system

Abstract

The system according to the embodiment aims to provide information optimized for individuals and provide daily support in an integrated manner. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a summarizing unit, a summarizing unit, a calorie calculation unit, a recommendation unit, and an expense summary unit. The reception unit receives questions. The generation unit analyzes the questions received by the reception unit and generates answers suited to individuals. The summary unit acquires calendar information and generates summaries. The summarizing unit analyzes photos and generates summaries. The calorie calculation unit analyzes photos of meals and calculates calories. The recommendation unit recommends meals suited to individuals. The expense summary unit summarizes expenses.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide information optimized for individuals and provide daily support in a centralized manner.

[0005] The system according to the embodiment aims to provide information optimized for individuals and provide daily support in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a summarization unit, a summarization unit, a calorie calculation unit, a recommendation unit, and an expense summary unit. The reception unit receives questions. The generation unit analyzes the questions received by the reception unit and generates answers suited to individuals. The summary unit acquires calendar information and generates summaries. The summarization unit analyzes photos and generates summaries. The calorie calculation unit analyzes photos of meals and calculates calories. The recommendation unit recommends meals suited to individuals. The expense summary unit summarizes expenses. [Effects of the Invention]

[0007] The system according to the embodiment can provide information optimized for individuals and provide daily support in a centralized manner. [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 messaging app-based personalized optimization service system according to an embodiment of the present invention is a system that incorporates a generation AI and provides personalized services based on a messaging app. This system allows a user to input a question through a messaging app, and the generation AI generates a personalized answer to the question. It also works with a calendar app to provide a daily summary of events. It also includes a function for summarizing memories, such as travels, with photos, and a function for sending photos of meals to the generation AI to calculate the calories consumed for the day. This enables services such as personalized meal recommendations and a summary of today's expenses to be provided. For example, a user inputs a question such as "What are your plans for today?" through a messaging app. This question is sent to the generation AI. The generation AI analyzes the input question and generates a personalized answer. For example, it generates an answer such as "Today's plans include a meeting in the morning and free time in the afternoon." It also works with a calendar app to provide a daily summary of events. For example, it generates a summary such as "Today's plans include a meeting in the morning and free time in the afternoon." It also includes a function to summarize travel memories with photos. For example, it can generate a summary such as, "My travel memories include visiting tourist spot A and taking photos." It also provides a function to send photos of meals to the generation AI and calculate the calories consumed for the day. For example, it can calculate the calories consumed for today by saying, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." This makes it possible to provide services such as personalized meal recommendations and summaries of today's expenses. For example, it can generate a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." This allows the messaging app-based personalized optimization service system to provide users with personalized services.

[0029] A messaging app-based personalized optimization service system according to an embodiment includes a reception unit, a generation unit, a summarization unit, a calorie calculation unit, a recommendation unit, and an expense summarization unit. The reception unit receives questions entered by a user through a messaging app. Questions may be in text format or audio format, but are not limited to these examples. The reception unit receives a question, for example, a user input such as "What are your plans for today?" The generation unit uses a generation AI to analyze the question received by the reception unit and generate an answer optimized for the individual. For example, the generation AI generates an answer such as "Today's plans include a meeting in the morning and free time in the afternoon." The summarization unit acquires calendar information and summarizes the events of the day. For example, the summarization unit generates a summary such as "Today's events include a meeting in the morning and free time in the afternoon." The summarization unit analyzes photos and summarizes memories, such as travel memories. The summarization unit generates a summary such as, "To make a memory of my trip, I visited tourist spot A and took photos." The calorie calculation unit analyzes photos of meals and calculates the calories consumed for the day. The calorie calculation unit calculates calories such as, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." The recommendation unit recommends meals optimized for individuals. The recommendation unit recommends, for example, "I recommend eating lots of vegetables for dinner tonight." The expense summary unit summarizes today's expenses. The expense summary unit generates a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." This allows the messaging app-based personal optimization service system according to the embodiment to provide users with services optimized for individuals.

[0030] The generation unit can analyze questions using the generation AI and generate answers optimized for individuals. For example, when a user asks, "What are your plans for today?", the generation AI generates an answer such as, "Today's plans include a meeting in the morning and free time in the afternoon." The generation unit can also generate more accurate answers based on the user's past question history and profile information. For example, the generation AI analyzes the user's past question history and generates appropriate answers to questions frequently asked by the user. Furthermore, the generation unit can generate optimal answers in real time based on the user's current situation and environment. For example, the generation AI generates optimal answers based on the user's current location information and calendar information. In this way, the generation AI can generate answers optimized for individuals to questions.

[0031] The summarizing unit can acquire calendar information and summarize the events of the day. For example, the summarizing unit acquires schedule information for the day from a calendar app and summarizes the events of the day based on that information. For example, the summarizing unit generates a summary such as, "Today's events were a meeting in the morning and free time in the afternoon." The summarizing unit can also summarize the progress of the user's tasks and events based on the calendar information. For example, the summarizing unit generates a summary such as, "Today's tasks were three completed and two incomplete." Furthermore, the summarizing unit can visually display the user's activities for the day based on the calendar information. For example, the summarizing unit visually displays the user's activities for the day using graphs and charts. This makes it possible to summarize the events of the day based on the calendar information.

[0032] The summarization unit can analyze photos and summarize travel memories. For example, the summarization unit analyzes travel photos taken by a user and summarizes the travel memories based on the analysis. For example, the summarization unit generates a summary such as, "My travel memories include visiting tourist spot A and taking photos." The summarization unit can also extract travel highlights based on the results of photo analysis and reflect them in the summary. For example, the summarization unit can generate a summary such as, "The highlights of my trip were taking photos at tourist spot A and eating at tourist spot B." The summarization unit can also visually display travel memories based on the results of photo analysis. For example, the summarization unit can display photos in a slideshow format, allowing users to visually enjoy their travel memories. This allows travel memories to be summarized by analyzing photos.

[0033] The calorie calculation unit can analyze photos of meals and calculate the calorie intake for the day. For example, the calorie calculation unit analyzes photos of meals taken by the user and calculates the calorie intake for the day based on the photos. For example, the calorie calculation unit may calculate calories such as, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." The calorie calculation unit can also evaluate the nutritional balance of each meal based on the analysis results of the meal photos. For example, the calorie calculation unit may evaluate, "Breakfast is high in carbohydrates, and lunch is high in protein." Furthermore, the calorie calculation unit can record the user's dietary history and analyze long-term calorie intake trends based on the analysis results of the meal photos. For example, the calorie calculation unit may analyze, "The average calorie intake for the past week is 2000 kcal." This allows the calorie calculation for the day to be performed by analyzing the meal photos.

[0034] The recommendation unit can recommend meals optimized for individuals. The recommendation unit makes meal recommendations optimized for individuals based on, for example, the user's meal history and preference information. For example, the recommendation unit makes a recommendation such as, "I recommend eating lots of vegetables for dinner tonight." The recommendation unit can also propose an optimal meal plan taking into account the user's nutritional balance. For example, the recommendation unit makes a suggestion such as, "This week's meal plan is fish dishes on Monday and vegetable dishes on Tuesday." Furthermore, the recommendation unit can also recommend meals customized according to the user's health condition and goals. For example, the recommendation unit makes a recommendation such as, "For those on a diet, I recommend low-calorie meals." This makes it possible to make meal recommendations optimized for individuals.

[0035] The expense summarizing unit can summarize today's expenses. For example, the expense summarizing unit summarizes today's expenses based on expense information entered by the user. For example, the expense summarizing unit generates a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." The expense summarizing unit can also classify expense information by category and summarize expenses for each category. For example, the expense summarizing unit generates a summary such as, "Today's food expenses are 2,000 yen and transportation expenses are 500 yen." Furthermore, the expense summarizing unit can analyze the user's spending habits based on the expense information and provide advice on saving money. For example, the expense summarizing unit can provide advice such as, "This month's food expenses are over budget, so I recommend that you reduce your food expenses next month." This allows today's expenses to be summarized.

[0036] The reception unit can analyze the user's past question history and select the optimal 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 question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. This makes it possible to analyze the user's past question history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit can filter questions based on the user's current areas of interest when receiving the questions. For example, the reception unit preferentially receives questions related to topics that the user has recently been interested in. The reception unit can also filter questions based on areas in which the user has shown interest in the past. Furthermore, the reception unit can preferentially receive questions that are highly relevant based on the user's current activity status. This makes it possible to filter questions based on the user's current areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform question filtering.

[0038] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. This makes it possible to prioritize receiving highly relevant questions by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to filter the questions.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize receiving questions related to topics in which the user has recently shown interest on social media. The reception unit can also prioritize receiving questions related to accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving highly relevant questions based on the user's social media activity history. This makes it possible to analyze the user's social media activity and receive related questions. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI perform question filtering.

[0040] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0041] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a general-purpose generation algorithm to general questions. Furthermore, the generation unit can also apply an emotion-conscious generation algorithm to emotional questions. This makes it possible to apply different generation algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0042] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. For example, the generation unit prioritizes answers to recently submitted questions. The generation unit can also postpone answers to questions that were submitted earlier. Furthermore, the generation unit can gradually adjust the priority of answers depending on the time of submission. This makes it possible to determine the priority of answers based on the time of submission of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0043] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit prioritizes answers to questions with high relevance. The generation unit can also postpone answers to questions with low relevance. Furthermore, the generation unit can gradually adjust the order of answers according to the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0044] When generating a summary, the summarizing unit can optimize the current summary by referring to past calendar information. The summarizing unit optimizes the current summary based on, for example, past calendar information. The summarizing unit can also extract important events from the past calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the past calendar information and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to the past calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input past calendar information data into the generation AI and cause the generation AI to optimize the summary.

[0045] The summarizing unit can apply different summarizing methods to different categories of calendar information when generating summaries. For example, the summarizing unit generates detailed summaries for work-related calendar information. The summarizing unit can also generate concise summaries for private calendar information. Furthermore, the summarizing unit can gradually adjust the summarizing method according to the category of calendar information. This makes it possible to apply different summarizing methods to different categories of calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, or without, AI, for example. For example, the summarizing unit can input category data of calendar information to the generation AI and cause the generation AI to apply the summarizing method.

[0046] When generating a summary, the summarizing unit can analyze changes in the summary based on the submission date of the calendar information. For example, the summarizing unit prioritizes summarizing the most recent calendar information. The summarizing unit can also postpone the processing of calendar information that was submitted earlier. Furthermore, the summarizing unit can analyze changes in the summary in stages according to the submission date. This makes it possible to analyze changes in the summary based on the submission date of the calendar information. Some or all of the above-mentioned processing in the summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the summarizing unit can input submission date data of the calendar information to the generation AI and cause the generation AI to analyze changes in the summary.

[0047] When generating a summary, the summarizing unit can optimize the summary by referring to the related data of the calendar information. The summarizing unit can optimize the current summary based on, for example, the related data of the calendar information. The summarizing unit can also extract important events from the related data of the calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the related data of the calendar information and include the most relevant information in the summary. This makes it possible to optimize the summary by referring to the related data of the calendar information. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the related data of the calendar information to the generation AI and cause the generation AI to optimize the summary.

[0048] When generating a summary, the summarization unit can optimize the current summary by referring to past photo data. The summarization unit, for example, optimizes the current summary based on past photo data. The summarization unit can also extract important events from past photo data and reflect them in the summary. Furthermore, the summarization unit can analyze past photo data and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to past photo data. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input past photo data to a generation AI and cause the generation AI to optimize the summary.

[0049] The summarization unit can apply different summarization techniques to different photo categories when generating summaries. For example, the summarization unit generates detailed summaries for travel-related photos. The summarization unit can also generate concise summaries for event-related photos. Furthermore, the summarization unit can gradually adjust the summarization technique depending on the photo category. This allows different summarization techniques to be applied to different photo categories. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input photo category data to the generation AI and cause the generation AI to apply the summarization technique.

[0050] When generating a summary, the summarization unit can analyze changes in the summary based on the time the photo was taken. For example, the summarization unit prioritizes summarization of recently taken photos. The summarization unit can also postpone summarization of older photos. Furthermore, the summarization unit can analyze changes in the summary in stages depending on the time the photo was taken. This makes it possible to analyze changes in the summary based on the time the photo was taken. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input photo taking time data into the generation AI and have the generation AI analyze changes in the summary.

[0051] The summarization unit can optimize the summary by referring to the photo-related data when generating the summary. For example, the summarization unit optimizes the current summary based on the photo-related data. The summarization unit can also extract important events from the photo-related data and reflect them in the summary. Furthermore, the summarization unit can analyze the photo-related data and include the most relevant information in the summary. This allows the summary to be optimized by referring to the photo-related data. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input the photo-related data into the generation AI and cause the generation AI to optimize the summary.

[0052] When calculating calories, the calorie calculation unit can optimize the current calorie calculation by referring to past meal data. The calorie calculation unit optimizes the current calorie calculation based on, for example, past meal data. The calorie calculation unit can also extract important meal information from the past meal data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the past meal data and include the most relevant information in the calorie calculation. This allows the current calorie calculation to be optimized by referring to the past meal data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input past meal data into the generation AI and cause the generation AI to optimize the calorie calculation.

[0053] The calorie calculation unit can apply different calorie calculation methods to different meal categories when calculating calories. For example, the calorie calculation unit performs detailed calorie calculation for breakfast. The calorie calculation unit can also perform simple calorie calculation for lunch. Furthermore, the calorie calculation unit can gradually adjust the calorie calculation method according to the meal category. This allows different calorie calculation methods to be applied to different meal categories. Some or all of the above-described processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input meal category data into the generation AI and cause the generation AI to apply the calorie calculation method.

[0054] When calculating calories, the calorie calculation unit can analyze changes in calorie count based on the time when the meal was photographed. For example, the calorie calculation unit prioritizes calorie calculation for photos of meals that were photographed recently. The calorie calculation unit can also postpone calculation of calories for photos of meals that were photographed older. Furthermore, the calorie calculation unit can analyze changes in calorie count in stages depending on the time when the photos were photographed. This makes it possible to analyze changes in calorie count based on the time when the meal was photographed. Some or all of the above-described processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input data on the time when the meal was photographed into the generation AI and have the generation AI analyze changes in calorie count.

[0055] The calorie calculation unit can optimize the calorie calculation by referring to meal-related data when calculating calories. The calorie calculation unit, for example, optimizes the current calorie calculation based on the meal-related data. The calorie calculation unit can also extract important meal information from the meal-related data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the meal-related data and include the most relevant information in the calorie calculation. This allows the calorie calculation to be optimized by referring to the meal-related data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, or without, AI, for example. For example, the calorie calculation unit can input meal-related data into a generation AI and cause the generation AI to optimize the calorie calculation.

[0056] When generating recommendations, the recommendation unit can optimize current recommendations by referring to past meal data. The recommendation unit, for example, optimizes current recommendations based on past meal data. The recommendation unit can also extract important meal information from past meal data and reflect it in recommendations. Furthermore, the recommendation unit can analyze past meal data and include the most relevant information in recommendations. This makes it possible to optimize current recommendations by referring to past meal data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input past meal data into the generation AI and cause the generation AI to optimize the recommendations.

[0057] The recommendation unit can apply different recommendation methods to different meal categories when generating recommendations. For example, the recommendation unit can make detailed recommendations for breakfast. The recommendation unit can also make brief recommendations for lunch. Furthermore, the recommendation unit can gradually adjust the recommendation method according to the meal category. This makes it possible to apply different recommendation methods to different meal categories. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input meal category data to the generation AI and cause the generation AI to apply the recommendation method.

[0058] When generating recommendations, the recommendation unit can analyze changes in recommendations based on the time when the meal was photographed. For example, the recommendation unit prioritizes recommendations of photos of meals that were photographed recently. The recommendation unit can also postpone recommendations of photos of meals that were photographed older. Furthermore, the recommendation unit can analyze changes in recommendations in stages according to the time when the photos were taken. This makes it possible to analyze changes in recommendations based on the time when the meal was photographed. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the time when the meal was photographed into the generation AI and cause the generation AI to analyze changes in recommendations.

[0059] The recommendation unit can optimize the recommendation by referring to the meal-related data when generating the recommendation. The recommendation unit, for example, optimizes the current recommendation based on the meal-related data. The recommendation unit can also extract important meal information from the meal-related data and reflect it in the recommendation. Furthermore, the recommendation unit can analyze the meal-related data and include the most relevant information in the recommendation. This makes it possible to optimize the recommendation by referring to the meal-related data. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the meal-related data into the generation AI and cause the generation AI to optimize the recommendation.

[0060] When generating an expense summary, the expense summarizing unit can optimize the current expense summary by referring to past expense data. The expense summarizing unit, for example, optimizes the current expense summary based on past expense data. The expense summarizing unit can also extract important expense information from the past expense data and reflect it in the expense summary. Furthermore, the expense summarizing unit can analyze past expense data and include the most relevant information in the expense summary. This makes it possible to optimize the current expense summary by referring to the past expense data. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input past expense data into the generation AI and cause the generation AI to optimize the expense summary.

[0061] When generating an expense summary, the expense summarizing unit can apply different expense summarizing methods to each expense category. For example, the expense summarizing unit generates a detailed expense summary for food expenses. The expense summarizing unit can also generate a concise expense summary for transportation expenses. Furthermore, the expense summarizing unit can gradually adjust the expense summarizing method according to the expense category. This makes it possible to apply different expense summarizing methods to each expense category. Some or all of the above-described processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input expense category data into the generation AI and cause the generation AI to apply the expense summarizing method.

[0062] When generating an expense summary, the expense summarizing unit can analyze changes in the expense summary based on the time of occurrence of the expenses. For example, the expense summarizing unit prioritizes summarizing recent expenses. The expense summarizing unit can also postpone expenses that occurred earlier. Furthermore, the expense summarizing unit can analyze changes in the expense summary in stages according to the time of occurrence. This makes it possible to analyze changes in the expense summary based on the time of occurrence of the expenses. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input data on the time of occurrence of expenses into the generation AI and have the generation AI analyze changes in the expense summary.

[0063] When generating an expense summary, the expense summarizing unit can optimize the expense summary by referring to expense-related data. The expense summarizing unit, for example, optimizes the current expense summary based on expense-related data. The expense summarizing unit can also extract important expense information from the expense-related data and reflect it in the expense summary. Furthermore, the expense summarizing unit can analyze expense-related data and include the most relevant information in the expense summary. This makes it possible to optimize the expense summary by referring to expense-related data. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input expense-related data into a generation AI and cause the generation AI to optimize the expense summary.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past question history and select the optimal reception method. For example, questions frequently asked by the user in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked during a specific time period based on the user's past question history. This makes it possible to analyze the user's past question history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using 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 reception method.

[0066] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer is generated for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0067] When generating a summary, the summarizing unit can optimize the current summary by referring to past calendar information. For example, the current summary is optimized based on the past calendar information. The summarizing unit can also extract important events from the past calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the past calendar information and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to the past calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input past calendar information data into the generation AI and cause the generation AI to optimize the summary.

[0068] When generating a summary, the summarization unit can optimize the current summary by referring to past photo data. For example, the current summary is optimized based on the past photo data. The summarization unit can also extract important events from the past photo data and reflect them in the summary. Furthermore, the summarization unit can analyze the past photo data and include the most relevant information in the summary. This allows the current summary to be optimized by referring to the past photo data. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input past photo data to a generation AI and cause the generation AI to optimize the summary.

[0069] When calculating calories, the calorie calculation unit can optimize the current calorie calculation by referring to past meal data. For example, the current calorie calculation is optimized based on the past meal data. The calorie calculation unit can also extract important meal information from the past meal data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the past meal data and include the most relevant information in the calorie calculation. This allows the current calorie calculation to be optimized by referring to the past meal data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, or without, AI, for example. For example, the calorie calculation unit can input past meal data into the generation AI and have the generation AI optimize the calorie calculation.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives a question input by the user through a messaging app. The question may be in text format or voice format. For example, the reception unit receives a question such as "What are your plans for today?" Step 2: The generation unit uses the generation AI to analyze the question received by the reception unit and generate an answer optimized for the individual. For example, the generation AI might generate an answer such as, "Today's schedule is a meeting in the morning, and free time in the afternoon." Step 3: The summary unit acquires the calendar information and summarizes the events of the day. For example, it generates a summary such as "Today's events were that I had a meeting in the morning and free time in the afternoon." Step 4: The summarization unit analyzes the photos and summarizes the memories of the trip, etc. For example, it generates a summary such as "My memories of the trip were visiting tourist spot A and taking photos." Step 5: The calorie calculation unit analyzes the food photos and calculates the calories for the day. For example, it calculates the calorie intake for today as follows: "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." Step 6: The recommendation unit makes personalized meal recommendations, such as "We recommend eating lots of vegetables for dinner tonight." Step 7: The expense summary unit summarizes today's expenses. For example, it generates a summary such as "Today's expenses are 2,000 yen for food and 500 yen for transportation."

[0072] (Example 2) A messaging app-based personalized optimization service system according to an embodiment of the present invention is a system that incorporates a generation AI and provides personalized services based on a messaging app. This system allows a user to input a question through a messaging app, and the generation AI generates a personalized answer to the question. It also works with a calendar app to provide a daily summary of events. It also includes a function for summarizing memories, such as travels, with photos, and a function for sending photos of meals to the generation AI to calculate the calories consumed for the day. This enables services such as personalized meal recommendations and a summary of today's expenses to be provided. For example, a user inputs a question such as "What are your plans for today?" through a messaging app. This question is sent to the generation AI. The generation AI analyzes the input question and generates a personalized answer. For example, it generates an answer such as "Today's plans include a meeting in the morning and free time in the afternoon." It also works with a calendar app to provide a daily summary of events. For example, it generates a summary such as "Today's plans include a meeting in the morning and free time in the afternoon." It also includes a function to summarize travel memories with photos. For example, it can generate a summary such as, "My travel memories include visiting tourist spot A and taking photos." It also provides a function to send photos of meals to the generation AI and calculate the calories consumed for the day. For example, it can calculate the calories consumed for today by saying, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." This makes it possible to provide services such as personalized meal recommendations and summaries of today's expenses. For example, it can generate a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." This allows the messaging app-based personalized optimization service system to provide users with personalized services.

[0073] A messaging app-based personalized optimization service system according to an embodiment includes a reception unit, a generation unit, a summarization unit, a calorie calculation unit, a recommendation unit, and an expense summarization unit. The reception unit receives questions entered by a user through a messaging app. Questions may be in text format or audio format, but are not limited to these examples. The reception unit receives a question, for example, a user input such as "What are your plans for today?" The generation unit uses a generation AI to analyze the question received by the reception unit and generate an answer optimized for the individual. For example, the generation AI generates an answer such as "Today's plans include a meeting in the morning and free time in the afternoon." The summarization unit acquires calendar information and summarizes the events of the day. For example, the summarization unit generates a summary such as "Today's events include a meeting in the morning and free time in the afternoon." The summarization unit analyzes photos and summarizes memories, such as travel memories. The summarization unit generates a summary such as, "To make a memory of my trip, I visited tourist spot A and took photos." The calorie calculation unit analyzes photos of meals and calculates the calories consumed for the day. The calorie calculation unit calculates calories such as, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." The recommendation unit recommends meals optimized for individuals. The recommendation unit recommends, for example, "I recommend eating lots of vegetables for dinner tonight." The expense summary unit summarizes today's expenses. The expense summary unit generates a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." This allows the messaging app-based personal optimization service system according to the embodiment to provide users with services optimized for individuals.

[0074] The generation unit can analyze questions using the generation AI and generate answers optimized for individuals. For example, when a user asks, "What are your plans for today?", the generation AI generates an answer such as, "Today's plans include a meeting in the morning and free time in the afternoon." The generation unit can also generate more accurate answers based on the user's past question history and profile information. For example, the generation AI analyzes the user's past question history and generates appropriate answers to questions frequently asked by the user. Furthermore, the generation unit can generate optimal answers in real time based on the user's current situation and environment. For example, the generation AI generates optimal answers based on the user's current location information and calendar information. In this way, the generation AI can generate answers optimized for individuals to questions.

[0075] The summarizing unit can acquire calendar information and summarize the events of the day. For example, the summarizing unit acquires schedule information for the day from a calendar app and summarizes the events of the day based on that information. For example, the summarizing unit generates a summary such as, "Today's events were a meeting in the morning and free time in the afternoon." The summarizing unit can also summarize the progress of the user's tasks and events based on the calendar information. For example, the summarizing unit generates a summary such as, "Today's tasks were three completed and two incomplete." Furthermore, the summarizing unit can visually display the user's activities for the day based on the calendar information. For example, the summarizing unit visually displays the user's activities for the day using graphs and charts. This makes it possible to summarize the events of the day based on the calendar information.

[0076] The summarization unit can analyze photos and summarize travel memories. For example, the summarization unit analyzes travel photos taken by a user and summarizes the travel memories based on the analysis. For example, the summarization unit generates a summary such as, "My travel memories include visiting tourist spot A and taking photos." The summarization unit can also extract travel highlights based on the results of photo analysis and reflect them in the summary. For example, the summarization unit can generate a summary such as, "The highlights of my trip were taking photos at tourist spot A and eating at tourist spot B." The summarization unit can also visually display travel memories based on the results of photo analysis. For example, the summarization unit can display photos in a slideshow format, allowing users to visually enjoy their travel memories. This allows travel memories to be summarized by analyzing photos.

[0077] The calorie calculation unit can analyze photos of meals and calculate the calorie intake for the day. For example, the calorie calculation unit analyzes photos of meals taken by the user and calculates the calorie intake for the day based on the photos. For example, the calorie calculation unit may calculate calories such as, "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." The calorie calculation unit can also evaluate the nutritional balance of each meal based on the analysis results of the meal photos. For example, the calorie calculation unit may evaluate, "Breakfast is high in carbohydrates, and lunch is high in protein." Furthermore, the calorie calculation unit can record the user's dietary history and analyze long-term calorie intake trends based on the analysis results of the meal photos. For example, the calorie calculation unit may analyze, "The average calorie intake for the past week is 2000 kcal." This allows the calorie calculation for the day to be performed by analyzing the meal photos.

[0078] The recommendation unit can recommend meals optimized for individuals. The recommendation unit makes meal recommendations optimized for individuals based on, for example, the user's meal history and preference information. For example, the recommendation unit makes a recommendation such as, "I recommend eating lots of vegetables for dinner tonight." The recommendation unit can also propose an optimal meal plan taking into account the user's nutritional balance. For example, the recommendation unit makes a suggestion such as, "This week's meal plan is fish dishes on Monday and vegetable dishes on Tuesday." Furthermore, the recommendation unit can also recommend meals customized according to the user's health condition and goals. For example, the recommendation unit makes a recommendation such as, "For those on a diet, I recommend low-calorie meals." This makes it possible to make meal recommendations optimized for individuals.

[0079] The expense summarizing unit can summarize today's expenses. For example, the expense summarizing unit summarizes today's expenses based on expense information entered by the user. For example, the expense summarizing unit generates a summary such as, "Today's expenses are 2,000 yen for food and 500 yen for transportation." The expense summarizing unit can also classify expense information by category and summarize expenses for each category. For example, the expense summarizing unit generates a summary such as, "Today's food expenses are 2,000 yen and transportation expenses are 500 yen." Furthermore, the expense summarizing unit can analyze the user's spending habits based on the expense information and provide advice on saving money. For example, the expense summarizing unit can provide advice such as, "This month's food expenses are over budget, so I recommend that you reduce your food expenses next month." This allows today's expenses to be summarized.

[0080] Furthermore, the messaging app-based personalized optimization service system includes a reception unit that estimates a user's emotions and adjusts the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit temporarily delays the reception of the question to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept the question and respond quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize the reception of the question and start processing it quickly. This allows the timing of question reception to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The reception unit can analyze the user's past question history and select the optimal 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 question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. This makes it possible to analyze the user's past question history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0082] The reception unit can filter questions based on the user's current areas of interest when receiving the questions. For example, the reception unit preferentially receives questions related to topics that the user has recently been interested in. The reception unit can also filter questions based on areas in which the user has shown interest in the past. Furthermore, the reception unit can preferentially receive questions that are highly relevant based on the user's current activity status. This makes it possible to filter questions based on the user's current areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform question filtering.

[0083] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit postpones questions of lower importance. Furthermore, when the user is relaxed, the reception unit can prioritize questions of higher importance. Furthermore, when the user is in a hurry, the reception unit can prioritize questions of higher urgency. This allows the priority of questions to be determined based on 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0084] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. This makes it possible to prioritize receiving highly relevant questions by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to filter the questions.

[0085] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize receiving questions related to topics in which the user has recently shown interest on social media. The reception unit can also prioritize receiving questions related to accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving highly relevant questions based on the user's social media activity history. This makes it possible to analyze the user's social media activity and receive related questions. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI perform question filtering.

[0086] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a polite and detailed answer. If the user is in a hurry, the generation unit can also generate a concise and to-the-point answer. Furthermore, if the user is excited, the generation unit can generate an answer with a visually appealing effect. This allows the way the answer is expressed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the answer is expressed.

[0087] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0088] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a general-purpose generation algorithm to general questions. Furthermore, the generation unit can also apply an emotion-conscious generation algorithm to emotional questions. This makes it possible to apply different generation algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point answer. Furthermore, if the user is relaxed, the generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is excited, the generation unit can generate an answer with a visually stimulating effect. This allows the length of the answer to be adjusted 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, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0090] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. For example, the generation unit prioritizes answers to recently submitted questions. The generation unit can also postpone answers to questions that were submitted earlier. Furthermore, the generation unit can gradually adjust the priority of answers depending on the time of submission. This makes it possible to determine the priority of answers based on the time of submission of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0091] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit prioritizes answers to questions with high relevance. The generation unit can also postpone answers to questions with low relevance. Furthermore, the generation unit can gradually adjust the order of answers according to the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0092] The summarizing unit can estimate the user's emotions and adjust the display method of the summary based on the estimated user's emotions. For example, if the user is nervous, the summarizing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the summarizing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the summarizing unit can provide a display method that focuses on the main points. This allows the display method of the summary to be adjusted based on 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 summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the summary.

[0093] When generating a summary, the summarizing unit can optimize the current summary by referring to past calendar information. The summarizing unit optimizes the current summary based on, for example, past calendar information. The summarizing unit can also extract important events from the past calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the past calendar information and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to the past calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input past calendar information data into the generation AI and cause the generation AI to optimize the summary.

[0094] The summarizing unit can apply different summarizing methods to different categories of calendar information when generating summaries. For example, the summarizing unit generates detailed summaries for work-related calendar information. The summarizing unit can also generate concise summaries for private calendar information. Furthermore, the summarizing unit can gradually adjust the summarizing method according to the category of calendar information. This makes it possible to apply different summarizing methods to different categories of calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, or without, AI, for example. For example, the summarizing unit can input category data of calendar information to the generation AI and cause the generation AI to apply the summarizing method.

[0095] The summarizing unit can estimate the user's emotions and adjust the importance of the summary based on the estimated user's emotions. For example, when the user is stressed, the summarizing unit postpones information of low importance. Furthermore, when the user is relaxed, the summarizing unit can prioritize summarizing information of high importance. Furthermore, when the user is in a hurry, the summarizing unit can prioritize information of high urgency. This allows the importance of the summary to be adjusted based on 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 summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarizing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the summary.

[0096] When generating a summary, the summarizing unit can analyze changes in the summary based on the submission date of the calendar information. For example, the summarizing unit prioritizes summarizing the most recent calendar information. The summarizing unit can also postpone the processing of calendar information that was submitted earlier. Furthermore, the summarizing unit can analyze changes in the summary in stages according to the submission date. This makes it possible to analyze changes in the summary based on the submission date of the calendar information. Some or all of the above-mentioned processing in the summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the summarizing unit can input submission date data of the calendar information to the generation AI and cause the generation AI to analyze changes in the summary.

[0097] When generating a summary, the summarizing unit can optimize the summary by referring to the related data of the calendar information. The summarizing unit can optimize the current summary based on, for example, the related data of the calendar information. The summarizing unit can also extract important events from the related data of the calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the related data of the calendar information and include the most relevant information in the summary. This makes it possible to optimize the summary by referring to the related data of the calendar information. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the related data of the calendar information to the generation AI and cause the generation AI to optimize the summary.

[0098] The summarization unit can estimate the user's emotions and adjust the display method of the summary based on the estimated user's emotions. For example, if the user is nervous, the summarization unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the summarization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a display method that focuses on the main points. This allows the display method of the summary to be adjusted 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 summarization unit can be performed using, for example, an AI, or without an AI. For example, the summarization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the summary.

[0099] When generating a summary, the summarization unit can optimize the current summary by referring to past photo data. The summarization unit, for example, optimizes the current summary based on past photo data. The summarization unit can also extract important events from past photo data and reflect them in the summary. Furthermore, the summarization unit can analyze past photo data and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to past photo data. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input past photo data to a generation AI and cause the generation AI to optimize the summary.

[0100] The summarization unit can apply different summarization techniques to different photo categories when generating summaries. For example, the summarization unit generates detailed summaries for travel-related photos. The summarization unit can also generate concise summaries for event-related photos. Furthermore, the summarization unit can gradually adjust the summarization technique depending on the photo category. This allows different summarization techniques to be applied to different photo categories. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input photo category data to the generation AI and cause the generation AI to apply the summarization technique.

[0101] The summarization unit can estimate the user's emotions and adjust the importance of summarization based on the estimated user's emotions. For example, when the user is stressed, the summarization unit postpones information of low importance. Furthermore, when the user is relaxed, the summarization unit can prioritize summarizing information of high importance. Furthermore, when the user is in a hurry, the summarization unit can prioritize summarizing information of high urgency. This allows the importance of summarization to be adjusted based on 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 summarization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the summarization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the summarization.

[0102] When generating a summary, the summarization unit can analyze changes in the summary based on the time the photo was taken. For example, the summarization unit prioritizes summarization of recently taken photos. The summarization unit can also postpone summarization of older photos. Furthermore, the summarization unit can analyze changes in the summary in stages depending on the time the photo was taken. This makes it possible to analyze changes in the summary based on the time the photo was taken. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input photo taking time data into the generation AI and have the generation AI analyze changes in the summary.

[0103] The summarization unit can optimize the summary by referring to the photo-related data when generating the summary. For example, the summarization unit optimizes the current summary based on the photo-related data. The summarization unit can also extract important events from the photo-related data and reflect them in the summary. Furthermore, the summarization unit can analyze the photo-related data and include the most relevant information in the summary. This allows the summary to be optimized by referring to the photo-related data. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input the photo-related data into the generation AI and cause the generation AI to optimize the summary.

[0104] The calorie calculation unit can estimate the user's emotions and adjust the display method of the calorie count based on the estimated user's emotions. For example, if the user is nervous, the calorie calculation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the calorie calculation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the calorie calculation unit can provide a display method that focuses on the main points. This allows the display method of the calorie count to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 calorie calculation unit can be performed using, for example, an AI, or without an AI. For example, the calorie calculation unit can input the user's emotional data into the generation AI and have the generation AI adjust the display method of the calorie count.

[0105] When calculating calories, the calorie calculation unit can optimize the current calorie calculation by referring to past meal data. The calorie calculation unit optimizes the current calorie calculation based on, for example, past meal data. The calorie calculation unit can also extract important meal information from the past meal data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the past meal data and include the most relevant information in the calorie calculation. This allows the current calorie calculation to be optimized by referring to the past meal data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input past meal data into the generation AI and cause the generation AI to optimize the calorie calculation.

[0106] The calorie calculation unit can apply different calorie calculation methods to different meal categories when calculating calories. For example, the calorie calculation unit performs detailed calorie calculation for breakfast. The calorie calculation unit can also perform simple calorie calculation for lunch. Furthermore, the calorie calculation unit can gradually adjust the calorie calculation method according to the meal category. This allows different calorie calculation methods to be applied to different meal categories. Some or all of the above-described processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input meal category data into the generation AI and cause the generation AI to apply the calorie calculation method.

[0107] The calorie calculation unit can estimate the user's emotions and adjust the importance of calorie calculation based on the estimated user's emotions. For example, if the user is feeling stressed, the calorie calculation unit postpones information of low importance. Furthermore, if the user is relaxed, the calorie calculation unit can prioritize calorie calculation for information of high importance. Furthermore, if the user is in a hurry, the calorie calculation unit can prioritize calorie calculation for information of high urgency. This allows the importance of calorie calculation to be adjusted based on 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 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 calorie calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calorie calculation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the importance of calorie calculation.

[0108] When calculating calories, the calorie calculation unit can analyze changes in calorie count based on the time when the meal was photographed. For example, the calorie calculation unit prioritizes calorie calculation for photos of meals that were photographed recently. The calorie calculation unit can also postpone calculation of calories for photos of meals that were photographed older. Furthermore, the calorie calculation unit can analyze changes in calorie count in stages depending on the time when the photos were photographed. This makes it possible to analyze changes in calorie count based on the time when the meal was photographed. Some or all of the above-described processing in the calorie calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calorie calculation unit can input data on the time when the meal was photographed into the generation AI and have the generation AI analyze changes in calorie count.

[0109] The calorie calculation unit can optimize the calorie calculation by referring to meal-related data when calculating calories. The calorie calculation unit, for example, optimizes the current calorie calculation based on the meal-related data. The calorie calculation unit can also extract important meal information from the meal-related data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the meal-related data and include the most relevant information in the calorie calculation. This allows the calorie calculation to be optimized by referring to the meal-related data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, or without, AI, for example. For example, the calorie calculation unit can input meal-related data into a generation AI and cause the generation AI to optimize the calorie calculation.

[0110] The recommendation unit can estimate the user's emotions and adjust the display method of recommendations based on the estimated user emotions. For example, if the user is nervous, the recommendation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the recommendation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the recommendation unit can also provide a display method that focuses on the main points. This allows the display method of recommendations to be adjusted 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of recommendations.

[0111] When generating recommendations, the recommendation unit can optimize current recommendations by referring to past meal data. The recommendation unit, for example, optimizes current recommendations based on past meal data. The recommendation unit can also extract important meal information from past meal data and reflect it in recommendations. Furthermore, the recommendation unit can analyze past meal data and include the most relevant information in recommendations. This makes it possible to optimize current recommendations by referring to past meal data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input past meal data into the generation AI and cause the generation AI to optimize the recommendations.

[0112] The recommendation unit can apply different recommendation methods to different meal categories when generating recommendations. For example, the recommendation unit can make detailed recommendations for breakfast. The recommendation unit can also make brief recommendations for lunch. Furthermore, the recommendation unit can gradually adjust the recommendation method according to the meal category. This makes it possible to apply different recommendation methods to different meal categories. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input meal category data to the generation AI and cause the generation AI to apply the recommendation method.

[0113] The recommendation unit can estimate the user's emotions and adjust the importance of recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can postpone information of low importance. Furthermore, if the user is relaxed, the recommendation unit can also prioritize recommending information of high importance. Furthermore, if the user is in a hurry, the recommendation unit can also prioritize recommending information of high urgency. This allows the importance of recommendations to be adjusted based on 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 recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input user emotion data into the generation AI and cause the generation AI to adjust the importance of recommendations.

[0114] When generating recommendations, the recommendation unit can analyze changes in recommendations based on the time when the meal was photographed. For example, the recommendation unit prioritizes recommendations of photos of meals that were photographed recently. The recommendation unit can also postpone recommendations of photos of meals that were photographed older. Furthermore, the recommendation unit can analyze changes in recommendations in stages according to the time when the photos were taken. This makes it possible to analyze changes in recommendations based on the time when the meal was photographed. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the time when the meal was photographed into the generation AI and cause the generation AI to analyze changes in recommendations.

[0115] The recommendation unit can optimize the recommendation by referring to the meal-related data when generating the recommendation. The recommendation unit, for example, optimizes the current recommendation based on the meal-related data. The recommendation unit can also extract important meal information from the meal-related data and reflect it in the recommendation. Furthermore, the recommendation unit can analyze the meal-related data and include the most relevant information in the recommendation. This makes it possible to optimize the recommendation by referring to the meal-related data. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the meal-related data into the generation AI and cause the generation AI to optimize the recommendation.

[0116] The expense summarizing unit can estimate the user's emotions and adjust the display method of the expense summary based on the estimated user emotions. For example, if the user is nervous, the expense summarizing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the expense summarizing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the expense summarizing unit can provide a display method that focuses on the main points. This allows the display method of the expense summary to be adjusted based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the expense summarizing unit can be performed using, for example, AI, or without AI. For example, the expense summarizing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the expense summary.

[0117] When generating an expense summary, the expense summarizing unit can optimize the current expense summary by referring to past expense data. The expense summarizing unit, for example, optimizes the current expense summary based on past expense data. The expense summarizing unit can also extract important expense information from the past expense data and reflect it in the expense summary. Furthermore, the expense summarizing unit can analyze past expense data and include the most relevant information in the expense summary. This makes it possible to optimize the current expense summary by referring to the past expense data. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input past expense data into the generation AI and cause the generation AI to optimize the expense summary.

[0118] When generating an expense summary, the expense summarizing unit can apply different expense summarizing methods to each expense category. For example, the expense summarizing unit generates a detailed expense summary for food expenses. The expense summarizing unit can also generate a concise expense summary for transportation expenses. Furthermore, the expense summarizing unit can gradually adjust the expense summarizing method according to the expense category. This makes it possible to apply different expense summarizing methods to each expense category. Some or all of the above-described processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input expense category data into the generation AI and cause the generation AI to apply the expense summarizing method.

[0119] The expense summarizing unit can estimate the user's emotions and adjust the importance of the expense summary based on the estimated user emotions. For example, when the user is feeling stressed, the expense summarizing unit postpones information of low importance. Furthermore, when the user is relaxed, the expense summarizing unit can prioritize information of high importance when summarizing expenses. Furthermore, when the user is in a hurry, the expense summarizing unit can prioritize information of high urgency when summarizing expenses. This allows the importance of the expense summary to be adjusted based on 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 expense summarizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the expense summarizing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the expense summary.

[0120] When generating an expense summary, the expense summarizing unit can analyze changes in the expense summary based on the time of occurrence of the expenses. For example, the expense summarizing unit prioritizes summarizing recent expenses. The expense summarizing unit can also postpone expenses that occurred earlier. Furthermore, the expense summarizing unit can analyze changes in the expense summary in stages according to the time of occurrence. This makes it possible to analyze changes in the expense summary based on the time of occurrence of the expenses. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input data on the time of occurrence of expenses into the generation AI and have the generation AI analyze changes in the expense summary.

[0121] When generating an expense summary, the expense summarizing unit can optimize the expense summary by referring to expense-related data. The expense summarizing unit, for example, optimizes the current expense summary based on expense-related data. The expense summarizing unit can also extract important expense information from the expense-related data and reflect it in the expense summary. Furthermore, the expense summarizing unit can analyze expense-related data and include the most relevant information in the expense summary. This makes it possible to optimize the expense summary by referring to expense-related data. Some or all of the above-mentioned processing in the expense summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense summarizing unit can input expense-related data into a generation AI and cause the generation AI to optimize the expense summary. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, summarization unit, calorie calculation unit, recommendation unit, expense summarization unit, and emotion estimation reception unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives questions entered by a user through a messaging app. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers optimized for individuals using a generation AI. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information and summarizes the events of the day. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos to summarize memories, such as travels. The calorie calculation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos of meals to calculate the calories consumed for the day. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and makes meal recommendations optimized for individuals. The expense summary unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes today's expenses. The emotion estimation reception unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of receiving questions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, summarization unit, calorie calculation unit, recommendation unit, expense summary unit, and emotion estimation reception unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions entered by a user through a messaging app. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers optimized for individuals using a generation AI. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information and summarizes events for the day. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos to summarize memories, such as travels. The calorie calculation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos of meals to calculate calories for the day. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and makes meal recommendations optimized for individuals. The expense summary unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes today's expenses. The emotion estimation reception unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of receiving questions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, generation unit, summarization unit, calorie calculation unit, recommendation unit, expense summarization unit, and emotion estimation reception unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives questions entered by a user through a messaging app. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers optimized for individuals using a generation AI. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information and summarizes the events of the day. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos to summarize memories, such as travels. The calorie calculation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos of meals to calculate the calories consumed for the day. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and makes meal recommendations optimized for individuals. The expense summary unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes today's expenses. The emotion estimation reception unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of receiving questions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, summarization unit, calorie calculation unit, recommendation unit, expense summary unit, and emotion estimation reception unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives questions entered by a user through a messaging app. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers optimized for individuals using a generation AI. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information and summarizes the events of the day. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos to summarize memories, such as travels. The calorie calculation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes photos of meals to calculate the calories for the day. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and makes meal recommendations optimized for individuals. The expense summary unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes today's expenses. The emotion estimation reception unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of receiving questions.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The reception unit can analyze the user's past question history and select the optimal reception method. For example, questions frequently asked by the user in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked during a specific time period based on the user's past question history. This makes it possible to analyze the user's past question history and select the optimal reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using 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 reception method.

[0124] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer is generated for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0125] When generating a summary, the summarizing unit can optimize the current summary by referring to past calendar information. For example, the current summary is optimized based on the past calendar information. The summarizing unit can also extract important events from the past calendar information and reflect them in the summary. Furthermore, the summarizing unit can analyze the past calendar information and include the most relevant information in the summary. This makes it possible to optimize the current summary by referring to the past calendar information. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input past calendar information data into the generation AI and cause the generation AI to optimize the summary.

[0126] When generating a summary, the summarization unit can optimize the current summary by referring to past photo data. For example, the current summary is optimized based on the past photo data. The summarization unit can also extract important events from the past photo data and reflect them in the summary. Furthermore, the summarization unit can analyze the past photo data and include the most relevant information in the summary. This allows the current summary to be optimized by referring to the past photo data. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input past photo data to a generation AI and cause the generation AI to optimize the summary.

[0127] When calculating calories, the calorie calculation unit can optimize the current calorie calculation by referring to past meal data. For example, the current calorie calculation is optimized based on the past meal data. The calorie calculation unit can also extract important meal information from the past meal data and reflect it in the calorie calculation. Furthermore, the calorie calculation unit can analyze the past meal data and include the most relevant information in the calorie calculation. This allows the current calorie calculation to be optimized by referring to the past meal data. Some or all of the above-mentioned processing in the calorie calculation unit may be performed using, or without, AI, for example. For example, the calorie calculation unit can input past meal data into the generation AI and have the generation AI optimize the calorie calculation.

[0128] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay the reception of questions to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept questions and respond quickly. Furthermore, if the user is in a hurry, the reception unit can prioritize the reception of questions and start processing quickly. This allows the timing of question reception to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0129] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a polite and detailed answer. If the user is in a hurry, the generation unit can also generate a concise and to-the-point answer. Furthermore, if the user is excited, the generation unit can generate an answer with a visually appealing effect. This allows the way the answer is expressed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the answer is expressed.

[0130] The summarizing unit can estimate the user's emotions and adjust the display method of the summary based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the summarizing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the summarizing unit can provide a display method that focuses on the main points. This allows the display method of the summary to be adjusted based on 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 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 summarizing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the summarizing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the summary.

[0131] The summarization unit can estimate the user's emotions and adjust the display method of the summary based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the summarization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a display method that focuses on the main points. This allows the display method of the summary to be adjusted 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 summarization unit can be performed using, for example, an AI, or without an AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the summary.

[0132] The expense summary unit can estimate the user's emotions and adjust the display method of the expense summary based on the estimated user emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the expense summary unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the expense summary unit can provide a display method that focuses on the main points. This allows the display method of the expense summary to be adjusted based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the expense summary unit can be performed using, for example, AI, or without AI. For example, the expense summary unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the expense summary.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit receives a question input by the user through a messaging app. The question may be in text format or voice format. For example, the reception unit receives a question such as "What are your plans for today?" Step 2: The generation unit uses the generation AI to analyze the question received by the reception unit and generate an answer optimized for the individual. For example, the generation AI might generate an answer such as, "Today's schedule is a meeting in the morning, and free time in the afternoon." Step 3: The summary unit acquires the calendar information and summarizes the events of the day. For example, it generates a summary such as "Today's events were that I had a meeting in the morning and free time in the afternoon." Step 4: The summarization unit analyzes the photos and summarizes the memories of the trip, etc. For example, it generates a summary such as "My memories of the trip were visiting tourist spot A and taking photos." Step 5: The calorie calculation unit analyzes the food photos and calculates the calories for the day. For example, it calculates the calorie intake for today as follows: "Today's calorie intake is 500 kcal for breakfast, 700 kcal for lunch, and 800 kcal for dinner." Step 6: The recommendation unit makes personalized meal recommendations, such as "We recommend eating lots of vegetables for dinner tonight." Step 7: The expense summary unit summarizes today's expenses. For example, it generates a summary such as "Today's expenses are 2,000 yen for food and 500 yen for transportation."

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0165] In the 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.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] 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.

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

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 section for accepting questions; a generation unit that analyzes the question received by the reception unit and generates an answer that is suitable for the individual; a summarizing unit that acquires calendar information and generates summaries; a summarization unit that analyzes the photograph and generates a summary; A calorie calculation unit that analyzes photos of food and calculates calories; A recommendation department that recommends meals suitable for individuals; An expense summary unit that summarizes expenses. A system characterized by:

2. The generation unit The AI ​​analyzes the question and generates an answer optimized for the individual.

2. The system of claim 1.

3. The gathering portion is Get calendar information and summarize today's events 2. The system of claim 1.

4. The summarizing unit Analyze photos and summarize your travel memories 2. The system of claim 1.

5. The calorie calculation unit Analyze photos of your meals and calculate your daily calorie intake 2. The system of claim 1.

6. The recommendation unit Providing personalized meal recommendations 2. The system of claim 1.

7. The expense summary unit Summarize today's expenses 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

Images