System

Abstract

An object of the system according to the embodiment is to allow a food manufacturing company to efficiently generate a food label conforming to regulations.SOLUTION: A system includes a reception unit, an analysis unit, a reference unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The reference unit refers to the regulation database based on the information analyzed by the analysis unit. The generation unit generates the label information based on the regulation database referred to by the reference unit. The providing unit provides the label information generated by the generating unit.SELECTED DRAWING: Figure 1

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 technology has the drawback of requiring time and effort for food manufacturers to create food labels that comply with regulations.

[0005] The system according to the embodiment aims to enable food manufacturers to efficiently generate compliant food labels. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a reference unit, a generating unit, and a providing unit. The receiving unit receives information from a user. The analyzing unit analyzes the information received by the receiving unit. The reference unit references a regulated database based on the information analyzed by the analyzing unit. The generating unit generates label information based on the regulated database referenced by the reference unit. The providing unit provides the label information generated by the generating unit. [Effects of the Invention]

[0007] Embodiments of the system enable food manufacturers to efficiently produce compliant food labels. [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 food label generation support system according to an embodiment of the present invention accepts information from a user, analyzes it with a generation AI, and generates and provides label information by referencing a regulatory database. In the food label generation support system, a user inputs information about a product's ingredients and manufacturing process, and the generation AI analyzes the information and generates regulatory-compliant food label text. For example, in the food label generation support system, a user inputs the types of ingredients and details of the manufacturing process. This information is then input into the generation AI, which analyzes the input information and references a regulatory database to generate appropriate label information. The generated label information is then provided to the user, who can make any necessary modifications. This allows food manufacturers to quickly create accurate label information that complies with regulations. For example, the food label generation support system can accurately provide important information for consumers, such as allergen information and nutritional information. Furthermore, the generation AI can also adapt to regulatory changes. For example, when new regulations are enacted, the generation AI can automatically update the database and generate label information based on the new regulations. This allows food manufacturers to always provide label information that complies with the latest regulations. This makes the food label generation support system a powerful tool for significantly streamlining label creation operations for food manufacturers and ensuring regulatory compliance. For example, the food label generation support system can accurately provide important information to consumers, such as allergen information and nutritional information. The generation AI can also adapt to changes in regulations. For example, when new regulations come into effect, the generation AI can automatically update its database and generate label information based on the new regulations. This allows food manufacturers to always provide label information that complies with the latest regulations.

[0029] A food label generation support system according to an embodiment includes a reception unit, an analysis unit, a reference unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, for example, information about product ingredients and manufacturing methods, but is not limited to such examples. The reception unit receives, for example, text information entered by a user. The reception unit can also receive image data uploaded by a user. The reception unit can also receive voice input. For example, the reception unit converts information entered by the user's voice into text data using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit causes the generation AI to analyze the text information and extract important keywords. The analysis unit can also cause the generation AI to analyze image data and extract text information within the image. The analysis unit can also cause the generation AI to analyze audio data and extract text information within the audio. The reference unit references a regulatory database based on the information analyzed by the analysis unit. The regulatory database may include, but is not limited to, legal and regulatory information, industry standards, and guidelines regarding food labels in each country. For example, the reference unit searches the regulatory database based on keywords extracted by the analysis unit. The reference unit may also search the regulatory database based on text information in an image extracted by the analysis unit. The reference unit may also search the regulatory database based on text information in audio extracted by the analysis unit. The generation unit generates label information based on the regulatory database referenced by the reference unit. The label information may include, but is not limited to, allergen information, nutritional information, and warning labels. For example, the generation unit may use a generation AI to generate allergen information. The generation unit may also use a generation AI to generate nutritional information. The generation unit may also use a generation AI to generate warning labels. The provision unit provides the label information generated by the generation unit. The provision may be performed, for example, by displaying the generated label information to a user, but is not limited to this example.For example, the providing unit may display the generated label information to the user via a web application or a mobile application. The providing unit may also send the generated label information to the user by email. The providing unit may also print the generated label information using a printer and provide it to the user. This allows the food label generation support system according to the embodiment to accept and analyze information from the user, generate and provide label information by referencing a regulatory database.

[0030] The food label generation support system includes a correction unit that provides the generated label information to the user and allows the user to correct it. The correction unit provides the generated label information to the user and allows the user to correct it. Corrections include, but are not limited to, editing text, changing numerical values, and modifying images. For example, the correction unit provides an interface that allows the user to edit the text of the generated label information. The correction unit can also provide an interface that allows the user to change numerical values ​​of the generated label information. The correction unit can also provide an interface that allows the user to modify images of the generated label information. This allows the user to correct the generated label information. Some or all of the above-described processing in the correction unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the correction unit can input the information corrected by the user to the generation AI, which can analyze the corrections and regenerate the label information.

[0031] The correction unit can send the information corrected by the user to the analysis unit again. The correction unit sends the information corrected by the user to the analysis unit again. Re-analysis includes, for example, differences from the initial analysis and handling of additional data, but is not limited to these examples. For example, the correction unit inputs the information corrected by the user to the generation AI, and the generation AI analyzes the correction content and generates label information again. The correction unit can also send the information corrected by the user to the analysis unit, which then analyzes it again. The correction unit can also send the information corrected by the user to the analysis unit, which then handles the additional data. This allows the corrected information to be sent to the analysis unit again.

[0032] The food label generation support system includes an update unit that updates the regulation database to accommodate regulatory changes. The update unit updates the regulation database to accommodate regulatory changes. Regulatory changes include, but are not limited to, the addition of new laws and regulations and revisions to existing regulations. For example, the update unit automatically updates the regulation database when new laws and regulations are enacted. The update unit can also automatically update the regulation database when existing regulations are revised. The update unit can also manually update the regulation database based on the regulatory changes. This allows the regulation database to be updated to accommodate regulatory changes. Some or all of the above-described processing in the update unit may be performed, for example, using or without the generation AI. For example, the update unit can input regulatory change information into the generation AI, which can then automatically update the regulation database.

[0033] The analysis unit can analyze information about the product's materials and manufacturing methods. The analysis unit analyzes information about the product's materials and manufacturing methods. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit causes the generation AI to analyze information about the product's materials and extract important keywords. The analysis unit can also cause the generation AI to analyze information about the product's manufacturing method and extract important steps. The analysis unit can also cause the generation AI to comprehensively analyze information about the product's materials and manufacturing methods and extract data necessary for generating label information. This makes it possible to analyze information about the product's materials and manufacturing methods. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input information about the product's materials and manufacturing methods into the generation AI, which can then analyze the information and extract the necessary data.

[0034] The generation unit can generate detailed label information for allergen information and nutritional information. The generation unit generates detailed label information for allergen information and nutritional information. The label information includes, for example, the type and content of allergens, and nutritional information such as calories, protein, fat, and carbohydrates, but is not limited to these examples. For example, the generation unit uses a generation AI to generate allergen information. The generation unit can also use a generation AI to generate nutritional information. The generation unit can also use a generation AI to generate detailed label information. This makes it possible to generate detailed label information such as allergen information and nutritional information. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can cause the generation AI to generate allergen information and nutritional information.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The input history includes, but is not limited to, past input data, input frequency, and input patterns. For example, the reception unit automatically displays ingredients and recipes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and recipes to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which then selects the optimal reception method.

[0036] The reception unit may filter information based on the user's current project or field of interest when receiving the information. The reception unit may filter information based on the user's current project or field of interest when receiving the information. Projects include, but are not limited to, research projects, development projects, and marketing projects. Fields of interest include, but are not limited to, technical fields, business fields, and hobbies. For example, the reception unit may preferentially display materials and manufacturing methods related to the user's current project. The reception unit may also filter and display related information based on the user's field of interest. The reception unit may also suggest optimal information based on fields in which the user has previously shown interest. This allows information to be filtered based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's project or field of interest into the generation AI, which may then perform the filtering.

[0037] The reception unit can select an appropriate reception means depending on the user's input method when receiving information. The reception unit can select an appropriate reception means depending on the user's input method (voice, text, image, etc.) when receiving information. Input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can receive information using voice recognition technology when the user inputs ingredients or manufacturing methods using voice. The reception unit can also receive information using text analysis technology when the user inputs information using text. The reception unit can also receive information using image recognition technology when the user provides information using images. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can select the optimal reception means.

[0038] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. When receiving information, the reception unit prioritizes receiving highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving information about materials and manufacturing methods related to that area. Furthermore, if the user is traveling, the reception unit can also suggest optimal information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can prioritize receiving information based on the country's regulations. This allows highly relevant information to be prioritized taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into a generation AI, which can select highly relevant information.

[0039] The reception unit may analyze the user's social media activity and receive related information when receiving information. The reception unit may analyze the user's social media activity and receive related information when receiving information. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit may preferentially receive information about ingredients or recipes shared by the user on social media. The reception unit may also analyze the content of the user's social media posts and receive related information. The reception unit may also receive related information based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's social media data into a generation AI, which may select related information.

[0040] The reception unit can customize the reception method based on the user's past feedback when receiving information. The reception unit customizes the reception method based on the user's past feedback when receiving information. Feedback includes, but is not limited to, the user's evaluation comments, survey results, and usage history. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially receive specific information based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. This allows the reception method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI, which can then select the optimal reception method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The importance includes, but is not limited to, for example, the scope of impact, urgency, and user interest. For example, the analysis unit performs a detailed analysis of important information. The analysis unit can also perform a simplified analysis of general information. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. Categories include, but are not limited to, product categories, service categories, and technology categories. For example, the analysis unit applies a specific analysis algorithm to information about ingredients. The analysis unit can also apply a different analysis algorithm to information about manufacturing methods. The analysis unit can also apply dedicated analysis algorithms to allergen information and nutritional information. This makes it possible to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information category data into the generation AI, which can then apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. During analysis, the analysis unit determines the priority of analysis based on the time of information submission. The submission time includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the submission time specified by the user. The analysis unit can also prioritize analysis of information related to regulatory changes. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input information submission time data into the generation AI, and the generation AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit can adjust the order of analysis based on the relevance of information during analysis. Relevance includes, but is not limited to, information similarity, related keywords, and common themes, for example. For example, the analysis unit prioritizes analysis of highly related information. The analysis unit can also adjust the order of analysis based on relevance specified by a user. The analysis unit can also adjust the order of analysis taking into account relevance based on regulations. This makes it possible to adjust the order of analysis based on the relevance of information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information relevance data into a generation AI, which can then adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general consumer, the analysis unit can also provide analysis results that avoid technical terms. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted based on the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms in the analysis.

[0047] The reference unit can improve the accuracy of the reference based on the interrelationships of information during the reference. The reference unit can improve the accuracy of the reference based on the interrelationships of information during the reference. Interrelationships include, but are not limited to, information associations, common themes, and correlations. For example, the reference unit links related information to each other for reference. The reference unit can also provide optimal reference results by taking into account the interrelationships of information. The reference unit can also improve the accuracy of the reference based on the interrelationships of information specified by the user. This allows the accuracy of the reference to be improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reference unit can input information interrelationship data into the generation AI, which can then improve the accuracy of the reference.

[0048] The reference unit can perform the reference while taking into consideration the attribute information of the information submitter. The reference unit can perform the reference while taking into consideration the attribute information of the information submitter. Attribute information includes, but is not limited to, age, gender, occupation, and region. For example, if the information submitter is an expert, the reference unit will preferentially reference the information. Furthermore, if the information submitter is a general consumer, the reference unit can also reference the information for reference purposes. Furthermore, the reference unit can determine the priority of the reference based on the attribute information of the information submitter. This allows the reference to be performed while taking into consideration the attribute information of the information submitter. Some or all of the above-described processing in the reference unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reference unit can input the attribute information of the information submitter into the generation AI, and the generation AI can determine the priority of the reference.

[0049] The reference unit can weight the reference based on the frequency of information submission at the time of reference. The reference unit weights the reference based on the frequency of information submission at the time of reference. The submission frequency includes, but is not limited to, for example, the number of submissions, the submission interval, and the timing of submission. For example, the reference unit preferentially references information that is submitted more frequently. The reference unit can also reference information that is submitted less frequently for reference purposes. The reference unit can also weight the reference based on the frequency of information submission. This allows the reference to be weighted based on the frequency of information submission. Some or all of the above-mentioned processing in the reference unit may be performed using, or without using, a generation AI. For example, the reference unit can input information submission frequency data into the generation AI, and the generation AI can weight the reference.

[0050] The reference unit can perform references based on the geographical distribution of the information when referencing. The reference unit can perform references based on the geographical distribution of the information when referencing. Geographical distribution includes, but is not limited to, regional data, geographical bias, and distribution patterns. For example, if a user is in a specific region, the reference unit can preferentially refer to information related to that region. Furthermore, if a user is traveling, the reference unit can suggest optimal information based on the user's current location. Furthermore, if a user is in a specific country, the reference unit can preferentially refer to information based on the regulations of that country. This allows references to be performed taking the geographical distribution of the information into consideration. Some or all of the above-described processing in the reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reference unit can input geographical distribution data of the information into a generation AI, which can then provide optimal reference results.

[0051] The referencing unit can improve the accuracy of the reference by referring to related literature of the information during the reference. The referencing unit can improve the accuracy of the reference by referring to related literature of the information during the reference. Related literature includes, but is not limited to, academic papers, technical reports, industry news, etc. For example, the referencing unit can improve the reliability of the information by referring to related literature. The referencing unit can also provide optimal reference results based on the related literature of the information. The referencing unit can also improve the accuracy of the reference based on related literature specified by the user. This can improve the accuracy of the reference by referring to the related literature of the information. Some or all of the above-mentioned processing in the referencing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the referencing unit can input related literature data of the information into the generation AI, which can improve the accuracy of the reference.

[0052] The reference unit can perform references based on the market value of the information when making a reference. The reference unit can perform references based on the market value of the information when making a reference. Market values ​​include, but are not limited to, sales data, market share, and customer ratings, for example. For example, the reference unit preferentially references information with high market value. The reference unit can also reference information with low market value for reference purposes. The reference unit can also determine the priority of references based on the market value of the information. This allows references to be made taking the market value of the information into consideration. Some or all of the above-described processing in the reference unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reference unit can input market value data of the information into the generation AI, and the generation AI can determine the priority of references.

[0053] The generation unit can improve the accuracy of generation based on the interrelationships of information during generation. The generation unit can improve the accuracy of generation based on the interrelationships of information during generation. Interrelationships include, but are not limited to, information associations, common themes, and correlations. For example, the generation unit generates related information by linking them to each other. The generation unit can also provide optimal generation results by taking into account the interrelationships of information. The generation unit can also improve the accuracy of generation based on the interrelationships of information specified by the user. This can improve the accuracy of generation by taking into account the interrelationships of information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information interrelationship data into the generation AI, which can then improve the accuracy of generation.

[0054] The generation unit can generate information taking into consideration attribute information of the information submitter when generating the information. The generation unit generates information taking into consideration attribute information of the information submitter when generating the information. Attribute information includes, but is not limited to, age, gender, occupation, and region, for example. For example, if the information submitter is an expert, the generation unit can generate the information preferentially. Furthermore, if the information submitter is a general consumer, the generation unit can generate the information for reference only. Furthermore, the generation unit can determine the priority of generation based on the attribute information of the information submitter. This allows generation to be performed taking into consideration the attribute information of the information submitter. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the information submitter into the generation AI, and the generation AI can determine the priority of generation.

[0055] The generation unit can weight the generation based on the frequency of information submission at the time of generation. The generation unit weights the generation based on the frequency of information submission at the time of generation. The submission frequency includes, but is not limited to, for example, the number of submissions, the submission interval, and the timing of submission. For example, the generation unit prioritizes generating information that is submitted more frequently. The generation unit can also generate information that is submitted less frequently for reference purposes. The generation unit can also weight the generation based on the frequency of information submission. This allows the generation to be weighted based on the frequency of information submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information submission frequency data into the generation AI, and the generation AI can weight the generation.

[0056] The generation unit can generate the information based on the geographical distribution of the information at the time of generation. The generation unit generates the information based on the geographical distribution of the information at the time of generation. Geographical distribution includes, but is not limited to, regional data, geographical bias, and distribution patterns, for example. For example, if the user is in a specific region, the generation unit can prioritize generating information related to that region. Furthermore, if the user is traveling, the generation unit can also suggest optimal information based on the user's current location. Furthermore, if the user is in a specific country, the generation unit can prioritize generating information based on the regulations of that country. This allows generation to take the geographical distribution of the information into consideration. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical distribution data of the information into the generation AI, which can then provide optimal generation results.

[0057] The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. Related literature includes, but is not limited to, academic papers, technical reports, and industry news, for example. For example, the generation unit can improve the reliability of the information by referring to related literature. The generation unit can also provide optimal generation results based on literature related to the information. The generation unit can also improve the accuracy of generation based on literature specified by the user. This can improve the accuracy of generation by referring to literature related to the information. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input literature data related to the information into the generation AI, which can improve the accuracy of generation.

[0058] The generation unit can generate information based on the market value of the information at the time of generation. The generation unit generates information based on the market value of the information at the time of generation. Market value includes, but is not limited to, sales data, market share, customer ratings, etc. For example, the generation unit generates information with high market value preferentially. The generation unit can also generate information with low market value for reference only. The generation unit can also determine the priority of generation based on the market value of the information. This allows generation to be performed taking into account the market value of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market value data of the information into the generation AI, and the generation AI can determine the priority of generation.

[0059] The providing unit can select an appropriate display method based on the user's past operation history when providing the display data. The providing unit selects an appropriate display method based on the user's past operation history when providing the display data. The operation history includes, but is not limited to, past operation data, operation frequency, and operation patterns. For example, the providing unit prioritizes providing display methods used by the user in the past. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also customize the display method based on the user's past operation history. This makes it possible to select an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's operation history data into the generation AI, which can select an optimal display method.

[0060] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit customizes the display content according to the user's current task when providing the information. Tasks include, but are not limited to, work tasks, project tasks, and daily tasks. For example, the providing unit prioritizes displaying information related to the user's current task. The providing unit can also suggest optimal display content based on the user's current task. The providing unit can also customize the display content taking into account the user's current task. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's task data into the generation AI, which can then provide optimal display content.

[0061] The providing unit can improve the display method based on user feedback when providing the data. The providing unit improves the display method based on user feedback when providing the data. Feedback includes, for example, user evaluation comments, survey results, and usage history, but is not limited to these examples. For example, the providing unit improves the display method based on the feedback provided by the user. The providing unit can also suggest an optimal display method based on the user feedback. The providing unit can also customize the display interface by reflecting the user feedback. This makes it possible to improve the display method by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback data into the generation AI, which can select the optimal display method.

[0062] The providing unit can select an appropriate display method based on the user's device information at the time of providing. The providing unit selects an appropriate display method based on the user's device information at the time of providing. Device information includes, but is not limited to, the device type, OS version, and hardware specifications. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal display method to be selected taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can select the optimal display method.

[0063] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit can make the display content multilingual according to the user's language setting when providing the content. Language settings include, but are not limited to, the user's language selection, the device's language setting, and the region setting. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI, which can then provide optimal display content.

[0064] The providing unit can customize the information based on the user's occupation and lifestyle when providing the information. The providing unit customizes the information based on the user's occupation and lifestyle when providing the information. Occupations include, but are not limited to, occupational classification, industry, and job content. Lifestyles include, but are not limited to, lifestyle habits, hobbies, and values. For example, if the user is a food manufacturer, the providing unit can prioritize providing information related to the business. Furthermore, if the user is a general consumer, the providing unit can prioritize providing information aimed at consumers. Furthermore, the providing unit can suggest optimal information based on the user's lifestyle. This allows customization based on the user's occupation and lifestyle. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's occupation and lifestyle data into the generation AI, which can then provide optimal information.

[0065] The correction unit can select an appropriate correction method based on the user's past correction history when making corrections. The correction unit can select an appropriate correction method based on the user's past correction history when making corrections. The correction history includes, but is not limited to, past correction data, correction frequency, and correction patterns. For example, the correction unit can propose an optimal correction method based on the corrections made by the user in the past. The correction unit can also extract specific patterns from the user's past correction history and reflect them in the corrections. The correction unit can also customize the correction interface by referring to the user's past correction history. This makes it possible to select an optimal correction method by referring to the user's past correction history. Some or all of the above-described processing in the correction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the correction unit can input the user's correction history data into a generation AI, which can select an optimal correction method.

[0066] The correction unit can customize the correction content according to the user's current task during correction. The correction unit can customize the correction content according to the user's current task during correction. Tasks include, but are not limited to, work tasks, project tasks, and daily tasks. For example, the correction unit can prioritize displaying correction content related to the user's current task. The correction unit can also suggest optimal correction content based on the user's current task. The correction unit can also customize the correction content taking into account the user's current task. This allows the correction content to be customized according to the user's current task. Some or all of the above-described processing in the correction unit can be performed using, or without, a generation AI. For example, the correction unit can input the user's task data into the generation AI, which can then provide optimal correction content.

[0067] The correction unit can improve the correction method based on user feedback during correction. The correction unit improves the correction method based on user feedback during correction. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history, for example. For example, the correction unit improves the correction method based on feedback provided by the user. The correction unit can also propose an optimal correction method based on the user feedback. The correction unit can also customize the correction interface by reflecting the user feedback. This makes it possible to improve the correction method by reflecting the user feedback. Some or all of the above-described processing in the correction unit may be performed using, or without, a generation AI. For example, the correction unit can input user feedback data into the generation AI, which can then select the optimal correction method.

[0068] The correction unit can select an appropriate correction method based on the user's device information during correction. The correction unit can select an appropriate correction method based on the user's device information during correction. Device information includes, but is not limited to, the device type, OS version, and hardware specifications. For example, if the user is using a smartphone, the correction unit can provide a correction method tailored to the screen size. Furthermore, if the user is using a tablet, the correction unit can provide a correction method optimized for a large screen. Furthermore, if the user is using a smartwatch, the correction unit can provide a simple and highly visible correction method. This allows the optimal correction method to be selected taking the user's device information into consideration. Some or all of the above-described processing in the correction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the correction unit can input the user's device information into the generation AI, which can then select the optimal correction method.

[0069] The correction unit can make the correction content multilingual in accordance with the user's language setting during correction. The correction unit can make the correction content multilingual in accordance with the user's language setting during correction. Language settings include, but are not limited to, the user's language selection, the device's language setting, and the region setting. For example, the correction unit automatically sets the correction content based on the user's device's language setting. The correction unit can also provide a language switching function when the user uses multiple languages. The correction unit can also provide the correction content in a specific language when the user selects that language. This makes it possible to make the correction content multilingual in accordance with the user's language setting. Some or all of the above-described processing in the correction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the correction unit can input the user's language setting data into the generation AI, which can then provide optimal correction content.

[0070] The correction unit can customize the content based on the user's occupation and lifestyle when making corrections. The correction unit customizes the content based on the user's occupation and lifestyle when making corrections. Occupations include, but are not limited to, occupational classification, industry, and job content. Lifestyles include, but are not limited to, lifestyle habits, hobbies, and values. For example, if the user is a food manufacturer, the correction unit can prioritize providing corrections related to the user's business. Furthermore, if the user is a general consumer, the correction unit can prioritize providing corrections aimed at consumers. Furthermore, the correction unit can suggest optimal corrections based on the user's lifestyle. This allows customization based on the user's occupation and lifestyle. Some or all of the above-described processing in the correction unit may be performed using, or without using, a generation AI. For example, the correction unit can input the user's occupation and lifestyle data into the generation AI, which can then provide optimal corrections.

[0071] The update unit can optimize the update algorithm based on past update data during an update. The update unit optimizes the update algorithm based on past update data during an update. Past update data includes, but is not limited to, past data sets, update reports, and feedback results. For example, the update unit proposes an optimal update algorithm based on the past update data. The update unit can also extract specific patterns from the past update data and reflect them in the update. The update unit can also adjust the update algorithm by referring to the past update data. This makes it possible to optimize the update algorithm by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input past update data into a generation AI, which can then optimize the update algorithm.

[0072] The update unit can update the update data based on user feedback during an update. The update unit updates the update data based on user feedback during an update. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history, for example. For example, the update unit improves the update data based on feedback provided by the user. The update unit can also propose optimal update data based on the user feedback. The update unit can also customize the update data by reflecting the user feedback. This allows the update data to be updated by reflecting the user feedback. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input user feedback data into the generation AI, which can then provide optimal update data.

[0073] The update unit can adjust the update frequency based on regulatory changes during updates. The update unit can adjust the update frequency based on regulatory changes during updates. Regulatory changes include, but are not limited to, the addition of new laws and regulations and revisions to existing regulations. For example, the update unit can increase the update frequency when regulatory changes occur frequently. The update unit can also decrease the update frequency when regulatory changes occur infrequently. The update unit can also propose an optimal update frequency based on regulatory changes. This makes it possible to adjust the update frequency based on regulatory changes. Some or all of the above-described processing in the update unit can be performed using, or without, a generation AI. For example, the update unit can input regulatory change data into the generation AI, which can then determine the optimal update frequency.

[0074] The update unit can weight the update data based on the submission timing of the restrictions at the time of update. The update unit can weight the update data based on the submission timing of the restrictions at the time of update. The submission timing includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, if the submission timing of the restrictions is approaching, the update unit prioritizes updating the data. Furthermore, if the submission timing of the restrictions is far away, the update unit can also update the data for reference only. Furthermore, the update unit can weight the update data based on the submission timing of the restrictions. In this way, the update data can be weighted based on the submission timing of the restrictions. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input the submission timing data of the restrictions into the generation AI, and the generation AI can optimally weight the update data.

[0075] The update unit can integrate information from different data sources to enrich the updated data during an update. The update unit integrates information from different data sources to enrich the updated data during an update. Data sources include, but are not limited to, internal databases, external APIs, and public datasets. For example, the update unit integrates information from different data sources to enrich the updated data. The update unit can also provide optimal updated data based on information from different data sources. The update unit can also customize the updated data by referring to information from different data sources. This makes it possible to integrate information from different data sources and enrich the updated data. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input information from different data sources into a generation AI, which can then provide optimal updated data.

[0076] The update unit can adjust the update algorithm based on user feedback during an update. The update unit adjusts the update algorithm based on user feedback during an update. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history. For example, the update unit improves the update algorithm based on user feedback. The update unit can also propose an optimal update algorithm based on user feedback. The update unit can also customize the update algorithm by reflecting user feedback. This allows the update algorithm to be adjusted by reflecting user feedback. Some or all of the above-described processing in the update unit may be performed using, or without using, a generation AI. For example, the update unit can input user feedback data into a generation AI, which can then provide an optimal update algorithm.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The reception unit can automatically suggest related past label information based on the user's input. For example, if the user inputs a specific ingredient, label information in which a similar ingredient was used in the past is presented. Also, if the user inputs a specific manufacturing method, past label information related to that manufacturing method can be displayed. Furthermore, related regulatory information and guidelines can be automatically suggested based on the information input by the user. This allows the user to efficiently generate label information while referring to past information.

[0079] The correction unit may have a sharing function for allowing a user to share corrected information with other users. For example, a user can share corrected label information with other users and receive feedback. The correction unit may also provide an interface that allows other users to comment on or rate the shared information. Furthermore, the correction unit may use the shared information as reference information when other users make corrections. This enables more accurate and effective label information to be generated through information sharing and feedback between users.

[0080] The correction unit can have a function for automatically managing versions of information corrected by the user. For example, every time the user makes a correction, the information before the correction and the information after the correction are automatically saved and managed as a version history. An interface can also be provided that allows the user to revert to a previous version. Furthermore, the correction unit can visually display the differences between versions, allowing the user to easily check the correction content. This allows the user to manage the correction history and revert to a previous version if necessary.

[0081] The update unit may have a function for notifying the user of the updated content when the regulation database is updated. For example, when a new regulation is added or an existing regulation is revised, the update unit automatically sends a notification to the user. The update unit may also provide guidelines for the user to take necessary actions based on the notification content. Furthermore, the update unit may provide a support function for the user to regenerate label information based on the updated content after receiving the notification. This allows the user to always provide label information that complies with the latest regulations.

[0082] The processing flow of the first embodiment will be briefly explained below.

[0083] Step 1: The reception unit receives information from the user. This information includes information about the product's materials and manufacturing methods. The reception unit can receive text information entered by the user, uploaded image data, and voice input. In the case of voice input, it converts the voice input into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using data mining, statistical analysis, and machine learning algorithms. The analysis unit extracts important keywords from the text information and also extracts text information from image data and audio data. Step 3: The reference unit references the regulatory database based on the information analyzed by the analysis unit. The regulatory database contains legal and regulatory information on food labels from each country, as well as industry standards and guidelines. The reference unit searches the regulatory database based on the keywords and text information extracted by the analysis unit. Step 4: The generation unit generates label information based on the regulatory database referenced by the reference unit. The label information includes allergen information, nutritional information, warning labels, etc. The generation unit generates this label information using generation AI. Step 5: The providing unit provides the label information generated by the generating unit. The providing unit provides the generated label information to the user by displaying the label information to the user. The providing unit can display the label information to the user through a web application or a mobile application, or can provide the label information by sending it by email or printing it out on a printer.

[0084] (Example 2) A food label generation support system according to an embodiment of the present invention accepts information from a user, analyzes it with a generation AI, and generates and provides label information by referencing a regulatory database. In the food label generation support system, a user inputs information about a product's ingredients and manufacturing process, and the generation AI analyzes the information and generates regulatory-compliant food label text. For example, in the food label generation support system, a user inputs the types of ingredients and details of the manufacturing process. This information is then input into the generation AI, which analyzes the input information and references a regulatory database to generate appropriate label information. The generated label information is then provided to the user, who can make any necessary modifications. This allows food manufacturers to quickly create accurate label information that complies with regulations. For example, the food label generation support system can accurately provide important information for consumers, such as allergen information and nutritional information. Furthermore, the generation AI can also adapt to regulatory changes. For example, when new regulations are enacted, the generation AI can automatically update the database and generate label information based on the new regulations. This allows food manufacturers to always provide label information that complies with the latest regulations. This makes the food label generation support system a powerful tool for significantly streamlining label creation operations for food manufacturers and ensuring regulatory compliance. For example, the food label generation support system can accurately provide important information to consumers, such as allergen information and nutritional information. The generation AI can also adapt to changes in regulations. For example, when new regulations come into effect, the generation AI can automatically update its database and generate label information based on the new regulations. This allows food manufacturers to always provide label information that complies with the latest regulations.

[0085] A food label generation support system according to an embodiment includes a reception unit, an analysis unit, a reference unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, for example, information about product ingredients and manufacturing methods, but is not limited to such examples. The reception unit receives, for example, text information entered by a user. The reception unit can also receive image data uploaded by a user. The reception unit can also receive voice input. For example, the reception unit converts information entered by the user's voice into text data using voice recognition technology. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit causes the generation AI to analyze the text information and extract important keywords. The analysis unit can also cause the generation AI to analyze image data and extract text information within the image. The analysis unit can also cause the generation AI to analyze audio data and extract text information within the audio. The reference unit references a regulatory database based on the information analyzed by the analysis unit. The regulatory database may include, but is not limited to, legal and regulatory information, industry standards, and guidelines regarding food labels in each country. For example, the reference unit searches the regulatory database based on keywords extracted by the analysis unit. The reference unit may also search the regulatory database based on text information in an image extracted by the analysis unit. The reference unit may also search the regulatory database based on text information in audio extracted by the analysis unit. The generation unit generates label information based on the regulatory database referenced by the reference unit. The label information may include, but is not limited to, allergen information, nutritional information, and warning labels. For example, the generation unit may use a generation AI to generate allergen information. The generation unit may also use a generation AI to generate nutritional information. The generation unit may also use a generation AI to generate warning labels. The provision unit provides the label information generated by the generation unit. The provision may be performed, for example, by displaying the generated label information to a user, but is not limited to this example.For example, the providing unit may display the generated label information to the user via a web application or a mobile application. The providing unit may also send the generated label information to the user by email. The providing unit may also print the generated label information using a printer and provide it to the user. This allows the food label generation support system according to the embodiment to accept and analyze information from the user, generate and provide label information by referencing a regulatory database.

[0086] The food label generation support system includes a correction unit that provides the generated label information to the user and allows the user to correct it. The correction unit provides the generated label information to the user and allows the user to correct it. Corrections include, but are not limited to, editing text, changing numerical values, and modifying images. For example, the correction unit provides an interface that allows the user to edit the text of the generated label information. The correction unit can also provide an interface that allows the user to change numerical values ​​of the generated label information. The correction unit can also provide an interface that allows the user to modify images of the generated label information. This allows the user to correct the generated label information. Some or all of the above-described processing in the correction unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the correction unit can input the information corrected by the user to the generation AI, which can analyze the corrections and regenerate the label information.

[0087] The correction unit can send the information corrected by the user to the analysis unit again. The correction unit sends the information corrected by the user to the analysis unit again. Re-analysis includes, for example, differences from the initial analysis and handling of additional data, but is not limited to these examples. For example, the correction unit inputs the information corrected by the user to the generation AI, and the generation AI analyzes the correction content and generates label information again. The correction unit can also send the information corrected by the user to the analysis unit, which then analyzes it again. The correction unit can also send the information corrected by the user to the analysis unit, which then handles the additional data. This allows the corrected information to be sent to the analysis unit again.

[0088] The food label generation support system includes an update unit that updates the regulation database to accommodate regulatory changes. The update unit updates the regulation database to accommodate regulatory changes. Regulatory changes include, but are not limited to, the addition of new laws and regulations and revisions to existing regulations. For example, the update unit automatically updates the regulation database when new laws and regulations are enacted. The update unit can also automatically update the regulation database when existing regulations are revised. The update unit can also manually update the regulation database based on the regulatory changes. This allows the regulation database to be updated to accommodate regulatory changes. Some or all of the above-described processing in the update unit may be performed, for example, using or without the generation AI. For example, the update unit can input regulatory change information into the generation AI, which can then automatically update the regulation database.

[0089] The analysis unit can analyze information about the product's materials and manufacturing methods. The analysis unit analyzes information about the product's materials and manufacturing methods. The analysis can be performed using, for example, data mining, statistical analysis, or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit causes the generation AI to analyze information about the product's materials and extract important keywords. The analysis unit can also cause the generation AI to analyze information about the product's manufacturing method and extract important steps. The analysis unit can also cause the generation AI to comprehensively analyze information about the product's materials and manufacturing methods and extract data necessary for generating label information. This makes it possible to analyze information about the product's materials and manufacturing methods. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input information about the product's materials and manufacturing methods into the generation AI, which can then analyze the information and extract the necessary data.

[0090] The generation unit can generate detailed label information for allergen information and nutritional information. The generation unit generates detailed label information for allergen information and nutritional information. The label information includes, for example, the type and content of allergens, and nutritional information such as calories, protein, fat, and carbohydrates, but is not limited to these examples. For example, the generation unit uses a generation AI to generate allergen information. The generation unit can also use a generation AI to generate nutritional information. The generation unit can also use a generation AI to generate detailed label information. This makes it possible to generate detailed label information such as allergen information and nutritional information. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can cause the generation AI to generate allergen information and nutritional information.

[0091] The food label generation support system includes a reception unit that estimates a user's emotion and adjusts the timing of information reception based on the estimated user emotion. The reception unit estimates the user's emotion and adjusts the timing of information reception based on the estimated user emotion. The emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the reception unit may capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The reception unit may also record the user's voice and estimate the emotion using voice analysis technology. The reception unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The reception unit adjusts the timing of information reception based on the estimated emotion. For example, if the user is feeling stressed, the reception unit may provide a simple interface and minimize input steps. If the user is relaxed, the reception unit may provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit may prioritize voice input to allow for quick information input. This allows the timing of information reception to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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 reception unit may be performed using, or without, the generation AI. For example, the reception unit inputs the user's emotion data into the generation AI, which then estimates the emotion and adjusts the timing of receiving the information.

[0092] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit analyzes the user's past input history and selects the optimal reception method. The input history includes, but is not limited to, past input data, input frequency, and input patterns. For example, the reception unit automatically displays ingredients and recipes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients and recipes to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which then selects the optimal reception method.

[0093] The reception unit may filter information based on the user's current project or field of interest when receiving the information. The reception unit may filter information based on the user's current project or field of interest when receiving the information. Projects include, but are not limited to, research projects, development projects, and marketing projects. Fields of interest include, but are not limited to, technical fields, business fields, and hobbies. For example, the reception unit may preferentially display materials and manufacturing methods related to the user's current project. The reception unit may also filter and display related information based on the user's field of interest. The reception unit may also suggest optimal information based on fields in which the user has previously shown interest. This allows information to be filtered based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's project or field of interest into the generation AI, which may then perform the filtering.

[0094] The reception unit can select an appropriate reception means depending on the user's input method when receiving information. The reception unit can select an appropriate reception means depending on the user's input method (voice, text, image, etc.) when receiving information. Input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can receive information using voice recognition technology when the user inputs ingredients or manufacturing methods using voice. The reception unit can also receive information using text analysis technology when the user inputs information using text. The reception unit can also receive information using image recognition technology when the user provides information using images. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can select the optimal reception means.

[0095] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user emotion. The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The reception unit determines the priority of information based on the estimated emotion. For example, if the user is stressed, important information can be received with priority. If the user is relaxed, detailed information can be received with priority. If the user is in a hurry, information that requires quick processing can be received with priority. This makes it possible to determine the priority of information to be received based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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 reception unit may be performed using, or without, the generation AI. For example, the reception unit may input user emotion data into the generation AI, which may then estimate the emotion and determine the priority of the information.

[0096] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. When receiving information, the reception unit prioritizes receiving highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving information about materials and manufacturing methods related to that area. Furthermore, if the user is traveling, the reception unit can also suggest optimal information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can prioritize receiving information based on the country's regulations. This allows highly relevant information to be prioritized taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into a generation AI, which can select highly relevant information.

[0097] The reception unit may analyze the user's social media activity and receive related information when receiving information. The reception unit may analyze the user's social media activity and receive related information when receiving information. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit may preferentially receive information about ingredients or recipes shared by the user on social media. The reception unit may also analyze the content of the user's social media posts and receive related information. The reception unit may also receive related information based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's social media data into a generation AI, which may select related information.

[0098] The reception unit can customize the reception method based on the user's past feedback when receiving information. The reception unit customizes the reception method based on the user's past feedback when receiving information. Feedback includes, but is not limited to, the user's evaluation comments, survey results, and usage history. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially receive specific information based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. This allows the reception method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI, which can then select the optimal reception method.

[0099] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. Based on the estimated emotion, the analysis unit adjusts the presentation method of the analysis. For example, if the user is stressed, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a summary analysis result can be provided. This makes it possible to adjust the presentation method of the analysis based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may then estimate the emotion and adjust the method of expression for the analysis.

[0100] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The importance includes, but is not limited to, for example, the scope of impact, urgency, and user interest. For example, the analysis unit performs a detailed analysis of important information. The analysis unit can also perform a simplified analysis of general information. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0101] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on the category of information during analysis. Categories include, but are not limited to, product categories, service categories, and technology categories. For example, the analysis unit applies a specific analysis algorithm to information about ingredients. The analysis unit can also apply a different analysis algorithm to information about manufacturing methods. The analysis unit can also apply dedicated analysis algorithms to allergen information and nutritional information. This makes it possible to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information category data into the generation AI, which can then apply an appropriate analysis algorithm.

[0102] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0103] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The analysis unit adjusts the length of the analysis based on the estimated emotion. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may then estimate the emotion and adjust the length of the analysis.

[0104] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. During analysis, the analysis unit determines the priority of analysis based on the time of information submission. The submission time includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the submission time specified by the user. The analysis unit can also prioritize analysis of information related to regulatory changes. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input information submission time data into the generation AI, and the generation AI can determine the priority of analysis.

[0105] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit can adjust the order of analysis based on the relevance of information during analysis. Relevance includes, but is not limited to, information similarity, related keywords, and common themes, for example. For example, the analysis unit prioritizes analysis of highly related information. The analysis unit can also adjust the order of analysis based on relevance specified by a user. The analysis unit can also adjust the order of analysis taking into account relevance based on regulations. This makes it possible to adjust the order of analysis based on the relevance of information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information relevance data into a generation AI, which can then adjust the order of analysis.

[0106] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general consumer, the analysis unit can also provide analysis results that avoid technical terms. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted based on the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terms in the analysis.

[0107] The reference unit can estimate the user's emotion and adjust the reference standard based on the estimated user's emotion. The reference unit can estimate the user's emotion and adjust the reference standard based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the reference unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The reference unit can also record the user's voice and estimate the emotion using voice analysis technology. The reference unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. Based on the estimated emotion, the reference unit adjusts the reference standard. For example, if the user is stressed, a simple and highly visible reference result can be provided. If the user is relaxed, a detailed reference result can be provided. If the user is in a hurry, a reference result that focuses on the main points can be provided. This makes it possible to adjust the reference standard based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative 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 reference unit may be performed using, or without, the generation AI. For example, the reference unit may input user emotion data into the generation AI, which may then estimate the emotion and adjust the reference standard.

[0108] The reference unit can improve the accuracy of the reference based on the interrelationships of information during the reference. The reference unit can improve the accuracy of the reference based on the interrelationships of information during the reference. Interrelationships include, but are not limited to, information associations, common themes, and correlations. For example, the reference unit links related information to each other for reference. The reference unit can also provide optimal reference results by taking into account the interrelationships of information. The reference unit can also improve the accuracy of the reference based on the interrelationships of information specified by the user. This allows the accuracy of the reference to be improved by taking into account the interrelationships of information. Some or all of the above-mentioned processing in the reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reference unit can input information interrelationship data into the generation AI, which can then improve the accuracy of the reference.

[0109] The reference unit can perform the reference while taking into consideration the attribute information of the information submitter. The reference unit can perform the reference while taking into consideration the attribute information of the information submitter. Attribute information includes, but is not limited to, age, gender, occupation, and region. For example, if the information submitter is an expert, the reference unit will preferentially reference the information. Furthermore, if the information submitter is a general consumer, the reference unit can also reference the information for reference purposes. Furthermore, the reference unit can determine the priority of the reference based on the attribute information of the information submitter. This allows the reference to be performed while taking into consideration the attribute information of the information submitter. Some or all of the above-described processing in the reference unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reference unit can input the attribute information of the information submitter into the generation AI, and the generation AI can determine the priority of the reference.

[0110] The reference unit can weight the reference based on the frequency of information submission at the time of reference. The reference unit weights the reference based on the frequency of information submission at the time of reference. The submission frequency includes, but is not limited to, for example, the number of submissions, the submission interval, and the timing of submission. For example, the reference unit preferentially references information that is submitted more frequently. The reference unit can also reference information that is submitted less frequently for reference purposes. The reference unit can also weight the reference based on the frequency of information submission. This allows the reference to be weighted based on the frequency of information submission. Some or all of the above-mentioned processing in the reference unit may be performed using, or without using, a generation AI. For example, the reference unit can input information submission frequency data into the generation AI, and the generation AI can weight the reference.

[0111] The reference unit can estimate the user's emotion and adjust the order in which the reference results are displayed based on the estimated user's emotion. The reference unit can estimate the user's emotion and adjust the order in which the reference results are displayed based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the reference unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The reference unit can also record the user's voice and estimate the emotion using voice analysis technology. The reference unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. Based on the estimated emotion, the reference unit adjusts the order in which the reference results are displayed. For example, if the user is stressed, important information can be displayed first. If the user is relaxed, detailed information can be displayed first. If the user is in a hurry, information with a high level of detail can be displayed first. This makes it possible to adjust the order in which the reference results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 reference unit may be performed using, or without, the generation AI. For example, the reference unit may input user emotion data to the generation AI, which may infer the emotion and adjust the order in which the reference results are displayed.

[0112] The reference unit can perform references based on the geographical distribution of the information when referencing. The reference unit can perform references based on the geographical distribution of the information when referencing. Geographical distribution includes, but is not limited to, regional data, geographical bias, and distribution patterns. For example, if a user is in a specific region, the reference unit can preferentially refer to information related to that region. Furthermore, if a user is traveling, the reference unit can suggest optimal information based on the user's current location. Furthermore, if a user is in a specific country, the reference unit can preferentially refer to information based on the regulations of that country. This allows references to be performed taking the geographical distribution of the information into consideration. Some or all of the above-described processing in the reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reference unit can input geographical distribution data of the information into a generation AI, which can then provide optimal reference results.

[0113] The referencing unit can improve the accuracy of the reference by referring to related literature of the information during the reference. The referencing unit can improve the accuracy of the reference by referring to related literature of the information during the reference. Related literature includes, but is not limited to, academic papers, technical reports, industry news, etc. For example, the referencing unit can improve the reliability of the information by referring to related literature. The referencing unit can also provide optimal reference results based on the related literature of the information. The referencing unit can also improve the accuracy of the reference based on related literature specified by the user. This can improve the accuracy of the reference by referring to the related literature of the information. Some or all of the above-mentioned processing in the referencing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the referencing unit can input related literature data of the information into the generation AI, which can improve the accuracy of the reference.

[0114] The reference unit can perform references based on the market value of the information when making a reference. The reference unit can perform references based on the market value of the information when making a reference. Market values ​​include, but are not limited to, sales data, market share, and customer ratings, for example. For example, the reference unit preferentially references information with high market value. The reference unit can also reference information with low market value for reference purposes. The reference unit can also determine the priority of references based on the market value of the information. This allows references to be made taking the market value of the information into consideration. Some or all of the above-described processing in the reference unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reference unit can input market value data of the information into the generation AI, and the generation AI can determine the priority of references.

[0115] The generation unit can estimate the user's emotion and determine the priority of the label information to be generated based on the estimated user's emotion. The generation unit can estimate the user's emotion and determine the priority of the label information to be generated based on the estimated user's emotion. The emotion estimation can be performed, for example, using an emotion recognition algorithm, but is not limited to this example. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The generation unit determines the priority of the label information to be generated based on the estimated emotion. For example, if the user is feeling stressed, important label information can be generated with priority. If the user is relaxed, detailed label information can be generated with priority. If the user is in a hurry, label information that covers the main points can be generated with priority. In this way, the priority of the label information to be generated can be determined based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input user emotion data into the generation AI, which may then estimate the emotion and determine the priority of the label information.

[0116] The generation unit can improve the accuracy of generation based on the interrelationships of information during generation. The generation unit can improve the accuracy of generation based on the interrelationships of information during generation. Interrelationships include, but are not limited to, information associations, common themes, and correlations. For example, the generation unit generates related information by linking them to each other. The generation unit can also provide optimal generation results by taking into account the interrelationships of information. The generation unit can also improve the accuracy of generation based on the interrelationships of information specified by the user. This can improve the accuracy of generation by taking into account the interrelationships of information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information interrelationship data into the generation AI, which can then improve the accuracy of generation.

[0117] The generation unit can generate information taking into consideration attribute information of the information submitter when generating the information. The generation unit generates information taking into consideration attribute information of the information submitter when generating the information. Attribute information includes, but is not limited to, age, gender, occupation, and region, for example. For example, if the information submitter is an expert, the generation unit can generate the information preferentially. Furthermore, if the information submitter is a general consumer, the generation unit can generate the information for reference only. Furthermore, the generation unit can determine the priority of generation based on the attribute information of the information submitter. This allows generation to be performed taking into consideration the attribute information of the information submitter. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the information submitter into the generation AI, and the generation AI can determine the priority of generation.

[0118] The generation unit can weight the generation based on the frequency of information submission at the time of generation. The generation unit weights the generation based on the frequency of information submission at the time of generation. The submission frequency includes, but is not limited to, for example, the number of submissions, the submission interval, and the timing of submission. For example, the generation unit prioritizes generating information that is submitted more frequently. The generation unit can also generate information that is submitted less frequently for reference purposes. The generation unit can also weight the generation based on the frequency of information submission. This allows the generation to be weighted based on the frequency of information submission. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information submission frequency data into the generation AI, and the generation AI can weight the generation.

[0119] The generation unit can estimate the user's emotion and adjust the display method of the generated label information based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the display method of the generated label information based on the estimated user emotion. Emotion estimation can be performed, for example, using an emotion recognition algorithm, but is not limited to this example. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The generation unit adjusts the display method of the generated label information based on the estimated emotion. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the generated label information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input user emotion data into the generation AI, which may then estimate the emotion and adjust the display method of the label information.

[0120] The generation unit can generate the information based on the geographical distribution of the information at the time of generation. The generation unit generates the information based on the geographical distribution of the information at the time of generation. Geographical distribution includes, but is not limited to, regional data, geographical bias, and distribution patterns, for example. For example, if the user is in a specific region, the generation unit can prioritize generating information related to that region. Furthermore, if the user is traveling, the generation unit can also suggest optimal information based on the user's current location. Furthermore, if the user is in a specific country, the generation unit can prioritize generating information based on the regulations of that country. This allows generation to take the geographical distribution of the information into consideration. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical distribution data of the information into the generation AI, which can then provide optimal generation results.

[0121] The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. Related literature includes, but is not limited to, academic papers, technical reports, and industry news, for example. For example, the generation unit can improve the reliability of the information by referring to related literature. The generation unit can also provide optimal generation results based on literature related to the information. The generation unit can also improve the accuracy of generation based on literature specified by the user. This can improve the accuracy of generation by referring to literature related to the information. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input literature data related to the information into the generation AI, which can improve the accuracy of generation.

[0122] The generation unit can generate information based on the market value of the information at the time of generation. The generation unit generates information based on the market value of the information at the time of generation. Market value includes, but is not limited to, sales data, market share, customer ratings, etc. For example, the generation unit generates information with high market value preferentially. The generation unit can also generate information with low market value for reference only. The generation unit can also determine the priority of generation based on the market value of the information. This allows generation to be performed taking into account the market value of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market value data of the information into the generation AI, and the generation AI can determine the priority of generation.

[0123] The providing unit can estimate the user's emotion and adjust the display method of the label information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the display method of the label information to be provided based on the estimated user's emotion. The emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to this example. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The providing unit adjusts the display method of the label information to be provided based on the estimated emotion. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the label information to be provided based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI, which may estimate the emotion and adjust the display method of the label information.

[0124] The providing unit can select an appropriate display method based on the user's past operation history when providing the display data. The providing unit selects an appropriate display method based on the user's past operation history when providing the display data. The operation history includes, but is not limited to, past operation data, operation frequency, and operation patterns. For example, the providing unit prioritizes providing display methods used by the user in the past. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also customize the display method based on the user's past operation history. This makes it possible to select an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's operation history data into the generation AI, which can select an optimal display method.

[0125] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit customizes the display content according to the user's current task when providing the information. Tasks include, but are not limited to, work tasks, project tasks, and daily tasks. For example, the providing unit prioritizes displaying information related to the user's current task. The providing unit can also suggest optimal display content based on the user's current task. The providing unit can also customize the display content taking into account the user's current task. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's task data into the generation AI, which can then provide optimal display content.

[0126] The providing unit can improve the display method based on user feedback when providing the data. The providing unit improves the display method based on user feedback when providing the data. Feedback includes, for example, user evaluation comments, survey results, and usage history, but is not limited to these examples. For example, the providing unit improves the display method based on the feedback provided by the user. The providing unit can also suggest an optimal display method based on the user feedback. The providing unit can also customize the display interface by reflecting the user feedback. This makes it possible to improve the display method by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback data into the generation AI, which can select the optimal display method.

[0127] The providing unit can estimate the user's emotion and adjust the operation procedure of the label information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the operation procedure of the label information to be provided based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. Based on the estimated emotion, the providing unit can adjust the operation procedure of the label information to be provided. For example, if the user is stressed, the providing unit can simplify the operation procedure. If the user is relaxed, the providing unit can provide detailed operation procedures. If the user is in a hurry, the providing unit can provide procedures that allow for quick operation. This makes it possible to adjust the operation procedure of the label information to be provided based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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 providing unit may be performed using, or without, the generation AI. For example, the providing unit may input user emotion data to the generation AI, which may then estimate the emotion and adjust the operation procedure.

[0128] The providing unit can select an appropriate display method based on the user's device information at the time of providing. The providing unit selects an appropriate display method based on the user's device information at the time of providing. Device information includes, but is not limited to, the device type, OS version, and hardware specifications. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal display method to be selected taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can select the optimal display method.

[0129] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit can make the display content multilingual according to the user's language setting when providing the content. Language settings include, but are not limited to, the user's language selection, the device's language setting, and the region setting. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI, which can then provide optimal display content.

[0130] The providing unit can customize the information based on the user's occupation and lifestyle when providing the information. The providing unit customizes the information based on the user's occupation and lifestyle when providing the information. Occupations include, but are not limited to, occupational classification, industry, and job content. Lifestyles include, but are not limited to, lifestyle habits, hobbies, and values. For example, if the user is a food manufacturer, the providing unit can prioritize providing information related to the business. Furthermore, if the user is a general consumer, the providing unit can prioritize providing information aimed at consumers. Furthermore, the providing unit can suggest optimal information based on the user's lifestyle. This allows customization based on the user's occupation and lifestyle. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's occupation and lifestyle data into the generation AI, which can then provide optimal information.

[0131] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the correction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The correction unit can also record the user's voice and estimate the emotion using voice analysis technology. The correction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The correction unit adjusts the correction method based on the estimated emotion. For example, if the user is stressed, a simple correction interface can be provided. If the user is relaxed, detailed correction options can be provided. If the user is in a hurry, a means for quick correction can be provided. This makes it possible to adjust the correction method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the correction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the correction unit may input user emotion data into the generation AI, which may estimate the emotion and adjust the correction method.

[0132] The correction unit can select an appropriate correction method based on the user's past correction history when making corrections. The correction unit can select an appropriate correction method based on the user's past correction history when making corrections. The correction history includes, but is not limited to, past correction data, correction frequency, and correction patterns. For example, the correction unit can propose an optimal correction method based on the corrections made by the user in the past. The correction unit can also extract specific patterns from the user's past correction history and reflect them in the corrections. The correction unit can also customize the correction interface by referring to the user's past correction history. This makes it possible to select an optimal correction method by referring to the user's past correction history. Some or all of the above-described processing in the correction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the correction unit can input the user's correction history data into a generation AI, which can select an optimal correction method.

[0133] The correction unit can customize the correction content according to the user's current task during correction. The correction unit can customize the correction content according to the user's current task during correction. Tasks include, but are not limited to, work tasks, project tasks, and daily tasks. For example, the correction unit can prioritize displaying correction content related to the user's current task. The correction unit can also suggest optimal correction content based on the user's current task. The correction unit can also customize the correction content taking into account the user's current task. This allows the correction content to be customized according to the user's current task. Some or all of the above-described processing in the correction unit can be performed using, or without, a generation AI. For example, the correction unit can input the user's task data into the generation AI, which can then provide optimal correction content.

[0134] The correction unit can improve the correction method based on user feedback during correction. The correction unit improves the correction method based on user feedback during correction. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history, for example. For example, the correction unit improves the correction method based on feedback provided by the user. The correction unit can also propose an optimal correction method based on the user feedback. The correction unit can also customize the correction interface by reflecting the user feedback. This makes it possible to improve the correction method by reflecting the user feedback. Some or all of the above-described processing in the correction unit may be performed using, or without, a generation AI. For example, the correction unit can input user feedback data into the generation AI, which can then select the optimal correction method.

[0135] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. Emotion estimation is performed, for example, using an emotion recognition algorithm, but is not limited to such an example. For example, the correction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The correction unit can also record the user's voice and estimate the emotion using voice analysis technology. The correction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The correction unit determines the priority of corrections based on the estimated emotions. For example, if the user is stressed, important corrections can be prioritized. If the user is relaxed, detailed corrections can be prioritized. If the user is in a hurry, content that requires quick correction can be prioritized. In this way, the priority of corrections can be determined 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 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 correction unit may be performed using, or without, the generation AI. For example, the correction unit may input user emotion data into the generation AI, which may then estimate the emotion and determine the priority of correction.

[0136] The correction unit can select an appropriate correction method based on the user's device information during correction. The correction unit can select an appropriate correction method based on the user's device information during correction. Device information includes, but is not limited to, the device type, OS version, and hardware specifications. For example, if the user is using a smartphone, the correction unit can provide a correction method tailored to the screen size. Furthermore, if the user is using a tablet, the correction unit can provide a correction method optimized for a large screen. Furthermore, if the user is using a smartwatch, the correction unit can provide a simple and highly visible correction method. This allows the optimal correction method to be selected taking the user's device information into consideration. Some or all of the above-described processing in the correction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the correction unit can input the user's device information into the generation AI, which can then select the optimal correction method.

[0137] The correction unit can make the correction content multilingual in accordance with the user's language setting during correction. The correction unit can make the correction content multilingual in accordance with the user's language setting during correction. Language settings include, but are not limited to, the user's language selection, the device's language setting, and the region setting. For example, the correction unit automatically sets the correction content based on the user's device's language setting. The correction unit can also provide a language switching function when the user uses multiple languages. The correction unit can also provide the correction content in a specific language when the user selects that language. This makes it possible to make the correction content multilingual in accordance with the user's language setting. Some or all of the above-described processing in the correction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the correction unit can input the user's language setting data into the generation AI, which can then provide optimal correction content.

[0138] The correction unit can customize the content based on the user's occupation and lifestyle when making corrections. The correction unit customizes the content based on the user's occupation and lifestyle when making corrections. Occupations include, but are not limited to, occupational classification, industry, and job content. Lifestyles include, but are not limited to, lifestyle habits, hobbies, and values. For example, if the user is a food manufacturer, the correction unit can prioritize providing corrections related to the user's business. Furthermore, if the user is a general consumer, the correction unit can prioritize providing corrections aimed at consumers. Furthermore, the correction unit can suggest optimal corrections based on the user's lifestyle. This allows customization based on the user's occupation and lifestyle. Some or all of the above-described processing in the correction unit may be performed using, or without using, a generation AI. For example, the correction unit can input the user's occupation and lifestyle data into the generation AI, which can then provide optimal corrections.

[0139] The update unit can estimate the user's emotion and select update data based on the estimated user emotion. The update unit can estimate the user's emotion and select update data based on the estimated user emotion. The emotion estimation can be performed using, for example, an emotion recognition algorithm, but is not limited to this example. For example, the update unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The update unit can also record the user's voice and estimate the emotion using voice analysis technology. The update unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The update unit selects update data based on the estimated emotion. For example, if the user is stressed, important update data can be prioritized. If the user is relaxed, detailed update data can be prioritized. If the user is in a hurry, data that needs to be updated quickly can be prioritized. This allows update data to be selected based on the user's emotion. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the update unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the update unit may input user emotion data to the generation AI, which may estimate the emotion and select update data.

[0140] The update unit can optimize the update algorithm based on past update data during an update. The update unit optimizes the update algorithm based on past update data during an update. Past update data includes, but is not limited to, past data sets, update reports, and feedback results. For example, the update unit proposes an optimal update algorithm based on the past update data. The update unit can also extract specific patterns from the past update data and reflect them in the update. The update unit can also adjust the update algorithm by referring to the past update data. This makes it possible to optimize the update algorithm by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input past update data into a generation AI, which can then optimize the update algorithm.

[0141] The update unit can update the update data based on user feedback during an update. The update unit updates the update data based on user feedback during an update. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history, for example. For example, the update unit improves the update data based on feedback provided by the user. The update unit can also propose optimal update data based on the user feedback. The update unit can also customize the update data by reflecting the user feedback. This allows the update data to be updated by reflecting the user feedback. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input user feedback data into the generation AI, which can then provide optimal update data.

[0142] The update unit can adjust the update frequency based on regulatory changes during updates. The update unit can adjust the update frequency based on regulatory changes during updates. Regulatory changes include, but are not limited to, the addition of new laws and regulations and revisions to existing regulations. For example, the update unit can increase the update frequency when regulatory changes occur frequently. The update unit can also decrease the update frequency when regulatory changes occur infrequently. The update unit can also propose an optimal update frequency based on regulatory changes. This makes it possible to adjust the update frequency based on regulatory changes. Some or all of the above-described processing in the update unit can be performed using, or without, a generation AI. For example, the update unit can input regulatory change data into the generation AI, which can then determine the optimal update frequency.

[0143] The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. The emotion estimation can be performed, for example, using an emotion recognition algorithm, but is not limited to this example. For example, the update unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The update unit can also record the user's voice and estimate the emotion using voice analysis technology. The update unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion recognition algorithm. The update unit adjusts the update frequency based on the estimated emotion. For example, if the user is stressed, the update frequency can be reduced. Also, if the user is relaxed, the update frequency can be increased. Also, if the user is in a hurry, data that needs to be updated quickly can be prioritized. This makes it possible to adjust the update frequency based on the user's emotion. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generative 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 update unit may be performed using, or without, the generation AI. For example, the update unit may input user emotion data into the generation AI, which may estimate the emotion and adjust the update frequency.

[0144] The update unit can weight the update data based on the submission timing of the restrictions at the time of update. The update unit can weight the update data based on the submission timing of the restrictions at the time of update. The submission timing includes, but is not limited to, for example, the submission date, submission time, and submission frequency. For example, if the submission timing of the restrictions is approaching, the update unit prioritizes updating the data. Furthermore, if the submission timing of the restrictions is far away, the update unit can also update the data for reference only. Furthermore, the update unit can weight the update data based on the submission timing of the restrictions. In this way, the update data can be weighted based on the submission timing of the restrictions. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input the submission timing data of the restrictions into the generation AI, and the generation AI can optimally weight the update data.

[0145] The update unit can integrate information from different data sources to enrich the updated data during an update. The update unit integrates information from different data sources to enrich the updated data during an update. Data sources include, but are not limited to, internal databases, external APIs, and public datasets. For example, the update unit integrates information from different data sources to enrich the updated data. The update unit can also provide optimal updated data based on information from different data sources. The update unit can also customize the updated data by referring to information from different data sources. This makes it possible to integrate information from different data sources and enrich the updated data. Some or all of the above-described processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input information from different data sources into a generation AI, which can then provide optimal updated data.

[0146] The update unit can adjust the update algorithm based on user feedback during an update. The update unit adjusts the update algorithm based on user feedback during an update. Feedback includes, but is not limited to, user evaluation comments, survey results, and usage history. For example, the update unit improves the update algorithm based on user feedback. The update unit can also propose an optimal update algorithm based on user feedback. The update unit can also customize the update algorithm by reflecting user feedback. This allows the update algorithm to be adjusted by reflecting user feedback. Some or all of the above-described processing in the update unit may be performed using, or without using, a generation AI. For example, the update unit can input user feedback data into a generation AI, which can then provide an optimal update algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, reference unit, generation unit, provision unit, correction unit, update unit, and emotion estimation function, 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 receives information from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to a regulated database. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates label information. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated label information to the user. The correction unit is realized by the control unit 46A of the smart device 14 and allows the user to correct the label information. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the regulated database. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and adjusts the operation of the reception unit and the provision unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, reference unit, generation unit, provision unit, correction unit, update unit, and emotion estimation function, 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 receives information from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to a regulated database. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates label information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated label information to the user. The correction unit is realized by the control unit 46A of the smart glasses 214 and allows the user to correct the label information. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the regulated database. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the operation of the reception unit and the provision unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, reference unit, generation unit, provision unit, correction unit, update unit, and emotion estimation function is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives information from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to a regulated database. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates label information. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated label information to the user. The correction unit is realized by the control unit 46A of the headset type terminal 314 and allows the user to correct the label information. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the regulated database. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the operation of the reception unit and the provision unit. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, reference unit, generation unit, provision unit, correction unit, update unit, and emotion estimation function, 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 receives information from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to a regulated database. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates label information. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated label information to the user. The correction unit is realized by the control unit 46A of the robot 414 and allows the user to correct the label information. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the regulated database. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and adjusts the operation of the reception unit and the provision unit.

[0147] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0148] The reception unit can automatically suggest related past label information based on the user's input. For example, if the user inputs a specific ingredient, label information in which a similar ingredient was used in the past is presented. Also, if the user inputs a specific manufacturing method, past label information related to that manufacturing method can be displayed. Furthermore, related regulatory information and guidelines can be automatically suggested based on the information input by the user. This allows the user to efficiently generate label information while referring to past information.

[0149] The correction unit may have a sharing function for allowing a user to share corrected information with other users. For example, a user can share corrected label information with other users and receive feedback. The correction unit may also provide an interface that allows other users to comment on or rate the shared information. Furthermore, the correction unit may use the shared information as reference information when other users make corrections. This enables more accurate and effective label information to be generated through information sharing and feedback between users.

[0150] The correction unit can have a function for automatically managing versions of information corrected by the user. For example, every time the user makes a correction, the information before the correction and the information after the correction are automatically saved and managed as a version history. An interface can also be provided that allows the user to revert to a previous version. Furthermore, the correction unit can visually display the differences between versions, allowing the user to easily check the correction content. This allows the user to manage the correction history and revert to a previous version if necessary.

[0151] The update unit may have a function for notifying the user of the updated content when the regulation database is updated. For example, when a new regulation is added or an existing regulation is revised, the update unit automatically sends a notification to the user. The update unit may also provide guidelines for the user to take necessary actions based on the notification content. Furthermore, the update unit may provide a support function for the user to regenerate label information based on the updated content after receiving the notification. This allows the user to always provide label information that complies with the latest regulations.

[0152] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be displayed concisely, emphasizing only the important points. If the user is relaxed, detailed analysis results can be provided to allow the user to understand more deeply. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be provided quickly. This makes it possible to display optimal analysis results according to the user's emotions.

[0153] The generation unit can estimate the user's emotions and adjust the level of detail of the generated label information based on the estimated user emotions. For example, if the user is feeling stressed, simple, highly visible label information can be generated. If the user is relaxed, label information including detailed information can be generated. Furthermore, if the user is in a hurry, label information that covers the main points can be quickly generated. This makes it possible to generate optimal label information based on the user's emotions.

[0154] The providing unit can estimate the user's emotions and adjust the format of the label information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a format that focuses on the main points can be provided. This makes it possible to provide label information in an optimal format based on the user's emotions.

[0155] The providing unit can estimate the user's emotion and adjust the color of the label information to be provided based on the estimated user's emotion. For example, if the user is feeling stressed, label information in a subdued color can be provided. If the user is relaxed, label information in a bright color can be provided. Furthermore, if the user is in a hurry, label information in a highly visible color can be provided. This makes it possible to provide label information in the optimal color based on the user's emotion.

[0156] The providing unit can estimate the user's emotion and adjust the layout of the label information to be provided based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and organized layout can be provided. If the user is relaxed, a layout including detailed information can be provided. Furthermore, if the user is in a hurry, a layout that focuses on the main points can be provided. In this way, it is possible to provide label information in an optimal layout based on the user's emotion.

[0157] The providing unit can estimate the user's emotion and adjust the font size of the label information to be provided based on the estimated user's emotion. For example, if the user is feeling stressed, the label information can be provided in a large font size. If the user is relaxed, the label information can be provided in a standard font size. Furthermore, if the user is in a hurry, the label information can be provided in a font size with high visibility. This makes it possible to provide label information in an optimal font size based on the user's emotion.

[0158] The processing flow of the second embodiment will be briefly explained below.

[0159] Step 1: The reception unit receives information from the user. This information includes information about the product's materials and manufacturing methods. The reception unit can receive text information entered by the user, uploaded image data, and voice input. In the case of voice input, it converts the voice input into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using data mining, statistical analysis, and machine learning algorithms. The analysis unit extracts important keywords from the text information and also extracts text information from image data and audio data. Step 3: The reference unit references the regulatory database based on the information analyzed by the analysis unit. The regulatory database contains legal and regulatory information on food labels from each country, as well as industry standards and guidelines. The reference unit searches the regulatory database based on the keywords and text information extracted by the analysis unit. Step 4: The generation unit generates label information based on the regulatory database referenced by the reference unit. The label information includes allergen information, nutritional information, warning labels, etc. The generation unit generates this label information using generation AI. Step 5: The providing unit provides the label information generated by the generating unit. The providing unit provides the generated label information to the user by displaying the label information to the user. The providing unit can display the label information to the user through a web application or a mobile application, or can provide the label information by sending it by email or printing it out on a printer.

[0160] 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.

[0161] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0180] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0181] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0196] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0211] 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.

[0212] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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."

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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, in order to avoid confusion and to 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.

[0230] 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.

[0231] [Explanation of symbols]

[0232] 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 information from a user; an analysis unit that analyzes the information received by the reception unit; a reference unit that references a regulatory database based on the information analyzed by the analysis unit; a generation unit that generates label information based on the regulatory database referenced by the reference unit; a providing unit that provides the label information generated by the generating unit. A system characterized by:

2. The generated label information is provided to the user, and a correction section is provided that allows the user to correct the information.

2. The system of claim 1.

3. The correction unit The user corrects the information and sends it back to the analysis unit.

3. The system of claim 2.

4. Equipped with an update unit that updates the regulatory database to accommodate changes in regulations 2. The system of claim 1.

5. The analysis unit Analyze information about product materials and manufacturing methods 2. The system of claim 1.

6. The generation unit Generate detailed label information for allergen information and nutrition facts 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit Filter information as it arrives based on the user's current projects and interests 2. The system of claim 1.

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