system

Abstract

The system according to the embodiment aims to analyze a user's food expense data and provide suggestions for effective household management. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives food expense data from a user. The analysis unit analyzes the data received by the reception unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit.

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 effectively utilized users' food expense data to sufficiently support household management, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's food expense data and provide suggestions for effective household management. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives food expense data from a user. The analysis unit analyzes the data received by the reception unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's food expense data and make suggestions for effective household 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 household management system according to an embodiment of the present invention allows users to efficiently manage and save on their daily food expenses. This household management system allows users to input their daily food expenses, and AI analyzes the data to identify food expense trends and wasteful spending patterns. It then makes suggestions for optimizing the user's food expenses. For example, the system suggests ways to reduce costs by buying specific ingredients in bulk, and recipes to reduce waste. This system allows users to efficiently manage and save on their food expenses. First, the user inputs their daily food expenses. This involves inputting detailed information about the ingredients purchased, including their prices and purchase dates. For example, the user enters information about vegetables, meat, and seasonings purchased at the supermarket. This information is then input into AI. Next, the AI ​​analyzes the input information. The AI ​​identifies food expense trends and wasteful spending patterns. For example, it can identify cases where a specific ingredient is frequently purchased or where a large amount of ingredient is wasted. This allows the user to understand their food expense trends. Furthermore, the AI ​​makes suggestions for optimizing the user's food expenses. For example, it suggests ways to reduce costs by buying specific ingredients in bulk, and recipes to reduce waste. This allows the user to efficiently manage and save on food expenses. This system allows users to efficiently manage and save on food expenses. For example, by utilizing recipes suggested by AI, waste can be reduced and food expenses can be cut. Also, by purchasing certain ingredients in bulk, costs can be reduced. This can improve food expenses, which are the biggest concern for those who manage their household finances. In this way, the household management system can efficiently manage and save on users' food expenses.

[0029] A household management system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives food expense data from a user. The food expense data from the user includes, but is not limited to, the items purchased, the amount, and the date and time of purchase. The reception unit can provide, for example, a method in which the user manually inputs the data or a method in which the data is automatically acquired by scanning a receipt. For example, the reception unit can allow the user to take a photo of a receipt using a smartphone camera and convert the contents of the receipt into text data using OCR technology. The reception unit can also allow the user to input food expense data using voice input. For example, the user can register the food expense data in the reception unit by vocally inputting, for example, "Today's food expense is 2,000 yen." The analysis unit analyzes the data received by the reception unit. The analysis unit can identify food expense trends and wasteful spending patterns using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can identify if the user frequently purchases certain ingredients or if the user wastes a lot of ingredients. The analysis unit can also learn from past data and make predictions. For example, the analysis unit can predict future food expense trends based on the user's past food expense data. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest ways to reduce costs by purchasing specific ingredients in bulk or recipes to reduce waste. For example, the suggestion unit can suggest recipes suitable for the user, taking into account the user's preferences and allergy information. The suggestion unit can also have a function to automatically generate a shopping list based on the proposed recipe. For example, the suggestion unit can list necessary ingredients based on the proposed recipe, allowing the user to shop efficiently. Furthermore, the suggestion unit can also have a function to display the nutritional value of the proposed recipe. For example, the suggestion unit can display the nutritional value, such as calories, vitamins, and minerals, of the proposed recipe, allowing the user to select meals that take nutritional balance into consideration. As a result, the household management system according to the embodiment can efficiently manage and save the user's food expenses.

[0030] The reception unit may have a receipt scanning function. Examples of the receipt scanning function include, but are not limited to, a method of converting the contents of a receipt into text data using OCR technology. For example, the reception unit may allow a user to take a photo of a receipt using a smartphone camera and convert the contents of the receipt into text data using OCR technology. The reception unit may also have a function to adjust the scan resolution. For example, scanning at high resolution can accurately read even small characters on a receipt. The reception unit may also have a function to automatically classify the data of scanned receipts. For example, analyzing the contents of a receipt and classifying them by ingredient can make it easier for users to review the data later. This allows users to easily enter food expense data by scanning a receipt. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input image data of a scanned receipt into a generation AI and have the generation AI convert the image data into text data.

[0031] The analysis unit can learn from past data and make predictions. Specific methods for learning from past data include, but are not limited to, methods using machine learning algorithms. For example, the analysis unit can predict future food expense trends based on a user's past food expense data. The analysis unit can perform data cleaning and normalization as a data preprocessing method. For example, the analysis unit can improve the quality of the learning data by interpolating missing values ​​and scaling the data. The analysis unit can also use regression analysis, clustering, or classification algorithms as learning algorithms. For example, the analysis unit can predict increases or decreases in a user's food expenses using regression analysis. It can group users' food expense patterns using clustering and make common suggestions to users with similar patterns. It can classify users' food expense data into categories using a classification algorithm and make appropriate suggestions for each category. By learning from past data, more accurate analysis results can be obtained. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past food expense data into the generation AI and have the generation AI learn from the data and make predictions.

[0032] The suggestion unit may have a function to make suggestions taking into account the user's preferences and allergy information. Specific methods for collecting the user's preferences and allergy information include, but are not limited to, questionnaires and analysis of past data. For example, the suggestion unit may collect preference and allergy information by having the user respond to a questionnaire. The suggestion unit may also analyze the user's past food expense data to estimate the user's preferences and allergy information. For example, the suggestion unit may estimate the user's preferences and allergy information based on ingredients the user frequently purchases and ingredients the user avoids. The suggestion unit may suggest recipes suitable for the user based on the collected preference and allergy information. For example, the suggestion unit may suggest recipes using ingredients the user prefers or recipes that avoid allergies. This allows suggestions to be made taking into account the user's preferences and allergy information. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's preference and allergy information into the generation AI and cause the generation AI to suggest recipes suitable for the user.

[0033] The suggestion unit may have a function to automatically generate a shopping list based on a proposed recipe. Specific methods for generating a shopping list include, but are not limited to, a method of listing ingredients needed based on a proposed recipe. For example, the suggestion unit may automatically generate a list of ingredients needed based on a proposed recipe. The suggestion unit may consider the recipe's portion sizes and the user's family composition to determine the quantities of listed ingredients. For example, the suggestion unit may calculate the quantity of ingredients needed based on the recipe's portion sizes and adjust the quantity according to the user's family composition. The suggestion unit may also have a function to display the shopping list on the user's smartphone or tablet. For example, the suggestion unit may display the generated shopping list on the user's smartphone so that the user can check the list while shopping. This improves shopping efficiency by automatically generating a shopping list based on a proposed recipe. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input data of the proposed recipe into a generation AI and cause the generation AI to generate a shopping list.

[0034] The suggestion unit may have a function to display the nutritional value of the proposed recipe. Specific methods for calculating nutritional value include, but are not limited to, methods for calculating calories, vitamins, and minerals. For example, the suggestion unit may calculate the calories of the proposed recipe and display them to the user. The suggestion unit may also calculate the vitamin and mineral content and display them to the user. The suggestion unit may provide a graphical, textual, or interactive display method for displaying nutritional value. For example, the suggestion unit may display the nutritional value of the proposed recipe in a graphical format, providing it in a format that is visually easy for the user to understand. The suggestion unit may also display detailed nutritional value information in text, allowing the user to check specific values. Furthermore, the suggestion unit may provide an interactive display method, allowing the user to click and check the detailed nutritional value information. This allows the user to select a nutritionally balanced meal by displaying the nutritional value of the proposed recipe. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data of the proposed recipe into a generation AI and cause the generation AI to calculate and display the nutritional value.

[0035] The reception unit can analyze the user's past food expense data input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice input or manual input) that the user has frequently used in the past. The reception unit can also send reminders that avoid time periods when the user has tended to forget to input data in the past. Furthermore, the reception unit can analyze patterns of data previously input by the user and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0036] When inputting food expense data, the reception unit can filter the data based on the user's current diet and health condition. For example, if the user is on a diet, the reception unit can prompt the user to refrain from inputting high-calorie ingredients. The reception unit can also prioritize the input of ingredients that are high in specific nutrients based on the results of a health checkup. Furthermore, if the user has allergies, the reception unit can filter the data to avoid inputting ingredients that are allergic to the user. This allows for more appropriate data input by filtering data based on the user's diet and health condition. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data into a generation AI and have the generation AI perform filtering.

[0037] When inputting food expense data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is at a particular supermarket, the reception unit can prioritize inputting data on ingredients purchased at that supermarket. In addition, if the user is eating out, the reception unit can also input food expense data based on restaurant menu information. Furthermore, if the user is traveling, the reception unit can also input food expense data based on information on local ingredients and restaurants. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and have the generation AI select highly relevant data.

[0038] When inputting food expense data, the reception unit can analyze the user's social media activity and input related data. For example, the reception unit can input food expense data based on photos of meals shared by the user on social media. The reception unit can also input food expense data based on information about restaurants the user has checked in to on social media. The reception unit can also input food expense data based on ingredients and recipes mentioned by the user on social media. This allows for efficient input of related data by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI select related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the food expense data. For example, the analysis unit can perform a detailed analysis on expensive food expense data to identify wasteful spending patterns. The analysis unit can also perform a simplified analysis on everyday food expense data to grasp overall trends. Furthermore, the analysis unit can perform a detailed analysis on specific ingredients to suggest cost-reduction methods. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data to a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of food expense data. For example, the analysis unit can perform an analysis of fresh foods taking into account expiration dates. For processed foods, the analysis unit can also perform an analysis of processed foods taking into account the balance between nutritional value and cost. Furthermore, for dining out expenses, the analysis unit can analyze the relationship between frequency and cost and make savings suggestions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data into the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of food expense data. For example, the analysis unit can prioritize analysis of recently submitted food expense data to grasp the latest trends. The analysis unit can also prioritize analysis of data submitted in a concentrated manner during a specific period to identify wasteful spending during that period. Furthermore, the analysis unit can prioritize analysis of data submitted periodically to grasp long-term trends. In this way, by determining the priority of analysis based on the time of submission of food expense data, the latest trends can be grasped. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data into a generation AI and have the generation AI determine the priority of analysis based on the time of submission.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of food expense data. For example, the analysis unit can analyze food expense data of the same category together to grasp overall trends. The analysis unit can also prioritize analyzing data related to specific ingredients to identify wasteful spending patterns. Furthermore, the analysis unit can prioritize analyzing data that is highly relevant to the user's past food expense data to grasp trends. This enables efficient analysis by adjusting the order of analysis based on the relevance of food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data to a generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0043] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the ingredient. For example, the suggestion unit can make detailed suggestions for expensive ingredients and suggest ways to reduce costs. The suggestion unit can also make simplified suggestions for everyday ingredients and grasp overall trends. Furthermore, the suggestion unit can make detailed suggestions for specific ingredients and suggest ways to reduce waste. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the ingredient. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion based on the importance.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of ingredient. For example, for fresh foods, the suggestion unit can make suggestions taking into account the expiration date. For processed foods, the suggestion unit can also make suggestions taking into account the balance between nutritional value and cost. Furthermore, for eating out expenses, the suggestion unit can analyze the relationship between frequency and cost and make suggestions for saving money. In this way, by applying different suggestion algorithms depending on the category of ingredient, more accurate suggestions are possible. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data into the generation AI and cause the generation AI to apply a suggestion algorithm depending on the category.

[0045] When making suggestions, the suggestion unit can determine the priority of suggestions based on the purchase date of the ingredients. For example, the suggestion unit can make suggestions to consume ingredients that are close to their expiration date as soon as possible. The suggestion unit can also make suggestions to consume ingredients purchased in bulk efficiently. Furthermore, the suggestion unit can make suggestions for seasonal ingredients that are in season. This enables efficient suggestions by determining the priority of suggestions based on the purchase date of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data into the generation AI and cause the generation AI to determine the priority of suggestions based on the purchase date.

[0046] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit can group ingredients from the same category together to suggest ways to consume them efficiently. The suggestion unit can also prioritize suggestions of recipes related to specific ingredients to suggest ways to reduce waste. Furthermore, the suggestion unit can prioritize suggestions of ingredients that are highly relevant to the user's past food expense data to identify trends. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of ingredients. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input ingredient data into a generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0047] The receipt scanning function can analyze the user's past scanning history to select the optimal scanning method when scanning a receipt. For example, the receipt scanning function can prioritize scanning methods (such as manual scanning or automatic scanning) that the user has frequently used in the past. The receipt scanning function can also send reminders that avoid time periods when the user has tended to forget to scan in the past. Furthermore, the receipt scanning function can analyze patterns in data scanned by the user in the past and suggest the most efficient scanning method. This allows the optimal scanning method to be suggested by analyzing the user's past scanning history. Some or all of the above-described processing in the receipt scanning function may be performed using, or without, AI. For example, the receipt scanning function can input the user's past scan data into a generation AI and have the generation AI select the optimal scanning method.

[0048] The receipt scanning function can prioritize scanning highly relevant receipts by taking into account the user's geographical location information when scanning receipts. For example, if the user is at a particular supermarket, the receipt scanning function can prioritize scanning receipts purchased at that supermarket. The receipt scanning function can also prioritize scanning restaurant receipts if the user is eating out. Furthermore, if the user is traveling, the receipt scanning function can prioritize scanning local receipts. This allows highly relevant receipts to be scanned by taking the user's geographical location information into account. Some or all of the above-described processing in the receipt scanning function may be performed using AI, for example, or without AI. For example, the receipt scanning function can input the user's geographical location data into a generation AI and have the generation AI select highly relevant receipts.

[0049] The function for making predictions by learning from past data can optimize the learning algorithm by referring to past learning data during learning. The function for making predictions by learning from past data can, for example, select an optimal algorithm based on past learning data and perform learning. The function for making predictions by learning from past data can also extract specific patterns from past learning data and optimize the algorithm. Furthermore, the function for making predictions by learning from past data can analyze past learning data and propose the most efficient learning method. This allows the optimal learning algorithm to be applied by referring to past learning data. Some or all of the above-mentioned processing in the function for making predictions by learning from past data can be performed, for example, using AI or without AI. For example, the function for making predictions by learning from past data can input past learning data into a generation AI and have the generation AI optimize the algorithm.

[0050] The function for learning from past data and making predictions can weight the learning data based on the time of submission of food expense data during learning. For example, the function for learning from past data and making predictions can emphasize recently submitted food expense data and learn the latest trends. The function for learning from past data and making predictions can also emphasize data submitted in a concentrated manner during a specific period and learn wasteful spending during that period. Furthermore, the function for learning from past data and making predictions can emphasize periodically submitted data and learn long-term trends. Thus, by weighting the learning data based on the time of submission of food expense data, the latest trends can be learned. Some or all of the above-described processing in the function for learning from past data and making predictions can be performed using, for example, AI, or without AI. For example, the function for learning from past data and making predictions can input food expense data into a generation AI and have the generation AI weight the learning data based on the time of submission.

[0051] The function that considers a user's preferences and allergy information can select the optimal input method by referring to the user's past dietary history when inputting preferences and allergy information. The function that considers a user's preferences and allergy information can, for example, suggest input of preferences based on ingredients that the user has frequently eaten in the past. The function that considers a user's preferences and allergy information can also suggest input of allergy information based on ingredients that the user has avoided in the past. Furthermore, the function that considers a user's preferences and allergy information can analyze the user's past dietary history and suggest the most efficient input method. In this way, the optimal input method can be suggested by referring to the user's past dietary history. Some or all of the above-described processing in the function that considers a user's preferences and allergy information can be performed, for example, using AI or without AI. For example, the function that considers a user's preferences and allergy information can input the user's past dietary data into a generation AI and have the generation AI select the optimal input method.

[0052] The function that considers a user's preferences and allergy information can prioritize inputting highly relevant information by taking into account the user's geographical location information when inputting preference and allergy information. For example, when a user is in a specific region, the function that considers a user's preferences and allergy information can prioritize inputting preferences and allergy information for ingredients common in that region. Furthermore, when a user is traveling, the function that considers a user's preferences and allergy information can also prioritize inputting preference and allergy information based on information about local ingredients and restaurants. Furthermore, when a user is in a specific supermarket, the function that considers a user's preferences and allergy information can prioritize inputting preference and allergy information for ingredients purchased at that supermarket. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the function that considers a user's preferences and allergy information may be performed, for example, using AI, or may be performed without using AI. For example, the function that considers a user's preferences and allergy information can input the user's geographical location data into a generation AI and have the generation AI select highly relevant information.

[0053] The function for automatically generating a shopping list based on a proposed recipe can generate an optimal list by referring to the user's past shopping history when generating the shopping list. The function for automatically generating a shopping list based on a proposed recipe can, for example, generate a shopping list based on ingredients that the user frequently purchased in the past. The function for automatically generating a shopping list based on a proposed recipe can also analyze patterns of ingredients that the user has purchased in the past to generate the most efficient list. Furthermore, the function for automatically generating a shopping list based on a proposed recipe can also generate a list that is efficient by referring to the user's past shopping history. In this way, an optimal shopping list can be generated by referring to the user's past shopping history. Some or all of the above-described processing in the function for automatically generating a shopping list based on a proposed recipe may be performed, for example, using AI or may be performed without using AI. For example, the function for automatically generating a shopping list based on a proposed recipe can input the user's past shopping data into a generation AI and have the generation AI generate an optimal list.

[0054] The function for automatically generating a shopping list based on a proposed recipe can prioritize generating a highly relevant list by taking into account the user's geographical location information when generating the shopping list. For example, when a user is at a particular supermarket, the function for automatically generating a shopping list based on a proposed recipe can prioritize adding ingredients that can be purchased at that supermarket to the list. Furthermore, when a user is traveling, the function for automatically generating a shopping list based on a proposed recipe can also generate a list based on information about local ingredients and restaurants. Furthermore, when a user is in a particular region, the function for automatically generating a shopping list based on a proposed recipe can prioritize adding ingredients that are common in that region to the list. This allows for the generation of a highly relevant shopping list by taking into account the user's geographical location information. Some or all of the above-described processing in the function for automatically generating a shopping list based on a proposed recipe may be performed using, or without, AI. For example, the function for automatically generating a shopping list based on a proposed recipe can input the user's geographical location data into a generation AI and cause the generation AI to generate a highly relevant list.

[0055] The function for displaying the nutritional value of a proposed recipe can select the optimal display method by referring to the user's past dietary history when displaying nutritional values. The function for displaying the nutritional value of a proposed recipe can, for example, suggest a display method based on the nutritional values ​​of ingredients that the user has frequently eaten in the past. The function for displaying the nutritional value of a proposed recipe can also suggest a display method based on the nutritional values ​​of ingredients that the user has avoided in the past. Furthermore, the function for displaying the nutritional value of a proposed recipe can analyze the user's past dietary history and suggest the most efficient display method. In this way, the optimal nutritional value display method can be suggested by referring to the user's past dietary history. Some or all of the above-described processing in the function for displaying the nutritional value of a proposed recipe may be performed, for example, using AI or without AI. For example, the function for displaying the nutritional value of a proposed recipe can input the user's past dietary data into a generation AI and have the generation AI select the optimal display method.

[0056] The function for displaying the nutritional value of a suggested recipe can prioritize relevant nutritional values ​​when displaying nutritional values, taking into account the user's geographical location information. For example, if the user is in a specific area, the function for displaying the nutritional value of a suggested recipe can prioritize the nutritional values ​​of ingredients common in that area. Furthermore, if the user is traveling, the function for displaying the nutritional value of a suggested recipe can also prioritize the nutritional values ​​of ingredients purchased at a specific supermarket when the user is in that supermarket. This allows for the prioritized display of relevant nutritional values ​​by taking into account the user's geographical location information. Some or all of the above-described processing in the function for displaying the nutritional value of a suggested recipe may be performed using AI, for example, or may be performed without AI. For example, the function for displaying the nutritional value of a suggested recipe can input the user's geographical location data into a generation AI and cause the generation AI to display relevant nutritional values.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When accepting a user's food expense data, the reception unit can automatically complete the input by referencing the user's purchase history. For example, it can automatically suggest ingredients that the user has frequently purchased in the past, eliminating the need for input. The reception unit can also suggest the optimal purchase timing by taking into account price fluctuations of ingredients that the user has purchased in the past. Furthermore, the reception unit can estimate the inventory status of specific ingredients based on the user's purchase history and send reminders when inventory is low. This allows the user to efficiently enter food expense data and avoid unnecessary purchases.

[0059] When analyzing the user's food expense data, the analysis unit can compare costs by referring to food ingredient price data by region. For example, the analysis unit can compare food ingredient prices in the user's region with those in other regions and suggest the most cost-effective purchasing locations. The analysis unit can also suggest the optimal time to purchase ingredients, taking into account seasonal fluctuations in food ingredient prices. Furthermore, the analysis unit can predict price trends for specific ingredients based on the user's food expense data and provide advice on future cost reductions. This allows the user to efficiently manage and save on food expenses.

[0060] The suggestion unit can have a function to suggest food storage methods based on the user's food expense data. For example, the suggestion unit can suggest the optimal storage temperature and storage period for a specific food item, thereby reducing food waste. The suggestion unit can also notify the user of the storage method for purchased food items as a reminder, encouraging them to consume the food at the appropriate time. Furthermore, the suggestion unit can also suggest cooking methods to maximize the nutritional value of the food items based on the storage method. This allows the user to use food items efficiently without wasting them.

[0061] The suggestion unit can have a function to suggest substitute ingredients based on the user's food expense data. For example, if a particular ingredient is expensive or difficult to obtain, the suggestion unit can suggest alternative ingredients with equivalent nutritional value. The suggestion unit can also suggest appropriate alternative ingredients taking into account the user's preferences and allergy information. Furthermore, the suggestion unit can suggest recipes using alternative ingredients, allowing the user to try new dishes. This allows the user to enjoy a wide variety of meals while saving on food expenses.

[0062] The suggestion unit can have a function to suggest where to purchase ingredients based on the user's food expense data. For example, the suggestion unit can compare prices at supermarkets and online stores in the user's area and suggest the most cost-effective place to purchase. The suggestion unit can also notify the user when a specific ingredient is on sale, allowing the user to purchase it at a lower price. Furthermore, the suggestion unit can provide point rewards and discount information at specific stores based on the user's purchase history. This allows the user to shop efficiently while saving on food expenses.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives food expense data from the user. The food expense data from the user includes the purchased items, the amount, the purchase date and time, etc. The reception unit provides a method for the user to manually enter the data or a method for automatically acquiring the data by scanning a receipt. For example, the user can take a photo of the receipt using the camera on their smartphone and convert the contents of the receipt into text data using OCR technology. The user can also enter food expense data using voice input. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit uses statistical analysis and machine learning algorithms to identify food expense trends and waste patterns. For example, it can identify when a user frequently purchases certain ingredients or wastes a lot of ingredients. It can also learn from past data to predict future food expense trends. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit suggests ways to reduce costs by buying specific ingredients in bulk, or recipes to reduce waste. For example, it can suggest recipes that are suitable for the user, taking into account the user's preferences and allergy information. It can also have a function to automatically generate a shopping list based on the suggested recipes, and a function to display the nutritional value of the suggested recipes.

[0065] (Example 2) A household management system according to an embodiment of the present invention allows users to efficiently manage and save on their daily food expenses. This household management system allows users to input their daily food expenses, and AI analyzes the data to identify food expense trends and wasteful spending patterns. It then makes suggestions for optimizing the user's food expenses. For example, the system suggests ways to reduce costs by buying specific ingredients in bulk, and recipes to reduce waste. This system allows users to efficiently manage and save on their food expenses. First, the user inputs their daily food expenses. This involves inputting detailed information about the ingredients purchased, including their prices and purchase dates. For example, the user enters information about vegetables, meat, and seasonings purchased at the supermarket. This information is then input into AI. Next, the AI ​​analyzes the input information. The AI ​​identifies food expense trends and wasteful spending patterns. For example, it can identify cases where a specific ingredient is frequently purchased or where a large amount of ingredient is wasted. This allows the user to understand their food expense trends. Furthermore, the AI ​​makes suggestions for optimizing the user's food expenses. For example, it suggests ways to reduce costs by buying specific ingredients in bulk, and recipes to reduce waste. This allows the user to efficiently manage and save on food expenses. This system allows users to efficiently manage and save on food expenses. For example, by utilizing recipes suggested by AI, waste can be reduced and food expenses can be cut. Also, by purchasing certain ingredients in bulk, costs can be reduced. This can improve food expenses, which are the biggest concern for those who manage their household finances. In this way, the household management system can efficiently manage and save on users' food expenses.

[0066] A household management system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives food expense data from a user. The food expense data from the user includes, but is not limited to, the items purchased, the amount, and the date and time of purchase. The reception unit can provide, for example, a method in which the user manually inputs the data or a method in which the data is automatically acquired by scanning a receipt. For example, the reception unit can allow the user to take a photo of a receipt using a smartphone camera and convert the contents of the receipt into text data using OCR technology. The reception unit can also allow the user to input food expense data using voice input. For example, the user can register the food expense data in the reception unit by vocally inputting, for example, "Today's food expense is 2,000 yen." The analysis unit analyzes the data received by the reception unit. The analysis unit can identify food expense trends and wasteful spending patterns using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can identify if the user frequently purchases certain ingredients or if the user wastes a lot of ingredients. The analysis unit can also learn from past data and make predictions. For example, the analysis unit can predict future food expense trends based on the user's past food expense data. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest ways to reduce costs by purchasing specific ingredients in bulk or recipes to reduce waste. For example, the suggestion unit can suggest recipes suitable for the user, taking into account the user's preferences and allergy information. The suggestion unit can also have a function to automatically generate a shopping list based on the proposed recipe. For example, the suggestion unit can list necessary ingredients based on the proposed recipe, allowing the user to shop efficiently. Furthermore, the suggestion unit can also have a function to display the nutritional value of the proposed recipe. For example, the suggestion unit can display the nutritional value, such as calories, vitamins, and minerals, of the proposed recipe, allowing the user to select meals that take nutritional balance into consideration. As a result, the household management system according to the embodiment can efficiently manage and save the user's food expenses.

[0067] The reception unit may have a receipt scanning function. Examples of the receipt scanning function include, but are not limited to, a method of converting the contents of a receipt into text data using OCR technology. For example, the reception unit may allow a user to take a photo of a receipt using a smartphone camera and convert the contents of the receipt into text data using OCR technology. The reception unit may also have a function to adjust the scan resolution. For example, scanning at high resolution can accurately read even small characters on a receipt. The reception unit may also have a function to automatically classify the data of scanned receipts. For example, analyzing the contents of a receipt and classifying them by ingredient can make it easier for users to review the data later. This allows users to easily enter food expense data by scanning a receipt. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input image data of a scanned receipt into a generation AI and have the generation AI convert the image data into text data.

[0068] The analysis unit can learn from past data and make predictions. Specific methods for learning from past data include, but are not limited to, methods using machine learning algorithms. For example, the analysis unit can predict future food expense trends based on a user's past food expense data. The analysis unit can perform data cleaning and normalization as a data preprocessing method. For example, the analysis unit can improve the quality of the learning data by interpolating missing values ​​and scaling the data. The analysis unit can also use regression analysis, clustering, or classification algorithms as learning algorithms. For example, the analysis unit can predict increases or decreases in a user's food expenses using regression analysis. It can group users' food expense patterns using clustering and make common suggestions to users with similar patterns. It can classify users' food expense data into categories using a classification algorithm and make appropriate suggestions for each category. By learning from past data, more accurate analysis results can be obtained. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past food expense data into the generation AI and have the generation AI learn from the data and make predictions.

[0069] The suggestion unit may have a function to make suggestions taking into account the user's preferences and allergy information. Specific methods for collecting the user's preferences and allergy information include, but are not limited to, questionnaires and analysis of past data. For example, the suggestion unit may collect preference and allergy information by having the user respond to a questionnaire. The suggestion unit may also analyze the user's past food expense data to estimate the user's preferences and allergy information. For example, the suggestion unit may estimate the user's preferences and allergy information based on ingredients the user frequently purchases and ingredients the user avoids. The suggestion unit may suggest recipes suitable for the user based on the collected preference and allergy information. For example, the suggestion unit may suggest recipes using ingredients the user prefers or recipes that avoid allergies. This allows suggestions to be made taking into account the user's preferences and allergy information. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's preference and allergy information into the generation AI and cause the generation AI to suggest recipes suitable for the user.

[0070] The suggestion unit may have a function to automatically generate a shopping list based on a proposed recipe. Specific methods for generating a shopping list include, but are not limited to, a method of listing ingredients needed based on a proposed recipe. For example, the suggestion unit may automatically generate a list of ingredients needed based on a proposed recipe. The suggestion unit may consider the recipe's portion sizes and the user's family composition to determine the quantities of listed ingredients. For example, the suggestion unit may calculate the quantity of ingredients needed based on the recipe's portion sizes and adjust the quantity according to the user's family composition. The suggestion unit may also have a function to display the shopping list on the user's smartphone or tablet. For example, the suggestion unit may display the generated shopping list on the user's smartphone so that the user can check the list while shopping. This improves shopping efficiency by automatically generating a shopping list based on a proposed recipe. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input data of the proposed recipe into a generation AI and cause the generation AI to generate a shopping list.

[0071] The suggestion unit may have a function to display the nutritional value of the proposed recipe. Specific methods for calculating nutritional value include, but are not limited to, methods for calculating calories, vitamins, and minerals. For example, the suggestion unit may calculate the calories of the proposed recipe and display them to the user. The suggestion unit may also calculate the vitamin and mineral content and display them to the user. The suggestion unit may provide a graphical, textual, or interactive display method for displaying nutritional value. For example, the suggestion unit may display the nutritional value of the proposed recipe in a graphical format, providing it in a format that is visually easy for the user to understand. The suggestion unit may also display detailed nutritional value information in text, allowing the user to check specific values. Furthermore, the suggestion unit may provide an interactive display method, allowing the user to click and check the detailed nutritional value information. This allows the user to select a nutritionally balanced meal by displaying the nutritional value of the proposed recipe. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data of the proposed recipe into a generation AI and cause the generation AI to calculate and display the nutritional value.

[0072] The reception unit can estimate the user's emotions and adjust the timing of food expense data input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can reduce the frequency of reminders and prompts for input. Furthermore, if the user is relaxed, the reception unit can periodically send reminders to prompt input. Furthermore, if the user is busy, the reception unit can provide an option to postpone input so that the data can be input all at once later. This reduces the burden of input by adjusting the timing of food expense data input 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, 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 reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past food expense data input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice input or manual input) that the user has frequently used in the past. The reception unit can also send reminders that avoid time periods when the user has tended to forget to input data in the past. Furthermore, the reception unit can analyze patterns of data previously input by the user and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0074] When inputting food expense data, the reception unit can filter the data based on the user's current diet and health condition. For example, if the user is on a diet, the reception unit can prompt the user to refrain from inputting high-calorie ingredients. The reception unit can also prioritize the input of ingredients that are high in specific nutrients based on the results of a health checkup. Furthermore, if the user has allergies, the reception unit can filter the data to avoid inputting ingredients that are allergic to the user. This allows for more appropriate data input by filtering data based on the user's diet and health condition. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data into a generation AI and have the generation AI perform filtering.

[0075] The reception unit can estimate the user's emotions and prioritize the food expense data to be entered based on the estimated user emotions. For example, if the user is tired, the reception unit can prompt the user to prioritize the input of only important food expense data. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed food expense data. Furthermore, if the user is in a hurry, the reception unit can provide a simple input method and allow the user to add details later. This enables efficient data entry by prioritizing input data 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, 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 reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] When inputting food expense data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is at a particular supermarket, the reception unit can prioritize inputting data on ingredients purchased at that supermarket. In addition, if the user is eating out, the reception unit can also input food expense data based on restaurant menu information. Furthermore, if the user is traveling, the reception unit can also input food expense data based on information on local ingredients and restaurants. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and have the generation AI select highly relevant data.

[0077] When inputting food expense data, the reception unit can analyze the user's social media activity and input related data. For example, the reception unit can input food expense data based on photos of meals shared by the user on social media. The reception unit can also input food expense data based on information about restaurants the user has checked in to on social media. The reception unit can also input food expense data based on ingredients and recipes mentioned by the user on social media. This allows for efficient input of related data by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI select related data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results to encourage deeper insights. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This makes the analysis results easier to understand by adjusting the presentation method of the analysis 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, 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the food expense data. For example, the analysis unit can perform a detailed analysis on expensive food expense data to identify wasteful spending patterns. The analysis unit can also perform a simplified analysis on everyday food expense data to grasp overall trends. Furthermore, the analysis unit can perform a detailed analysis on specific ingredients to suggest cost-reduction methods. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data to a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of food expense data. For example, the analysis unit can perform an analysis of fresh foods taking into account expiration dates. For processed foods, the analysis unit can also perform an analysis of processed foods taking into account the balance between nutritional value and cost. Furthermore, for dining out expenses, the analysis unit can analyze the relationship between frequency and cost and make savings suggestions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data into the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0081] 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 to encourage deeper insight. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis result becomes easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of food expense data. For example, the analysis unit can prioritize analysis of recently submitted food expense data to grasp the latest trends. The analysis unit can also prioritize analysis of data submitted in a concentrated manner during a specific period to identify wasteful spending during that period. Furthermore, the analysis unit can prioritize analysis of data submitted periodically to grasp long-term trends. In this way, by determining the priority of analysis based on the time of submission of food expense data, the latest trends can be grasped. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data into a generation AI and have the generation AI determine the priority of analysis based on the time of submission.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of food expense data. For example, the analysis unit can analyze food expense data of the same category together to grasp overall trends. The analysis unit can also prioritize analyzing data related to specific ingredients to identify wasteful spending patterns. Furthermore, the analysis unit can prioritize analyzing data that is highly relevant to the user's past food expense data to grasp trends. This enables efficient analysis by adjusting the order of analysis based on the relevance of food expense data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food expense data to a generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0084] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, visually easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper insight. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. This allows the suggestions to be more easily understood by adjusting the way the suggestions are presented based on 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 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 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 the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the ingredient. For example, the suggestion unit can make detailed suggestions for expensive ingredients and suggest ways to reduce costs. The suggestion unit can also make simplified suggestions for everyday ingredients and grasp overall trends. Furthermore, the suggestion unit can make detailed suggestions for specific ingredients and suggest ways to reduce waste. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the ingredient. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion based on the importance.

[0086] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of ingredient. For example, for fresh foods, the suggestion unit can make suggestions taking into account the expiration date. For processed foods, the suggestion unit can also make suggestions taking into account the balance between nutritional value and cost. Furthermore, for eating out expenses, the suggestion unit can analyze the relationship between frequency and cost and make suggestions for saving money. In this way, by applying different suggestion algorithms depending on the category of ingredient, more accurate suggestions are possible. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data into the generation AI and cause the generation AI to apply a suggestion algorithm depending on the category.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper insight. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This makes the suggestions easier to understand by adjusting the length of the suggestions according to the user's emotions. The 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] When making suggestions, the suggestion unit can determine the priority of suggestions based on the purchase date of the ingredients. For example, the suggestion unit can make suggestions to consume ingredients that are close to their expiration date as soon as possible. The suggestion unit can also make suggestions to consume ingredients purchased in bulk efficiently. Furthermore, the suggestion unit can make suggestions for seasonal ingredients that are in season. This enables efficient suggestions by determining the priority of suggestions based on the purchase date of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient data into the generation AI and cause the generation AI to determine the priority of suggestions based on the purchase date.

[0089] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit can group ingredients from the same category together to suggest ways to consume them efficiently. The suggestion unit can also prioritize suggestions of recipes related to specific ingredients to suggest ways to reduce waste. Furthermore, the suggestion unit can prioritize suggestions of ingredients that are highly relevant to the user's past food expense data to identify trends. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of ingredients. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input ingredient data into a generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0090] The receipt scanning function can estimate a user's emotions and adjust the timing of receipt scanning based on the estimated user emotions. For example, if a user is feeling stressed, the receipt scanning function can reduce the frequency of scan reminders and prompts. The receipt scanning function can also send periodic reminders to encourage scanning when the user is relaxed. Furthermore, if a user is busy, the receipt scanning function can provide an option to postpone scanning, allowing the user to scan all at once later. This reduces the burden of scanning by adjusting the timing of receipt scanning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the receipt scanning function may be performed using AI, or without AI. For example, the receipt scanning function can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The receipt scanning function can analyze the user's past scanning history to select the optimal scanning method when scanning a receipt. For example, the receipt scanning function can prioritize scanning methods (such as manual scanning or automatic scanning) that the user has frequently used in the past. The receipt scanning function can also send reminders that avoid time periods when the user has tended to forget to scan in the past. Furthermore, the receipt scanning function can analyze patterns in data scanned by the user in the past and suggest the most efficient scanning method. This allows the optimal scanning method to be suggested by analyzing the user's past scanning history. Some or all of the above-described processing in the receipt scanning function may be performed using, or without, AI. For example, the receipt scanning function can input the user's past scan data into a generation AI and have the generation AI select the optimal scanning method.

[0092] The receipt scanning function can estimate a user's emotions and prioritize which receipts to scan based on the estimated user emotions. For example, if a user is tired, the receipt scanning function can prompt the user to prioritize scanning only important receipts. The receipt scanning function can also prompt the user to scan detailed receipts if the user is relaxed. Furthermore, if a user is in a hurry, the receipt scanning function can provide a simple scanning method and allow the user to add details later. This enables efficient scanning by prioritizing which receipts to scan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the receipt scanning function may be performed using AI, or without AI. For example, the receipt scanning function can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The receipt scanning function can prioritize scanning highly relevant receipts by taking into account the user's geographical location information when scanning receipts. For example, if the user is at a particular supermarket, the receipt scanning function can prioritize scanning receipts purchased at that supermarket. The receipt scanning function can also prioritize scanning restaurant receipts if the user is eating out. Furthermore, if the user is traveling, the receipt scanning function can prioritize scanning local receipts. This allows highly relevant receipts to be scanned by taking the user's geographical location information into account. Some or all of the above-described processing in the receipt scanning function may be performed using AI, for example, or without AI. For example, the receipt scanning function can input the user's geographical location data into a generation AI and have the generation AI select highly relevant receipts.

[0094] The function for learning from past data and making predictions can estimate a user's emotions and select training data based on the estimated user emotions. For example, when a user is feeling stressed, the function for learning from past data and making predictions can prioritize learning simple, visually easy-to-understand data. Furthermore, when a user is relaxed, the function for learning from past data and making predictions can also learn detailed data to facilitate deeper insights. Furthermore, when a user is in a hurry, the function for learning from past data and making predictions can prioritize learning concise data that covers the key points. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) and multimodal generation AIs. Some or all of the above-described processing in the function for learning from past data and making predictions can be performed using, for example, AI, or without AI. For example, the function for learning from past data and making predictions can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The function for making predictions by learning from past data can optimize the learning algorithm by referring to past learning data during learning. The function for making predictions by learning from past data can, for example, select an optimal algorithm based on past learning data and perform learning. The function for making predictions by learning from past data can also extract specific patterns from past learning data and optimize the algorithm. Furthermore, the function for making predictions by learning from past data can analyze past learning data and propose the most efficient learning method. This allows the optimal learning algorithm to be applied by referring to past learning data. Some or all of the above-mentioned processing in the function for making predictions by learning from past data can be performed, for example, using AI or without AI. For example, the function for making predictions by learning from past data can input past learning data into a generation AI and have the generation AI optimize the algorithm.

[0096] The function for making predictions based on learning from past data can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the function for making predictions based on learning from past data can reduce the frequency of learning and reduce the burden on the user when the user is stressed. Furthermore, the function for making predictions based on learning from past data can increase the frequency of learning and promote deeper insights when the user is relaxed. Furthermore, the function for making predictions based on learning from past data can adjust the frequency of learning and efficiently learn when the user is in a hurry. This enables efficient learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 function for making predictions based on learning from past data can be performed using, for example, an AI, or without an AI. For example, the function for making predictions based on learning from past data can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The function for learning from past data and making predictions can weight the learning data based on the time of submission of food expense data during learning. For example, the function for learning from past data and making predictions can emphasize recently submitted food expense data and learn the latest trends. The function for learning from past data and making predictions can also emphasize data submitted in a concentrated manner during a specific period and learn wasteful spending during that period. Furthermore, the function for learning from past data and making predictions can emphasize periodically submitted data and learn long-term trends. Thus, by weighting the learning data based on the time of submission of food expense data, the latest trends can be learned. Some or all of the above-described processing in the function for learning from past data and making predictions can be performed using, for example, AI, or without AI. For example, the function for learning from past data and making predictions can input food expense data into a generation AI and have the generation AI weight the learning data based on the time of submission.

[0098] The function that considers user preferences and allergy information can estimate the user's emotions and adjust the input method for preferences and allergy information based on the estimated user emotions. For example, the function that considers user preferences and allergy information can provide a simple input method and minimize input steps when the user is stressed. The function that considers user preferences and allergy information can also provide detailed input options and suggest customizable input methods when the user is relaxed. Furthermore, the function that considers user preferences and allergy information can prioritize voice input when the user is in a hurry, allowing the user to quickly input preferences and allergy information. This enables efficient input by adjusting the input method for preferences and allergy information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the function that considers user preferences and allergy information may be performed using, for example, AI, or without AI. For example, a function that takes into account the user's preferences and allergy information can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0099] The function that considers a user's preferences and allergy information can select the optimal input method by referring to the user's past dietary history when inputting preferences and allergy information. The function that considers a user's preferences and allergy information can, for example, suggest input of preferences based on ingredients that the user has frequently eaten in the past. The function that considers a user's preferences and allergy information can also suggest input of allergy information based on ingredients that the user has avoided in the past. Furthermore, the function that considers a user's preferences and allergy information can analyze the user's past dietary history and suggest the most efficient input method. In this way, the optimal input method can be suggested by referring to the user's past dietary history. Some or all of the above-described processing in the function that considers a user's preferences and allergy information can be performed, for example, using AI or without AI. For example, the function that considers a user's preferences and allergy information can input the user's past dietary data into a generation AI and have the generation AI select the optimal input method.

[0100] The function that considers user preferences and allergy information can estimate the user's emotions and prioritize the preferences and allergy information based on the estimated user emotions. For example, the function that considers user preferences and allergy information can prompt the user to prioritize input of only important preferences and allergy information when the user is tired. The function that considers user preferences and allergy information can also prompt the user to input detailed preferences and allergy information when the user is relaxed. Furthermore, the function that considers user preferences and allergy information can provide a simple input method for the user when the user is in a hurry, allowing the user to add details later. This enables efficient input by prioritizing preferences and allergy information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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 function that considers user preferences and allergy information can be performed using, for example, AI, or without AI. For example, the function that considers user preferences and allergy information can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0101] The function that considers a user's preferences and allergy information can prioritize inputting highly relevant information by taking into account the user's geographical location information when inputting preference and allergy information. For example, when a user is in a specific region, the function that considers a user's preferences and allergy information can prioritize inputting preferences and allergy information for ingredients common in that region. Furthermore, when a user is traveling, the function that considers a user's preferences and allergy information can also prioritize inputting preference and allergy information based on information about local ingredients and restaurants. Furthermore, when a user is in a specific supermarket, the function that considers a user's preferences and allergy information can prioritize inputting preference and allergy information for ingredients purchased at that supermarket. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the function that considers a user's preferences and allergy information may be performed, for example, using AI, or may be performed without using AI. For example, the function that considers a user's preferences and allergy information can input the user's geographical location data into a generation AI and have the generation AI select highly relevant information.

[0102] The function for automatically generating a shopping list based on suggested recipes can estimate a user's emotions and adjust the timing of generating the shopping list based on the estimated user emotions. For example, the function for automatically generating a shopping list based on suggested recipes can reduce the frequency of prompting the user to create a shopping list by sending fewer reminders when the user is stressed. The function for automatically generating a shopping list based on suggested recipes can also periodically send reminders to prompt the user to create a shopping list when the user is relaxed. Furthermore, the function for automatically generating a shopping list based on suggested recipes can also provide a user with an option to postpone list generation when the user is busy, allowing the user to generate the list all at once later. This reduces the burden of list generation by adjusting the timing of shopping list generation 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 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 function for automatically generating a shopping list based on suggested recipes may be performed using, for example, AI, or without AI. For example, a function that automatically generates a shopping list based on suggested recipes can input the user's facial expression data into the generation AI, which can then infer the user's emotions.

[0103] The function for automatically generating a shopping list based on a proposed recipe can generate an optimal list by referring to the user's past shopping history when generating the shopping list. The function for automatically generating a shopping list based on a proposed recipe can, for example, generate a shopping list based on ingredients that the user frequently purchased in the past. The function for automatically generating a shopping list based on a proposed recipe can also analyze patterns of ingredients that the user has purchased in the past to generate the most efficient list. Furthermore, the function for automatically generating a shopping list based on a proposed recipe can also generate a list that is efficient by referring to the user's past shopping history. In this way, an optimal shopping list can be generated by referring to the user's past shopping history. Some or all of the above-described processing in the function for automatically generating a shopping list based on a proposed recipe may be performed, for example, using AI or may be performed without using AI. For example, the function for automatically generating a shopping list based on a proposed recipe can input the user's past shopping data into a generation AI and have the generation AI generate an optimal list.

[0104] The function for automatically generating a shopping list based on suggested recipes can estimate a user's emotions and prioritize the shopping list based on the estimated user emotions. For example, the function for automatically generating a shopping list based on suggested recipes can prompt a user to prioritize adding only essential ingredients to the list if the user is tired. The function for automatically generating a shopping list based on suggested recipes can also prompt a user to create a detailed list if the user is relaxed. Furthermore, the function for automatically generating a shopping list based on suggested recipes can provide a simple method for creating a list if the user is in a hurry, allowing the user to add details later. This enables efficient shopping by prioritizing the shopping list based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 function for automatically generating a shopping list based on suggested recipes can be performed using, for example, AI, or without AI. For example, the function for automatically generating a shopping list based on suggested recipes can input a user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The function for automatically generating a shopping list based on a proposed recipe can prioritize generating a highly relevant list by taking into account the user's geographical location information when generating the shopping list. For example, when a user is at a particular supermarket, the function for automatically generating a shopping list based on a proposed recipe can prioritize adding ingredients that can be purchased at that supermarket to the list. Furthermore, when a user is traveling, the function for automatically generating a shopping list based on a proposed recipe can also generate a list based on information about local ingredients and restaurants. Furthermore, when a user is in a particular region, the function for automatically generating a shopping list based on a proposed recipe can prioritize adding ingredients that are common in that region to the list. This allows for the generation of a highly relevant shopping list by taking into account the user's geographical location information. Some or all of the above-described processing in the function for automatically generating a shopping list based on a proposed recipe may be performed using, or without, AI. For example, the function for automatically generating a shopping list based on a proposed recipe can input the user's geographical location data into a generation AI and cause the generation AI to generate a highly relevant list.

[0106] The function for displaying the nutritional values ​​of a proposed recipe can estimate a user's emotions and adjust the way the nutritional values ​​are displayed based on the estimated user emotions. For example, when a user is stressed, the function for displaying the nutritional values ​​of a proposed recipe can display a simple, visually easy-to-understand nutritional value display. When a user is relaxed, the function for displaying the nutritional values ​​of a proposed recipe can also display detailed nutritional values ​​to encourage deeper insight. When a user is in a hurry, the function for displaying the nutritional values ​​of a proposed recipe can also display a concise nutritional value display that focuses on the key points. This allows the user to better understand the nutritional values ​​by adjusting the way the nutritional values ​​are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 function for displaying the nutritional values ​​of a proposed recipe can be performed using AI, or without AI. For example, the function for displaying the nutritional values ​​of a proposed recipe can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The function for displaying the nutritional value of a proposed recipe can select the optimal display method by referring to the user's past dietary history when displaying nutritional values. The function for displaying the nutritional value of a proposed recipe can, for example, suggest a display method based on the nutritional values ​​of ingredients that the user has frequently eaten in the past. The function for displaying the nutritional value of a proposed recipe can also suggest a display method based on the nutritional values ​​of ingredients that the user has avoided in the past. Furthermore, the function for displaying the nutritional value of a proposed recipe can analyze the user's past dietary history and suggest the most efficient display method. In this way, the optimal nutritional value display method can be suggested by referring to the user's past dietary history. Some or all of the above-described processing in the function for displaying the nutritional value of a proposed recipe may be performed, for example, using AI or without AI. For example, the function for displaying the nutritional value of a proposed recipe can input the user's past dietary data into a generation AI and have the generation AI select the optimal display method.

[0108] The function for displaying the nutritional values ​​of suggested recipes can estimate the user's emotions and prioritize the nutritional values ​​based on the estimated user emotions. For example, if the user is tired, the function for displaying the nutritional values ​​of suggested recipes can prompt the user to prioritize displaying only important nutritional values. The function for displaying the nutritional values ​​of suggested recipes can also prompt the user to display detailed nutritional values ​​when the user is relaxed. Furthermore, if the user is in a hurry, the function for displaying the nutritional values ​​of suggested recipes can provide a simple display method and allow the user to add details later. This enables efficient display of nutritional values ​​by prioritizing nutritional values ​​according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 function for displaying the nutritional values ​​of suggested recipes can be performed using AI, for example, or without AI. For example, the function for displaying the nutritional values ​​of suggested recipes can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The function for displaying the nutritional value of a suggested recipe can prioritize relevant nutritional values ​​when displaying nutritional values, taking into account the user's geographical location information. For example, if the user is in a specific area, the function for displaying the nutritional value of a suggested recipe can prioritize the nutritional values ​​of ingredients common in that area. Furthermore, if the user is traveling, the function for displaying the nutritional value of a suggested recipe can also prioritize the nutritional values ​​of ingredients purchased at a specific supermarket when the user is in that supermarket. This allows for the prioritized display of relevant nutritional values ​​by taking into account the user's geographical location information. Some or all of the above-described processing in the function for displaying the nutritional value of a suggested recipe may be performed using AI, for example, or may be performed without AI. For example, the function for displaying the nutritional value of a suggested recipe can input the user's geographical location data into a generation AI and cause the generation AI to display relevant nutritional values. === Hard Collateral 1-1 === Each of the multiple elements, including the reception 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 reception unit is realized by the control unit 46A of the smart device 14, and allows a user to take a photo of a receipt using a smartphone camera and convert the contents of the receipt into text data using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and can identify food expense trends and wasteful spending patterns using statistical analysis and machine learning algorithms. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and can suggest ways to buy specific ingredients in bulk and recipes to reduce waste. === Hard Collateral 1-2 === Each of the multiple elements, including the reception 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 reception unit is realized by the control unit 46A of the smart glasses 214, and the user can take a picture of a receipt using the smart glasses' camera and convert the contents of the receipt into text data using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and can identify food expense trends and wasteful spending patterns using statistical analysis and machine learning algorithms. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and can suggest ways to buy specific ingredients in bulk and recipes to reduce waste. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and suggestion unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314, and the user can take a photo of a receipt using the camera of the headset terminal and convert the contents of the receipt into text data using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and can identify food expense trends and wasteful spending patterns using statistical analysis and machine learning algorithms. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and can suggest ways to buy specific ingredients in bulk and recipes to reduce waste. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user can take a photo of a receipt using the robot's camera and convert the contents of the receipt into text data using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and can identify food expense trends and wasteful spending patterns using statistical analysis and machine learning algorithms. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and can suggest ways to buy specific ingredients in bulk and recipes to reduce waste.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When accepting a user's food expense data, the reception unit can automatically complete the input by referencing the user's purchase history. For example, it can automatically suggest ingredients that the user has frequently purchased in the past, eliminating the need for input. The reception unit can also suggest the optimal purchase timing by taking into account price fluctuations of ingredients that the user has purchased in the past. Furthermore, the reception unit can estimate the inventory status of specific ingredients based on the user's purchase history and send reminders when inventory is low. This allows the user to efficiently enter food expense data and avoid unnecessary purchases.

[0112] When analyzing the user's food expense data, the analysis unit can compare costs by referring to food ingredient price data by region. For example, the analysis unit can compare food ingredient prices in the user's region with those in other regions and suggest the most cost-effective purchasing locations. The analysis unit can also suggest the optimal time to purchase ingredients, taking into account seasonal fluctuations in food ingredient prices. Furthermore, the analysis unit can predict price trends for specific ingredients based on the user's food expense data and provide advice on future cost reductions. This allows the user to efficiently manage and save on food expenses.

[0113] The suggestion unit can have a function to suggest food storage methods based on the user's food expense data. For example, the suggestion unit can suggest the optimal storage temperature and storage period for a specific food item, thereby reducing food waste. The suggestion unit can also notify the user of the storage method for purchased food items as a reminder, encouraging them to consume the food at the appropriate time. Furthermore, the suggestion unit can also suggest cooking methods to maximize the nutritional value of the food items based on the storage method. This allows the user to use food items efficiently without wasting them.

[0114] The suggestion unit can have a function to suggest substitute ingredients based on the user's food expense data. For example, if a particular ingredient is expensive or difficult to obtain, the suggestion unit can suggest alternative ingredients with equivalent nutritional value. The suggestion unit can also suggest appropriate alternative ingredients taking into account the user's preferences and allergy information. Furthermore, the suggestion unit can suggest recipes using alternative ingredients, allowing the user to try new dishes. This allows the user to enjoy a wide variety of meals while saving on food expenses.

[0115] The suggestion unit can have a function to suggest where to purchase ingredients based on the user's food expense data. For example, the suggestion unit can compare prices at supermarkets and online stores in the user's area and suggest the most cost-effective place to purchase. The suggestion unit can also notify the user when a specific ingredient is on sale, allowing the user to purchase it at a lower price. Furthermore, the suggestion unit can provide point rewards and discount information at specific stores based on the user's purchase history. This allows the user to shop efficiently while saving on food expenses.

[0116] The reception unit can estimate the user's emotions and adjust the food expense data input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple input method can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input food expense data quickly. This allows efficient data input by adjusting the input method according to the user's emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can provide simple, visually easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results to encourage deeper insight. Furthermore, if the user is in a hurry, it can provide concise analysis results that focus on the main points. In this way, by adjusting the notification method of the analysis results according to the user's emotions, the analysis results can be made easier to understand.

[0118] The suggestion unit can estimate the user's emotions and prioritize the suggestions based on the estimated user's emotions. For example, if the user is tired, the suggestion unit can prioritize displaying only important suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper insight. If the user is in a hurry, the suggestion unit can provide a simple suggestion method, allowing the user to add details later. This allows efficient suggestions to be made by prioritizing the suggestions based on the user's emotions.

[0119] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper insight. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. By adjusting the way suggestions are presented according to the user's emotions, the suggestions become easier to understand.

[0120] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper insight. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of the suggestions according to the user's emotions, the suggestions can be more easily understood.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit receives food expense data from the user. The food expense data from the user includes the purchased items, the amount, the purchase date and time, etc. The reception unit provides a method for the user to manually enter the data or a method for automatically acquiring the data by scanning a receipt. For example, the user can take a photo of the receipt using the camera on their smartphone and convert the contents of the receipt into text data using OCR technology. The user can also enter food expense data using voice input. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit uses statistical analysis and machine learning algorithms to identify food expense trends and waste patterns. For example, it can identify when a user frequently purchases certain ingredients or wastes a lot of ingredients. It can also learn from past data to predict future food expense trends. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestion unit suggests ways to reduce costs by buying specific ingredients in bulk, or recipes to reduce waste. For example, it can suggest recipes that are suitable for the user, taking into account the user's preferences and allergy information. It can also have a function to automatically generate a shopping list based on the suggested recipes, and a function to display the nutritional value of the suggested recipes.

[0123] 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.

[0124] 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.

[0125] 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.

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

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. 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 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.

[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 (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).

[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] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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. a reception unit that receives food expense data from a user; an analysis unit that analyzes the data accepted by the acceptance unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit. A system characterized by:

2. The reception unit Includes receipt scanning functionality 2. The system of claim 1.

3. The analysis unit Learning from past data to make predictions 2. The system of claim 1.

4. The proposal unit Equipped with a function that makes suggestions taking into account the user's preferences and allergy information 2. The system of claim 1.

5. The proposal unit It has the ability to automatically generate a shopping list based on suggested recipes.

2. The system of claim 1.

6. The proposal unit It has the ability to display the nutritional value of suggested recipes.

2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the timing of inputting food expense data based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past food expense data entry history and select the optimal entry method 2. The system of claim 1.

Images