity information indicating trends in popular products within the aforementioned business sector. A product suggestion device according to claim 1 or 2.

4. When the perspective is product-based, the input unit generates a prompt that includes conditions based on at least one of the following: the unit price of each of the multiple products being proposed, and the sales performance of those multiple products. The product suggestion device according to claim 1.

5. The input unit generates the prompt containing conditions based on at least one of the location of the proposed destination and the affiliated group to which the proposed destination belongs, when the viewpoint is the proposed destination's viewpoint. The product suggestion device according to claim 1.

6. The input unit, when the perspective is that of a retailer, generates a prompt that includes conditions based on at least one of the following for each of the multiple products being proposed: inventory, gross profit, and expiration date. The product suggestion device according to claim 1.

7. The input unit generates a prompt that includes an extraction instruction to extract products from the plurality of products that meet the highest sales criteria, and an instruction to generate the suggested information based on the results extracted by the extraction instruction. The product suggestion device according to claim 2.

8. The computer displays a screen for inputting perspective information that indicates the viewpoint of a product proposal, which is one of the following viewpoints: product viewpoint, target viewpoint, or retailer viewpoint, and accepts the input perspective information. The computer provides a new product suggestion model, which is a trained model, with information indicating the type of business of the proposed target and the purchase history of the proposed target's products over a past reference period. It also generates a prompt that includes conditions corresponding to the viewpoint indicated by the viewpoint information and instructions for generating suggestion information indicating products to be proposed to the proposed target based on the type of business of the proposed target and the purchase history. The computer then inputs the prompt into the new product suggestion model. A product suggestion method in which a computer outputs suggestion information generated by the new product suggestion model in correspondence with the business type, the purchase history, and the conditions described in the prompt.

9. A perspective information indicating a perspective for product proposals, comprising a screen for inputting perspective information indicating one of the following perspectives: product perspective, proposed to customer perspective, and retailer perspective, and a perspective reception process for receiving the input perspective information. Input processing involves providing a new product proposal model, which is a trained model, with information indicating the type of business of the proposed target and the purchase history of the proposed target's products during the past reference period, generating a prompt that includes conditions corresponding to the viewpoint information received by the viewpoint reception processing and instructions for generating proposal information indicating products to be proposed to the proposed target based on the type of business of the proposed target and the purchase history, and inputting the prompt into the new product proposal model. An output process that outputs proposal information generated by the new product proposal model in correspondence with the business type and purchase history given by the input process and the conditions described in the prompt. A product suggestion program that makes a computer function as a product suggestion device.

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