system

Abstract

The system according to the embodiment aims to automate and efficiently create and update information. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a feedback unit. The reception unit allows a user to select the type of information they wish to generate and input the necessary data. The generation unit analyzes the data input by the reception unit and generates information in a predetermined format. The customization unit customizes the information generated by the generation unit to meet the user's requirements. The feedback unit receives user feedback on the information customized by the customization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring time and cost to manually create and update information such as catalogs and menus.

[0005] The system according to the embodiment aims to automate and efficiently create and update information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a customization unit, and a feedback unit. The reception unit allows a user to select the type of information they wish to generate and input the necessary data. The generation unit analyzes the data input by the reception unit and generates information in a predetermined format. The customization unit customizes the information generated by the generation unit to meet the user's requirements. The feedback unit receives user feedback on the information customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate and efficiently create and update information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate and update information such as product catalogs, restaurant menus, local facility information, and hotel room guides. In the information generation system, a user selects the type of information they want to generate and inputs the necessary data. The generation AI then analyzes the input data and generates information in an appropriate format. For example, in the case of a product catalog, users input the product name, price, description, and image, and the generation AI automatically generates a catalog page based on this information. Similarly, restaurant menus, local facility information, and hotel room guides can be automatically generated simply by inputting the necessary data. The generated information can be customized to meet user needs. For example, promotional information for specific products can be added, and seasonal menus can be automatically updated. Furthermore, the generation AI can improve the accuracy of the information based on user feedback. This allows the information generation system to always provide up-to-date and accurate information. This significantly reduces the time and cost required to create and update information. For example, the information users want to generate can be quickly and accurately generated, customized, and feedback can be collected. Furthermore, the generated information can be customized to meet user needs, ensuring that the information provided is always up-to-date and accurate.

[0029] An information generation system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a feedback unit. The reception unit selects the type of information a user wants to generate and inputs the necessary data. Examples of the types of information a user wants to generate include, but are not limited to, product catalogs, restaurant menus, local facility information, and hotel room guides. The reception unit can input, for example, text data, numerical data, and image data. The generation unit uses a generation AI to analyze the data input by the reception unit and generate information in a predetermined format. The generation unit selects an optimal format based on the input data and generates information. The generation AI can be, for example, a text generation AI (e.g., GPT-4 (registered trademark)) or a multimodal generation AI, but is not limited to, examples. The generation unit automatically generates a catalog page based on, for example, product names, prices, descriptions, images, and the like. The customization unit customizes the information generated by the generation unit to meet user requirements. For example, the customization unit adds promotional information about specific products to the generated information. The customization unit can also automatically update the generated information to include seasonal menus. The feedback unit receives user feedback on the information customized by the customization unit. The feedback unit can receive, for example, user ratings, comments, correction requests, etc. This enables the information generation system according to the embodiment to efficiently generate, customize, and collect feedback on information that the user wants to generate.

[0030] The generation unit can analyze the input data through the generation AI and generate information in an appropriate format. For example, the generation AI selects an optimal format based on the input data and generates information. The generation AI can be, for example, a text generation AI (e.g., GPT-4) or a multimodal generation AI, but is not limited to these examples. The generation unit automatically generates a catalog page based on, for example, product names, prices, descriptions, images, etc. The generation unit can also automatically generate restaurant menus, local facility information, hotel room guides, etc., simply by inputting the necessary data. This improves the accuracy of information generation by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit inputs input data to the generation AI, and the generation AI generates information.

[0031] The customization unit can add promotional information about a specific product to the generated information. For example, the customization unit adds promotional information about a specific product to the generated information. Promotional information about a specific product includes, but is not limited to, product features, prices, and promotion periods. For example, the customization unit adds promotional information about a specific product to a generated catalog page. The customization unit can also add promotional information about a specific dish to a generated restaurant menu. By adding promotional information, it becomes possible to provide information tailored to user needs. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the generated information into AI, which adds the promotional information.

[0032] The customization unit can automatically update the seasonal menu based on the generated information. The customization unit, for example, automatically updates the seasonal menu based on the generated information. Seasonal menus include, but are not limited to, spring menus, summer menus, autumn menus, and winter menus. The customization unit, for example, automatically adds seasonal dishes to the generated restaurant menu. The customization unit can also automatically add seasonal event information to the generated local facility information. This allows the automatic updating of seasonal menus to always provide the latest information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the generated information into AI, which then automatically updates the seasonal menu.

[0033] The feedback unit receives user feedback, allowing the generation AI to improve the accuracy of the information. The feedback unit, for example, receives user feedback, allowing the generation AI to improve the accuracy of the information. User feedback includes, but is not limited to, ratings, comments, and correction requests. For example, the feedback unit receives user ratings on the generated catalog page, allowing the generation AI to improve the accuracy of the information based on the ratings. The feedback unit can also receive user comments on the generated restaurant menu, allowing the generation AI to improve the accuracy of the information based on the comments. This allows the accuracy of the information to be improved based on user feedback, thereby always providing accurate information. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit inputs user feedback into AI, allowing the AI ​​to improve the accuracy of the information.

[0034] The reception unit can analyze the user's past data input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of data previously input by the user and select an optimal input method. This makes it possible to suggest an optimal input method by analyzing the past data input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past input history data to a generation AI, which then selects the optimal input method.

[0035] The reception unit can filter data based on the user's current project or areas of interest when inputting the data. For example, the reception unit displays only data related to the user's current project. The reception unit can also prioritize displaying highly relevant data based on the user's areas of interest. The reception unit can also filter data based on areas in which the user has previously shown interest. This makes it possible to provide highly relevant data by filtering data based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the user's area of ​​interest data to a generation AI, which then performs the filtering.

[0036] The reception unit can select an appropriate input means depending on the user's input method when inputting data. For example, if the user desires voice input, the reception unit can provide a voice input interface. Furthermore, if the user desires text input, the reception unit can provide a text input interface. Furthermore, if the user desires image input, the reception unit can input data using image recognition technology. This allows efficient data input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method data to a generation AI, which then selects an appropriate input means.

[0037] When inputting data, the reception unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying data related to the user's current location. The reception unit can also filter highly relevant data based on the user's geographical location information. The reception unit can also prioritize inputting related data based on places the user has visited in the past. This makes it possible to provide highly relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information data to a generation AI, and the generation AI filters out highly relevant data.

[0038] The reception unit can analyze the user's social media activity and input relevant data when inputting data. The reception unit can input relevant data based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input relevant data. The reception unit can also input relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be provided by analyzing social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the user's social media activity data to the generation AI, and the generation AI inputs the relevant data.

[0039] The reception unit can customize the input method based on the user's past feedback when entering data. The reception unit can suggest an optimal input method based on, for example, feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's feedback. This makes it possible to provide an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback data to a generation AI, which then customizes the input method.

[0040] The generation unit can adjust the level of detail of the generated information based on the importance of the data when generating the information. For example, for data with high importance, the generation unit generates information including a detailed description and an image. For data with low importance, the generation unit can generate only a brief description and basic information. The generation unit can also dynamically adjust the level of detail of the information according to the importance of the data. This enables efficient information generation by adjusting the level of detail of the generated information according to the importance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the importance of the data into the generation AI, and the generation AI adjusts the level of detail of the information.

[0041] The generation unit can apply different generation algorithms depending on the type of data when generating information. For example, in the case of a product catalog, the generation unit applies an algorithm that generates information based on the product name, price, description, and image. In the case of a restaurant menu, the generation unit can apply an algorithm that generates information based on the dish name, price, description, and image. In the case of local facility information, the generation unit can apply an algorithm that generates information based on the facility name, location, business hours, and image. This improves the accuracy of information generation by applying the optimal generation algorithm depending on the type of data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the type of data into the generation AI, which then applies the optimal generation algorithm.

[0042] When generating information, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit improves the accuracy of generation, for example, based on information generated by the user in the past. The generation unit can also improve accuracy by learning optimal generation patterns from the user's past generation results. The generation unit can also improve accuracy by improving the generation algorithm based on user feedback. This improves the accuracy of generation by referring to past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past generation result data into the generation AI, and the generation AI improves the accuracy of generation.

[0043] When generating information, the generation unit can determine the generation priority according to the data submission time. For example, the generation unit prioritizes the generation of data with an approaching deadline. Furthermore, the generation unit can postpone the generation of data with a more distant submission time. Furthermore, the generation unit can dynamically adjust the generation priority according to the data submission time. This enables efficient information generation by determining the generation priority according to the data submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs the data submission time into the generation AI, and the generation AI determines the generation priority.

[0044] The generation unit can adjust the order of generation according to the relevance of data when generating information. For example, the generation unit generates highly relevant data preferentially. Furthermore, the generation unit can postpone the generation of less relevant data. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of data. This enables efficient information generation by adjusting the order of generation according to the relevance of data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the relevance of data to the generation AI, and the generation AI adjusts the order of generation.

[0045] When generating information, the generation unit can adjust the use of technical terminology in the generated information based on the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate information that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can generate easy-to-understand information that avoids technical terminology. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated information according to the user's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's level of expertise into the generation AI, and the generation AI adjusts the use of technical terminology.

[0046] During customization, the customization unit can analyze the user's past customization history and select an appropriate customization method. The customization unit can, for example, suggest an optimal customization method based on the user's past customizations. The customization unit can also learn specific patterns from the user's past customization history and select an optimal customization method. The customization unit can also analyze the user's past customization history and suggest the most efficient customization method. In this way, the optimal customization method can be suggested by analyzing the past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's past customization history data into a generation AI, which then selects the optimal customization method.

[0047] During customization, the customization unit can customize the customization means based on the user's current requirements. The customization unit provides the customization means based on, for example, information currently required by the user. The customization unit can also dynamically adjust customization options according to the user's current needs. The customization unit can also analyze the user's current needs and suggest an optimal customization method. This enables more appropriate customization by providing the customization means based on the current needs. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's current needs data into a generation AI, which then customizes the customization means.

[0048] During customization, the customization unit can improve the customization method based on user feedback. The customization unit improves the customization method based on, for example, feedback provided by the user. The customization unit can also learn the optimal customization method from the user feedback and reflect it in the next customization. The customization unit can also analyze the user feedback and optimize the customization procedure. This allows the customization method to be improved by reflecting the feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs user feedback data into a generation AI, which then improves the customization method.

[0049] During customization, the customization unit can select an appropriate customization method based on the user's geographical location information. For example, the customization unit prioritizes customization related to the user's current location. The customization unit can also suggest an optimal customization method based on the user's geographical location information. The customization unit can also perform relevant customization based on places the user has visited in the past. This makes it possible to provide an optimal customization method by taking the geographical location information into consideration. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's geographical location information data into a generation AI, which then selects the optimal customization method.

[0050] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest relevant customizations based on information shared by the user on social media. The customization unit can also analyze the content posted by the user on social media and suggest relevant customizations. The customization unit can also suggest relevant customizations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, highly relevant customization methods can be provided. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's social media activity data into a generation AI, which then suggests customization methods.

[0051] During customization, the customization unit can customize the customization method based on the user's past feedback. The customization unit can, for example, suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also optimize the customization means from the user's past feedback. The customization unit can also improve the customization procedure by reflecting the user's feedback. This makes it possible to provide an optimal customization method by reflecting the past feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's past feedback data into a generation AI, which then customizes the customization method.

[0052] When collecting feedback, the feedback unit can analyze the user's past feedback history and select an appropriate collection method. The feedback unit, for example, suggests an optimal collection method based on feedback provided by the user in the past. The feedback unit can also learn specific patterns from the user's past feedback history and select an optimal collection method. The feedback unit can also analyze the user's past feedback history and suggest the most efficient collection method. In this way, the optimal collection method can be provided by analyzing the past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's past feedback history data into a generation AI, which then selects the optimal collection method.

[0053] When collecting feedback, the feedback unit can customize the collection means based on the user's current requirements. For example, the feedback unit provides the feedback means based on the information the user currently needs. The feedback unit can also dynamically adjust feedback options according to the user's current needs. The feedback unit can also analyze the user's current needs and suggest an optimal feedback method. This allows for more appropriate feedback to be collected by providing the collection means based on the current needs. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit inputs the user's current needs data into a generation AI, which then customizes the collection means.

[0054] The feedback unit can improve the collection method based on the user's feedback when collecting feedback. The feedback unit improves the feedback collection method, for example, based on feedback provided by the user. The feedback unit can also learn the optimal collection method from the user's feedback and reflect it in the next feedback collection. The feedback unit can also analyze the user's feedback and optimize the collection procedure. In this way, the collection method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's feedback data into a generation AI, which then improves the collection method.

[0055] When collecting feedback, the feedback unit can select an appropriate collection method based on the user's geographical location information. For example, the feedback unit prioritizes collecting feedback related to the user's current location. The feedback unit can also suggest an optimal collection method based on the user's geographical location information. The feedback unit can also collect related feedback based on places the user has visited in the past. This makes it possible to provide an optimal collection method by taking geographical location information into consideration. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit inputs the user's geographical location information data into a generation AI, which then selects the optimal collection method.

[0056] When collecting feedback, the feedback unit can analyze the user's social media activity and suggest collection means. For example, the feedback unit collects relevant feedback based on information shared by the user on social media. The feedback unit can also collect relevant feedback by analyzing the content of the user's social media posts. The feedback unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, highly relevant feedback can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's social media activity data into a generation AI, which then suggests collection means.

[0057] When collecting feedback, the feedback unit can customize the collection method based on the user's past feedback. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also optimize the collection means from the user's past feedback. The feedback unit can also improve the collection procedure by reflecting the user's feedback. In this way, the optimal collection method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's past feedback data into a generation AI, and the generation AI customizes the collection method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past behavioral patterns and suggest the optimal timing for data entry. For example, if the user has tended to enter data during a specific time period in the past, a notification can be sent to prompt the user to enter data during that time period. Also, if the user has entered data on a specific day of the week in the past, a data entry reminder can be set for that day. Furthermore, the reception unit can analyze the success rate and error rate of the user's past data entry attempts and suggest the most efficient timing. This can improve data entry efficiency by suggesting the optimal timing for data entry based on the user's behavioral patterns.

[0060] The generation unit can reflect the user's preferences in the information it generates. For example, it can adjust the layout of the information it generates based on the format or design that the user has used in the past. Also, if the user prefers a particular color or font, it can generate information that matches that preference. It can also learn the style of information that the user has given high ratings to in the past and generate new information based on that. This makes it possible to generate information that matches the user's preferences, thereby improving user satisfaction.

[0061] The reception unit can preferentially input highly relevant data in consideration of the user's geographical location information. For example, data related to the user's current location can be preferentially displayed. Also, highly relevant data can be filtered based on the user's geographical location information. Furthermore, related data can be preferentially input based on places the user has visited in the past. In this way, highly relevant data can be provided by taking the user's geographical location information into consideration.

[0062] When generating information, the generation unit can apply different generation algorithms depending on the type of data. For example, in the case of a product catalog, an algorithm can be applied that generates information based on the product name, price, description, and image. In the case of a restaurant menu, an algorithm can be applied that generates information based on the dish name, price, description, and image. Furthermore, in the case of local facility information, an algorithm can be applied that generates information based on the facility name, location, business hours, and image. This allows the accuracy of information generation to be improved by applying the optimal generation algorithm depending on the type of data.

[0063] The feedback unit can analyze the user's social media activity and collect relevant feedback. For example, relevant feedback can be collected based on information shared by the user on social media. Also, relevant feedback can be collected by analyzing the content posted by the user on social media. Furthermore, relevant feedback can be collected by referring to the activities of the user's friends on social media. In this way, highly relevant feedback can be collected by analyzing social media activity.

[0064] The reception unit can customize the input method based on the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's feedback. In this way, the optimal input method can be provided by reflecting the past feedback.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit selects the type of information the user wants to generate and inputs the necessary data. The types of information the user wants to generate include, for example, product catalogs, restaurant menus, local facility information, hotel room guides, etc. The reception unit can input text data, numerical data, image data, etc. Step 2: The generation unit uses a generation AI to analyze the data input by the reception unit and generate information in a specified format. The generation unit selects the optimal format based on the input data and generates the information. The generation AI may be a text generation AI (e.g., GPT-4) or a multimodal generation AI. The generation unit automatically generates a catalog page based on the product name, price, description, images, etc. Step 3: The customization unit customizes the information generated by the generation unit to meet the user's needs. The customization unit adds promotional information about specific products to the generated information. The customization unit can also automatically update the generated information with seasonal menus. Step 4: The feedback unit receives user feedback on the information customized by the customization unit. The feedback unit can receive user ratings, comments, correction requests, and the like.

[0067] (Example 2) An information generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate and update information such as product catalogs, restaurant menus, local facility information, and hotel room guides. In the information generation system, a user selects the type of information they want to generate and inputs the necessary data. The generation AI then analyzes the input data and generates information in an appropriate format. For example, in the case of a product catalog, users input the product name, price, description, and image, and the generation AI automatically generates a catalog page based on this information. Similarly, restaurant menus, local facility information, and hotel room guides can be automatically generated simply by inputting the necessary data. The generated information can be customized to meet user needs. For example, promotional information for specific products can be added, and seasonal menus can be automatically updated. Furthermore, the generation AI can improve the accuracy of the information based on user feedback. This allows the information generation system to always provide up-to-date and accurate information. This significantly reduces the time and cost required to create and update information. For example, the information users want to generate can be quickly and accurately generated, customized, and feedback can be collected. Furthermore, the generated information can be customized to meet user needs, ensuring that the information provided is always up-to-date and accurate.

[0068] An information generation system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a feedback unit. The reception unit selects the type of information a user wants to generate and inputs the necessary data. Examples of the types of information a user wants to generate include, but are not limited to, product catalogs, restaurant menus, local facility information, and hotel room guides. The reception unit can input, for example, text data, numerical data, and image data. The generation unit uses a generation AI to analyze the data input by the reception unit and generate information in a predetermined format. The generation unit selects an optimal format based on the input data and generates information. The generation AI can be, for example, a text generation AI (e.g., GPT-4) or a multimodal generation AI, but is not limited to, examples. The generation unit automatically generates a catalog page based on, for example, product names, prices, descriptions, images, and the like. The customization unit customizes the information generated by the generation unit to meet user requirements. For example, the customization unit adds promotional information about specific products to the generated information. The customization unit can also automatically update the generated information to include seasonal menus. The feedback unit receives user feedback on the information customized by the customization unit. The feedback unit can receive, for example, user ratings, comments, correction requests, etc. This enables the information generation system according to the embodiment to efficiently generate, customize, and collect feedback on information that the user wants to generate.

[0069] The generation unit can analyze the input data through the generation AI and generate information in an appropriate format. For example, the generation AI selects an optimal format based on the input data and generates information. The generation AI can be, for example, a text generation AI (e.g., GPT-4) or a multimodal generation AI, but is not limited to these examples. The generation unit automatically generates a catalog page based on, for example, product names, prices, descriptions, images, etc. The generation unit can also automatically generate restaurant menus, local facility information, hotel room guides, etc., simply by inputting the necessary data. This improves the accuracy of information generation by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit inputs input data to the generation AI, and the generation AI generates information.

[0070] The customization unit can add promotional information about a specific product to the generated information. For example, the customization unit adds promotional information about a specific product to the generated information. Promotional information about a specific product includes, but is not limited to, product features, prices, and promotion periods. For example, the customization unit adds promotional information about a specific product to a generated catalog page. The customization unit can also add promotional information about a specific dish to a generated restaurant menu. By adding promotional information, it becomes possible to provide information tailored to user needs. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the generated information into AI, which adds the promotional information.

[0071] The customization unit can automatically update the seasonal menu based on the generated information. The customization unit, for example, automatically updates the seasonal menu based on the generated information. Seasonal menus include, but are not limited to, spring menus, summer menus, autumn menus, and winter menus. The customization unit, for example, automatically adds seasonal dishes to the generated restaurant menu. The customization unit can also automatically add seasonal event information to the generated local facility information. This allows the automatic updating of seasonal menus to always provide the latest information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the generated information into AI, which then automatically updates the seasonal menu.

[0072] The feedback unit receives user feedback, allowing the generation AI to improve the accuracy of the information. The feedback unit, for example, receives user feedback, allowing the generation AI to improve the accuracy of the information. User feedback includes, but is not limited to, ratings, comments, and correction requests. For example, the feedback unit receives user ratings on the generated catalog page, allowing the generation AI to improve the accuracy of the information based on the ratings. The feedback unit can also receive user comments on the generated restaurant menu, allowing the generation AI to improve the accuracy of the information based on the comments. This allows the accuracy of the information to be improved based on user feedback, thereby always providing accurate information. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit inputs user feedback into AI, allowing the AI ​​to improve the accuracy of the information.

[0073] The reception unit can predict the user's emotions and adjust the timing of data input based on the predicted user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of data input, providing a relaxing environment. Furthermore, if the user is relaxed, the reception unit can speed up the timing of data input, allowing for efficient data input. Furthermore, if the user is in a hurry, the reception unit can optimize the timing of data input and complete the input quickly. This allows for more appropriate data input by adjusting the timing of data input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the timing of data input.

[0074] The reception unit can analyze the user's past data input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of data previously input by the user and select an optimal input method. This makes it possible to suggest an optimal input method by analyzing the past data input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past input history data to a generation AI, which then selects the optimal input method.

[0075] The reception unit can filter data based on the user's current project or areas of interest when inputting the data. For example, the reception unit displays only data related to the user's current project. The reception unit can also prioritize displaying highly relevant data based on the user's areas of interest. The reception unit can also filter data based on areas in which the user has previously shown interest. This makes it possible to provide highly relevant data by filtering data based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the user's area of ​​interest data to a generation AI, which then performs the filtering.

[0076] The reception unit can select an appropriate input means depending on the user's input method when inputting data. For example, if the user desires voice input, the reception unit can provide a voice input interface. Furthermore, if the user desires text input, the reception unit can provide a text input interface. Furthermore, if the user desires image input, the reception unit can input data using image recognition technology. This allows efficient data input by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method data to a generation AI, which then selects an appropriate input means.

[0077] The reception unit can predict the user's emotions and determine the priority of data to be input based on the predicted user emotions. For example, if the user is feeling stressed, the reception unit postpones input of less important data. Furthermore, if the user is relaxed, the reception unit can prioritize input of more important data. Furthermore, if the user is in a hurry, the reception unit can input the most important data first. Thus, by determining the priority of data according to the user's emotions, important data can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs the user's facial expression data to the generation AI, which then estimates the emotion and determines the priority of the data.

[0078] When inputting data, the reception unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying data related to the user's current location. The reception unit can also filter highly relevant data based on the user's geographical location information. The reception unit can also prioritize inputting related data based on places the user has visited in the past. This makes it possible to provide highly relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the user's geographical location information data to a generation AI, and the generation AI filters out highly relevant data.

[0079] The reception unit can analyze the user's social media activity and input relevant data when inputting data. The reception unit can input relevant data based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input relevant data. The reception unit can also input relevant data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be provided by analyzing social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the user's social media activity data to the generation AI, and the generation AI inputs the relevant data.

[0080] The reception unit can customize the input method based on the user's past feedback when entering data. The reception unit can suggest an optimal input method based on, for example, feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's feedback. This makes it possible to provide an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback data to a generation AI, which then customizes the input method.

[0081] The generation unit can predict the user's emotions and adjust the way the information is presented based on the predicted user emotions. For example, if the user is relaxed, the generation unit generates information using soft colors and a simple design. If the user is in a hurry, the generation unit can generate information using a concise design that emphasizes the main points. If the user is excited, the generation unit can generate information using a visually stimulating design. This allows for more appropriate information to be provided by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the way the information is presented.

[0082] The generation unit can adjust the level of detail of the generated information based on the importance of the data when generating the information. For example, for data with high importance, the generation unit generates information including a detailed description and an image. For data with low importance, the generation unit can generate only a brief description and basic information. The generation unit can also dynamically adjust the level of detail of the information according to the importance of the data. This enables efficient information generation by adjusting the level of detail of the generated information according to the importance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the importance of the data into the generation AI, and the generation AI adjusts the level of detail of the information.

[0083] The generation unit can apply different generation algorithms depending on the type of data when generating information. For example, in the case of a product catalog, the generation unit applies an algorithm that generates information based on the product name, price, description, and image. In the case of a restaurant menu, the generation unit can apply an algorithm that generates information based on the dish name, price, description, and image. In the case of local facility information, the generation unit can apply an algorithm that generates information based on the facility name, location, business hours, and image. This improves the accuracy of information generation by applying the optimal generation algorithm depending on the type of data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the type of data into the generation AI, which then applies the optimal generation algorithm.

[0084] When generating information, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit improves the accuracy of generation, for example, based on information generated by the user in the past. The generation unit can also improve accuracy by learning optimal generation patterns from the user's past generation results. The generation unit can also improve accuracy by improving the generation algorithm based on user feedback. This improves the accuracy of generation by referring to past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past generation result data into the generation AI, and the generation AI improves the accuracy of generation.

[0085] The generation unit can predict the user's emotions and adjust the length of the information to be generated based on the predicted user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point information. Furthermore, if the user is relaxed, the generation unit can generate longer information with detailed explanations. Furthermore, if the user is excited, the generation unit can generate information with visually stimulating effects. This allows for more appropriate information to be provided by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the length of the information.

[0086] When generating information, the generation unit can determine the generation priority according to the data submission time. For example, the generation unit prioritizes the generation of data with an approaching deadline. Furthermore, the generation unit can postpone the generation of data with a more distant submission time. Furthermore, the generation unit can dynamically adjust the generation priority according to the data submission time. This enables efficient information generation by determining the generation priority according to the data submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs the data submission time into the generation AI, and the generation AI determines the generation priority.

[0087] The generation unit can adjust the order of generation according to the relevance of data when generating information. For example, the generation unit generates highly relevant data preferentially. Furthermore, the generation unit can postpone the generation of less relevant data. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of data. This enables efficient information generation by adjusting the order of generation according to the relevance of data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the relevance of data to the generation AI, and the generation AI adjusts the order of generation.

[0088] When generating information, the generation unit can adjust the use of technical terminology in the generated information based on the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate information that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can generate easy-to-understand information that avoids technical terminology. Furthermore, the generation unit can dynamically adjust the use of technical terminology in the generated information according to the user's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's level of expertise into the generation AI, and the generation AI adjusts the use of technical terminology.

[0089] The customization unit can predict the user's emotions and adjust the customization method based on the predicted user emotions. For example, the customization unit can provide detailed customization options when the user is relaxed. Furthermore, the customization unit can provide concise customization options when the user is in a hurry. Furthermore, the customization unit can provide visually stimulating customization options when the user is excited. This enables more appropriate customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the customization method.

[0090] During customization, the customization unit can analyze the user's past customization history and select an appropriate customization method. The customization unit can, for example, suggest an optimal customization method based on the user's past customizations. The customization unit can also learn specific patterns from the user's past customization history and select an optimal customization method. The customization unit can also analyze the user's past customization history and suggest the most efficient customization method. In this way, the optimal customization method can be suggested by analyzing the past customization history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's past customization history data into a generation AI, which then selects the optimal customization method.

[0091] During customization, the customization unit can customize the customization means based on the user's current requirements. The customization unit provides the customization means based on, for example, information currently required by the user. The customization unit can also dynamically adjust customization options according to the user's current needs. The customization unit can also analyze the user's current needs and suggest an optimal customization method. This enables more appropriate customization by providing the customization means based on the current needs. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's current needs data into a generation AI, which then customizes the customization means.

[0092] During customization, the customization unit can improve the customization method based on user feedback. The customization unit improves the customization method based on, for example, feedback provided by the user. The customization unit can also learn the optimal customization method from the user feedback and reflect it in the next customization. The customization unit can also analyze the user feedback and optimize the customization procedure. This allows the customization method to be improved by reflecting the feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs user feedback data into a generation AI, which then improves the customization method.

[0093] The customization unit can predict the user's emotions and determine the priority of customization based on the predicted user emotions. For example, if the user is feeling stressed, the customization unit postpones less important customization. Furthermore, if the user is relaxed, the customization unit can prioritize more important customization. Furthermore, if the user is in a hurry, the customization unit can perform the most important customization first. This allows the priority of customization to be determined according to the user's emotions, thereby prioritizing important customization. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and determines the priority of customization.

[0094] During customization, the customization unit can select an appropriate customization method based on the user's geographical location information. For example, the customization unit prioritizes customization related to the user's current location. The customization unit can also suggest an optimal customization method based on the user's geographical location information. The customization unit can also perform relevant customization based on places the user has visited in the past. This makes it possible to provide an optimal customization method by taking the geographical location information into consideration. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's geographical location information data into a generation AI, which then selects the optimal customization method.

[0095] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit can suggest relevant customizations based on information shared by the user on social media. The customization unit can also analyze the content posted by the user on social media and suggest relevant customizations. The customization unit can also suggest relevant customizations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, highly relevant customization methods can be provided. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's social media activity data into a generation AI, which then suggests customization methods.

[0096] During customization, the customization unit can customize the customization method based on the user's past feedback. The customization unit can, for example, suggest an optimal customization method based on feedback provided by the user in the past. The customization unit can also optimize the customization means from the user's past feedback. The customization unit can also improve the customization procedure by reflecting the user's feedback. This makes it possible to provide an optimal customization method by reflecting the past feedback. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit inputs the user's past feedback data into a generation AI, which then customizes the customization method.

[0097] The feedback unit can predict the user's emotions and adjust the feedback collection method based on the predicted user emotions. For example, if the user is relaxed, the feedback unit can request detailed feedback. If the user is in a hurry, the feedback unit can request brief feedback. If the user is excited, the feedback unit can provide a visually stimulating feedback form. This allows for more appropriate feedback to be collected by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the feedback collection method.

[0098] When collecting feedback, the feedback unit can analyze the user's past feedback history and select an appropriate collection method. The feedback unit, for example, suggests an optimal collection method based on feedback provided by the user in the past. The feedback unit can also learn specific patterns from the user's past feedback history and select an optimal collection method. The feedback unit can also analyze the user's past feedback history and suggest the most efficient collection method. In this way, the optimal collection method can be provided by analyzing the past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's past feedback history data into a generation AI, which then selects the optimal collection method.

[0099] When collecting feedback, the feedback unit can customize the collection means based on the user's current requirements. For example, the feedback unit provides the feedback means based on the information the user currently needs. The feedback unit can also dynamically adjust feedback options according to the user's current needs. The feedback unit can also analyze the user's current needs and suggest an optimal feedback method. This allows for more appropriate feedback to be collected by providing the collection means based on the current needs. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit inputs the user's current needs data into a generation AI, which then customizes the collection means.

[0100] The feedback unit can improve the collection method based on the user's feedback when collecting feedback. The feedback unit improves the feedback collection method, for example, based on feedback provided by the user. The feedback unit can also learn the optimal collection method from the user's feedback and reflect it in the next feedback collection. The feedback unit can also analyze the user's feedback and optimize the collection procedure. In this way, the collection method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's feedback data into a generation AI, which then improves the collection method.

[0101] The feedback unit can predict the user's emotions and determine the priority of feedback based on the predicted user emotions. For example, if the user is feeling stressed, the feedback unit can postpone feedback of lower importance. Also, if the user is relaxed, the feedback unit can prioritize collecting feedback of higher importance. Also, if the user is in a hurry, the feedback unit can collect the most important feedback first. In this way, by determining the priority of feedback according to the user's emotions, it is possible to prioritize and collect important feedback. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit inputs the user's facial expression data into the generation AI, which then estimates the emotions and determines the priority of the feedback.

[0102] When collecting feedback, the feedback unit can select an appropriate collection method based on the user's geographical location information. For example, the feedback unit prioritizes collecting feedback related to the user's current location. The feedback unit can also suggest an optimal collection method based on the user's geographical location information. The feedback unit can also collect related feedback based on places the user has visited in the past. This makes it possible to provide an optimal collection method by taking geographical location information into consideration. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit inputs the user's geographical location information data into a generation AI, which then selects the optimal collection method.

[0103] When collecting feedback, the feedback unit can analyze the user's social media activity and suggest collection means. For example, the feedback unit collects relevant feedback based on information shared by the user on social media. The feedback unit can also collect relevant feedback by analyzing the content of the user's social media posts. The feedback unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, highly relevant feedback can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's social media activity data into a generation AI, which then suggests collection means.

[0104] When collecting feedback, the feedback unit can customize the collection method based on the user's past feedback. For example, the feedback unit can suggest an optimal collection method based on feedback provided by the user in the past. The feedback unit can also optimize the collection means from the user's past feedback. The feedback unit can also improve the collection procedure by reflecting the user's feedback. In this way, the optimal collection method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit inputs the user's past feedback data into a generation AI, and the generation AI customizes the collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, customization unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user selects the type of information they want to generate and inputs the necessary data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI and generates information in a predetermined format. The customization unit is realized, for example, by the control unit 46A of the smart device 14, and customizes the generated information to meet the user's requirements. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and receives user feedback on the customized information. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, customization unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user selects the type of information they want to generate and inputs the required data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI and generates information in a predetermined format. The customization unit is realized, for example, by the control unit 46A of the smart glasses 214, and customizes the generated information to meet the user's requirements. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and receives user feedback on the customized information. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, customization unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user selects the type of information they want to generate and inputs the necessary data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI and generates information in a predetermined format. The customization unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and customizes the generated information to meet the user's requirements. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and receives user feedback on the customized information. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, customization unit, and feedback unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user selects the type of information they want to generate and inputs the necessary data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI and generates information in a predetermined format. The customization unit is realized, for example, by the control unit 46A of the robot 414, and customizes the generated information to meet the user's requirements. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and receives user feedback on the customized information.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the user's past behavioral patterns and suggest the optimal timing for data entry. For example, if the user has tended to enter data during a specific time period in the past, a notification can be sent to prompt the user to enter data during that time period. Also, if the user has entered data on a specific day of the week in the past, a data entry reminder can be set for that day. Furthermore, the reception unit can analyze the success rate and error rate of the user's past data entry attempts and suggest the most efficient timing. This can improve data entry efficiency by suggesting the optimal timing for data entry based on the user's behavioral patterns.

[0107] The generation unit can reflect the user's preferences in the information it generates. For example, it can adjust the layout of the information it generates based on the format or design that the user has used in the past. Also, if the user prefers a particular color or font, it can generate information that matches that preference. It can also learn the style of information that the user has given high ratings to in the past and generate new information based on that. This makes it possible to generate information that matches the user's preferences, thereby improving user satisfaction.

[0108] The customization unit can predict the user's emotions and make customization suggestions based on the predicted emotions. For example, if the user is feeling stressed, simple and intuitive customization options can be provided. If the user is feeling relaxed, detailed customization options can be provided, allowing the user to freely enjoy customization. Furthermore, if the user is excited, visually stimulating customization options can be provided. In this way, by making customization suggestions according to the user's emotions, a more appropriate customization experience can be provided.

[0109] The feedback unit can predict the user's emotions and adjust the feedback collection method based on the predicted emotions. For example, if the user is relaxed, detailed feedback can be requested. If the user is in a hurry, brief feedback can be requested. Furthermore, if the user is excited, a visually stimulating feedback form can be provided. In this way, by adjusting the feedback collection method according to the user's emotions, more appropriate feedback can be collected.

[0110] The reception unit can preferentially input highly relevant data in consideration of the user's geographical location information. For example, data related to the user's current location can be preferentially displayed. Also, highly relevant data can be filtered based on the user's geographical location information. Furthermore, related data can be preferentially input based on places the user has visited in the past. In this way, highly relevant data can be provided by taking the user's geographical location information into consideration.

[0111] When generating information, the generation unit can apply different generation algorithms depending on the type of data. For example, in the case of a product catalog, an algorithm can be applied that generates information based on the product name, price, description, and image. In the case of a restaurant menu, an algorithm can be applied that generates information based on the dish name, price, description, and image. Furthermore, in the case of local facility information, an algorithm can be applied that generates information based on the facility name, location, business hours, and image. This allows the accuracy of information generation to be improved by applying the optimal generation algorithm depending on the type of data.

[0112] The customization unit can predict the user's emotions and determine the priority of customization based on the predicted emotions. For example, if the user is feeling stressed, customization with low importance can be postponed. Also, if the user is relaxed, customization with high importance can be given priority. Furthermore, if the user is in a hurry, the most important customization can be performed first. In this way, by determining the priority of customization according to the user's emotions, important customization can be given priority.

[0113] The feedback unit can analyze the user's social media activity and collect relevant feedback. For example, relevant feedback can be collected based on information shared by the user on social media. Also, relevant feedback can be collected by analyzing the content posted by the user on social media. Furthermore, relevant feedback can be collected by referring to the activities of the user's friends on social media. In this way, highly relevant feedback can be collected by analyzing social media activity.

[0114] The generation unit can predict the user's emotions and adjust the way the information is presented based on the predicted emotions. For example, if the user is relaxed, the information can be generated with soft colors and a simple design. If the user is in a hurry, the information can be generated with a concise design that emphasizes the main points. Furthermore, if the user is excited, the information can be generated with a visually stimulating design. In this way, the way the information is presented can be adjusted according to the user's emotions, thereby providing more appropriate information.

[0115] The reception unit can customize the input method based on the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's feedback. In this way, the optimal input method can be provided by reflecting the past feedback.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit selects the type of information the user wants to generate and inputs the necessary data. The types of information the user wants to generate include, for example, product catalogs, restaurant menus, local facility information, hotel room guides, etc. The reception unit can input text data, numerical data, image data, etc. Step 2: The generation unit uses a generation AI to analyze the data input by the reception unit and generate information in a specified format. The generation unit selects the optimal format based on the input data and generates the information. The generation AI may be a text generation AI (e.g., GPT-4) or a multimodal generation AI. The generation unit automatically generates a catalog page based on the product name, price, description, images, etc. Step 3: The customization unit customizes the information generated by the generation unit to meet the user's needs. The customization unit adds promotional information about specific products to the generated information. The customization unit can also automatically update the generated information with seasonal menus. Step 4: The feedback unit receives user feedback on the information customized by the customization unit. The feedback unit can receive user ratings, comments, correction requests, and the like.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for allowing a user to select the type of information they wish to generate and input necessary data; a generating unit that analyzes the data input by the receiving unit and generates information in a predetermined format; a customization unit that customizes the information generated by the generation unit in accordance with a user's request; a feedback unit that receives user feedback on the information customized by the customization unit. A system characterized by:

2. The generation unit Generative AI analyzes the input data and generates information in the appropriate format.

2. The system of claim 1.

3. The customization unit Add promotional information about specific products to the generated information 2. The system of claim 1.

4. The customization unit Automatically update seasonal menus with generated information 2. The system of claim 1.

5. The feedback unit Generative AI receives user feedback and improves the accuracy of information 2. The system of claim 1.

6. The reception unit Predicting user emotions and adjusting the timing of data input based on the predicted user emotions 2. The system of claim 1.

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

8. The reception unit Filter data entry based on the user's current project or area of ​​interest 2. The system of claim 1.

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