System

Abstract

An object of a system according to an embodiment is to enable a user to easily obtain a detailed review or recommended information on a specific dish or place.SOLUTION: A system includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a question from a user. The collection part collects data from the review site on the basis of the question received by the reception part. The generation unit analyzes the data collected by the collection unit and generates a detailed review and recommendation information. The provision unit provides the user with the information generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to obtain detailed reviews and recommendations about specific dishes or places.

[0005] The system according to the embodiment aims to enable users to easily obtain detailed reviews and recommendations about specific dishes and places. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives questions from users. The collection unit collects data from review sites based on the questions received by the reception unit. The generation unit analyzes the data collected by the collection unit and generates detailed reviews and recommendation information. The provision unit provides the information generated by the generation unit to users. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily obtain detailed reviews and recommendations about specific dishes and places. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention integrates a generation AI with a local review site to generate detailed reviews and recommendations when users ask questions about specific dishes or places. Furthermore, an app is developed that can provide information about local foods and restaurants in real time, making it easy for users to access. Multilingual support is also added, making the system accessible to users around the world and allowing them to explore global food cultures. This system revolutionizes the provision of gourmet information and improves the dining experience. The system generates and provides detailed reviews and recommendations based on user questions. For example, when a user inputs a question about a specific dish or place, the generation AI integrates with a local review site to generate detailed reviews and recommendations. Furthermore, through the app that provides real-time information about local foods and restaurants, users can obtain real-time information about nearby restaurants and food based on their current location. Furthermore, multilingual support makes the system accessible to users around the world, allowing them to explore global food cultures. This allows users to easily obtain detailed reviews and recommendations, improving their dining experience. For example, when searching for delicious local restaurants while traveling, referring to the information provided by the generation AI can improve their dining experience. In addition, users can obtain real-time information about local foods and restaurants, allowing them to enjoy their meals based on the latest information. Furthermore, multilingual support allows access by users around the world, allowing them to explore global food cultures. For example, users from different countries can obtain information in their own languages ​​and enjoy different food cultures. In this way, the provision of gourmet information is revolutionized and the dining experience is improved.

[0029] An information provision system according to an embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a user's question. The user's question may include, but is not limited to, a question about a specific dish or location. For example, the reception unit allows a user to input a question such as, "What are some recommended restaurants in this area?" The collection unit collects data from review sites based on the question received by the reception unit. Data collection may include, but is not limited to, web scraping or data acquisition using an API. The collection unit collects data such as restaurant ratings, reviews, and menu details from review sites. The generation unit analyzes the data collected by the collection unit and generates detailed reviews and recommendation information. The generation unit analyzes the collected data using, for example, a generation AI and provides optimal information to the user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates detailed reviews and recommendation information based on the data collected from the review sites. For example, in response to a question such as, "What are some recommended restaurants in this area?", the generation AI analyzes data collected from review sites and provides information on the most suitable restaurants. The providing unit provides the information generated by the generation unit to the user. The providing unit, for example, displays the generated information on the user's device. This allows the information provision system according to the embodiment to generate and provide detailed reviews and recommendations based on the user's questions. For example, when a user inputs a question about a specific dish or place, the generation AI connects with a local review site to generate detailed reviews and recommendations. Furthermore, through an app that provides real-time information about local food and restaurants, users can obtain real-time information about nearby restaurants and foods based on their current location. Furthermore, multilingual support enables access by users worldwide, allowing them to explore global food cultures. This allows users to easily obtain detailed reviews and recommendations, improving their dining experience. For example, when searching for delicious local restaurants while traveling, referring to the information provided by the generation AI can improve their dining experience.In addition, users can obtain real-time information about local foods and restaurants, allowing them to enjoy their meals based on the latest information. Furthermore, multilingual support allows access by users around the world, allowing them to explore global food cultures. For example, users from different countries can obtain information in their own languages ​​and enjoy different food cultures. In this way, the provision of gourmet information is revolutionized and the dining experience is improved.

[0030] The information provision system includes a real-time provision unit that provides information about local food or restaurants in real time. The real-time provision unit provides the information about local food or restaurants in real time. For example, when a user inputs their current location, the real-time provision unit displays information about nearby restaurants and recommended menus. The real-time provision unit, for example, identifies the user's current location using GPS data and provides information about restaurants in the vicinity. The real-time provision unit can also provide restaurant ratings and reviews that are updated in real time based on the user's current location. This allows information about local food and restaurants to be provided in real time. For example, when a user is traveling and looking for a delicious local restaurant, the user can refer to the information provided by the real-time provision unit to have a better dining experience. Furthermore, since information about local food and restaurants can be obtained in real time, the user can enjoy their meal based on the latest information. Some or all of the above-described processing by the real-time provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the real-time provision unit may input GPS data into the generation AI and cause the generation AI to obtain optimal restaurant information based on the user's current location.

[0031] The information providing system includes a multilingual support unit that provides multilingual support. The multilingual support unit provides multilingual support. The multilingual support unit provides information in, for example, a user's language. The multilingual support unit can provide information in multiple languages, such as Japanese, English, and Chinese. The multilingual support unit can, for example, use a generation AI to perform translation tailored to the user's language. This multilingual support enables access to users around the world. For example, users in different countries can obtain information in their respective languages ​​and enjoy different food cultures. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, the generation AI. For example, the multilingual support unit can input the user's language settings into the generation AI and cause the generation AI to perform translation tailored to the user's language.

[0032] The reception unit can analyze the user's past question history and select the optimal question reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question content that will be used during a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history data into the generation AI and have the generation AI select the optimal question reception method.

[0033] When receiving a question, the reception unit can filter the questions based on the user's current interests and concerns. For example, the reception unit can prioritize receiving questions related to the user's recent search for a dish or place. The reception unit can also analyze the user's social media activity and filter related questions. Furthermore, the reception unit can reflect the user's past feedback and prioritize receiving questions based on the user's interests and concerns. In this way, by filtering questions based on the user's current interests and concerns, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to filter related questions.

[0034] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question by text, the reception unit can also receive the question using text analysis technology. Furthermore, when the user uploads an image, the reception unit can also receive related questions using image analysis technology. This allows the reception of questions to be made more efficient by selecting the optimal reception 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, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform voice recognition.

[0035] The collection unit can evaluate the reliability of review sites and select appropriate data collection destinations when collecting data. The collection unit, for example, collects data from reliable sites based on the review site's evaluation score. The collection unit can also collect data from reliable sites based on the number of users of the review site. Furthermore, the collection unit can collect data from reliable sites based on the update frequency of the review site. In this way, reliable data can be collected by evaluating the reliability of the review site. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the review site's evaluation score data into the generation AI and have the generation AI perform a reliability evaluation.

[0036] When collecting data, the collection unit can filter the collected data based on the user's current interests and concerns. For example, the collection unit can prioritize collecting relevant data based on dishes or places recently searched by the user. The collection unit can also analyze the user's social media activities and filter relevant data. Furthermore, the collection unit can prioritize collecting data based on interests and concerns by reflecting the user's past feedback. This allows for prioritized collection of highly relevant data by filtering data based on the user's current interests and concerns. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and have the generation AI filter the relevant data.

[0037] When collecting data, the collection unit can improve the accuracy of the collected data by referring to the user's past question history. For example, the collection unit prioritizes collecting related data based on the content of questions asked by the user in the past. The collection unit can also prioritize collecting specific data from the user's past question history. Furthermore, the collection unit can analyze the user's past question history and collect the most relevant data. By referring to the user's past question history, the accuracy of the collected data is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the collected data.

[0038] The generation unit can adjust the level of detail of the generated information based on the importance of the review when generating information. For example, the generation unit can generate highly rated reviews in detail with priority. The generation unit can also generate low-rated reviews in a concise manner. Furthermore, the generation unit can generate moderately rated reviews in appropriate detail. In this way, by adjusting the level of detail of the information based on the importance of the review, more appropriate information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input review evaluation data into the generation AI and cause the generation AI to adjust the level of detail of the generated information.

[0039] When generating information, the generation unit can apply different generation algorithms depending on the review category. For example, the generation unit applies a generation algorithm that includes details about the food and an evaluation of the service to restaurant reviews. The generation unit can also apply a generation algorithm that includes scenery and access information to tourist spot reviews. Furthermore, the generation unit can apply a generation algorithm that includes product quality and price information to shopping reviews. In this way, by applying different generation algorithms depending on the review category, more appropriate information can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input review category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0040] When generating information, the generation unit can improve the accuracy of the generated information by referring to the user's past generation results. The generation unit, for example, generates related information based on reviews generated by the user in the past. The generation unit can also preferentially generate specific information from the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and generate the most relevant information. This improves the accuracy of the generated information by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of the generated information.

[0041] When providing information, the providing unit can select an appropriate information providing method by referring to the user's past browsing history. The providing unit, for example, provides related information based on information previously viewed by the user. The providing unit can also preferentially provide specific information from the user's past browsing history. Furthermore, the providing unit can analyze the user's past browsing history and provide the most relevant information. This makes it possible to select the optimal information providing method by referring to the user's past browsing history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past browsing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0042] When providing information, the providing unit can customize the provided content based on the user's current interests and concerns. For example, the providing unit can provide relevant information based on dishes or places recently searched by the user. The providing unit can also analyze the user's social media activities and provide relevant information. Furthermore, the providing unit can provide information based on the user's interests and concerns by reflecting the user's past feedback. This allows the information to be customized based on the user's current interests and concerns, thereby providing more relevant information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI and have the generation AI customize the relevant information.

[0043] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, if a user provides feedback on the provided information, the providing unit improves the information providing method based on the feedback. The providing unit can also analyze the user's past feedback and select the optimal information providing method. Furthermore, the providing unit can also customize the providing interface by reflecting user feedback. In this way, the information providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the information providing method.

[0044] The real-time providing unit can provide appropriate information by referring to the user's current location when providing real-time information. For example, the real-time providing unit can provide information about nearby restaurants in real time based on the user's current location. If the user is traveling, the real-time providing unit can also provide information about restaurants in the user's travel destination in real time. Furthermore, if the user is interested in a particular area, the real-time providing unit can also provide information about that area in real time. This makes it possible to provide optimal information in real time based on the user's current location. Some or all of the above-described processing in the real-time providing unit may be performed using, or without, a generation AI. For example, the real-time providing unit can input the user's current location data into the generation AI and cause the generation AI to provide optimal information.

[0045] When providing real-time information, the real-time providing unit can analyze the user's past behavioral history and customize the provided content. The real-time providing unit can, for example, provide relevant information in real time based on places the user has visited in the past. The real-time providing unit can also provide specific information preferentially in real time from the user's past behavioral history. Furthermore, the real-time providing unit can analyze the user's past behavioral history and provide the most relevant information in real time. In this way, by analyzing the user's past behavioral history, highly relevant information can be provided in real time. Some or all of the above-described processing in the real-time providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the real-time providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to customize the provided content.

[0046] The real-time providing unit can improve the providing method by reflecting user feedback when providing real-time information. For example, if a user provides feedback on information provided in real time, the real-time providing unit improves the providing method based on that feedback. The real-time providing unit can also analyze the user's past feedback and select the optimal providing method. Furthermore, the real-time providing unit can also customize the providing interface by reflecting user feedback. In this way, the real-time information providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the real-time providing unit may be performed using, or without, a generation AI. For example, the real-time providing unit can input user feedback data into the generation AI and cause the generation AI to improve the providing method.

[0047] When supporting multiple languages, the multilingual support unit can select an appropriate response method by referring to the user's language history. The multilingual support unit can provide an optimal translation based on, for example, languages ​​used by the user in the past. The multilingual support unit can also prioritize providing a specific language from the user's language history. Furthermore, the multilingual support unit can analyze the user's language history and provide the most relevant language. This allows the optimal translation to be provided by referring to the user's language history. Some or all of the above-mentioned processing in the multilingual support unit can be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's language history data into the generation AI and have the generation AI select the optimal response method.

[0048] When supporting multiple languages, the multilingual support unit can customize the support content based on the user's current language setting. The multilingual support unit provides optimal translations based on, for example, the language setting of the user's device. The multilingual support unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the multilingual support unit can provide information in that language. This allows for customizing the support content based on the user's current language setting to provide more appropriate translations. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's language setting data into the generation AI and have the generation AI customize the support content.

[0049] The multilingual support unit can improve the support method by reflecting user feedback when supporting multiple languages. For example, if a user provides feedback on a provided translation, the multilingual support unit improves the support method based on that feedback. The multilingual support unit can also analyze the user's past feedback and select the optimal support method. Furthermore, the multilingual support unit can also customize the support interface by reflecting user feedback. In this way, the multilingual support method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, or without, a generation AI, for example. For example, the multilingual support unit can input user feedback data into the generation AI and have the generation AI improve the support method.

[0050] When providing multilingual support, the multilingual support unit can select an appropriate response method by taking into account the user's geographical location information. The multilingual support unit, for example, provides the most appropriate translation based on the user's current location. Furthermore, if the user is traveling, the multilingual support unit can also provide translation based on the language of the destination. Furthermore, if the user is interested in a particular region, the multilingual support unit can provide information in the language of that region. This allows the most appropriate translation to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's geographical location data into the generation AI and have the generation AI select the most appropriate response method.

[0051] When providing multilingual support, the multilingual support unit can analyze the user's social media activity and customize the support content. The multilingual support unit can provide optimal translations based on, for example, the language the user uses on social media. The multilingual support unit can also analyze the user's social media posts and provide relevant information. Furthermore, the multilingual support unit can provide relevant information by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant translations can be provided. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's social media data into the generation AI and have the generation AI customize the support content.

[0052] The multilingual support unit can customize the response method by reflecting the user's past feedback when providing multilingual support. For example, the multilingual support unit can suggest an optimal response method based on feedback provided by the user in the past. The multilingual support unit can also prioritize providing specific information from the user's past feedback. Furthermore, the multilingual support unit can customize the response interface by reflecting the user's feedback. In this way, the multilingual support method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's past feedback data into the generation AI and have the generation AI customize the response method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The information providing system can also include a health monitoring unit that monitors the user's health condition and makes meal suggestions based on the health condition. For example, if the user's blood sugar level is high, a low-carbohydrate meal can be suggested. Also, if the user has just exercised, a high-protein meal can be suggested. Furthermore, if the user has an allergy, meals that address the allergy can be suggested. This makes it possible to suggest meals that are appropriate for the user's health condition, supporting a healthier diet.

[0055] The information providing system may also include a meal history analysis unit that analyzes the user's meal history and suggests new meals based on the past meal history. For example, similar dishes may be suggested based on dishes that the user has previously liked. Different dishes may also be suggested based on dishes that the user has previously avoided. Furthermore, the nutritional balance may be analyzed from the user's meal history to suggest nutritionally balanced meals. This makes it possible to suggest personalized meals based on the user's meal history.

[0056] The information providing system may also include a preference learning unit that learns the user's food preferences and suggests meals based on the preferences. For example, if the user likes spicy food, spicy dishes may be preferentially suggested. Also, if the user is vegetarian, vegetarian dishes may be suggested. Furthermore, if the user likes cuisine from a particular region, dishes from that region may be suggested. This makes it possible to suggest meals based on the user's preferences, providing a more satisfying dining experience.

[0057] The information provision system may also include a preference learning unit that learns the user's food preferences and adjusts the food provision method based on the preferences. For example, if the user likes spicy food, a detailed description of spicy food may be provided. If the user is vegetarian, a detailed description of vegetarian food may be provided. Furthermore, if the user likes food from a particular region, a detailed description of that region's food may be provided. This enables a method of provision based on the user's preferences, resulting in more satisfying information provision.

[0058] The information providing system may also include a preference learning and evaluation unit that learns the user's food preferences and evaluates meals based on the preferences. For example, if the user likes spicy food, it may prioritize evaluating spicy food. Also, if the user is vegetarian, it may prioritize evaluating vegetarian food. Furthermore, if the user likes food from a particular region, it may prioritize evaluating food from that region. This allows evaluations based on the user's preferences, making it possible to provide more appropriate evaluations.

[0059] The information provision system may also include a preference learning feedback unit that learns the user's food preferences and provides food feedback based on the preferences. For example, if the user likes spicy food, feedback on spicy food may be given priority. Also, if the user is vegetarian, feedback on vegetarian food may be given priority. Furthermore, if the user likes food from a particular region, feedback on food from that region may be given priority. This makes it possible to provide feedback based on the user's preferences, making it possible to provide more appropriate feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts a user's question. The user's question may include a question about a specific dish or location. For example, the user can enter a question such as, "What are some recommended restaurants in this area?" Step 2: The collection unit collects data from review sites based on the questions received by the reception unit. Data collection includes web scraping and data acquisition using APIs. For example, data such as restaurant ratings, reviews, and menu details are collected from review sites. Step 3: The generation unit analyzes the data collected by the collection unit and generates detailed reviews and recommendation information. The generation unit uses a generation AI to analyze the collected data and provide optimal information to the user. For example, the generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates detailed reviews and recommendation information based on the data collected from review sites. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit displays the generated information on the user's device, allowing the user to obtain detailed reviews and recommended information.

[0062] (Example 2) A system according to an embodiment of the present invention integrates a generation AI with a local review site to generate detailed reviews and recommendations when users ask questions about specific dishes or places. Furthermore, an app is developed that can provide information about local foods and restaurants in real time, making it easy for users to access. Multilingual support is also added, making the system accessible to users around the world and allowing them to explore global food cultures. This system revolutionizes the provision of gourmet information and improves the dining experience. The system generates and provides detailed reviews and recommendations based on user questions. For example, when a user inputs a question about a specific dish or place, the generation AI integrates with a local review site to generate detailed reviews and recommendations. Furthermore, through the app that provides real-time information about local foods and restaurants, users can obtain real-time information about nearby restaurants and food based on their current location. Furthermore, multilingual support makes the system accessible to users around the world, allowing them to explore global food cultures. This allows users to easily obtain detailed reviews and recommendations, improving their dining experience. For example, when searching for delicious local restaurants while traveling, referring to the information provided by the generation AI can improve their dining experience. In addition, users can obtain real-time information about local foods and restaurants, allowing them to enjoy their meals based on the latest information. Furthermore, multilingual support allows access by users around the world, allowing them to explore global food cultures. For example, users from different countries can obtain information in their own languages ​​and enjoy different food cultures. In this way, the provision of gourmet information is revolutionized and the dining experience is improved.

[0063] An information provision system according to an embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives a user's question. The user's question may include, but is not limited to, a question about a specific dish or location. For example, the reception unit allows a user to input a question such as, "What are some recommended restaurants in this area?" The collection unit collects data from review sites based on the question received by the reception unit. Data collection may include, but is not limited to, web scraping or data acquisition using an API. The collection unit collects data such as restaurant ratings, reviews, and menu details from review sites. The generation unit analyzes the data collected by the collection unit and generates detailed reviews and recommendation information. The generation unit analyzes the collected data using, for example, a generation AI and provides optimal information to the user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates detailed reviews and recommendation information based on the data collected from the review sites. For example, in response to a question such as, "What are some recommended restaurants in this area?", the generation AI analyzes data collected from review sites and provides information on the most suitable restaurants. The providing unit provides the information generated by the generation unit to the user. The providing unit, for example, displays the generated information on the user's device. This allows the information provision system according to the embodiment to generate and provide detailed reviews and recommendations based on the user's questions. For example, when a user inputs a question about a specific dish or place, the generation AI connects with a local review site to generate detailed reviews and recommendations. Furthermore, through an app that provides real-time information about local food and restaurants, users can obtain real-time information about nearby restaurants and foods based on their current location. Furthermore, multilingual support enables access by users worldwide, allowing them to explore global food cultures. This allows users to easily obtain detailed reviews and recommendations, improving their dining experience. For example, when searching for delicious local restaurants while traveling, referring to the information provided by the generation AI can improve their dining experience.In addition, users can obtain real-time information about local foods and restaurants, allowing them to enjoy their meals based on the latest information. Furthermore, multilingual support allows access by users around the world, allowing them to explore global food cultures. For example, users from different countries can obtain information in their own languages ​​and enjoy different food cultures. In this way, the provision of gourmet information is revolutionized and the dining experience is improved.

[0064] The information provision system includes a real-time provision unit that provides information about local food or restaurants in real time. The real-time provision unit provides the information about local food or restaurants in real time. For example, when a user inputs their current location, the real-time provision unit displays information about nearby restaurants and recommended menus. The real-time provision unit, for example, identifies the user's current location using GPS data and provides information about restaurants in the vicinity. The real-time provision unit can also provide restaurant ratings and reviews that are updated in real time based on the user's current location. This allows information about local food and restaurants to be provided in real time. For example, when a user is traveling and looking for a delicious local restaurant, the user can refer to the information provided by the real-time provision unit to have a better dining experience. Furthermore, since information about local food and restaurants can be obtained in real time, the user can enjoy their meal based on the latest information. Some or all of the above-described processing by the real-time provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the real-time provision unit may input GPS data into the generation AI and cause the generation AI to obtain optimal restaurant information based on the user's current location.

[0065] The information providing system includes a multilingual support unit that provides multilingual support. The multilingual support unit provides multilingual support. The multilingual support unit provides information in, for example, a user's language. The multilingual support unit can provide information in multiple languages, such as Japanese, English, and Chinese. The multilingual support unit can, for example, use a generation AI to perform translation tailored to the user's language. This multilingual support enables access to users around the world. For example, users in different countries can obtain information in their respective languages ​​and enjoy different food cultures. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, the generation AI. For example, the multilingual support unit can input the user's language settings into the generation AI and cause the generation AI to perform translation tailored to the user's language.

[0066] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick question entry. This allows for more appropriate question reception by adjusting the question reception 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 reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0067] The reception unit can analyze the user's past question history and select the optimal question reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question content that will be used during a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history data into the generation AI and have the generation AI select the optimal question reception method.

[0068] When receiving a question, the reception unit can filter the questions based on the user's current interests and concerns. For example, the reception unit can prioritize receiving questions related to the user's recent search for a dish or place. The reception unit can also analyze the user's social media activity and filter related questions. Furthermore, the reception unit can reflect the user's past feedback and prioritize receiving questions based on the user's interests and concerns. In this way, by filtering questions based on the user's current interests and concerns, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to filter related questions.

[0069] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question by text, the reception unit can also receive the question using text analysis technology. Furthermore, when the user uploads an image, the reception unit can also receive related questions using image analysis technology. This allows the reception of questions to be made more efficient by selecting the optimal reception 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, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice data into the generation AI and have the generation AI perform voice recognition.

[0070] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is relaxed, the collection unit can prioritize collecting detailed reviews. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting concise reviews. Furthermore, if the user is excited, the collection unit can prioritize collecting visually stimulating reviews. This allows for more appropriate data collection by adjusting the data collection method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0071] The collection unit can evaluate the reliability of review sites and select appropriate data collection destinations when collecting data. The collection unit, for example, collects data from reliable sites based on the review site's evaluation score. The collection unit can also collect data from reliable sites based on the number of users of the review site. Furthermore, the collection unit can collect data from reliable sites based on the update frequency of the review site. In this way, reliable data can be collected by evaluating the reliability of the review site. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the review site's evaluation score data into the generation AI and have the generation AI perform a reliability evaluation.

[0072] When collecting data, the collection unit can filter the collected data based on the user's current interests and concerns. For example, the collection unit can prioritize collecting relevant data based on dishes or places recently searched by the user. The collection unit can also analyze the user's social media activities and filter relevant data. Furthermore, the collection unit can prioritize collecting data based on interests and concerns by reflecting the user's past feedback. This allows for prioritized collection of highly relevant data by filtering data based on the user's current interests and concerns. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and have the generation AI filter the relevant data.

[0073] When collecting data, the collection unit can improve the accuracy of the collected data by referring to the user's past question history. For example, the collection unit prioritizes collecting related data based on the content of questions asked by the user in the past. The collection unit can also prioritize collecting specific data from the user's past question history. Furthermore, the collection unit can analyze the user's past question history and collect the most relevant data. By referring to the user's past question history, the accuracy of the collected data is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the collected data.

[0074] The generation unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a detailed review. If the user is in a hurry, the generation unit can also generate a concise review. Furthermore, if the user is excited, the generation unit can generate a review with a visually stimulating effect. This allows for more appropriate information to be generated by adjusting the way the information is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0075] The generation unit can adjust the level of detail of the generated information based on the importance of the review when generating information. For example, the generation unit can generate highly rated reviews in detail with priority. The generation unit can also generate low-rated reviews in a concise manner. Furthermore, the generation unit can generate moderately rated reviews in appropriate detail. In this way, by adjusting the level of detail of the information based on the importance of the review, more appropriate information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input review evaluation data into the generation AI and cause the generation AI to adjust the level of detail of the generated information.

[0076] When generating information, the generation unit can apply different generation algorithms depending on the review category. For example, the generation unit applies a generation algorithm that includes details about the food and an evaluation of the service to restaurant reviews. The generation unit can also apply a generation algorithm that includes scenery and access information to tourist spot reviews. Furthermore, the generation unit can apply a generation algorithm that includes product quality and price information to shopping reviews. In this way, by applying different generation algorithms depending on the review category, more appropriate information can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input review category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0077] When generating information, the generation unit can improve the accuracy of the generated information by referring to the user's past generation results. The generation unit, for example, generates related information based on reviews generated by the user in the past. The generation unit can also preferentially generate specific information from the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and generate the most relevant information. This improves the accuracy of the generated information by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of the generated information.

[0078] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information. Furthermore, if the user is excited, the providing unit can provide information with a visually stimulating effect. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0079] When providing information, the providing unit can select an appropriate information providing method by referring to the user's past browsing history. The providing unit, for example, provides related information based on information previously viewed by the user. The providing unit can also preferentially provide specific information from the user's past browsing history. Furthermore, the providing unit can analyze the user's past browsing history and provide the most relevant information. This makes it possible to select the optimal information providing method by referring to the user's past browsing history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past browsing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0080] When providing information, the providing unit can customize the provided content based on the user's current interests and concerns. For example, the providing unit can provide relevant information based on dishes or places recently searched by the user. The providing unit can also analyze the user's social media activities and provide relevant information. Furthermore, the providing unit can provide information based on the user's interests and concerns by reflecting the user's past feedback. This allows the information to be customized based on the user's current interests and concerns, thereby providing more relevant information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI and have the generation AI customize the relevant information.

[0081] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, if a user provides feedback on the provided information, the providing unit improves the information providing method based on the feedback. The providing unit can also analyze the user's past feedback and select the optimal information providing method. Furthermore, the providing unit can also customize the providing interface by reflecting user feedback. In this way, the information providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the information providing method.

[0082] The real-time providing unit can estimate the user's emotions and adjust the method of providing real-time information based on the estimated user's emotions. For example, when the user is relaxed, the real-time providing unit can provide detailed information in real time. Furthermore, when the user is in a hurry, the real-time providing unit can provide concise information in real time. Furthermore, when the user is excited, the real-time providing unit can provide information with visually stimulating effects in real time. This enables more appropriate information to be provided by adjusting the method of providing real-time information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the real-time providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the real-time providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The real-time providing unit can provide appropriate information by referring to the user's current location when providing real-time information. For example, the real-time providing unit can provide information about nearby restaurants in real time based on the user's current location. If the user is traveling, the real-time providing unit can also provide information about restaurants in the user's travel destination in real time. Furthermore, if the user is interested in a particular area, the real-time providing unit can also provide information about that area in real time. This makes it possible to provide optimal information in real time based on the user's current location. Some or all of the above-described processing in the real-time providing unit may be performed using, or without, a generation AI. For example, the real-time providing unit can input the user's current location data into the generation AI and cause the generation AI to provide optimal information.

[0084] When providing real-time information, the real-time providing unit can analyze the user's past behavioral history and customize the provided content. The real-time providing unit can, for example, provide relevant information in real time based on places the user has visited in the past. The real-time providing unit can also provide specific information preferentially in real time from the user's past behavioral history. Furthermore, the real-time providing unit can analyze the user's past behavioral history and provide the most relevant information in real time. In this way, by analyzing the user's past behavioral history, highly relevant information can be provided in real time. Some or all of the above-described processing in the real-time providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the real-time providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to customize the provided content.

[0085] The real-time providing unit can improve the providing method by reflecting user feedback when providing real-time information. For example, if a user provides feedback on information provided in real time, the real-time providing unit improves the providing method based on that feedback. The real-time providing unit can also analyze the user's past feedback and select the optimal providing method. Furthermore, the real-time providing unit can also customize the providing interface by reflecting user feedback. In this way, the real-time information providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the real-time providing unit may be performed using, or without, a generation AI. For example, the real-time providing unit can input user feedback data into the generation AI and cause the generation AI to improve the providing method.

[0086] The multilingual support unit can estimate the user's emotions and adjust the multilingual support method based on the estimated user emotions. For example, the multilingual support unit can provide a detailed translation when the user is relaxed. Furthermore, the multilingual support unit can provide a concise translation when the user is in a hurry. Furthermore, the multilingual support unit can provide a translation with a visually stimulating effect when the user is excited. This allows for adjusting the multilingual support method according to the user's emotions to provide a more appropriate translation. 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 multilingual support unit can be performed using, for example, the generation AI, or without the generation AI. For example, the multilingual support unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0087] When supporting multiple languages, the multilingual support unit can select an appropriate response method by referring to the user's language history. The multilingual support unit can provide an optimal translation based on, for example, languages ​​used by the user in the past. The multilingual support unit can also prioritize providing a specific language from the user's language history. Furthermore, the multilingual support unit can analyze the user's language history and provide the most relevant language. This allows the optimal translation to be provided by referring to the user's language history. Some or all of the above-mentioned processing in the multilingual support unit can be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's language history data into the generation AI and have the generation AI select the optimal response method.

[0088] When supporting multiple languages, the multilingual support unit can customize the support content based on the user's current language setting. The multilingual support unit provides optimal translations based on, for example, the language setting of the user's device. The multilingual support unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the multilingual support unit can provide information in that language. This allows for customizing the support content based on the user's current language setting to provide more appropriate translations. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's language setting data into the generation AI and have the generation AI customize the support content.

[0089] The multilingual support unit can improve the support method by reflecting user feedback when supporting multiple languages. For example, if a user provides feedback on a provided translation, the multilingual support unit improves the support method based on that feedback. The multilingual support unit can also analyze the user's past feedback and select the optimal support method. Furthermore, the multilingual support unit can also customize the support interface by reflecting user feedback. In this way, the multilingual support method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the multilingual support unit may be performed using, or without, a generation AI, for example. For example, the multilingual support unit can input user feedback data into the generation AI and have the generation AI improve the support method.

[0090] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated user emotions. For example, if the user is stressed, the multilingual support unit can prioritize providing translations with high urgency. Furthermore, if the user is relaxed, the multilingual support unit can prioritize providing detailed translations. Furthermore, if the user is in a hurry, the multilingual support unit can prioritize providing concise translations. This allows for more appropriate translations to be provided by determining the priority of multilingual support according to the user's emotions. The 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 multilingual support unit may be performed using, for example, the generation AI. For example, the multilingual support unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0091] When providing multilingual support, the multilingual support unit can select an appropriate response method by taking into account the user's geographical location information. The multilingual support unit, for example, provides the most appropriate translation based on the user's current location. Furthermore, if the user is traveling, the multilingual support unit can also provide translation based on the language of the destination. Furthermore, if the user is interested in a particular region, the multilingual support unit can provide information in the language of that region. This allows the most appropriate translation to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's geographical location data into the generation AI and have the generation AI select the most appropriate response method.

[0092] When providing multilingual support, the multilingual support unit can analyze the user's social media activity and customize the support content. The multilingual support unit can provide optimal translations based on, for example, the language the user uses on social media. The multilingual support unit can also analyze the user's social media posts and provide relevant information. Furthermore, the multilingual support unit can provide relevant information by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant translations can be provided. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's social media data into the generation AI and have the generation AI customize the support content.

[0093] The multilingual support unit can customize the response method by reflecting the user's past feedback when providing multilingual support. For example, the multilingual support unit can suggest an optimal response method based on feedback provided by the user in the past. The multilingual support unit can also prioritize providing specific information from the user's past feedback. Furthermore, the multilingual support unit can customize the response interface by reflecting the user's feedback. In this way, the multilingual support method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the multilingual support unit may be performed using, or without, a generation AI. For example, the multilingual support unit can input the user's past feedback data into the generation AI and have the generation AI customize the response method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, generation unit, provision unit, real-time provision unit, and multilingual support unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive user questions using the reception device 38 of the smart device 14. The collection unit can collect data from review sites using the specific processing unit 290 of the data processing device 12. The generation unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and generate detailed reviews and recommendation information. The provision unit can provide the generated information to the user using the output device 40 of the smart device 14. The real-time provision unit can identify the user's current location using the GPS function of the smart device 14 and provide information about nearby restaurants. The multilingual support unit can translate information into the user's language using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, generation unit, provision unit, real-time provision unit, and multilingual support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a user's questions using the microphone 238 of the smart glasses 214. The collection unit can collect data from review sites using the specific processing unit 290 of the data processing device 12. The generation unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and generate detailed reviews and recommendation information. The provision unit can provide the generated information to the user using the speaker 240 of the smart glasses 214. The real-time provision unit can identify the user's current location using the GPS function of the smart glasses 214 and provide information about nearby restaurants. The multilingual support unit can perform translation tailored to the user's language using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, provision unit, real-time provision unit, and multilingual support unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive a user's question using the microphone 238 of the headset terminal 314. The collection unit can collect data from review sites using the specific processing unit 290 of the data processing device 12. The generation unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and generate detailed reviews and recommendation information. The provision unit can provide the generated information to the user using the speaker 240 of the headset terminal 314. The real-time provision unit can identify the user's current location using the GPS function of the headset terminal 314 and provide information about nearby restaurants. The multilingual support unit can perform translation tailored to the user's language using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, collection unit, generation unit, provision unit, real-time provision unit, and multilingual support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive a user's question using the microphone 238 of the robot 414. The collection unit can collect data from review sites using the specific processing unit 290 of the data processing device 12. The generation unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and generate detailed reviews and recommendation information. The provision unit can provide the generated information to the user using the speaker 240 of the robot 414. The real-time provision unit can identify the user's current location using the GPS function of the robot 414 and provide information about nearby restaurants. The multilingual support unit can perform translation tailored to the user's language using the specific processing unit 290 of the data processing device 12.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The information providing system can also include a health monitoring unit that monitors the user's health condition and makes meal suggestions based on the health condition. For example, if the user's blood sugar level is high, a low-carbohydrate meal can be suggested. Also, if the user has just exercised, a high-protein meal can be suggested. Furthermore, if the user has an allergy, meals that address the allergy can be suggested. This makes it possible to suggest meals that are appropriate for the user's health condition, supporting a healthier diet.

[0096] The information providing system may also include a meal history analysis unit that analyzes the user's meal history and suggests new meals based on the past meal history. For example, similar dishes may be suggested based on dishes that the user has previously liked. Different dishes may also be suggested based on dishes that the user has previously avoided. Furthermore, the nutritional balance may be analyzed from the user's meal history to suggest nutritionally balanced meals. This makes it possible to suggest personalized meals based on the user's meal history.

[0097] The information provision system may also include an emotion-based meal suggestion unit that estimates the user's emotions and suggests meals based on the estimated emotions. For example, if the user is feeling stressed, a meal with a relaxing effect may be suggested. If the user is happy, a special dessert may be suggested. Furthermore, if the user is tired, a meal suitable for replenishing energy may be suggested. This makes it possible to suggest meals according to the user's emotions, thereby providing a more satisfying dining experience.

[0098] The information providing system may also include a preference learning unit that learns the user's food preferences and suggests meals based on the preferences. For example, if the user likes spicy food, spicy dishes may be preferentially suggested. Also, if the user is vegetarian, vegetarian dishes may be suggested. Furthermore, if the user likes cuisine from a particular region, dishes from that region may be suggested. This makes it possible to suggest meals based on the user's preferences, providing a more satisfying dining experience.

[0099] The information provision system may also include an emotion-based provision unit that estimates the user's emotion and adjusts the food provision method based on the estimated emotion. For example, if the user is relaxed, a detailed menu description may be provided. If the user is in a hurry, a concise menu description may be provided. Furthermore, if the user is excited, a visually appealing menu may be provided. This enables a provision method that corresponds to the user's emotion, resulting in more appropriate information provision.

[0100] The information provision system may also include a preference learning unit that learns the user's food preferences and adjusts the food provision method based on the preferences. For example, if the user likes spicy food, a detailed description of spicy food may be provided. If the user is vegetarian, a detailed description of vegetarian food may be provided. Furthermore, if the user likes food from a particular region, a detailed description of that region's food may be provided. This enables a method of provision based on the user's preferences, resulting in more satisfying information provision.

[0101] The information providing system may also include an emotion-based evaluation unit that estimates the user's emotion and evaluates the meal based on the estimated emotion. For example, if the user is relaxed, a detailed evaluation may be performed. If the user is in a hurry, a brief evaluation may be performed. Furthermore, if the user is excited, a visually appealing evaluation may be performed. This allows evaluation according to the user's emotion, and more appropriate evaluations may be provided.

[0102] The information providing system may also include a preference learning and evaluation unit that learns the user's food preferences and evaluates meals based on the preferences. For example, if the user likes spicy food, it may prioritize evaluating spicy food. Also, if the user is vegetarian, it may prioritize evaluating vegetarian food. Furthermore, if the user likes food from a particular region, it may prioritize evaluating food from that region. This allows evaluations based on the user's preferences, making it possible to provide more appropriate evaluations.

[0103] The information provision system may also include an emotion-based feedback unit that estimates the user's emotion and provides meal feedback based on the estimated emotion. For example, if the user is relaxed, detailed feedback may be provided. If the user is in a hurry, brief feedback may be provided. Furthermore, if the user is excited, visually appealing feedback may be provided. This allows for feedback according to the user's emotion, making it possible to provide more appropriate feedback.

[0104] The information provision system may also include a preference learning feedback unit that learns the user's food preferences and provides food feedback based on the preferences. For example, if the user likes spicy food, feedback on spicy food may be given priority. Also, if the user is vegetarian, feedback on vegetarian food may be given priority. Furthermore, if the user likes food from a particular region, feedback on food from that region may be given priority. This makes it possible to provide feedback based on the user's preferences, making it possible to provide more appropriate feedback.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The reception unit accepts a user's question. The user's question may include a question about a specific dish or location. For example, the user can enter a question such as, "What are some recommended restaurants in this area?" Step 2: The collection unit collects data from review sites based on the questions received by the reception unit. Data collection includes web scraping and data acquisition using APIs. For example, data such as restaurant ratings, reviews, and menu details are collected from review sites. Step 3: The generation unit analyzes the data collected by the collection unit and generates detailed reviews and recommendation information. The generation unit uses a generation AI to analyze the collected data and provide optimal information to the user. For example, the generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates detailed reviews and recommendation information based on the data collected from review sites. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit displays the generated information on the user's device, allowing the user to obtain detailed reviews and recommended information.

[0107] 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.

[0108] 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.

[0109] 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.

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

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0114] The 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.

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

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

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

[0118] Fig. 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.

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

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

[0121] In the 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.

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

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

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

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

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

[0130] The 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.

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

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

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

[0134] Fig. 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

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

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a collection unit that collects data from review sites based on the questions received by the reception unit; a generation unit that analyzes the data collected by the collection unit and generates detailed reviews and recommendation information; a providing unit that provides the information generated by the generating unit to a user. A system characterized by:

2. A real-time provider provides real-time information about local food or restaurants. The system of claim 1 .

3. Equipped with a multilingual support department that provides multilingual support The system of claim 1 .

4. The reception unit Estimate the user's emotions and adjust the way questions are accepted based on the estimated user emotions. The system of claim 1 .

5. The reception unit Analyze the user's past question history and select the appropriate method for accepting questions The system of claim 1 .

6. The reception unit When questions are asked, they are filtered based on the user's current interests. The system of claim 1 .

7. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method. The system of claim 1 .

8. The collecting unit Inferring user emotions and adjusting data collection methods based on the estimated user emotions The system of claim 1 .

9. The collecting unit When collecting data, evaluate the credibility of review sites and select appropriate sources. The system of claim 1 .

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