system

Abstract

An object of the system according to the embodiment is to spread and improve reliability of insect food through determination of edible insects and research on health effects.SOLUTION: In one embodiment, a system comprises a generative AI, an image analyzer, a nutritional value analyzer, an allergy analyzer, and a PR activator. The production AI uses the production AI to identify edible insects or to study health effects. The image analysis unit analyzes an image of an insect. The nutrition value analysis part analyzes the nutrition value of the insect. The allergy analysis unit analyzes an allergy risk of an insect. The PR action part performs PR action of insect eating.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 not adequately researched the identification of edible insects or their health benefits, leaving room for improvement in the widespread adoption of insect eating.

[0005] The system of the embodiment aims to popularize and increase the credibility of eating insects through research into the identification of edible insects and their health benefits. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, an image analysis unit, a nutritional value analysis unit, an allergy analysis unit, and a PR activity unit. The generation AI uses the generation AI to identify edible insects or research their health effects. The image analysis unit analyzes images of insects. The nutritional value analysis unit analyzes the nutritional value of insects. The allergy analysis unit analyzes the allergy risk of insects. The PR activity unit carries out PR activities for eating insects. [Effects of the Invention]

[0007] The system according to the embodiment can promote the spread of insect eating and increase its credibility through research into the identification of edible insects and their health benefits. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The insect eating promotion system according to an embodiment of the present invention uses generative AI and image analysis technology to identify edible insects, research their health benefits, and provide that information. This allows the insect eating promotion system to perform edible insect identification, health benefit research, nutritional value analysis, allergy risk analysis, and PR activities in an integrated manner.

[0029] The insect eating promotion system according to the embodiment includes a generation AI, an image analysis unit, a nutritional value analysis unit, an allergy analysis unit, and a PR activity unit. The generation AI distinguishes edible insects and researches their health benefits. For example, the generation AI analyzes images of insects to identify their species. The generation AI also conducts scientific research on the health benefits of eating insects and provides the results as information. The image analysis unit analyzes images of insects. For example, the image analysis unit analyzes the morphological characteristics of the insects to identify their species. The image analysis unit can also perform DNA analysis of the insects to identify their species. The nutritional value analysis unit analyzes the nutritional value of the insects. For example, the nutritional value analysis unit analyzes the components of the insects to identify their nutrient content. The nutritional value analysis unit can also calculate the calories of the insects. The allergy analysis unit analyzes the allergy risk of the insects. For example, the allergy analysis unit identifies allergen components of the insects and evaluates the allergy risk. The allergy analysis unit can also evaluate allergic reactions. The PR activity unit conducts PR activities for eating insects. For example, the PR department will partner with celebrities and influencers to spread the appeal of eating insects. The PR department will also link with portal sites and apps to provide information about eating insects. This will enable the insect eating promotion system to identify edible insects, research their health benefits, analyze their nutritional value, analyze allergy risks, and carry out PR activities all in one place.

[0030] The generative AI can track the growth process of insects in real time and analyze the nutritional value and edibility of each stage. The generative AI can, for example, track the growth process of insects in real time and analyze the nutritional value at each stage. For example, the growth process from larvae to adults can be recorded with a camera, and the video data can be input into the generative AI for analysis. The generative AI can also analyze the edibility of each stage. For example, it can evaluate the taste and texture of each stage and identify the stage that is suitable for eating. This makes it possible to track the growth process of insects in real time and analyze the nutritional value and edibility of each stage.

[0031] The generation AI can analyze data on the insect's ecological environment and identify the optimal rearing conditions. For example, environmental data such as temperature, humidity, and light intensity can be collected using sensors, and this data can be input into the generation AI for analysis. The generation AI can also optimize the rearing conditions. For example, it can adjust the rearing temperature and type of food to optimize insect growth. This allows the generation AI to analyze data on the insect's ecological environment and identify the optimal rearing conditions.

[0032] The generative AI can analyze the sounds and movement patterns of insects to help identify their species. For example, the generative AI can analyze the sounds of insects to help identify their species. For example, the sounds of insects can be recorded and the audio data can be input into the generative AI for analysis. The generative AI can also analyze the movement patterns of insects. For example, it can analyze the insect's movement speed and movement trajectory to identify its species. This allows the generative AI to analyze the sounds and movement patterns of insects to help identify its species.

[0033] Generative AI can analyze the DNA data of insects and identify edible species based on genetic characteristics. Generative AI can, for example, analyze the DNA data of insects and identify edible species based on genetic characteristics. For example, DNA sequencing data can be input into the generative AI for analysis. Generative AI can also identify edible species using genetic markers. For example, it can identify insects with specific genetic mutations as edible species. This makes it possible to analyze the DNA data of insects and identify edible species based on genetic characteristics.

[0034] The generative AI can suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects. For example, the generative AI can suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects. For example, for a user who is deficient in a particular nutrient, it can suggest recipes that use insects that contain a lot of that nutrient. The generative AI can also suggest recipes that take allergy risk into consideration. For example, it can suggest recipes that use insects with a low risk of allergies. This makes it possible to suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects.

[0035] Generative AI can track and analyze the long-term health effects of eating insects. For example, Generative AI can collect regular health check data and input it into Generative AI for analysis. Generative AI can also evaluate the preventive effects of chronic diseases. For example, it can analyze the impact of eating insects on improving immunity. This makes it possible to track and analyze the long-term health effects of eating insects.

[0036] The generation AI can analyze the allergen components of insects and propose processing methods to minimize allergy risk. For example, the generation AI can analyze the allergen components of insects and propose processing methods to minimize allergy risk. For example, it can propose a heat treatment method to break down the allergen components. The generation AI can also propose a method of breaking down the allergen components using fermentation. For example, it can break down the allergen components using specific microorganisms. This makes it possible to analyze the allergen components of insects and propose processing methods to minimize allergy risk.

[0037] Generative AI can analyze past food safety data and formulate optimal food safety standards. Generative AI can, for example, analyze past food safety data and formulate optimal food safety standards. For example, it can perform risk assessments based on past food safety accident data and set standards. Generative AI can also set standard values ​​for microorganisms. For example, it can set the acceptable concentration of a specific microorganism as the standard. This makes it possible to analyze past food safety data and formulate optimal food safety standards.

[0038] Generative AI can monitor the hygiene of insect breeding environments in real time to ensure safety. Generative AI can, for example, monitor the hygiene of insect breeding environments in real time. For example, it can use sensors to detect temperature, humidity, and the presence of microorganisms, and input that data into generative AI for analysis. Generative AI can also suggest measures to improve hygiene. For example, it can optimize cleaning frequency and suggest disinfection methods. This makes it possible to monitor the hygiene of insect breeding environments in real time to ensure safety.

[0039] Generative AI can analyze microbial risks during the processing of insect food and propose measures to minimize the risks. Generative AI can, for example, analyze microbial risks during the processing of insect food and propose measures to minimize the risks. For example, it can propose temperature control and sterilization methods during the processing. Generative AI can also propose optimization of storage methods. For example, it can propose freezing and the use of vacuum packs. This makes it possible to analyze microbial risks during the processing of insect food and propose measures to minimize the risks.

[0040] Generative AI can analyze consumer purchasing history and preference data to formulate optimal PR strategies. Generative AI can, for example, analyze consumer purchasing history and formulate optimal PR strategies. For example, it can suggest insect edible products that consumers might be interested in based on past purchasing data. Generative AI can also analyze preference data and create PR messages tailored to the target demographic. For example, it can create advertisements based on preferred flavors and ingredients. This makes it possible to analyze consumer purchasing history and preference data and formulate optimal PR strategies.

[0041] Generative AI can analyze social media reactions in real time and develop effective PR campaigns. Generative AI can, for example, analyze social media reactions in real time and develop effective PR campaigns. For example, it can analyze consumer posts and comments and evaluate the effectiveness of a campaign. Generative AI can also analyze social media trends and create timely PR messages. For example, it can develop a campaign using trending hashtags. This allows generative AI to analyze social media reactions in real time and develop effective PR campaigns.

[0042] Generative AI can analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region. For example, generative AI can analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region. For example, it can create PR messages that take cultural background and eating habits into consideration. Generative AI can also analyze consumer responses in each region and formulate PR strategies tailored to the target demographic. For example, it can create advertisements based on the preferences and needs of each region. This makes it possible to analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region.

[0043] Generative AI can analyze consumer feedback and adjust PR campaigns in real time. Generative AI can, for example, analyze consumer feedback and adjust PR campaigns in real time. For example, it can change the campaign content based on consumer response data. Generative AI can also optimize PR messages based on feedback. For example, it can create advertisements that reflect consumer opinions and requests. This makes it possible to analyze consumer feedback and adjust PR campaigns in real time.

[0044] The generative AI can suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects. For example, the generative AI can suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects. For example, it can suggest protein bars made with insects that are high in protein. The generative AI can also suggest processed foods that have been fortified with vitamins and minerals. For example, it can suggest supplements made with insects that are high in specific vitamins. This makes it possible to suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects.

[0045] Generative AI can learn from past successes and failures and generate proposals with the highest probability of success. For example, generative AI can learn from past successes and failures and generate proposals with the highest probability of success when proposing new plans. For example, it can automatically generate optimal plans based on past data. Generative AI can also analyze factors that led to success or failure and improve the quality of proposals. For example, it can extract commonalities between successes and failures and make proposals based on those. This allows it to learn from past successes and failures and generate proposals with the highest probability of success.

[0046] Generative AI can collect user feedback on proposed plans in real time and make immediate improvements. For example, generative AI can build a system that collects user feedback on proposed plans in real time and makes immediate improvements based on the results. For example, it can use online surveys and comment functions. Generative AI can also improve the quality of plans based on the feedback. For example, it can add features or change the design to reflect user opinions. This allows proposed plans to be collected from users in real time and made immediate improvements.

[0047] Generative AI can automatically generate planning proposals specialized for different markets and regions. For example, it can make proposals based on market needs and regional characteristics. Generative AI can also analyze consumer responses in each region and propose optimal plans. For example, it can propose plans based on regional preferences and needs. This makes it possible to automatically generate planning proposals specialized for different markets and regions.

[0048] Generative AI can automatically generate visual prototypes of proposed plans, making them easier to understand visually. Generative AI can automatically generate visual prototypes of proposed plans, for example, visually displaying product designs or service flows. Generative AI can also create 3D models and design mockups, for example, visually representing the appearance and functions of a product. This allows for the automatic generation of visual prototypes of proposed plans, making them easier to understand visually.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The insect eating promotion system can further include a history analysis unit that analyzes the user's dietary history. The history analysis unit collects the user's past dietary data and evaluates the effectiveness of introducing insect eating. For example, it tracks the user's health condition and weight changes after consuming insect eating, and specifically indicates the effectiveness of insect eating. The history analysis unit can also analyze the user's eating patterns and suggest the optimal timing for introducing insect eating. This makes it possible to evaluate the effectiveness of insect eating based on the user's dietary history and suggest the optimal introduction method.

[0051] The insect eating popularization system can further include an allergy-friendly recipe suggestion unit that analyzes the user's allergy information and suggests recipes that minimize the risk of allergies. The allergy-friendly recipe suggestion unit suggests safe insect eating recipes based on the user's allergy information. For example, it can suggest recipes that use insects that do not contain specific allergens. It can also suggest cooking methods that reduce the risk of allergies. This makes it possible to provide safe insect eating recipes that take allergy risks into consideration.

[0052] The insect eating popularization system can further include a preference recipe suggestion unit that analyzes the user's food preferences and suggests insect eating recipes based on the preferences. The preference recipe suggestion unit suggests recipes using the user's favorite flavors and ingredients based on the user's past food data and preference data. For example, a spicy insect eating recipe can be suggested to a user who likes spicy flavors. Also, a dessert-style insect eating recipe can be suggested to a user who likes sweet things. This makes it possible to suggest personalized recipes that match the user's preferences.

[0053] The insect eating popularization system can further include an exercise-linked recipe suggestion unit that analyzes the user's exercise data and suggests insect eating recipes combined with exercise. The exercise-linked recipe suggestion unit suggests recipes suitable for replenishing nutrition after exercise based on the user's exercise data. For example, it suggests insect eating recipes that are rich in protein, which is necessary after strength training. It can also suggest recipes suitable for replenishing energy needed after aerobic exercise. This allows for effective nutritional supplementation combined with exercise.

[0054] The insect food popularization system can further include a purchasing tendency suggestion unit that analyzes user purchasing data and suggests insect food products based on purchasing trends. The purchasing tendency suggestion unit suggests insect food products that the user may be interested in based on the user's past purchasing data. For example, if a user prefers products that are high in a specific nutrient, it can suggest insect food products that are high in that nutrient. It can also suggest insect food products of that brand to a user who prefers a specific brand. This makes it possible to make personalized suggestions based on the user's purchasing trends.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The generative AI will identify edible insects and research their health benefits. For example, the generative AI will analyze images of insects and identify their species. The generative AI will also conduct scientific research on the health benefits of eating insects and provide the results as information. Step 2: The image analysis unit analyzes the image of the insect. For example, the image analysis unit analyzes the morphological characteristics of the insect to identify its species. The image analysis unit can also perform DNA analysis of the insect to identify its species. Step 3: The nutritional value analysis unit analyzes the nutritional value of the insect. For example, the nutritional value analysis unit analyzes the components of the insect to determine the nutrient content. The nutritional value analysis unit can also calculate the calorie content of the insect. Step 4: The allergy analysis unit analyzes the allergy risk of the insect. For example, the allergy analysis unit identifies allergen components of the insect and evaluates the allergy risk. The allergy analysis unit can also evaluate allergic reactions. Step 5: The PR department will promote insect eating. For example, the PR department will partner with celebrities and influencers to spread the word about the appeal of insect eating. The PR department will also work with portal sites and apps to provide information about insect eating.

[0057] (Example 2) The insect eating promotion system according to an embodiment of the present invention uses generative AI and image analysis technology to identify edible insects, research their health benefits, and provide that information. This allows the insect eating promotion system to perform edible insect identification, health benefit research, nutritional value analysis, allergy risk analysis, and PR activities in an integrated manner.

[0058] The insect eating promotion system according to the embodiment includes a generation AI, an image analysis unit, a nutritional value analysis unit, an allergy analysis unit, and a PR activity unit. The generation AI distinguishes edible insects and researches their health benefits. For example, the generation AI analyzes images of insects to identify their species. The generation AI also conducts scientific research on the health benefits of eating insects and provides the results as information. The image analysis unit analyzes images of insects. For example, the image analysis unit analyzes the morphological characteristics of the insects to identify their species. The image analysis unit can also perform DNA analysis of the insects to identify their species. The nutritional value analysis unit analyzes the nutritional value of the insects. For example, the nutritional value analysis unit analyzes the components of the insects to identify their nutrient content. The nutritional value analysis unit can also calculate the calories of the insects. The allergy analysis unit analyzes the allergy risk of the insects. For example, the allergy analysis unit identifies allergen components of the insects and evaluates the allergy risk. The allergy analysis unit can also evaluate allergic reactions. The PR activity unit conducts PR activities for eating insects. For example, the PR department will partner with celebrities and influencers to spread the appeal of eating insects. The PR department will also link with portal sites and apps to provide information about eating insects. This will enable the insect eating promotion system to identify edible insects, research their health benefits, analyze their nutritional value, analyze allergy risks, and carry out PR activities all in one place.

[0059] The generative AI can track the growth process of insects in real time and analyze the nutritional value and edibility of each stage. The generative AI can, for example, track the growth process of insects in real time and analyze the nutritional value at each stage. For example, the growth process from larvae to adults can be recorded with a camera, and the video data can be input into the generative AI for analysis. The generative AI can also analyze the edibility of each stage. For example, it can evaluate the taste and texture of each stage and identify the stage that is suitable for eating. This makes it possible to track the growth process of insects in real time and analyze the nutritional value and edibility of each stage.

[0060] The generation AI can analyze data on the insect's ecological environment and identify the optimal rearing conditions. For example, environmental data such as temperature, humidity, and light intensity can be collected using sensors, and this data can be input into the generation AI for analysis. The generation AI can also optimize the rearing conditions. For example, it can adjust the rearing temperature and type of food to optimize insect growth. This allows the generation AI to analyze data on the insect's ecological environment and identify the optimal rearing conditions.

[0061] The generative AI can analyze the sounds and movement patterns of insects to help identify their species. For example, the generative AI can analyze the sounds of insects to help identify their species. For example, the sounds of insects can be recorded and the audio data can be input into the generative AI for analysis. The generative AI can also analyze the movement patterns of insects. For example, it can analyze the insect's movement speed and movement trajectory to identify its species. This allows the generative AI to analyze the sounds and movement patterns of insects to help identify its species.

[0062] Generative AI can analyze the DNA data of insects and identify edible species based on genetic characteristics. Generative AI can, for example, analyze the DNA data of insects and identify edible species based on genetic characteristics. For example, DNA sequencing data can be input into the generative AI for analysis. Generative AI can also identify edible species using genetic markers. For example, it can identify insects with specific genetic mutations as edible species. This makes it possible to analyze the DNA data of insects and identify edible species based on genetic characteristics.

[0063] The generative AI can suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects. For example, the generative AI can suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects. For example, for a user who is deficient in a particular nutrient, it can suggest recipes that use insects that contain a lot of that nutrient. The generative AI can also suggest recipes that take allergy risk into consideration. For example, it can suggest recipes that use insects with a low risk of allergies. This makes it possible to suggest recipes that are optimal for individual health conditions based on the nutritional value data of insects.

[0064] Generative AI can track and analyze the long-term health effects of eating insects. For example, Generative AI can collect regular health check data and input it into Generative AI for analysis. Generative AI can also evaluate the preventive effects of chronic diseases. For example, it can analyze the impact of eating insects on improving immunity. This makes it possible to track and analyze the long-term health effects of eating insects.

[0065] The generation AI can analyze the allergen components of insects and propose processing methods to minimize allergy risk. For example, the generation AI can analyze the allergen components of insects and propose processing methods to minimize allergy risk. For example, it can propose a heat treatment method to break down the allergen components. The generation AI can also propose a method of breaking down the allergen components using fermentation. For example, it can break down the allergen components using specific microorganisms. This makes it possible to analyze the allergen components of insects and propose processing methods to minimize allergy risk.

[0066] Generative AI can analyze past food safety data and formulate optimal food safety standards. Generative AI can, for example, analyze past food safety data and formulate optimal food safety standards. For example, it can perform risk assessments based on past food safety accident data and set standards. Generative AI can also set standard values ​​for microorganisms. For example, it can set the acceptable concentration of a specific microorganism as the standard. This makes it possible to analyze past food safety data and formulate optimal food safety standards.

[0067] Generative AI can monitor the hygiene of insect breeding environments in real time to ensure safety. Generative AI can, for example, monitor the hygiene of insect breeding environments in real time. For example, it can use sensors to detect temperature, humidity, and the presence of microorganisms, and input that data into generative AI for analysis. Generative AI can also suggest measures to improve hygiene. For example, it can optimize cleaning frequency and suggest disinfection methods. This makes it possible to monitor the hygiene of insect breeding environments in real time to ensure safety.

[0068] Generative AI can analyze microbial risks during the processing of insect food and propose measures to minimize the risks. Generative AI can, for example, analyze microbial risks during the processing of insect food and propose measures to minimize the risks. For example, it can propose temperature control and sterilization methods during the processing. Generative AI can also propose optimization of storage methods. For example, it can propose freezing and the use of vacuum packs. This makes it possible to analyze microbial risks during the processing of insect food and propose measures to minimize the risks.

[0069] Generative AI can analyze consumer purchasing history and preference data to formulate optimal PR strategies. Generative AI can, for example, analyze consumer purchasing history and formulate optimal PR strategies. For example, it can suggest insect edible products that consumers might be interested in based on past purchasing data. Generative AI can also analyze preference data and create PR messages tailored to the target demographic. For example, it can create advertisements based on preferred flavors and ingredients. This makes it possible to analyze consumer purchasing history and preference data and formulate optimal PR strategies.

[0070] Generative AI can analyze social media reactions in real time and develop effective PR campaigns. Generative AI can, for example, analyze social media reactions in real time and develop effective PR campaigns. For example, it can analyze consumer posts and comments and evaluate the effectiveness of a campaign. Generative AI can also analyze social media trends and create timely PR messages. For example, it can develop a campaign using trending hashtags. This allows generative AI to analyze social media reactions in real time and develop effective PR campaigns.

[0071] Generative AI can analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region. For example, generative AI can analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region. For example, it can create PR messages that take cultural background and eating habits into consideration. Generative AI can also analyze consumer responses in each region and formulate PR strategies tailored to the target demographic. For example, it can create advertisements based on the preferences and needs of each region. This makes it possible to analyze the acceptability of edible insects in different cultural spheres and formulate the optimal PR strategy for each region.

[0072] Generative AI can analyze consumer feedback and adjust PR campaigns in real time. Generative AI can, for example, analyze consumer feedback and adjust PR campaigns in real time. For example, it can change the campaign content based on consumer response data. Generative AI can also optimize PR messages based on feedback. For example, it can create advertisements that reflect consumer opinions and requests. This makes it possible to analyze consumer feedback and adjust PR campaigns in real time.

[0073] The generation AI can use its emotion estimation function to analyze the emotions consumers have toward eating insects and create promotional messages based on those emotions. For example, the generation AI uses its emotion estimation function to analyze the emotions consumers have toward eating insects. For example, it analyzes the consumer's facial expressions and voice and calculates an emotion score. The generation AI then creates promotional messages based on those emotions. For example, it creates catchphrases and advertising copy that elicit positive emotions. This makes it possible to analyze the emotions consumers have toward eating insects and create promotional messages based on those emotions.

[0074] The generative AI can use its emotion estimation function to monitor consumers' emotions toward eating insects in real time, and formulate a marketing strategy based on the results. The generative AI can, for example, use its emotion estimation function to monitor consumers' emotions toward eating insects in real time. For example, it can analyze consumers' facial expressions and voices to calculate an emotion score. The generative AI can then formulate a marketing strategy based on the results. For example, it can suggest promotional methods to elicit positive emotions. This makes it possible to monitor consumers' emotions toward eating insects in real time, and formulate a marketing strategy based on the results.

[0075] The generative AI can suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects. For example, the generative AI can suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects. For example, it can suggest protein bars made with insects that are high in protein. The generative AI can also suggest processed foods that have been fortified with vitamins and minerals. For example, it can suggest supplements made with insects that are high in specific vitamins. This makes it possible to suggest processed foods that have been fortified with specific nutrients based on the nutritional value data of insects.

[0076] The generation AI can use the emotion estimation function to identify the nutritional information that consumers are most interested in and create marketing materials based on that information. The generation AI can, for example, use the emotion estimation function to identify the nutritional information that consumers are most interested in. For example, it can analyze the consumer's facial expressions and voice and calculate an emotion score. The generation AI can then create marketing materials based on that information. For example, it can create presentation materials or brochures based on the nutritional information that consumers are interested in. This makes it possible to identify the nutritional information that consumers are most interested in and create marketing materials based on that information.

[0077] The generative AI can use its emotion estimation function to analyze the concerns consumers have about safety and provide information to alleviate those concerns. The generative AI, for example, uses its emotion estimation function to analyze the concerns consumers have about food safety. For example, it can analyze the consumer's facial expressions and voice to identify the cause of the concerns. The generative AI can also provide information to alleviate those concerns. For example, it can create FAQs and guidelines and provide them to consumers. This makes it possible to analyze the concerns consumers have about safety and provide information to alleviate those concerns.

[0078] Generative AI can use its emotion estimation function to monitor consumers' feelings about safety in real time and provide information to elicit positive emotions. Generative AI can, for example, use its emotion estimation function to monitor consumers' feelings about food safety in real time. For example, it can analyze consumers' facial expressions and voices and calculate an emotion score. Generative AI can also provide information to elicit positive emotions. For example, it can create guidelines and educational materials to give consumers a sense of security. This makes it possible to monitor consumers' feelings about safety in real time and provide information to elicit positive emotions.

[0079] Generative AI can learn from past successes and failures and generate proposals with the highest probability of success. For example, generative AI can learn from past successes and failures and generate proposals with the highest probability of success when proposing new plans. For example, it can automatically generate optimal plans based on past data. Generative AI can also analyze factors that led to success or failure and improve the quality of proposals. For example, it can extract commonalities between successes and failures and make proposals based on those. This allows it to learn from past successes and failures and generate proposals with the highest probability of success.

[0080] Generative AI can collect user feedback on proposed plans in real time and make immediate improvements. For example, generative AI can build a system that collects user feedback on proposed plans in real time and makes immediate improvements based on the results. For example, it can use online surveys and comment functions. Generative AI can also improve the quality of plans based on the feedback. For example, it can add features or change the design to reflect user opinions. This allows proposed plans to be collected from users in real time and made immediate improvements.

[0081] The generation AI can use the emotion estimation function to make personalized plan proposals based on the user's emotions. The generation AI, for example, uses the emotion estimation function to make personalized plan proposals based on the user's emotions. For example, it proposes optimal plans based on the user's emotion score. The generation AI also makes personalized proposals based on the user's preferences and past behavioral data. For example, it proposes plans that match the user's tastes. This makes it possible to make personalized plan proposals based on the user's emotions.

[0082] Generative AI can automatically generate planning proposals specialized for different markets and regions. For example, it can make proposals based on market needs and regional characteristics. Generative AI can also analyze consumer responses in each region and propose optimal plans. For example, it can propose plans based on regional preferences and needs. This makes it possible to automatically generate planning proposals specialized for different markets and regions.

[0083] Generative AI can automatically generate visual prototypes of proposed plans, making them easier to understand visually. Generative AI can automatically generate visual prototypes of proposed plans, for example, visually displaying product designs or service flows. Generative AI can also create 3D models and design mockups, for example, visually representing the appearance and functions of a product. This allows for the automatic generation of visual prototypes of proposed plans, making them easier to understand visually.

[0084] The generative AI uses the emotion estimation function to monitor users' emotional reactions to new project proposals, and can continuously improve the quality of the proposals. The generative AI, for example, uses the emotion estimation function to monitor users' emotional reactions to new project proposals in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generative AI then improves the quality of the proposals based on the results. For example, it makes suggestions that elicit positive reactions. This makes it possible to monitor users' emotional reactions to new project proposals and continuously improve the quality of the proposals.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The insect eating promotion system can further include a history analysis unit that analyzes the user's dietary history. The history analysis unit collects the user's past dietary data and evaluates the effectiveness of introducing insect eating. For example, it tracks the user's health condition and weight changes after consuming insect eating, and specifically indicates the effectiveness of insect eating. The history analysis unit can also analyze the user's eating patterns and suggest the optimal timing for introducing insect eating. This makes it possible to evaluate the effectiveness of insect eating based on the user's dietary history and suggest the optimal introduction method.

[0087] The insect eating popularization system can further include an emotional recipe suggestion unit that estimates the user's emotions and suggests insect eating recipes based on the estimated emotions. The emotional recipe suggestion unit analyzes the user's emotions and suggests recipes that correspond to those emotions. For example, if the user is feeling stressed, it can suggest insect eating recipes that have a relaxing effect. Also, if the user feels like they want to cheer up, it can suggest recipes that are suitable for replenishing energy. This makes it possible to suggest personalized recipes based on the user's emotions.

[0088] The insect eating popularization system can further include an allergy-friendly recipe suggestion unit that analyzes the user's allergy information and suggests recipes that minimize the risk of allergies. The allergy-friendly recipe suggestion unit suggests safe insect eating recipes based on the user's allergy information. For example, it can suggest recipes that use insects that do not contain specific allergens. It can also suggest cooking methods that reduce the risk of allergies. This makes it possible to provide safe insect eating recipes that take allergy risks into consideration.

[0089] The insect eating promotion system can further include an emotional PR activity unit that estimates the user's emotions and carries out PR activities for insect eating based on the estimated emotions. The emotional PR activity unit analyzes the user's emotions and creates a PR message based on those emotions. For example, if the user is interested, it can create an appealing PR message to further stimulate that interest. Also, if the user is feeling anxious, it can create a PR message that provides a sense of security to alleviate that anxiety. This enables effective PR activities based on the user's emotions.

[0090] The insect eating popularization system can further include a preference recipe suggestion unit that analyzes the user's food preferences and suggests insect eating recipes based on the preferences. The preference recipe suggestion unit suggests recipes using the user's favorite flavors and ingredients based on the user's past food data and preference data. For example, a spicy insect eating recipe can be suggested to a user who likes spicy flavors. Also, a dessert-style insect eating recipe can be suggested to a user who likes sweet things. This makes it possible to suggest personalized recipes that match the user's preferences.

[0091] The insect eating promotion system can further include an emotion and health explanation unit that estimates the user's emotions and explains the health benefits of eating insects based on the estimated emotions. The emotion and health explanation unit analyzes the user's emotions and explains the health benefits according to those emotions. For example, if the user is interested in health, the specific health benefits of eating insects can be explained in detail. Furthermore, if the user is feeling anxious, scientific evidence can be provided to alleviate that anxiety. This makes it possible to explain effective health benefits based on the user's emotions.

[0092] The insect eating popularization system can further include an exercise-linked recipe suggestion unit that analyzes the user's exercise data and suggests insect eating recipes combined with exercise. The exercise-linked recipe suggestion unit suggests recipes suitable for replenishing nutrition after exercise based on the user's exercise data. For example, it suggests insect eating recipes that are rich in protein, which is necessary after strength training. It can also suggest recipes suitable for replenishing energy needed after aerobic exercise. This allows for effective nutritional supplementation combined with exercise.

[0093] The insect eating promotion system can further include an emotion introduction support unit that estimates the user's emotions and supports the introduction of insect eating based on the estimated emotions. The emotion introduction support unit analyzes the user's emotions and provides support according to those emotions. For example, if the user is interested, it can provide information to further stimulate that interest. Also, if the user is feeling anxious, it can provide support to alleviate that anxiety. This makes it possible to provide effective support for the introduction of insect eating based on the user's emotions.

[0094] The insect food popularization system can further include a purchasing tendency suggestion unit that analyzes user purchasing data and suggests insect food products based on purchasing trends. The purchasing tendency suggestion unit suggests insect food products that the user may be interested in based on the user's past purchasing data. For example, if a user prefers products that are high in a specific nutrient, it can suggest insect food products that are high in that nutrient. It can also suggest insect food products of that brand to a user who prefers a specific brand. This makes it possible to make personalized suggestions based on the user's purchasing trends.

[0095] The insect eating popularization system can further include an emotional event planning unit that estimates the user's emotions and plans an insect eating event based on the estimated emotions. The emotional event planning unit analyzes the user's emotions and plans an event based on those emotions. For example, if the user is interested, it can plan a tasting event to further stimulate that interest. Also, if the user is feeling anxious, it can plan an educational event to alleviate that anxiety. This makes it possible to plan effective events based on the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The generative AI will identify edible insects and research their health benefits. For example, the generative AI will analyze images of insects and identify their species. The generative AI will also conduct scientific research on the health benefits of eating insects and provide the results as information. Step 2: The image analysis unit analyzes the image of the insect. For example, the image analysis unit analyzes the morphological characteristics of the insect to identify its species. The image analysis unit can also perform DNA analysis of the insect to identify its species. Step 3: The nutritional value analysis unit analyzes the nutritional value of the insect. For example, the nutritional value analysis unit analyzes the components of the insect to determine the nutrient content. The nutritional value analysis unit can also calculate the calorie content of the insect. Step 4: The allergy analysis unit analyzes the allergy risk of the insect. For example, the allergy analysis unit identifies allergen components of the insect and evaluates the allergy risk. The allergy analysis unit can also evaluate allergic reactions. Step 5: The PR department will promote insect eating. For example, the PR department will partner with celebrities and influencers to spread the word about the appeal of insect eating. The PR department will also work with portal sites and apps to provide information about insect eating.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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. [Explanation of symbols]

[0165] 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. Generative AI that uses generative AI to identify edible insects or research their health benefits; an image analysis unit that analyzes images of insects; a nutritional value analysis department that analyzes the nutritional value of insects; Allergy Analysis Department, which analyzes insect allergy risks; It also has a PR department that promotes insect eating. A system characterized by:

2. The generated AI is Tracking insect growth in real time and analyzing the nutritional value or edibility of each stage of development 2. The system of claim 1.

3. The generated AI is Proposing optimal recipes for individual health conditions based on nutritional data on insects 2. The system of claim 1.

4. The generated AI is Analyzing insect allergens and proposing processing methods to minimize allergy risks 2. The system of claim 1.

5. The generated AI is Analyzing consumer purchasing history or preference data to formulate optimal PR strategies 2. The system of claim 1.

6. The generated AI is Analyze consumer sentiment toward eating insects and create PR messages based on those sentiments 2. The system of claim 1.

7. The generated AI is Identify the nutritional information that consumers find most interesting and create marketing materials based on that information 2. The system of claim 1.

8. The generated AI is Monitor users' emotional reactions to new proposals and continuously improve the quality of proposals 2. The system of claim 1.

Images