system

Abstract

The system according to the embodiment aims to utilize information on purchase receipts to suggest alternative products from the perspective of rising prices and health management. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, and a suggestion unit. The acquisition unit acquires an image of a receipt. The analysis unit analyzes the image of the receipt acquired by the acquisition unit and extracts information about the purchased product. The suggestion unit suggests alternative products based on the information extracted by the analysis unit in light of rising prices or health management.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not been effective in efficiently utilizing information from purchase receipts to suggest alternative products from the perspective of rising prices and health management, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize information on purchase receipts to suggest alternative products from the perspective of rising prices and health management. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a suggestion unit. The acquisition unit acquires an image of a receipt. The analysis unit analyzes the image of the receipt acquired by the acquisition unit and extracts information about the purchased product. The suggestion unit suggests alternative products based on the information extracted by the analysis unit in light of rising prices or health management. [Effects of the Invention]

[0007] The system according to the embodiment can utilize information on purchase receipts to suggest alternative products in light of rising prices and health management. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A proposal system according to an embodiment of the present invention automatically reads purchase receipts and uses a generation AI to suggest alternative products from the perspective of rising prices and health management. In this proposal system, a user photographs the receipt for a purchased item using a smartphone or other device and uploads it to the system. The generation AI then analyzes the receipt and extracts information about the purchased item. Based on this information, the system makes the following proposals. For example, to suggest alternative products to address rising prices, the generation AI analyzes the price information of the purchased item and suggests cheaper alternatives to the same item but from different manufacturers. For example, if a user purchases a specific brand of milk, the generation AI suggests cheaper alternatives from other brands of milk. Furthermore, to suggest alternatives from a health management perspective, the generation AI analyzes the nutritional information of the purchased item and suggests lower-calorie alternatives to the same item but from different manufacturers. For example, if a user purchases a specific brand of yogurt, the generation AI suggests lower-calorie alternatives from other brands of yogurt. This system allows users to select optimal products from a health management perspective while also addressing rising prices. This allows the proposal system to select optimal products from a health management perspective.

[0029] The proposal system according to the embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit acquires an image of a receipt for a product purchased by a user. For example, the acquisition unit can take a photo of the receipt using a smartphone camera and upload the image to the system. The acquisition unit can also digitize the receipt using a scanner and acquire it as image data. The acquisition unit can also convert the contents of the receipt into text data using OCR technology. For example, the acquisition unit can analyze a receipt image taken with a smartphone camera using OCR technology and convert it into text data. The analysis unit uses a generation AI to analyze the image of the receipt acquired by the acquisition unit and extract information about the purchased product. The analysis unit extracts information such as the product name, price, and quantity listed on the receipt. The generation AI analyzes the contents of the receipt using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI receives an image of the receipt as input and extracts information such as the product name and price. The proposal unit suggests alternative products based on the information extracted by the analysis unit, taking into account factors such as rising prices and health management. The suggestion unit, for example, analyzes price information of a purchased product and suggests a cheaper substitute for the same product but from a different manufacturer. The suggestion unit also analyzes nutritional information of the purchased product and suggests a lower-calorie substitute for the same product but from a different manufacturer. For example, the suggestion unit uses a generation AI to analyze price information and nutritional information of a purchased product and suggests an optimal substitute. This allows the suggestion system according to the embodiment to enable the user to select the optimal product from the perspective of rising prices and health management.

[0030] The acquisition unit can acquire images of receipts uploaded by users. For example, the acquisition unit can upload images of receipts taken by users with a smartphone camera to the system. The acquisition unit can save the uploaded images of receipts in a database and pass them on to the analysis unit. For example, the acquisition unit allows a user to take an image of a receipt using a smartphone app and upload the image to the system. The acquisition unit saves the uploaded image in a database and passes it on to the analysis unit. This ensures that images of receipts uploaded by users can be acquired reliably. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input images of receipts uploaded by users into an AI model and have the AI ​​acquire the images.

[0031] The analysis unit can analyze the contents of the receipt and extract information about the purchased items. The analysis unit can analyze the contents of the receipt using, for example, OCR technology and extract information about the purchased items. For example, the analysis unit can extract information such as the product name, price, and quantity written on the receipt. The analysis unit can also analyze the contents of the receipt using a generation AI. For example, the generation AI can receive an image of the receipt as input and extract information such as the product name and price. The generation AI can analyze the contents of the receipt using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive an image of the receipt as input and extract information such as the product name and price. This allows the receipt to be accurately analyzed and information about the purchased items to be extracted. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input an image of the receipt into an AI model and have the AI ​​extract product information.

[0032] The suggestion unit can analyze price information of a purchased product and suggest a cheaper alternative to the same product but from a different manufacturer. For example, the suggestion unit can analyze price information of a purchased product and suggest a cheaper alternative to the same product but from a different manufacturer. For example, if a user purchases a specific brand of milk, the suggestion unit can suggest a cheaper alternative to other brands of milk. The suggestion unit can also analyze price information of a purchased product using a generation AI. For example, the generation AI can receive price information of a purchased product as input and suggest a cheaper alternative to the same product but from a different manufacturer. The generation AI can analyze price information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive price information of a purchased product as input and suggest a cheaper alternative to the same product but from a different manufacturer. This allows the user to respond to rising prices and select a cheaper alternative. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input price information of a purchased product into an AI model and cause the AI ​​to suggest a cheaper alternative.

[0033] The suggestion unit can analyze the nutritional information of a purchased product and suggest low-calorie substitutes for the same product but from different manufacturers. For example, the suggestion unit can analyze the nutritional information of a purchased product and suggest low-calorie substitutes for the same product but from different manufacturers. For example, if a user purchases a specific brand of yogurt, the suggestion unit can suggest other brands of yogurt that are lower in calories. The suggestion unit can also analyze the nutritional information of a purchased product using a generation AI. For example, the generation AI can receive the nutritional information of a purchased product as input and suggest low-calorie substitutes for the same product but from different manufacturers. The generation AI can analyze the nutritional information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive the nutritional information of a purchased product as input and suggest low-calorie substitutes for the same product but from different manufacturers. This allows the user to select the optimal product from a health management perspective. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the nutritional information of a purchased product into an AI model and cause the AI ​​to suggest low-calorie substitutes.

[0034] The acquisition unit can analyze the user's past receipt acquisition history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past receipt acquisition history and selects the optimal acquisition method. For example, the acquisition unit prioritizes and suggests acquisition methods (manual, voice instruction, etc.) that the user has frequently used in the past. The acquisition unit can also suggest a method for acquiring receipts during a specific time period based on the user's past acquisition history. The acquisition unit can also analyze the user's past acquisition history and suggest the most efficient acquisition method. This makes it possible to select the optimal acquisition method based on the user's past history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history data into an AI model and have the AI ​​select the optimal acquisition method.

[0035] The acquisition unit can perform filtering based on the user's current purchasing patterns and areas of interest when acquiring receipt images. For example, the acquisition unit can perform filtering based on the user's current purchasing patterns and areas of interest when acquiring receipt images. For example, the acquisition unit can filter receipt images based on product categories frequently purchased by the user. The acquisition unit can also filter receipt images based on the user's areas of interest (health foods, eco-friendly products, etc.). The acquisition unit can also analyze the user's purchasing patterns and preferentially acquire highly relevant receipt images. This makes it possible to acquire highly relevant receipt images based on the user's purchasing patterns and areas of interest. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI. For example, the acquisition unit can input the user's purchasing pattern data into an AI model and have the AI ​​perform the filtering.

[0036] When acquiring receipt images, the acquisition unit can prioritize acquiring highly relevant receipts by taking into account the user's geographical location information. For example, when acquiring receipt images, the acquisition unit prioritizes acquiring highly relevant receipts by taking into account the user's geographical location information. For example, if the user is in a specific store, the acquisition unit prioritizes acquiring receipts from that store. Also, if the user is in a specific area, the acquisition unit can prioritize acquiring receipts from stores in that area. Also, the acquisition unit can prioritize acquiring receipts from stores close to the user's current location. This makes it possible to acquire highly relevant receipts based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data into an AI model and cause the AI ​​to acquire highly relevant receipts.

[0037] The acquisition unit can analyze the user's social media activity when acquiring a receipt image and acquire related receipts. For example, the acquisition unit can analyze the user's social media activity when acquiring a receipt image and acquire related receipts. For example, the acquisition unit can prioritize acquiring receipts related to products mentioned by the user on social media. The acquisition unit can also prioritize acquiring receipts from stores where the user has checked in on social media. The acquisition unit can also acquire related receipts based on the user's purchasing intentions on social media. This makes it possible to acquire related receipts based on the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI. For example, the acquisition unit can input the user's social media data into an AI model and have the AI ​​acquire related receipts.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the receipt during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the receipt during analysis. For example, the analysis unit performs a detailed analysis on receipts with high importance. The analysis unit can also perform a concise analysis on receipts with low importance. The analysis unit can also adjust the depth of the analysis depending on the importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the receipt. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt importance data into an AI model and have the AI ​​adjust the level of detail.

[0039] The analysis unit can apply different analysis algorithms depending on the receipt category during analysis. For example, the analysis unit applies different analysis algorithms depending on the receipt category during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes nutritional information to receipts in the food category. The analysis unit can also apply an analysis algorithm that emphasizes price information to receipts in the home appliance category. The analysis unit can also apply an analysis algorithm that emphasizes brand information to receipts in the clothing category. This makes it possible to apply the optimal analysis algorithm depending on the receipt category. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt category data into an AI model and have the AI ​​apply the analysis algorithm.

[0040] The analysis unit can determine the analysis priority based on the submission date of the receipt during analysis. The analysis unit can, for example, determine the analysis priority based on the submission date of the receipt during analysis. For example, the analysis unit prioritizes the analysis of the most recent receipt. The analysis unit can also postpone the analysis of older receipts. The analysis unit can also adjust the analysis priority based on the submission date. This makes it possible to determine the analysis priority based on the submission date of the receipt. Some or all of the above-described processing by the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input receipt submission date data into an AI model and have AI determine the priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of receipts during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of receipts during analysis. For example, the analysis unit prioritizes analyzing receipts related to the user's current purchasing pattern. The analysis unit can also prioritize analyzing receipts related to the user's areas of interest. The analysis unit can also adjust the order of analysis according to the relevance of receipts. This makes it possible to adjust the order of analysis based on the relevance of receipts. Some or all of the above-described processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt relevance data into an AI model and have AI adjust the order.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high importance. The suggestion unit can also make a concise suggestion for a product with low importance. The suggestion unit can also adjust the depth of the suggestion according to the importance. This makes it possible to adjust the level of detail of the suggestion according to the importance of the product. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product importance data into an AI model and cause AI to adjust the level of detail.

[0043] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies a suggestion algorithm that emphasizes nutritional information to products in the food category. The suggestion unit can also apply a suggestion algorithm that emphasizes price information to products in the home appliance category. The suggestion unit can also apply a suggestion algorithm that emphasizes brand information to products in the clothing category. This makes it possible to apply the optimal suggestion algorithm depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product category data into an AI model and cause AI to apply the suggestion algorithm.

[0044] The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the submission time of the products when making suggestions. For example, the suggestion unit preferentially suggests the latest products. The suggestion unit can also postpone products that were submitted earlier. The suggestion unit can also adjust the priority of suggestions according to the submission time. This makes it possible to determine the priority of suggestions based on the submission time of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product submission time data into an AI model and have AI determine the priority.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit preferentially suggests products related to the user's current purchasing pattern. The suggestion unit can also preferentially suggest products related to the user's field of interest. The suggestion unit can also adjust the order of suggestions according to the relevance of products. This makes it possible to adjust the order of suggestions based on the relevance of products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product relevance data into an AI model and have AI adjust the order.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The proposed system may further include a prediction unit that analyzes the user's purchasing history and predicts future purchases based on past purchasing patterns. For example, the prediction unit may identify products that the user has frequently purchased in the past and predict when those products are likely to be purchased again. The prediction unit may also predict products that the user is likely to purchase based on seasons or events. Furthermore, the prediction unit may predict purchasing patterns based on changes in the user's lifestyle (e.g., moving or changes in family composition). This may help the user plan future purchases and prepare necessary products in advance.

[0048] The acquisition unit can also recognize the user's voice commands and acquire receipt images through voice input. For example, if the user commands "take a picture of the receipt," the acquisition unit will follow the command and activate the smartphone's camera to acquire a receipt image. The acquisition unit can also prioritize the acquisition of receipts from specific stores or products based on the voice command. Furthermore, the acquisition unit can analyze the voice command, understand the user's intention, and select the optimal acquisition method. This allows the user to easily acquire receipt images without using their hands.

[0049] The analysis unit can further combine the user's purchasing history with current market trends to predict future price fluctuations of purchased items. For example, the analysis unit can analyze past price data and current market trends to predict when a particular item is likely to increase in price. The analysis unit can also predict price fluctuations of items based on seasons or events. Furthermore, the analysis unit can make price predictions based on fluctuations in the supply chain or changes in economic conditions. This allows users to plan their purchases in anticipation of future price fluctuations.

[0050] The suggestion unit can further combine the user's purchase history and current health condition to suggest an optimal meal plan from the perspective of health management. For example, the suggestion unit can analyze the nutritional information of foods the user has purchased in the past and suggest a balanced meal plan. The suggestion unit can also customize the meal plan based on the user's health condition (e.g., allergies or deficiencies of specific nutrients). Furthermore, the suggestion unit can adjust the meal plan based on the user's lifestyle and exercise habits. This allows the user to select an optimal meal from the perspective of health management.

[0051] The suggestion unit can further combine the user's purchasing history with their current environmental awareness to suggest eco-friendly products. For example, the suggestion unit can analyze the environmental impact of products the user has purchased in the past and suggest more environmentally friendly alternatives. The suggestion unit can also select products based on the user's environmental awareness (e.g., plastic reduction and recycling). Furthermore, the suggestion unit can suggest eco-friendly products based on the user's lifestyle and local environmental policies. This allows the user to select environmentally friendly products.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The acquisition unit acquires an image of the receipt for the product purchased by the user. For example, the acquisition unit can take a picture of the receipt using a smartphone camera and upload the image to the system. The acquisition unit can also digitize the receipt using a scanner and acquire it as image data. Furthermore, the acquisition unit can convert the contents of the receipt into text data using OCR technology. Step 2: The analysis unit uses the generation AI to analyze the image of the receipt acquired by the acquisition unit and extract information about the purchased product. The analysis unit extracts information such as the product name, price, and quantity written on the receipt. The generation AI analyzes the contents of the receipt using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit suggests alternative products based on the information extracted by the analysis unit, taking into account rising prices and health management. For example, the suggestion unit analyzes the price information of the purchased product and suggests cheaper alternative products of the same product but from different manufacturers. The suggestion unit also analyzes the nutritional information of the purchased product and suggests lower-calorie alternative products of the same product but from different manufacturers. For example, the suggestion unit uses a generation AI to analyze the price information and nutritional information of the purchased product and suggests the optimal alternative.

[0054] (Example 2) A proposal system according to an embodiment of the present invention automatically reads purchase receipts and uses a generation AI to suggest alternative products from the perspective of rising prices and health management. In this proposal system, a user photographs the receipt for a purchased item using a smartphone or other device and uploads it to the system. The generation AI then analyzes the receipt and extracts information about the purchased item. Based on this information, the system makes the following proposals. For example, to suggest alternative products to address rising prices, the generation AI analyzes the price information of the purchased item and suggests cheaper alternatives to the same item but from different manufacturers. For example, if a user purchases a specific brand of milk, the generation AI suggests cheaper alternatives from other brands of milk. Furthermore, to suggest alternatives from a health management perspective, the generation AI analyzes the nutritional information of the purchased item and suggests lower-calorie alternatives to the same item but from different manufacturers. For example, if a user purchases a specific brand of yogurt, the generation AI suggests lower-calorie alternatives from other brands of yogurt. This system allows users to select optimal products from a health management perspective while also addressing rising prices. This allows the proposal system to select optimal products from a health management perspective.

[0055] The proposal system according to the embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit acquires an image of a receipt for a product purchased by a user. For example, the acquisition unit can take a photo of the receipt using a smartphone camera and upload the image to the system. The acquisition unit can also digitize the receipt using a scanner and acquire it as image data. The acquisition unit can also convert the contents of the receipt into text data using OCR technology. For example, the acquisition unit can analyze a receipt image taken with a smartphone camera using OCR technology and convert it into text data. The analysis unit uses a generation AI to analyze the image of the receipt acquired by the acquisition unit and extract information about the purchased product. The analysis unit extracts information such as the product name, price, and quantity listed on the receipt. The generation AI analyzes the contents of the receipt using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI receives an image of the receipt as input and extracts information such as the product name and price. The proposal unit suggests alternative products based on the information extracted by the analysis unit, taking into account factors such as rising prices and health management. The suggestion unit, for example, analyzes price information of a purchased product and suggests a cheaper substitute for the same product but from a different manufacturer. The suggestion unit also analyzes nutritional information of the purchased product and suggests a lower-calorie substitute for the same product but from a different manufacturer. For example, the suggestion unit uses a generation AI to analyze price information and nutritional information of a purchased product and suggests an optimal substitute. This allows the suggestion system according to the embodiment to enable the user to select the optimal product from the perspective of rising prices and health management.

[0056] The acquisition unit can acquire images of receipts uploaded by users. For example, the acquisition unit can upload images of receipts taken by users with a smartphone camera to the system. The acquisition unit can save the uploaded images of receipts in a database and pass them on to the analysis unit. For example, the acquisition unit allows a user to take an image of a receipt using a smartphone app and upload the image to the system. The acquisition unit saves the uploaded image in a database and passes it on to the analysis unit. This ensures that images of receipts uploaded by users can be acquired reliably. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input images of receipts uploaded by users into an AI model and have the AI ​​acquire the images.

[0057] The analysis unit can analyze the contents of the receipt and extract information about the purchased items. The analysis unit can analyze the contents of the receipt using, for example, OCR technology and extract information about the purchased items. For example, the analysis unit can extract information such as the product name, price, and quantity written on the receipt. The analysis unit can also analyze the contents of the receipt using a generation AI. For example, the generation AI can receive an image of the receipt as input and extract information such as the product name and price. The generation AI can analyze the contents of the receipt using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive an image of the receipt as input and extract information such as the product name and price. This allows the receipt to be accurately analyzed and information about the purchased items to be extracted. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input an image of the receipt into an AI model and have the AI ​​extract product information.

[0058] The suggestion unit can analyze price information of a purchased product and suggest a cheaper alternative to the same product but from a different manufacturer. For example, the suggestion unit can analyze price information of a purchased product and suggest a cheaper alternative to the same product but from a different manufacturer. For example, if a user purchases a specific brand of milk, the suggestion unit can suggest a cheaper alternative to other brands of milk. The suggestion unit can also analyze price information of a purchased product using a generation AI. For example, the generation AI can receive price information of a purchased product as input and suggest a cheaper alternative to the same product but from a different manufacturer. The generation AI can analyze price information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive price information of a purchased product as input and suggest a cheaper alternative to the same product but from a different manufacturer. This allows the user to respond to rising prices and select a cheaper alternative. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input price information of a purchased product into an AI model and cause the AI ​​to suggest a cheaper alternative.

[0059] The suggestion unit can analyze the nutritional information of a purchased product and suggest low-calorie substitutes for the same product but from different manufacturers. For example, the suggestion unit can analyze the nutritional information of a purchased product and suggest low-calorie substitutes for the same product but from different manufacturers. For example, if a user purchases a specific brand of yogurt, the suggestion unit can suggest other brands of yogurt that are lower in calories. The suggestion unit can also analyze the nutritional information of a purchased product using a generation AI. For example, the generation AI can receive the nutritional information of a purchased product as input and suggest low-calorie substitutes for the same product but from different manufacturers. The generation AI can analyze the nutritional information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI can receive the nutritional information of a purchased product as input and suggest low-calorie substitutes for the same product but from different manufacturers. This allows the user to select the optimal product from a health management perspective. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the nutritional information of a purchased product into an AI model and cause the AI ​​to suggest low-calorie substitutes.

[0060] The acquisition unit can estimate the user's emotions and adjust the timing of receipt image acquisition based on the estimated user emotions. For example, the acquisition unit can estimate the user's emotions and adjust the timing of receipt image acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can prompt the user to acquire a receipt image at a time when the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can notify the user to quickly acquire a receipt image. Furthermore, if the user is relaxed, the acquisition unit can automatically acquire a receipt image without the user noticing. This allows receipt images to be acquired at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using AI, or without AI. For example, the acquisition unit can input the user's emotion data into an AI model and have the AI ​​adjust the acquisition timing.

[0061] The acquisition unit can analyze the user's past receipt acquisition history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past receipt acquisition history and selects the optimal acquisition method. For example, the acquisition unit prioritizes and suggests acquisition methods (manual, voice instruction, etc.) that the user has frequently used in the past. The acquisition unit can also suggest a method for acquiring receipts during a specific time period based on the user's past acquisition history. The acquisition unit can also analyze the user's past acquisition history and suggest the most efficient acquisition method. This makes it possible to select the optimal acquisition method based on the user's past history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past acquisition history data into an AI model and have the AI ​​select the optimal acquisition method.

[0062] The acquisition unit can perform filtering based on the user's current purchasing patterns and areas of interest when acquiring receipt images. For example, the acquisition unit can perform filtering based on the user's current purchasing patterns and areas of interest when acquiring receipt images. For example, the acquisition unit can filter receipt images based on product categories frequently purchased by the user. The acquisition unit can also filter receipt images based on the user's areas of interest (health foods, eco-friendly products, etc.). The acquisition unit can also analyze the user's purchasing patterns and preferentially acquire highly relevant receipt images. This makes it possible to acquire highly relevant receipt images based on the user's purchasing patterns and areas of interest. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI. For example, the acquisition unit can input the user's purchasing pattern data into an AI model and have the AI ​​perform the filtering.

[0063] The acquisition unit can estimate the user's emotions and determine the priority of receipt images to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of receipt images to be acquired based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit may postpone acquiring receipt images of lower importance. Furthermore, if the user is relaxed, the acquisition unit may prioritize acquiring receipt images of higher importance. Furthermore, if the user is in a hurry, the acquisition unit may prioritize acquiring receipt images that can be acquired quickly. This allows the priority of receipt images to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without AI. For example, the acquisition unit may input the user's emotion data into an AI model and have the AI ​​determine the priority.

[0064] When acquiring receipt images, the acquisition unit can prioritize acquiring highly relevant receipts by taking into account the user's geographical location information. For example, when acquiring receipt images, the acquisition unit prioritizes acquiring highly relevant receipts by taking into account the user's geographical location information. For example, if the user is in a specific store, the acquisition unit prioritizes acquiring receipts from that store. Also, if the user is in a specific area, the acquisition unit can prioritize acquiring receipts from stores in that area. Also, the acquisition unit can prioritize acquiring receipts from stores close to the user's current location. This makes it possible to acquire highly relevant receipts based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data into an AI model and cause the AI ​​to acquire highly relevant receipts.

[0065] The acquisition unit can analyze the user's social media activity when acquiring a receipt image and acquire related receipts. For example, the acquisition unit can analyze the user's social media activity when acquiring a receipt image and acquire related receipts. For example, the acquisition unit can prioritize acquiring receipts related to products mentioned by the user on social media. The acquisition unit can also prioritize acquiring receipts from stores where the user has checked in on social media. The acquisition unit can also acquire related receipts based on the user's purchasing intentions on social media. This makes it possible to acquire related receipts based on the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI. For example, the acquisition unit can input the user's social media data into an AI model and have the AI ​​acquire related receipts.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually easy-to-understand analysis results when the user is stressed. This allows the analysis presentation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI model and have the AI ​​adjust the presentation method.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the receipt during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the receipt during analysis. For example, the analysis unit performs a detailed analysis on receipts with high importance. The analysis unit can also perform a concise analysis on receipts with low importance. The analysis unit can also adjust the depth of the analysis depending on the importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the receipt. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt importance data into an AI model and have the AI ​​adjust the level of detail.

[0068] The analysis unit can apply different analysis algorithms depending on the receipt category during analysis. For example, the analysis unit applies different analysis algorithms depending on the receipt category during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes nutritional information to receipts in the food category. The analysis unit can also apply an analysis algorithm that emphasizes price information to receipts in the home appliance category. The analysis unit can also apply an analysis algorithm that emphasizes brand information to receipts in the clothing category. This makes it possible to apply the optimal analysis algorithm depending on the receipt category. Some or all of the above-mentioned processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt category data into an AI model and have the AI ​​apply the analysis algorithm.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI model and have the AI ​​adjust the length of the analysis.

[0070] The analysis unit can determine the analysis priority based on the submission date of the receipt during analysis. The analysis unit can, for example, determine the analysis priority based on the submission date of the receipt during analysis. For example, the analysis unit prioritizes the analysis of the most recent receipt. The analysis unit can also postpone the analysis of older receipts. The analysis unit can also adjust the analysis priority based on the submission date. This makes it possible to determine the analysis priority based on the submission date of the receipt. Some or all of the above-described processing by the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input receipt submission date data into an AI model and have AI determine the priority.

[0071] The analysis unit can adjust the order of analysis based on the relevance of receipts during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of receipts during analysis. For example, the analysis unit prioritizes analyzing receipts related to the user's current purchasing pattern. The analysis unit can also prioritize analyzing receipts related to the user's areas of interest. The analysis unit can also adjust the order of analysis according to the relevance of receipts. This makes it possible to adjust the order of analysis based on the relevance of receipts. Some or all of the above-described processing by the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input receipt relevance data into an AI model and have AI adjust the order.

[0072] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually easy-to-understand suggestions when the user is stressed. This allows the way the suggestion is expressed to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI model and cause the AI ​​to adjust the way the suggestion is expressed.

[0073] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high importance. The suggestion unit can also make a concise suggestion for a product with low importance. The suggestion unit can also adjust the depth of the suggestion according to the importance. This makes it possible to adjust the level of detail of the suggestion according to the importance of the product. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product importance data into an AI model and cause AI to adjust the level of detail.

[0074] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the product category when making a suggestion. For example, the suggestion unit applies a suggestion algorithm that emphasizes nutritional information to products in the food category. The suggestion unit can also apply a suggestion algorithm that emphasizes price information to products in the home appliance category. The suggestion unit can also apply a suggestion algorithm that emphasizes brand information to products in the clothing category. This makes it possible to apply the optimal suggestion algorithm depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product category data into an AI model and cause AI to apply the suggestion algorithm.

[0075] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. This allows the length of the suggestion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI model and cause the AI ​​to adjust the length of the suggestion.

[0076] The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit, for example, determines the priority of suggestions based on the submission time of the products when making suggestions. For example, the suggestion unit preferentially suggests the latest products. The suggestion unit can also postpone products that were submitted earlier. The suggestion unit can also adjust the priority of suggestions according to the submission time. This makes it possible to determine the priority of suggestions based on the submission time of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product submission time data into an AI model and have AI determine the priority.

[0077] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit preferentially suggests products related to the user's current purchasing pattern. The suggestion unit can also preferentially suggest products related to the user's field of interest. The suggestion unit can also adjust the order of suggestions according to the relevance of products. This makes it possible to adjust the order of suggestions based on the relevance of products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product relevance data into an AI model and have AI adjust the order. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can photograph a receipt using the camera 42 of the smart device 14 and upload the image to the data processing device 12. The acquisition unit can also analyze the receipt image photographed by the camera 42 of the smart device 14 using OCR technology and convert it into text data. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the contents of the receipt using a generation AI and extracts information about the purchased product. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes price information and nutritional information about the purchased product and suggests optimal substitutes. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can photograph a receipt using the camera 42 of the smart glasses 214 and upload the image to the data processing device 12. The acquisition unit can also analyze the receipt image photographed by the camera 42 of the smart glasses 214 using OCR technology and convert it into text data. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the contents of the receipt using a generation AI and extracts information about the purchased product. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes price information and nutritional information of the purchased product and suggests optimal substitutes. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can take a picture of a receipt using the camera 42 of the headset terminal 314 and upload the image to the data processing device 12. The acquisition unit can also analyze the receipt image taken by the camera 42 of the headset terminal 314 using OCR technology and convert it into text data. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the contents of the receipt using a generation AI and extracts information about the purchased product. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes price information and nutritional information about the purchased product and suggests optimal substitutes. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can photograph a receipt using the camera 42 of the robot 414 and upload the image to the data processing device 12. The acquisition unit can also analyze the receipt image photographed by the camera 42 of the robot 414 using OCR technology and convert it into text data. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the contents of the receipt using a generation AI and extracts information about the purchased product. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes price information and nutritional information about the purchased product and suggests optimal substitutes.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The proposed system may further include a prediction unit that analyzes the user's purchasing history and predicts future purchases based on past purchasing patterns. For example, the prediction unit may identify products that the user has frequently purchased in the past and predict when those products are likely to be purchased again. The prediction unit may also predict products that the user is likely to purchase based on seasons or events. Furthermore, the prediction unit may predict purchasing patterns based on changes in the user's lifestyle (e.g., moving or changes in family composition). This may help the user plan future purchases and prepare necessary products in advance.

[0080] The acquisition unit can also recognize the user's voice commands and acquire receipt images through voice input. For example, if the user commands "take a picture of the receipt," the acquisition unit will follow the command and activate the smartphone's camera to acquire a receipt image. The acquisition unit can also prioritize the acquisition of receipts from specific stores or products based on the voice command. Furthermore, the acquisition unit can analyze the voice command, understand the user's intention, and select the optimal acquisition method. This allows the user to easily acquire receipt images without using their hands.

[0081] The analysis unit can further combine the user's purchasing history with current market trends to predict future price fluctuations of purchased items. For example, the analysis unit can analyze past price data and current market trends to predict when a particular item is likely to increase in price. The analysis unit can also predict price fluctuations of items based on seasons or events. Furthermore, the analysis unit can make price predictions based on fluctuations in the supply chain or changes in economic conditions. This allows users to plan their purchases in anticipation of future price fluctuations.

[0082] The suggestion unit can further combine the user's purchase history and current health condition to suggest an optimal meal plan from the perspective of health management. For example, the suggestion unit can analyze the nutritional information of foods the user has purchased in the past and suggest a balanced meal plan. The suggestion unit can also customize the meal plan based on the user's health condition (e.g., allergies or deficiencies of specific nutrients). Furthermore, the suggestion unit can adjust the meal plan based on the user's lifestyle and exercise habits. This allows the user to select an optimal meal from the perspective of health management.

[0083] The suggestion unit can further combine the user's purchasing history with their current environmental awareness to suggest eco-friendly products. For example, the suggestion unit can analyze the environmental impact of products the user has purchased in the past and suggest more environmentally friendly alternatives. The suggestion unit can also select products based on the user's environmental awareness (e.g., plastic reduction and recycling). Furthermore, the suggestion unit can suggest eco-friendly products based on the user's lifestyle and local environmental policies. This allows the user to select environmentally friendly products.

[0084] The acquisition unit can estimate the user's emotions and customize the receipt image acquisition method based on the estimated user emotions. For example, if the acquisition unit is stressed, the acquisition unit can enable the user to acquire a receipt image with a simple operation. If the user is relaxed, the acquisition unit can also provide detailed instructions to support the user in acquiring a receipt image. Furthermore, if the user is in a hurry, the acquisition unit can provide a shortcut to quickly acquire a receipt image. This makes it possible to provide the optimal acquisition method according to the user's emotions.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Alternatively, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results that are visually easy to understand when the user is feeling stressed. This makes it possible to adjust the presentation method of the analysis results according to the user's emotions.

[0086] The suggestion unit can estimate the user's emotions and customize the suggestion content based on the estimated user's emotions. For example, the suggestion unit can make detailed suggestions when the user is relaxed. Also, the suggestion unit can make concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can make visually easy-to-understand suggestions when the user is feeling stressed. In this way, the suggestion content can be customized according to the user's emotions.

[0087] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit selects the timing to make detailed suggestions. If the user is in a hurry, the suggestion unit can also quickly make concise suggestions. Furthermore, if the user is feeling stressed, the suggestion unit can also make suggestions at a timing when the user is relaxed. This allows suggestions to be made at the optimal timing according to the user's emotions.

[0088] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit can prioritize suggestions with high importance. Also, when the user is in a hurry, the suggestion unit can postpone suggestions with low importance. Furthermore, when the user is feeling stressed, the suggestion unit can prioritize suggestions that are easy to understand visually. In this way, the priority of suggestions can be determined according to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The acquisition unit acquires an image of the receipt for the product purchased by the user. For example, the acquisition unit can take a picture of the receipt using a smartphone camera and upload the image to the system. The acquisition unit can also digitize the receipt using a scanner and acquire it as image data. Furthermore, the acquisition unit can convert the contents of the receipt into text data using OCR technology. Step 2: The analysis unit uses the generation AI to analyze the image of the receipt acquired by the acquisition unit and extract information about the purchased product. The analysis unit extracts information such as the product name, price, and quantity written on the receipt. The generation AI analyzes the contents of the receipt using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit suggests alternative products based on the information extracted by the analysis unit, taking into account rising prices and health management. For example, the suggestion unit analyzes the price information of the purchased product and suggests cheaper alternative products of the same product but from different manufacturers. The suggestion unit also analyzes the nutritional information of the purchased product and suggests lower-calorie alternative products of the same product but from different manufacturers. For example, the suggestion unit uses a generation AI to analyze the price information and nutritional information of the purchased product and suggests the optimal alternative.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring an image of a receipt; an analysis unit that analyzes the image of the receipt acquired by the acquisition unit and extracts information about the purchased product; a suggestion unit that suggests alternative products from the perspective of price increases or health management based on the information extracted by the analysis unit. A system characterized by:

2. The acquisition unit Get the receipt image uploaded by the user The system of claim 1 .

3. The analysis unit Analyze the receipt and extract information about the purchased items The system of claim 1 .

4. The proposal unit Analyzes the price information of purchased items and suggests lower-priced alternatives to the same product from different manufacturers The system of claim 1 .

5. The proposal unit Analyzes the nutritional information of purchased products and suggests low-calorie alternatives to the same product from different manufacturers The system of claim 1 .

6. The acquisition unit To estimate a user's emotion and adjust the timing of acquiring a receipt image based on the estimated user's emotion. The system of claim 1 .

7. The acquisition unit Analyze the user's receipt acquisition history and select the optimal acquisition method The system of claim 1 .

8. The acquisition unit When capturing receipt images, filter them based on the user's current purchasing patterns and interests. The system of claim 1 .

Images