System

Abstract

To provide a system capable of making appropriate suggestions based on a comment of a user.SOLUTION: A system in an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives a comment from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit makes suggestions based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately provide appropriate suggestions based on user comments, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate suggestions based on user comments. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives a user's utterance. The analysis unit analyzes the information received by the reception unit. The proposal unit makes a proposal based on the information analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide appropriate suggestions based on user comments. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The proposed system according to the embodiment of the present invention is a system that analyzes the behavioral tendency of consumers and suggests the next action based on the location information of the registered smartphone and individual registration. When the user says "my child is coughing," the proposed system analyzes the information and makes the following suggestions. For example, (1) contacting kindergartens, nursery schools, schools, etc., (2) business hours and reservations of the family doctor or medical institutions that can handle the situation, (3) cancellation and rescheduling of after-school care and extracurricular activities, (4) notification to emergency contacts, (5) suggestions such as online shopping, and (6) home measures are all proposed. This allows the proposed system to allow the user to take prompt and appropriate action. For example, if a child has a cough, a reservation can be made quickly based on the information of the medical institution proposed by the system. In addition, the necessary procedures, such as notification to emergency contacts and cancellation of after-school care, can be carried out all at once. This reduces the burden on the user and enables efficient response.

[0029] The proposal system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives a user's utterance. The user's utterance includes, for example, voice, text, gesture, and the like, but is not limited to such examples. The reception unit converts the user's utterance into text data, for example, by using a voice recognition technology. The reception unit can also receive text input and gesture input. For example, the voice recognition technology analyzes the user's utterance in real time and saves it as text data. The text input can be input by the user using a keyboard or a touch screen. The gesture input is detected by using a camera or a sensor and recognized as a utterance. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, a method such as natural language processing, emotion analysis, keyword extraction, and the like, but is not limited to such examples. For example, the natural language processing technology analyzes the user's utterance and understands its intention. The emotion analysis technology estimates emotions from the user's utterance and reflects them in the analysis. The keyword extraction technology extracts important keywords from the user's utterance and uses them in the analysis. The suggestion unit makes a suggestion based on the information analyzed by the analysis unit. The suggestion is made in the form of, for example, a product suggestion, a service suggestion, an action suggestion, or the like, but is not limited to such examples. For example, the suggestion unit suggests related products or services based on the user's utterance. The suggestion unit can also suggest actions to support the user's behavior. In this way, the suggestion system according to the embodiment can analyze the user's utterance and make appropriate suggestions.

[0030] The reception unit receives user utterances. The user utterances include, but are not limited to, voice, text, gestures, and the like. The reception unit converts the user utterances into text data, for example, using a voice recognition technology. Specifically, the voice recognition technology analyzes the user utterances in real time and saves them as text data. This technology accurately converts the utterances into text by analyzing the waveform of the voice and breaking it down into phonemes and words. In addition, the voice recognition technology can perform highly accurate recognition by taking into account background noise and the accent of the speaker. Furthermore, the reception unit can also receive text input and gesture input. The text input can be input by the user using a keyboard or touch screen, and this allows the utterances to be smoothly accepted even in an environment where voice input is difficult. The gesture input detects the user's movements using a camera or a sensor and recognizes them as utterances. For example, the user can issue a specific instruction to the system by making a specific hand movement. The camera captures the user's hand movement in real time, and the sensor detects the speed and direction of the movement. This allows the reception unit to support a variety of input methods and accurately accept the user's utterances. Furthermore, the reception unit provides an interface for centrally managing these input data and transmitting them to the analysis unit, thereby enabling the reception unit to efficiently collect user comments and improve the performance of the entire system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, natural language processing, emotion analysis, keyword extraction, and the like, but is not limited to such examples. Specifically, the natural language processing technology analyzes the user's utterance and understands its intention. The natural language processing technology uses morphological analysis and syntactic analysis to analyze the grammatical structure of the utterance and extract the meaning. In addition, the user's intention can be accurately grasped by taking into account the context. The emotion analysis technology estimates the emotion from the user's utterance and reflects it in the analysis. For example, the emotional expression and tone contained in the utterance are analyzed to determine whether the user is happy, angry, or sad. This allows the system to take an appropriate response according to the user's emotion. The keyword extraction technology extracts important keywords from the user's utterance and uses them for analysis. For example, when a user mentions a specific product or service, the keyword is extracted and used as basic data for providing related information. Furthermore, the analysis unit can combine these technologies to comprehensively analyze the user's utterance. For example, the meaning extracted by natural language processing technology can be combined with the emotion estimated by emotion analysis technology to gain a deeper understanding of the user's intentions. In addition, more accurate analysis can be performed by utilizing past comment history and user profile information. This allows the analysis unit to analyze user comments from multiple angles, improving the response accuracy of the entire system.

[0032] The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions are made in the form of, for example, product suggestions, service suggestions, action suggestions, and the like, but are not limited to such examples. Specifically, the suggestion unit suggests related products and services based on the user's remarks. For example, when a user asks about a specific product, other products and accessories related to that product are suggested. In addition, personalized suggestions can be made taking into account the user's past purchase history and preferences. The suggestion unit uses AI to predict the user's needs and generate optimal suggestions. For example, a machine learning algorithm is used to analyze the user's past behavioral data and predict products and services that the user may be interested in in the future. Furthermore, the suggestion unit can also suggest actions to support the user's behavior. For example, when a user is planning a trip, the suggestion unit provides information on accommodations, tourist spots, transportation, and the like to support the trip planning. In addition, when a user is managing his or her health, the suggestion unit can provide appropriate exercise and diet advice. The suggestion unit provides an interface for presenting these suggestions to the user in an easy-to-understand manner. For example, the suggestion content is explained using visually easy-to-understand graphs and charts. In addition, by using voice and text explanations in combination, the suggestion content is made easier for the user to understand. This allows the suggestion unit to analyze the user's comments and make appropriate suggestions, thereby providing support that meets the user's needs.

[0033] The suggestion unit includes a provision unit that provides information on medical institutions. The provision unit provides the information on medical institutions. The information on medical institutions includes, for example, the location of the hospital, medical department, and consultation hours, but is not limited to such examples. The provision unit provides information on nearby medical institutions based on, for example, the user's location information. The provision unit can also check the reservation status of the medical institution and suggest it to the user. For example, the provision unit displays the location and consultation hours of the hospital closest to the user's current location. The medical department suggests an appropriate department according to the user's symptoms. The consultation hours display the time when a reservation can be made according to the user's convenience. This allows the provision unit to quickly find a medical institution.

[0034] The suggestion unit includes a notification unit that notifies the emergency contact. The notification unit notifies the emergency contact. The emergency contact includes, for example, family, friends, medical institutions, etc., but is not limited to such examples. The notification unit notifies, for example, the emergency contact registered by the user. The notification method includes, for example, text message, push notification, voice notification, etc., but is not limited to such examples. For example, when the user faces an emergency, the notification unit sends a text message to the registered family. The notification unit can also send a push notification to the user's friends. Furthermore, the notification unit can also send a voice notification to a medical institution to encourage a quick response. This enables the notification unit to respond quickly.

[0035] The suggestion unit includes a shopping unit that makes online shopping suggestions. The shopping unit makes online shopping suggestions. Online shopping suggestions include, for example, product categories, price ranges, and the user's purchasing history, but are not limited to these examples. The shopping unit suggests related products based on, for example, the user's purchasing history. The shopping unit can also suggest optimal products based on the user's current situation and areas of interest. For example, the shopping unit suggests products related to products the user has previously purchased. The price ranges are suggested according to the user's budget. The product categories are suggested based on the user's areas of interest. This allows the shopping unit to quickly purchase items the user needs.

[0036] The suggestion unit includes a countermeasure unit that provides a countermeasure at home. The countermeasure unit provides the countermeasure at home. The countermeasure at home includes, but is not limited to, first aid, home remedies, and advice on daily life. For example, the countermeasure unit suggests an appropriate countermeasure according to the symptoms of the user. The countermeasure unit can also provide an optimal countermeasure based on the user's home environment and available resources. For example, the countermeasure unit suggests first aid that the user should take for a child's cough. The home remedies provide simple treatments that the user can perform at home. The advice on daily life suggests specific methods for the user to maintain health. This allows the countermeasure unit to allow the user to take appropriate countermeasures.

[0037] The suggestion unit includes a cancellation unit that cancels and reschedules after-school classes and lessons. The cancellation unit cancels and reschedules after-school classes and lessons. The cancellation and rescheduling includes, for example, cancellation procedures and rescheduling conditions, but is not limited to such examples. The cancellation unit performs cancellation and rescheduling procedures based on, for example, information about after-school classes and lessons registered by the user. The cancellation unit can also suggest an optimal rescheduling date based on the user's schedule. For example, the cancellation unit can easily perform procedures when the user cancels after-school classes. The rescheduling conditions are suggested according to the user's convenience. This allows the cancellation unit to allow the user to respond quickly.

[0038] The reception unit analyzes the user's past speech history and selects an appropriate reception method. The reception unit analyzes the user's past speech history and selects the optimal reception method. The user's past speech history includes, for example, speech format, speech time, speech content, etc., but is not limited to such examples. The reception unit collects the user's past speech history using, for example, a database. The speech history can also be analyzed using text mining technology. For example, the database stores the user's speech history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the speech history. This allows the reception unit to select the optimal reception method based on the user's past speech history. For example, the reception unit preferentially accepts speech formats that the user has frequently used in the past. The reception unit predicts and suggests a speech format to be used in a specific time period from the user's past speech history. The reception unit preferentially accepts speech methods (such as voice and text) that the user has used in the past. A part 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.

[0039] The reception unit performs filtering based on the user's current situation and field of interest when receiving a utterance. The reception unit performs filtering based on the user's current situation and field of interest when receiving a utterance. The user's current situation and field of interest include, for example, location information, past behavior history, and questionnaire results, but are not limited to such examples. The reception unit, for example, identifies the user's current situation using location information. In addition, the reception unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's utterance history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the reception unit to filter the utterances based on the user's current situation and field of interest. For example, when the user inputs the current situation, related utterances are preferentially accepted based on the situation. Related utterances are filtered and accepted based on the user's field of interest. Related utterances are preferentially accepted based on the user's current location information. Part 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.

[0040] The reception unit preferentially receives highly relevant utterances in consideration of the geographical location information of the user when receiving a utterance. The reception unit preferentially receives highly relevant utterances in consideration of the geographical location information of the user when receiving a utterance. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The reception unit, for example, identifies the current location of the user using GPS data. The reception unit can also acquire the user's location information using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the reception unit to preferentially accept highly relevant utterances based on the user's geographical location information. For example, when the user is in a specific location, the reception unit preferentially accepts utterances related to that location. The reception unit preferentially accepts utterances related to a location close to the user's current location. When the user is moving, the reception unit preferentially accepts utterances related to the destination. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The reception unit analyzes the user's social media activity when receiving a comment, and receives related comments. The reception unit analyzes the user's social media activity when receiving a comment, and receives related comments. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The reception unit collects the user's activity data, for example, using a social media API. The content of the social media post can also be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the reception unit to preferentially receive related comments based on the user's social media activity. For example, based on information shared by the user on social media, the reception unit preferentially receives related comments. Based on the activities of the user's social media followers and friends, the reception unit receives related comments. Based on topics in which the user has shown interest on social media, the reception unit receives related comments. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the statement during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the statement during the analysis. The importance of the statement includes, for example, the frequency of appearance of keywords and the user's emotions, but is not limited to such examples. The analysis unit, for example, uses natural language processing technology to evaluate the importance of the statement. The importance of the statement can also be evaluated using emotion analysis technology. For example, the natural language processing technology analyzes the frequency of appearance of important keywords in the statement. The emotion analysis technology estimates emotions from the user's statement and evaluates the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the statement. For example, a detailed analysis is performed for important statements. A concise analysis is performed for general statements. A quick analysis is performed for statements with high urgency. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The analysis unit applies different analysis algorithms depending on the category of the utterance during analysis. The analysis unit applies different analysis algorithms depending on the category of the utterance during analysis. The categories of the utterance include, for example, questions, opinions, requests, etc., but are not limited to such examples. The analysis unit classifies the category of the utterance using, for example, natural language processing technology. The category of the utterance can also be classified using machine learning technology. For example, the natural language processing technology analyzes the content of the utterance and identifies the category. The machine learning technology predicts the category of the utterance based on past data. This allows the analysis unit to apply different analysis algorithms depending on the category of the utterance. For example, a medical-specialized analysis algorithm is applied to a medical-related utterance. For education-related utterances, an education-specialized analysis algorithm is applied. For shopping-related utterances, a shopping-specialized analysis algorithm is applied. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit determines the priority of analysis based on the time of submission of the utterance during analysis. The analysis unit determines the priority of analysis based on the time of submission of the utterance during analysis. The time of submission of the utterance includes, for example, a timestamp and a submission order, but is not limited to such examples. The analysis unit identifies the time of submission of the utterance using, for example, a timestamp. The priority of the utterance can also be determined based on the submission order. For example, the timestamp records the date and time when the utterance was made. The submission order indicates the order in which the utterance was made. This allows the analysis unit to determine the priority of analysis based on the time of submission of the utterance. For example, the latest utterance is analyzed with priority. The current utterance is analyzed while referring to past utterances. The utterances submitted in a specific time period are analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the utterances during analysis. The analysis unit adjusts the order of analysis based on the relevance of the utterances during analysis. The relevance of the utterances includes, for example, common keywords, related topics, etc., but is not limited to such examples. The analysis unit, for example, uses natural language processing technology to evaluate the relevance of the utterances. The relevance of the utterances can also be evaluated using machine learning technology. For example, the natural language processing technology analyzes common keywords in the utterances. The machine learning technology predicts the relevance of the utterances based on past data. This allows the analysis unit to adjust the order of analysis based on the relevance of the utterances. For example, highly relevant utterances are analyzed preferentially. Less relevant utterances are analyzed later. The order of analysis is dynamically adjusted based on the relevance of the utterances. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The suggestion unit adjusts the level of detail of the proposal based on the importance of the information at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the information at the time of suggestion. The importance of the information includes, for example, the user's interest level and the urgency of the information, but is not limited to such examples. The suggestion unit evaluates the importance of the information using, for example, natural language processing technology. The importance of the information can also be evaluated using sentiment analysis technology. For example, the natural language processing technology analyzes the frequency of appearance of important keywords in the information. The sentiment analysis technology estimates emotions from the user's remarks and evaluates the importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the information. For example, a detailed proposal is made for important information. A concise proposal is made for general information. A quick proposal is made for information with high urgency. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The suggestion unit applies different proposal algorithms according to the category of information at the time of suggestion. The suggestion unit applies different proposal algorithms according to the category of information at the time of suggestion. The categories of information include, for example, news, product information, service information, etc., but are not limited to such examples. The suggestion unit classifies the category of information using, for example, natural language processing technology. The category of information can also be classified using machine learning technology. For example, the natural language processing technology analyzes the content of the information and identifies the category. The machine learning technology predicts the category of information based on past data. This allows the suggestion unit to apply different proposal algorithms according to the category of information. For example, a proposal algorithm specialized in medical care is applied to medical information. For education-related information, a proposal algorithm specialized in education is applied. For shopping-related information, a proposal algorithm specialized in shopping is applied. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The time of submission of the information includes, for example, a timestamp, a submission order, etc., but is not limited to such examples. The suggestion unit, for example, uses a timestamp to identify the time of submission of the information. The priority of the information can also be determined based on the submission order. For example, the timestamp records the date and time when the information was provided. The submission order indicates the order in which the information was provided. This allows the suggestion unit to determine the priority of the proposal based on the time of submission of the information. For example, the latest information is preferentially proposed. Current information is proposed while referring to past information. Information submitted in a specific time period is preferentially proposed. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The suggestion unit adjusts the order of the proposal based on the relevance of the information at the time of proposal. The suggestion unit adjusts the order of the proposal based on the relevance of the information at the time of proposal. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to such examples. The suggestion unit evaluates the relevance of the information using, for example, natural language processing technology. The relevance of the information can also be evaluated using machine learning technology. For example, the natural language processing technology analyzes common keywords in the information. The machine learning technology predicts the relevance of the information based on past data. This allows the suggestion unit to adjust the order of the proposal based on the relevance of the information. For example, highly relevant information is suggested preferentially. Information with low relevance is suggested later. The order of the proposal is dynamically adjusted based on the relevance of the information. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The providing unit provides appropriate information by referring to the user's past medical history when providing the medical institution information. The providing unit provides optimal information by referring to the user's past medical history when providing the medical institution information. The user's past medical history includes, for example, an electronic medical record, a medical record, etc., but is not limited to such examples. The providing unit, for example, collects the user's past medical history using an electronic medical record system. The user's medical history can also be analyzed based on the medical record. For example, the electronic medical record system stores the user's medical information in a digital format and can be searched as necessary. The medical record records the user's past medical treatment contents in detail. As a result, the providing unit can provide optimal medical institution information based on the user's past medical history. For example, information on medical institutions visited by the user in the past is provided preferentially. The optimal medical institution is suggested from the user's past medical history. Related medical institution information is provided based on the user's past medical history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The providing unit provides appropriate information in consideration of the user's current health condition when providing the medical institution information. The providing unit provides optimal information in consideration of the user's current health condition when providing the medical institution information. The user's current health condition includes, for example, self-reporting, health checkup results, etc., but is not limited to such examples. The providing unit, for example, identifies the user's health condition based on the self-reporting. The providing unit can also evaluate the user's health condition based on the health checkup results. For example, the self-reporting is performed by the user inputting his / her own health condition. The health checkup results provide data that objectively evaluates the user's health condition. This allows the providing unit to provide optimal medical institution information based on the user's current health condition. For example, when the user inputs his / her current health condition, optimal medical institution information is provided based on that condition. Based on the user's health condition, medical institution information with a high urgency is provided preferentially. The user's health condition is monitored in real time, and optimal medical institution information is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The providing unit provides appropriate information in consideration of the geographical location information of the user when providing the medical institution information. The providing unit provides optimal information in consideration of the geographical location information of the user when providing the medical institution information. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The providing unit, for example, identifies the current location of the user using GPS data. In addition, the providing unit can acquire the user's location information using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. Thereby, the providing unit can provide optimal medical institution information based on the user's geographical location information. For example, the user preferentially provides information on medical institutions close to the current location. The providing unit provides optimal medical institution information based on the user's geographical location information. When the user is moving, the providing unit provides medical institution information related to the destination. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The providing unit analyzes the user's social media activity when providing the medical institution information, and provides related medical institution information. The providing unit analyzes the user's social media activity when providing the medical institution information, and provides related medical institution information. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The providing unit, for example, collects the user's activity data using a social media API. In addition, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the providing unit to provide related medical institution information based on the user's social media activity. For example, based on health information shared by the user on social media, related medical institution information is provided. Based on the activities of the user's social media followers and friends, related medical institution information is provided. Based on medical topics in which the user showed interest on social media, related medical institution information is provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The notification unit selects the optimal notification method by referring to the past notification history of the user at the time of notification. The notification unit selects the optimal notification method by referring to the past notification history of the user at the time of notification. The past notification history of the user includes, for example, a notification log, user feedback, etc., but is not limited to such examples. The notification unit, for example, uses a database to collect the past notification history of the user. The notification history can also be analyzed using text mining technology. For example, the database stores the notification history of the user in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the notification history. This allows the notification unit to select the optimal notification method based on the past notification history of the user. For example, the notification method that the user has used in the past with preference is provided preferentially. The notification method to be used in a specific time period is predicted and proposed from the past notification history of the user. The optimal notification method is provided based on the past notification history of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0055] The notification unit performs filtering based on the user's current situation and field of interest at the time of notification. The notification unit performs filtering based on the user's current situation and field of interest at the time of notification. The user's current situation and field of interest include, for example, location information, past behavior history, and survey results, but are not limited to such examples. The notification unit, for example, identifies the user's current situation using location information. In addition, the notification unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior patterns. The survey results identify the field of interest based on the questions answered by the user. This allows the notification unit to filter notifications based on the user's current situation and field of interest. For example, when a user inputs a current situation, related notifications are given priority based on that situation. Related notifications are filtered and provided based on the user's field of interest. Related notifications are given priority based on the user's current location information. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0056] The notification unit provides an appropriate notification method in consideration of the device information of the user at the time of notification. The notification unit provides an optimal notification method in consideration of the device information of the user at the time of notification. The device information of the user includes, for example, the type of device, the OS, the browser, etc., but is not limited to such examples. The notification unit, for example, identifies the type of device and provides a notification method corresponding to it. In addition, the optimal notification method can be selected based on the information of the OS and the browser. For example, when the user is using a smartphone, a notification method according to the screen size is provided. When the user is using a tablet, a notification method optimized for a large screen is provided. When the user is using a smart watch, a notification method that is simple and highly visible is provided. As a result, the notification unit can provide an optimal notification method based on the device information of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0057] The notification unit provides a notification in multiple languages ​​according to the language setting of the user at the time of notification. The notification unit provides a notification in multiple languages ​​according to the language setting of the user at the time of notification. The user's language setting includes, for example, the language setting of the device, the user's selected language, etc., but is not limited to such examples. The notification unit automatically sets the language of the notification based on the language setting of the device, for example. In addition, when the user uses multiple languages, a language switching function can be provided. For example, the language of the notification is automatically set based on the language setting of the user's device. When the user selects a specific language, the notification is provided in that language. This allows the notification unit to provide a notification in multiple languages ​​according to the language setting of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0058] The shopping unit provides optimal suggestions by referring to the user's past purchase history when making shopping suggestions. The shopping unit provides optimal suggestions by referring to the user's past purchase history when making shopping suggestions. The user's past purchase history includes, for example, purchase history data, user feedback, etc., but is not limited to such examples. The shopping unit, for example, uses a database to collect the user's past purchase history. The purchase history can also be analyzed using text mining technology. For example, the database stores the user's purchase history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the purchase history. This allows the shopping unit to provide optimal shopping suggestions based on the user's past purchase history. For example, based on products purchased by the user in the past, related products are suggested. Products to be purchased in a specific time period are predicted and suggested from the user's past purchase history. Based on the user's past purchase history, optimal shopping suggestions are provided based on the user's past purchase history. A part or all of the above-mentioned processing in the shopping unit may be performed using, for example, AI, or may be performed without using AI.

[0059] The shopping unit performs filtering based on the user's current situation and field of interest when making a shopping suggestion. The shopping unit performs filtering based on the user's current situation and field of interest when making a shopping suggestion. The user's current situation and field of interest include, for example, location information, past behavior history, and survey results, but are not limited to such examples. The shopping unit, for example, identifies the user's current situation using location information. In addition, the shopping unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior patterns. The survey results identify the field of interest based on the questions answered by the user. This allows the shopping unit to filter products based on the user's current situation and field of interest. For example, when a user inputs a current situation, related products are preferentially suggested based on the situation. Related products are filtered and suggested based on the user's field of interest. Related products are preferentially suggested based on the user's current location information. A part or all of the above-mentioned processing in the shopping unit may be performed using, for example, AI, or may be performed without using AI.

[0060] The shopping unit provides an appropriate proposal in consideration of the geographical location information of the user when making a shopping proposal. The shopping unit provides an optimal proposal in consideration of the geographical location information of the user when making a shopping proposal. The geographical location information of the user includes, for example, GPS data, an IP address, and the like, but is not limited to such examples. The shopping unit, for example, identifies the user's current location using GPS data. Also, the shopping unit can obtain the user's location information using the IP address. For example, the GPS data is obtained from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the shopping unit to provide optimal shopping proposals based on the user's geographical location information. For example, the user preferentially suggests products from a store close to the current location. The optimal shopping proposal is provided based on the user's geographical location information. When the user is on the move, products related to the move destination are suggested. A part or all of the above-mentioned processing in the shopping unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The shopping unit analyzes the social media activity of the user at the time of making a shopping suggestion, and suggests related products. The shopping unit analyzes the social media activity of the user at the time of making a shopping suggestion, and suggests related products. The social media activity of the user includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The shopping unit, for example, uses a social media API to collect activity data of the user. Also, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the content of the user's post and the number of likes. The text mining technology extracts important keywords and topics from the content of the post. This allows the shopping unit to suggest related products based on the user's social media activity. For example, based on information shared by the user on social media, the shopping unit suggests related products. Based on the activity of the user's social media followers and friends, the shopping unit suggests related products. Based on products in which the user has shown interest on social media, the shopping unit suggests related products. A part or all of the above-mentioned processing in the shopping unit may be performed, for example, using AI, or may be performed without using AI.

[0062] The countermeasure unit provides an optimal method by referring to the user's past countermeasure history when providing a countermeasure. The countermeasure unit provides an optimal method by referring to the user's past countermeasure history when providing a countermeasure. The user's past countermeasure history includes, for example, a countermeasure log, user feedback, etc., but is not limited to such examples. The countermeasure unit, for example, uses a database to collect the user's past countermeasure history. The countermeasure history can also be analyzed using text mining technology. For example, the database stores the user's countermeasure history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the countermeasure history. This allows the countermeasure unit to provide an optimal countermeasure based on the user's past countermeasure history. For example, the countermeasure used by the user in the past is provided preferentially. A countermeasure to be used in a specific time period is predicted and proposed from the user's past countermeasure history. The optimal countermeasure is provided based on the user's past countermeasure history. A part or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI.

[0063] The handling unit performs filtering based on the user's current situation and field of interest when providing a handling method. The handling unit performs filtering based on the user's current situation and field of interest when providing a handling method. The user's current situation and field of interest include, for example, location information, past behavior history, questionnaire results, etc., but are not limited to such examples. The handling unit, for example, identifies the user's current situation using location information. In addition, the handling unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the handling unit to filter handling methods based on the user's current situation and field of interest. For example, when a user inputs a current situation, related handling methods are preferentially provided based on the situation. Related handling methods are filtered and provided based on the user's field of interest. Related handling methods are preferentially provided based on the user's current location information. Part or all of the above-mentioned processing in the handling unit may be performed using, for example, AI, or may be performed without using AI.

[0064] The handling unit provides an appropriate method in consideration of the geographical location information of the user when providing a handling method. The handling unit provides an optimal method in consideration of the geographical location information of the user when providing a handling method. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The handling unit, for example, identifies the current location of the user using GPS data. Also, the user's location information can be acquired using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the handling unit to provide an optimal handling method based on the user's geographical location information. For example, the handling unit preferentially provides a handling method that can be executed in a location close to the user's current location. The handling unit provides an optimal handling method based on the user's geographical location information. When the user is moving, the handling unit provides a handling method that can be executed at the moving destination. A part or all of the above-mentioned processing in the handling unit may be performed using, for example, AI, or may be performed without using AI.

[0065] The countermeasure unit analyzes the user's social media activity when providing the countermeasure, and provides the relevant countermeasure. The countermeasure unit analyzes the user's social media activity when providing the countermeasure, and provides the relevant countermeasure. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The countermeasure unit, for example, collects the user's activity data using a social media API. Also, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the countermeasure unit to provide the relevant countermeasure based on the user's social media activity. For example, the countermeasure unit provides the relevant countermeasure based on the information shared by the user on social media. The countermeasure unit provides the relevant countermeasure based on the activity of the user's social media followers and friends. The countermeasure unit provides the relevant countermeasure based on the countermeasure in which the user showed interest on social media. A part or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The cancellation unit provides the optimal method by referring to the user's past cancellation history at the time of cancellation / transfer. The cancellation unit provides the optimal method by referring to the user's past cancellation history at the time of cancellation / transfer. The user's past cancellation history includes, for example, a cancellation log, user feedback, etc., but is not limited to such examples. The cancellation unit, for example, uses a database to collect the user's past cancellation history. The cancellation history can also be analyzed using text mining technology. For example, the database stores the user's cancellation history in chronological order and can be searched as needed. The text mining technology extracts important patterns and trends from the cancellation history. This allows the cancellation unit to provide the optimal cancellation / transfer method based on the user's past cancellation history. For example, the cancellation / transfer method used by the user in the past is provided with priority. The cancellation / transfer method to be used in a specific time period is predicted and proposed from the user's past cancellation history. The optimal cancellation / transfer method is provided based on the user's past cancellation history. Some or all of the above-mentioned processing in the cancellation unit may be performed, for example, using AI or may be performed without using AI.

[0067] The cancellation unit performs filtering based on the user's current situation and field of interest at the time of cancellation / transfer. The cancellation unit performs filtering based on the user's current situation and field of interest at the time of cancellation / transfer. The user's current situation and field of interest include, for example, location information, past behavior history, questionnaire results, etc., but are not limited to such examples. The cancellation unit, for example, identifies the user's current situation using location information. It is also possible to identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or IP address. The past behavior history is analyzed based on the user's remark history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the cancellation unit to filter cancellation / transfer methods based on the user's current situation and field of interest. For example, when a user inputs a current situation, related cancellation / transfer methods are preferentially provided based on the situation. Related cancellation / transfer methods are filtered and provided based on the user's field of interest. Related cancellation / transfer methods are preferentially provided based on the user's current location information. Some or all of the above-described processing in the cancellation unit may be performed using AI, for example, or may be performed without using AI.

[0068] The cancellation unit provides an appropriate method in consideration of the geographical location information of the user at the time of cancellation / transfer. The cancellation unit provides an optimal method in consideration of the geographical location information of the user at the time of cancellation / transfer. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The cancellation unit, for example, identifies the user's current location using GPS data. The cancellation unit can also obtain the user's location information using the IP address. For example, GPS data is obtained from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the cancellation unit to provide the optimal cancellation / transfer method based on the user's geographical location information. For example, the cancellation / transfer method that can be executed in a location close to the user's current location is preferentially provided. The optimal cancellation / transfer method is provided based on the user's geographical location information. If the user is on the move, a cancellation / transfer method that can be executed at the destination is provided. A part or all of the above-mentioned processing in the cancellation unit may be performed, for example, using AI, or may be performed without using AI.

[0069] The cancellation unit analyzes the user's social media activity at the time of cancellation and transfer, and provides a related cancellation and transfer method. The cancellation unit analyzes the user's social media activity at the time of cancellation and transfer, and provides a related cancellation and transfer method. The user's social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The cancellation unit, for example, uses a social media API to collect user activity data. The content of social media posts can also be analyzed using text mining technology. For example, the social media API obtains the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the cancellation unit to provide a related cancellation and transfer method based on the user's social media activity. For example, based on information shared by the user on social media, the cancellation unit provides a related cancellation and transfer method. Based on the activities of the user's social media followers and friends, the cancellation unit provides a related cancellation and transfer method. Based on the cancellation and transfer method in which the user showed interest on social media, the cancellation unit provides a related cancellation and transfer method. Some or all of the above-described processing in the cancellation unit may be performed using AI, for example, or may be performed without using AI.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit can learn the user's behavioral patterns based on the content of the user's remarks and predict the next remark. For example, if the user frequently makes specific remarks at a specific time period, the analysis unit can learn the pattern and predict the next remark. Also, if the user makes a specific remark under a specific situation, the analysis unit can detect the situation and predict the next remark. Furthermore, the analysis unit can extract the user's interests and concerns from the content of the user's remarks and predict the next remark based on that. This allows the analysis unit to more effectively analyze the user's remarks and make appropriate suggestions.

[0072] When providing information on medical institutions, the providing unit can refer to the user's past medical history and suggest the most suitable medical institution. For example, if the user has received treatment at a specific hospital in the past, information on that hospital is provided preferentially. The providing unit can also suggest an appropriate medical department based on the user's past medical records. Furthermore, the providing unit can check the reservation status of a specific medical institution from the user's past medical history and suggest an available time for reservation. This allows the providing unit to quickly find an appropriate medical institution for the user.

[0073] When notifying the user's emergency contacts, the notification unit can select the optimal notification method in consideration of the user's current situation. For example, when the user is on the move, push notification can be given priority. Also, when the user is at home, text messages can be sent preferentially. Furthermore, the notification unit can determine the priority of emergency contacts based on the user's current situation and notify the most appropriate contact. This allows the user to take prompt and appropriate action.

[0074] When suggesting related products based on the user's purchase history, the shopping unit can adjust the content of the suggestions by taking into account the user's current situation and areas of interest. For example, when suggesting products related to products previously purchased by the user, products that can be purchased at nearby stores are preferentially suggested based on the user's current location information. Also, related product categories can be suggested based on the user's areas of interest. Furthermore, products in the optimal price range can be suggested based on the user's current situation. This allows the shopping unit to quickly purchase items that the user needs.

[0075] When providing a home countermeasure, the countermeasure unit can suggest an optimal countermeasure in consideration of the user's home environment and available resources. For example, when the user is at home, a home remedy can be suggested preferentially. Also, when the user is out, first aid can be suggested preferentially. Furthermore, the countermeasure unit can suggest a countermeasure that takes into consideration available resources based on the user's home environment. This allows the countermeasure unit to allow the user to take an appropriate countermeasure.

[0076] The process flow of the first embodiment will be briefly described below.

[0077] Step 1: The reception unit receives a user's utterance. The user's utterance may include voice, text, gestures, etc. The reception unit converts the user's utterance into text data using voice recognition technology. It can also accept text input and gesture input. For example, voice recognition technology analyzes the user's utterance in real time and saves it as text data. The user can input text using a keyboard or touch screen. The gesture input is detected by using a camera or sensor to detect the user's movements and recognize them as utterances. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction. For example, natural language processing technology analyzes the user's comments and understands their intentions. Sentiment analysis technology infers emotions from the user's comments and reflects them in the analysis. Keyword extraction technology extracts important keywords from the user's comments and uses them in the analysis. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions are made in the form of product suggestions, service suggestions, action suggestions, etc. For example, the suggestion unit suggests related products or services based on the user's comments. The suggestion unit can also suggest actions to support the user's behavior.

[0078] (Example 2) The proposed system according to the embodiment of the present invention is a system that analyzes the behavioral tendency of consumers based on the location information of the registered smartphone and individual registration, and proposes the next action. When a user says "my child is coughing," the proposed system analyzes the information and makes the following proposals. For example, (1) contacting kindergartens, nursery schools, schools, etc., (2) business hours and reservations of the family doctor or medical institutions that can handle the situation, (3) cancellation and rescheduling of after-school care and extracurricular activities, (4) notification to emergency contacts, (5) suggestions such as online shopping, and (6) home measures are all proposed. This allows the proposed system to allow the user to take prompt and appropriate action. For example, if a child has a cough, a reservation can be made quickly based on the information of the medical institution proposed by the system. In addition, the necessary procedures, such as notification to emergency contacts and cancellation of after-school care, can be performed all at once. This reduces the burden on the user and enables efficient response.

[0079] The proposal system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives a user's utterance. The user's utterance includes, for example, voice, text, gesture, and the like, but is not limited to such examples. The reception unit converts the user's utterance into text data, for example, by using a voice recognition technology. The reception unit can also receive text input and gesture input. For example, the voice recognition technology analyzes the user's utterance in real time and saves it as text data. The text input can be input by the user using a keyboard or a touch screen. The gesture input is detected by using a camera or a sensor and recognized as a utterance. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, a method such as natural language processing, emotion analysis, keyword extraction, and the like, but is not limited to such examples. For example, the natural language processing technology analyzes the user's utterance and understands its intention. The emotion analysis technology estimates emotions from the user's utterance and reflects them in the analysis. The keyword extraction technology extracts important keywords from the user's utterance and uses them in the analysis. The suggestion unit makes a suggestion based on the information analyzed by the analysis unit. The suggestion is made in the form of, for example, a product suggestion, a service suggestion, an action suggestion, or the like, but is not limited to such examples. For example, the suggestion unit suggests related products or services based on the user's utterance. The suggestion unit can also suggest actions to support the user's behavior. In this way, the suggestion system according to the embodiment can analyze the user's utterance and make appropriate suggestions.

[0080] The reception unit receives user utterances. The user utterances include, but are not limited to, voice, text, gestures, and the like. The reception unit converts the user utterances into text data, for example, using a voice recognition technology. Specifically, the voice recognition technology analyzes the user utterances in real time and saves them as text data. This technology accurately converts the utterances into text by analyzing the waveform of the voice and breaking it down into phonemes and words. In addition, the voice recognition technology can perform highly accurate recognition by taking into account background noise and the accent of the speaker. Furthermore, the reception unit can also receive text input and gesture input. The text input can be input by the user using a keyboard or touch screen, and this allows the utterances to be smoothly accepted even in an environment where voice input is difficult. The gesture input detects the user's movements using a camera or a sensor and recognizes them as utterances. For example, the user can issue a specific instruction to the system by making a specific hand movement. The camera captures the user's hand movement in real time, and the sensor detects the speed and direction of the movement. This allows the reception unit to support a variety of input methods and accurately accept the user's utterances. Furthermore, the reception unit provides an interface for managing these input data in a centralized manner and transmitting them to the analysis unit, thereby enabling the reception unit to efficiently collect user comments and improve the performance of the entire system.

[0081] The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, natural language processing, emotion analysis, keyword extraction, and the like, but is not limited to such examples. Specifically, the natural language processing technology analyzes the user's utterance and understands its intention. The natural language processing technology uses morphological analysis and syntactic analysis to analyze the grammatical structure of the utterance and extract the meaning. In addition, the user's intention can be accurately grasped by taking into account the context. The emotion analysis technology estimates the emotion from the user's utterance and reflects it in the analysis. For example, the emotional expression and tone contained in the utterance are analyzed to determine whether the user is happy, angry, or sad. This allows the system to take an appropriate response according to the user's emotion. The keyword extraction technology extracts important keywords from the user's utterance and uses them for analysis. For example, when a user mentions a specific product or service, the keyword is extracted and used as basic data for providing related information. Furthermore, the analysis unit can combine these technologies to comprehensively analyze the user's utterance. For example, the meaning extracted by natural language processing technology can be combined with the emotion estimated by emotion analysis technology to gain a deeper understanding of the user's intentions. In addition, more accurate analysis can be performed by utilizing past comment history and user profile information. This allows the analysis unit to analyze user comments from multiple angles, improving the response accuracy of the entire system.

[0082] The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions are made in the form of, for example, product suggestions, service suggestions, action suggestions, and the like, but are not limited to such examples. Specifically, the suggestion unit suggests related products and services based on the user's remarks. For example, when a user asks about a specific product, other products and accessories related to that product are suggested. In addition, personalized suggestions can be made taking into account the user's past purchase history and preferences. The suggestion unit uses AI to predict the user's needs and generate optimal suggestions. For example, a machine learning algorithm is used to analyze the user's past behavioral data and predict products and services that the user may be interested in in the future. Furthermore, the suggestion unit can also suggest actions to support the user's behavior. For example, when a user is planning a trip, the suggestion unit provides information on accommodations, tourist spots, transportation, and the like to support the trip planning. In addition, when a user is managing his or her health, the suggestion unit can provide appropriate exercise and diet advice. The suggestion unit provides an interface for presenting these suggestions to the user in an easy-to-understand manner. For example, the suggestion content is explained using visually easy-to-understand graphs and charts. In addition, by using voice and text explanations in combination, the suggestion content is made easier for the user to understand. This allows the suggestion unit to analyze the user's comments and make appropriate suggestions, thereby providing support that meets the user's needs.

[0083] The suggestion unit includes a provision unit that provides information on medical institutions. The provision unit provides the information on medical institutions. The information on medical institutions includes, for example, the location of the hospital, medical department, and consultation hours, but is not limited to such examples. The provision unit provides information on nearby medical institutions based on, for example, the user's location information. The provision unit can also check the reservation status of the medical institution and suggest it to the user. For example, the provision unit displays the location and consultation hours of the hospital closest to the user's current location. The medical department suggests an appropriate department according to the user's symptoms. The consultation hours display the time when a reservation can be made according to the user's convenience. This allows the provision unit to quickly find a medical institution.

[0084] The suggestion unit includes a notification unit that notifies the emergency contact. The notification unit notifies the emergency contact. The emergency contact includes, for example, family, friends, medical institutions, etc., but is not limited to such examples. The notification unit notifies, for example, the emergency contact registered by the user. The notification method includes, for example, text message, push notification, voice notification, etc., but is not limited to such examples. For example, when the user faces an emergency, the notification unit sends a text message to the registered family. The notification unit can also send a push notification to the user's friends. Furthermore, the notification unit can also send a voice notification to a medical institution to encourage a quick response. This enables the notification unit to respond quickly.

[0085] The suggestion unit includes a shopping unit that makes online shopping suggestions. The shopping unit makes online shopping suggestions. Online shopping suggestions include, for example, product categories, price ranges, and the user's purchasing history, but are not limited to these examples. The shopping unit suggests related products based on, for example, the user's purchasing history. The shopping unit can also suggest optimal products based on the user's current situation and areas of interest. For example, the shopping unit suggests products related to products the user has previously purchased. The price ranges are suggested according to the user's budget. The product categories are suggested based on the user's areas of interest. This allows the shopping unit to quickly purchase items the user needs.

[0086] The suggestion unit includes a countermeasure unit that provides a countermeasure at home. The countermeasure unit provides the countermeasure at home. The countermeasure at home includes, but is not limited to, first aid, home remedies, and advice on daily life. For example, the countermeasure unit suggests an appropriate countermeasure according to the symptoms of the user. The countermeasure unit can also provide an optimal countermeasure based on the user's home environment and available resources. For example, the countermeasure unit suggests first aid that the user should take for a child's cough. The home remedies provide simple treatments that the user can perform at home. The advice on daily life suggests specific methods for the user to maintain health. This allows the countermeasure unit to allow the user to take appropriate countermeasures.

[0087] The suggestion unit includes a cancellation unit that cancels and reschedules after-school classes and lessons. The cancellation unit cancels and reschedules after-school classes and lessons. The cancellation and rescheduling includes, for example, cancellation procedures and rescheduling conditions, but is not limited to such examples. The cancellation unit performs cancellation and rescheduling procedures based on, for example, information about after-school classes and lessons registered by the user. The cancellation unit can also suggest an optimal rescheduling date based on the user's schedule. For example, the cancellation unit can easily perform procedures when the user cancels after-school classes. The rescheduling conditions are suggested according to the user's convenience. This allows the cancellation unit to allow the user to respond quickly.

[0088] The reception unit provides a method for estimating the user's emotion and adjusting the timing of receiving the utterance based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the timing of receiving the utterance based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the reception unit to adjust the timing of receiving the utterance according to the user's emotion. For example, if the user is stressed, the system immediately accepts the utterance and responds quickly. If the user is relaxed, the system accepts the utterance with a slight delay and collects detailed information. If the user is in a hurry, the system prioritizes voice input and quickly accepts the utterance. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The reception unit analyzes the user's past speech history and selects an appropriate reception method. The reception unit analyzes the user's past speech history and selects the optimal reception method. The user's past speech history includes, for example, speech format, speech time, speech content, etc., but is not limited to such examples. The reception unit collects the user's past speech history using, for example, a database. The speech history can also be analyzed using text mining technology. For example, the database stores the user's speech history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the speech history. This allows the reception unit to select the optimal reception method based on the user's past speech history. For example, the reception unit preferentially accepts speech formats that the user has frequently used in the past. The reception unit predicts and suggests a speech format to be used in a specific time period from the user's past speech history. The reception unit preferentially accepts speech methods (such as voice and text) that the user has used in the past. A part 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.

[0090] The reception unit performs filtering based on the user's current situation and field of interest when receiving a utterance. The reception unit performs filtering based on the user's current situation and field of interest when receiving a utterance. The user's current situation and field of interest include, for example, location information, past behavior history, and questionnaire results, but are not limited to such examples. The reception unit, for example, identifies the user's current situation using location information. In addition, the reception unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's utterance history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the reception unit to filter the utterances based on the user's current situation and field of interest. For example, when the user inputs the current situation, related utterances are preferentially accepted based on the situation. Related utterances are filtered and accepted based on the user's field of interest. Related utterances are preferentially accepted based on the user's current location information. Part 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.

[0091] The reception unit provides a method for estimating the user's emotion and determining the priority of utterances to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of utterances to be received based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the reception unit to determine the priority of utterances based on the user's emotion. For example, if the user is nervous, important utterances are preferentially accepted. If the user is relaxed, detailed utterances are preferentially accepted. If the user is in a hurry, utterances that require a quick response are preferentially accepted. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The reception unit preferentially receives highly relevant utterances in consideration of the geographical location information of the user when receiving a utterance. The reception unit preferentially receives highly relevant utterances in consideration of the geographical location information of the user when receiving a utterance. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The reception unit, for example, identifies the current location of the user using GPS data. The reception unit can also acquire the user's location information using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the reception unit to preferentially accept highly relevant utterances based on the user's geographical location information. For example, when the user is in a specific location, the reception unit preferentially accepts utterances related to that location. The reception unit preferentially accepts utterances related to a location close to the user's current location. When the user is moving, the reception unit preferentially accepts utterances related to the destination. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The reception unit analyzes the user's social media activity when receiving a comment, and receives related comments. The reception unit analyzes the user's social media activity when receiving a comment, and receives related comments. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The reception unit collects the user's activity data, for example, using a social media API. The content of the social media post can also be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the reception unit to preferentially receive related comments based on the user's social media activity. For example, based on information shared by the user on social media, the reception unit preferentially receives related comments. Based on the activities of the user's social media followers and friends, the reception unit receives related comments. Based on topics in which the user has shown interest on social media, the reception unit receives related comments. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The analysis unit provides a method for estimating the user's emotion and adjusting the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The user's emotion includes, for example, relaxation, hurry, excitement, etc., but is not limited to such examples. The analysis unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the analysis unit to adjust the expression method of the analysis based on the user's emotion. For example, if the user is relaxed, a detailed analysis result is provided. If the user is in a hurry, a concise analysis result that covers the main points is provided. If the user is excited, a visually stimulating analysis result is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] The analysis unit adjusts the level of detail of the analysis based on the importance of the statement during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the statement during the analysis. The importance of the statement includes, for example, the frequency of appearance of keywords and the user's emotions, but is not limited to such examples. The analysis unit, for example, uses natural language processing technology to evaluate the importance of the statement. The importance of the statement can also be evaluated using emotion analysis technology. For example, the natural language processing technology analyzes the frequency of appearance of important keywords in the statement. The emotion analysis technology estimates emotions from the user's statement and evaluates the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the statement. For example, a detailed analysis is performed for important statements. A concise analysis is performed for general statements. A quick analysis is performed for statements with high urgency. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The analysis unit applies different analysis algorithms depending on the category of the utterance during analysis. The analysis unit applies different analysis algorithms depending on the category of the utterance during analysis. The categories of the utterance include, for example, questions, opinions, requests, etc., but are not limited to such examples. The analysis unit classifies the category of the utterance using, for example, natural language processing technology. The category of the utterance can also be classified using machine learning technology. For example, the natural language processing technology analyzes the content of the utterance and identifies the category. The machine learning technology predicts the category of the utterance based on past data. This allows the analysis unit to apply different analysis algorithms depending on the category of the utterance. For example, a medical-specialized analysis algorithm is applied to a medical-related utterance. For education-related utterances, an education-specialized analysis algorithm is applied. For shopping-related utterances, a shopping-specialized analysis algorithm is applied. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0097] The analysis unit provides a method for estimating the user's emotion and adjusting the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The user's emotion includes, for example, haste, relaxation, excitement, etc., but is not limited to such examples. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. For example, if the user is in a hurry, a short and to the point analysis is performed. If the user is relaxed, a detailed analysis is performed. If the user is excited, a visually stimulating analysis is performed. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] The analysis unit determines the priority of analysis based on the time of submission of the utterance during analysis. The analysis unit determines the priority of analysis based on the time of submission of the utterance during analysis. The time of submission of the utterance includes, for example, a timestamp and a submission order, but is not limited to such examples. The analysis unit identifies the time of submission of the utterance using, for example, a timestamp. The priority of the utterance can also be determined based on the submission order. For example, the timestamp records the date and time when the utterance was made. The submission order indicates the order in which the utterance was made. This allows the analysis unit to determine the priority of analysis based on the time of submission of the utterance. For example, the latest utterance is analyzed with priority. The current utterance is analyzed while referring to past utterances. The utterances submitted in a specific time period are analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0099] The analysis unit adjusts the order of analysis based on the relevance of the utterances during analysis. The analysis unit adjusts the order of analysis based on the relevance of the utterances during analysis. The relevance of the utterances includes, for example, common keywords, related topics, etc., but is not limited to such examples. The analysis unit, for example, uses natural language processing technology to evaluate the relevance of the utterances. The relevance of the utterances can also be evaluated using machine learning technology. For example, the natural language processing technology analyzes common keywords in the utterances. The machine learning technology predicts the relevance of the utterances based on past data. This allows the analysis unit to adjust the order of analysis based on the relevance of the utterances. For example, highly relevant utterances are analyzed preferentially. Less relevant utterances are analyzed later. The order of analysis is dynamically adjusted based on the relevance of the utterances. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0100] The suggestion unit provides a method for estimating the user's emotion and adjusting the expression method of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion and adjusts the expression method of the proposal based on the estimated user's emotion. The user's emotion includes, for example, relaxation, hurry, excitement, etc., but is not limited to such examples. The suggestion unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the suggestion unit to adjust the expression method of the proposal based on the user's emotion. For example, if the user is relaxed, a detailed proposal is made. If the user is in a hurry, a concise proposal is made. If the user is excited, a visually stimulating proposal is made. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The suggestion unit adjusts the level of detail of the proposal based on the importance of the information at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the information at the time of suggestion. The importance of the information includes, for example, the user's interest level and the urgency of the information, but is not limited to such examples. The suggestion unit evaluates the importance of the information using, for example, natural language processing technology. The importance of the information can also be evaluated using sentiment analysis technology. For example, the natural language processing technology analyzes the frequency of appearance of important keywords in the information. The sentiment analysis technology estimates emotions from the user's remarks and evaluates the importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the information. For example, a detailed proposal is made for important information. A concise proposal is made for general information. A quick proposal is made for information with high urgency. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The suggestion unit applies different proposal algorithms according to the category of information at the time of suggestion. The suggestion unit applies different proposal algorithms according to the category of information at the time of suggestion. The categories of information include, for example, news, product information, service information, etc., but are not limited to such examples. The suggestion unit classifies the category of information using, for example, natural language processing technology. The category of information can also be classified using machine learning technology. For example, the natural language processing technology analyzes the content of the information and identifies the category. The machine learning technology predicts the category of information based on past data. This allows the suggestion unit to apply different proposal algorithms according to the category of information. For example, a proposal algorithm specialized in medical care is applied to medical information. For education-related information, a proposal algorithm specialized in education is applied. For shopping-related information, a proposal algorithm specialized in shopping is applied. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0103] The suggestion unit provides a method for estimating a user's emotion and adjusting the length of the proposal based on the estimated user's emotion. The suggestion unit estimates a user's emotion and adjusts the length of the proposal based on the estimated user's emotion. The user's emotion includes, for example, haste, relaxation, excitement, etc., but is not limited to such examples. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the suggestion unit to adjust the length of the proposal based on the user's emotion. For example, if the user is in a hurry, a short and to the point proposal is made. If the user is relaxed, a detailed proposal is made. If the user is excited, a visually stimulating proposal is made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] The suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The suggestion unit determines the priority of the proposal based on the time of submission of the information at the time of proposal. The time of submission of the information includes, for example, a timestamp, a submission order, etc., but is not limited to such examples. The suggestion unit, for example, uses a timestamp to identify the time of submission of the information. The priority of the information can also be determined based on the submission order. For example, the timestamp records the date and time when the information was provided. The submission order indicates the order in which the information was provided. This allows the suggestion unit to determine the priority of the proposal based on the time of submission of the information. For example, the latest information is preferentially proposed. Current information is proposed while referring to past information. Information submitted in a specific time period is preferentially proposed. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0105] The suggestion unit adjusts the order of the proposal based on the relevance of the information at the time of proposal. The suggestion unit adjusts the order of the proposal based on the relevance of the information at the time of proposal. The relevance of the information includes, for example, common keywords, related topics, etc., but is not limited to such examples. The suggestion unit evaluates the relevance of the information using, for example, natural language processing technology. The relevance of the information can also be evaluated using machine learning technology. For example, the natural language processing technology analyzes common keywords in the information. The machine learning technology predicts the relevance of the information based on past data. This allows the suggestion unit to adjust the order of the proposal based on the relevance of the information. For example, highly relevant information is suggested preferentially. Information with low relevance is suggested later. The order of the proposal is dynamically adjusted based on the relevance of the information. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0106] The providing unit provides a method for estimating the user's emotion and adjusting the method for providing medical institution information based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the method for providing medical institution information based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the providing unit to adjust the method for providing medical institution information based on the user's emotion. For example, if the user is nervous, simple and highly visible information is provided. If the user is relaxed, detailed medical institution information is provided. If the user is in a hurry, medical institution information with the main points is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The providing unit provides appropriate information by referring to the user's past medical history when providing the medical institution information. The providing unit provides optimal information by referring to the user's past medical history when providing the medical institution information. The user's past medical history includes, for example, an electronic medical record, a medical record, etc., but is not limited to such examples. The providing unit, for example, collects the user's past medical history using an electronic medical record system. The user's medical history can also be analyzed based on the medical record. For example, the electronic medical record system stores the user's medical information in a digital format and can be searched as necessary. The medical record records the user's past medical treatment contents in detail. As a result, the providing unit can provide optimal medical institution information based on the user's past medical history. For example, information on medical institutions visited by the user in the past is provided preferentially. The optimal medical institution is suggested from the user's past medical history. Related medical institution information is provided based on the user's past medical history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The providing unit provides appropriate information in consideration of the user's current health condition when providing the medical institution information. The providing unit provides optimal information in consideration of the user's current health condition when providing the medical institution information. The user's current health condition includes, for example, self-reporting, health checkup results, etc., but is not limited to such examples. The providing unit, for example, identifies the user's health condition based on the self-reporting. The providing unit can also evaluate the user's health condition based on the health checkup results. For example, the self-reporting is performed by the user inputting his / her own health condition. The health checkup results provide data that objectively evaluates the user's health condition. This allows the providing unit to provide optimal medical institution information based on the user's current health condition. For example, when the user inputs his / her current health condition, optimal medical institution information is provided based on that condition. Based on the user's health condition, medical institution information with a high urgency is provided preferentially. The user's health condition is monitored in real time, and optimal medical institution information is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0109] The providing unit provides a method for estimating the user's emotion and determining the priority of medical institution information based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of medical institution information based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the providing unit to determine the priority of medical institution information based on the user's emotion. For example, if the user is nervous, important medical institution information is provided preferentially. If the user is relaxed, detailed medical institution information is provided. If the user is in a hurry, medical institution information that requires a quick response is provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The providing unit provides appropriate information in consideration of the geographical location information of the user when providing the medical institution information. The providing unit provides optimal information in consideration of the geographical location information of the user when providing the medical institution information. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The providing unit, for example, identifies the current location of the user using GPS data. In addition, the providing unit can acquire the user's location information using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. Thereby, the providing unit can provide optimal medical institution information based on the user's geographical location information. For example, the user preferentially provides information on medical institutions close to the current location. The providing unit provides optimal medical institution information based on the user's geographical location information. When the user is moving, the providing unit provides medical institution information related to the destination. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0111] The providing unit analyzes the user's social media activity when providing the medical institution information, and provides related medical institution information. The providing unit analyzes the user's social media activity when providing the medical institution information, and provides related medical institution information. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The providing unit, for example, collects the user's activity data using a social media API. In addition, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the providing unit to provide related medical institution information based on the user's social media activity. For example, based on health information shared by the user on social media, related medical institution information is provided. Based on the activities of the user's social media followers and friends, related medical institution information is provided. Based on medical topics in which the user showed interest on social media, related medical institution information is provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0112] The notification unit provides a method for estimating the user's emotion and adjusting the notification method based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the notification method based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, and hurry, but is not limited to such examples. The notification unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the notification unit to adjust the notification method based on the user's emotion. For example, if the user is nervous, a simple and highly visible notification is provided. If the user is relaxed, a detailed notification is provided. If the user is in a hurry, a concise notification that covers the main points is provided. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The notification unit selects the optimal notification method by referring to the past notification history of the user at the time of notification. The notification unit selects the optimal notification method by referring to the past notification history of the user at the time of notification. The past notification history of the user includes, for example, a notification log, user feedback, etc., but is not limited to such examples. The notification unit, for example, uses a database to collect the past notification history of the user. The notification history can also be analyzed using text mining technology. For example, the database stores the notification history of the user in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the notification history. This allows the notification unit to select the optimal notification method based on the past notification history of the user. For example, the notification method that the user has used in the past with preference is provided preferentially. The notification method to be used in a specific time period is predicted and proposed from the past notification history of the user. The optimal notification method is provided based on the past notification history of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0114] The notification unit performs filtering based on the user's current situation and field of interest at the time of notification. The notification unit performs filtering based on the user's current situation and field of interest at the time of notification. The user's current situation and field of interest include, for example, location information, past behavior history, and survey results, but are not limited to such examples. The notification unit, for example, identifies the user's current situation using location information. In addition, the notification unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior patterns. The survey results identify the field of interest based on the questions answered by the user. This allows the notification unit to filter notifications based on the user's current situation and field of interest. For example, when a user inputs a current situation, related notifications are given priority based on that situation. Related notifications are filtered and provided based on the user's field of interest. Related notifications are given priority based on the user's current location information. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0115] The notification unit provides a method for estimating a user's emotion and determining a priority order of notifications based on the estimated user's emotion. The notification unit estimates a user's emotion and determines a priority order of notifications based on the estimated user's emotion. The user's emotion includes, but is not limited to, tension, relaxation, and hurry. The notification unit estimates the user's emotion using, for example, facial expression recognition technology. The notification unit can also estimate the user's emotion using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the notification unit to determine the priority order of notifications based on the user's emotion. For example, if the user is nervous, important notifications are given priority. If the user is relaxed, detailed notifications are given. If the user is in a hurry, notifications that require a quick response are given priority. The emotion estimation is realized using, for example, an emotion engine or a generation AI, etc., using an emotion estimation function. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0116] The notification unit provides an appropriate notification method in consideration of the device information of the user at the time of notification. The notification unit provides an optimal notification method in consideration of the device information of the user at the time of notification. The device information of the user includes, for example, the type of device, the OS, the browser, etc., but is not limited to such examples. The notification unit, for example, identifies the type of device and provides a notification method corresponding to it. In addition, the optimal notification method can be selected based on the information of the OS and the browser. For example, when the user is using a smartphone, a notification method according to the screen size is provided. When the user is using a tablet, a notification method optimized for a large screen is provided. When the user is using a smart watch, a notification method that is simple and highly visible is provided. As a result, the notification unit can provide an optimal notification method based on the device information of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0117] The notification unit provides a notification in multiple languages ​​according to the language setting of the user at the time of notification. The notification unit provides a notification in multiple languages ​​according to the language setting of the user at the time of notification. The user's language setting includes, for example, the language setting of the device, the user's selected language, etc., but is not limited to such examples. The notification unit automatically sets the language of the notification based on the language setting of the device, for example. In addition, when the user uses multiple languages, a language switching function can be provided. For example, the language of the notification is automatically set based on the language setting of the user's device. When the user selects a specific language, the notification is provided in that language. This allows the notification unit to provide a notification in multiple languages ​​according to the language setting of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0118] The shopping unit provides a method for estimating a user's emotion and adjusting a shopping suggestion method based on the estimated user's emotion. The shopping unit estimates a user's emotion and adjusts a shopping suggestion method based on the estimated user's emotion. The user's emotion includes, for example, relaxation, hurry, excitement, etc., but is not limited to such examples. The shopping unit estimates the user's emotion using, for example, facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the shopping unit to adjust the shopping suggestion method based on the user's emotion. For example, if the user is relaxed, a detailed shopping suggestion is made. If the user is in a hurry, a concise shopping suggestion is made. If the user is excited, a visually stimulating shopping suggestion is made. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The shopping unit provides optimal suggestions by referring to the user's past purchase history when making shopping suggestions. The shopping unit provides optimal suggestions by referring to the user's past purchase history when making shopping suggestions. The user's past purchase history includes, for example, purchase history data, user feedback, etc., but is not limited to such examples. The shopping unit, for example, uses a database to collect the user's past purchase history. The purchase history can also be analyzed using text mining technology. For example, the database stores the user's purchase history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the purchase history. This allows the shopping unit to provide optimal shopping suggestions based on the user's past purchase history. For example, based on products purchased by the user in the past, related products are suggested. Products to be purchased in a specific time period are predicted and suggested from the user's past purchase history. Based on the user's past purchase history, optimal shopping suggestions are provided based on the user's past purchase history. A part or all of the above-mentioned processing in the shopping unit may be performed using, for example, AI, or may be performed without using AI.

[0120] The shopping unit performs filtering based on the user's current situation and field of interest when making a shopping suggestion. The shopping unit performs filtering based on the user's current situation and field of interest when making a shopping suggestion. The user's current situation and field of interest include, for example, location information, past behavior history, and survey results, but are not limited to such examples. The shopping unit, for example, identifies the user's current situation using location information. In addition, the shopping unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior patterns. The survey results identify the field of interest based on the questions answered by the user. This allows the shopping unit to filter products based on the user's current situation and field of interest. For example, when a user inputs a current situation, related products are preferentially suggested based on the situation. Related products are filtered and suggested based on the user's field of interest. Related products are preferentially suggested based on the user's current location information. A part or all of the above-mentioned processing in the shopping unit may be performed using, for example, AI, or may be performed without using AI.

[0121] The shopping unit provides a method for estimating a user's emotion and determining the priority of shopping suggestions based on the estimated user's emotion. The shopping unit estimates a user's emotion and determines the priority of shopping suggestions based on the estimated user's emotion. The user's emotion includes, but is not limited to, tension, relaxation, and hurry. The shopping unit estimates the user's emotion, for example, using facial expression recognition technology. The shopping unit can also estimate the user's emotion using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the shopping unit to determine the priority of shopping suggestions based on the user's emotion. For example, if the user is nervous, important products are preferentially suggested. If the user is relaxed, detailed shopping suggestions are made. If the user is in a hurry, products that require a quick response are preferentially suggested. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] The shopping unit provides an appropriate proposal in consideration of the geographical location information of the user when making a shopping proposal. The shopping unit provides an optimal proposal in consideration of the geographical location information of the user when making a shopping proposal. The geographical location information of the user includes, for example, GPS data, an IP address, and the like, but is not limited to such examples. The shopping unit, for example, identifies the user's current location using GPS data. Also, the shopping unit can obtain the user's location information using the IP address. For example, the GPS data is obtained from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the shopping unit to provide optimal shopping proposals based on the user's geographical location information. For example, the user preferentially suggests products from a store close to the current location. The optimal shopping proposal is provided based on the user's geographical location information. When the user is on the move, products related to the move destination are suggested. A part or all of the above-mentioned processing in the shopping unit may be performed, for example, using AI, or may be performed without using AI.

[0123] The shopping unit analyzes the social media activity of the user at the time of making a shopping suggestion, and suggests related products. The shopping unit analyzes the social media activity of the user at the time of making a shopping suggestion, and suggests related products. The social media activity of the user includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The shopping unit, for example, uses a social media API to collect activity data of the user. Also, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the content of the user's post and the number of likes. The text mining technology extracts important keywords and topics from the content of the post. This allows the shopping unit to suggest related products based on the user's social media activity. For example, based on information shared by the user on social media, the shopping unit suggests related products. Based on the activity of the user's social media followers and friends, the shopping unit suggests related products. Based on products in which the user has shown interest on social media, the shopping unit suggests related products. A part or all of the above-mentioned processing in the shopping unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The coping unit estimates the user's emotion and provides a method for adjusting the method for providing the coping method based on the estimated user's emotion. The coping unit estimates the user's emotion and adjusts the method for providing the coping method based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The coping unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the coping unit to adjust the method for providing the coping method based on the user's emotion. For example, if the user is nervous, a simple and highly visible coping method is provided. If the user is relaxed, a detailed coping method is provided. If the user is in a hurry, a coping method that covers the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0125] The countermeasure unit provides an optimal method by referring to the user's past countermeasure history when providing a countermeasure. The countermeasure unit provides an optimal method by referring to the user's past countermeasure history when providing a countermeasure. The user's past countermeasure history includes, for example, a countermeasure log, user feedback, etc., but is not limited to such examples. The countermeasure unit, for example, uses a database to collect the user's past countermeasure history. The countermeasure history can also be analyzed using text mining technology. For example, the database stores the user's countermeasure history in chronological order and can be searched as necessary. The text mining technology extracts important patterns and trends from the countermeasure history. This allows the countermeasure unit to provide an optimal countermeasure based on the user's past countermeasure history. For example, the countermeasure used by the user in the past is provided preferentially. A countermeasure to be used in a specific time period is predicted and proposed from the user's past countermeasure history. The optimal countermeasure is provided based on the user's past countermeasure history. A part or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI.

[0126] The handling unit performs filtering based on the user's current situation and field of interest when providing a handling method. The handling unit performs filtering based on the user's current situation and field of interest when providing a handling method. The user's current situation and field of interest include, for example, location information, past behavior history, questionnaire results, etc., but are not limited to such examples. The handling unit, for example, identifies the user's current situation using location information. In addition, the handling unit can also identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or an IP address. The past behavior history is analyzed based on the user's remark history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the handling unit to filter handling methods based on the user's current situation and field of interest. For example, when a user inputs a current situation, related handling methods are preferentially provided based on the situation. Related handling methods are filtered and provided based on the user's field of interest. Related handling methods are preferentially provided based on the user's current location information. Part or all of the above-mentioned processing in the handling unit may be performed using, for example, AI, or may be performed without using AI.

[0127] The coping unit provides a method for estimating the user's emotion and determining the priority order of the coping methods based on the estimated user's emotion. The coping unit estimates the user's emotion and determines the priority order of the coping methods based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The coping unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the coping unit to determine the priority order of the coping methods based on the user's emotion. For example, if the user is nervous, important coping methods are provided with priority. If the user is relaxed, detailed coping methods are provided. If the user is in a hurry, coping methods that require a quick response are provided with priority. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0128] The handling unit provides an appropriate method in consideration of the geographical location information of the user when providing a handling method. The handling unit provides an optimal method in consideration of the geographical location information of the user when providing a handling method. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The handling unit, for example, identifies the current location of the user using GPS data. Also, the user's location information can be acquired using the IP address. For example, the GPS data is acquired from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the handling unit to provide an optimal handling method based on the user's geographical location information. For example, the handling unit preferentially provides a handling method that can be executed in a location close to the user's current location. The handling unit provides an optimal handling method based on the user's geographical location information. When the user is moving, the handling unit provides a handling method that can be executed at the moving destination. A part or all of the above-mentioned processing in the handling unit may be performed using, for example, AI, or may be performed without using AI.

[0129] The countermeasure unit analyzes the user's social media activity when providing the countermeasure, and provides the relevant countermeasure. The countermeasure unit analyzes the user's social media activity when providing the countermeasure, and provides the relevant countermeasure. The user's social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The countermeasure unit, for example, collects the user's activity data using a social media API. Also, the content of the social media post can be analyzed using text mining technology. For example, the social media API acquires the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the countermeasure unit to provide the relevant countermeasure based on the user's social media activity. For example, the countermeasure unit provides the relevant countermeasure based on the information shared by the user on social media. The countermeasure unit provides the relevant countermeasure based on the activity of the user's social media followers and friends. The countermeasure unit provides the relevant countermeasure based on the countermeasure in which the user showed interest on social media. A part or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI.

[0130] The cancellation unit estimates the user's emotion and provides a method for adjusting the cancellation / transfer method based on the estimated user's emotion. The cancellation unit estimates the user's emotion and adjusts the cancellation / transfer method based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The cancellation unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the cancellation unit to adjust the cancellation / transfer method based on the user's emotion. For example, if the user is nervous, a simple and highly visible cancellation / transfer method is provided. If the user is relaxed, a detailed cancellation / transfer method is provided. If the user is in a hurry, a cancellation / transfer method that covers the main points is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0131] The cancellation unit provides the optimal method by referring to the user's past cancellation history at the time of cancellation / transfer. The cancellation unit provides the optimal method by referring to the user's past cancellation history at the time of cancellation / transfer. The user's past cancellation history includes, for example, a cancellation log, user feedback, etc., but is not limited to such examples. The cancellation unit, for example, uses a database to collect the user's past cancellation history. The cancellation history can also be analyzed using text mining technology. For example, the database stores the user's cancellation history in chronological order and can be searched as needed. The text mining technology extracts important patterns and trends from the cancellation history. This allows the cancellation unit to provide the optimal cancellation / transfer method based on the user's past cancellation history. For example, the cancellation / transfer method used by the user in the past is provided with priority. The cancellation / transfer method to be used in a specific time period is predicted and proposed from the user's past cancellation history. The optimal cancellation / transfer method is provided based on the user's past cancellation history. Some or all of the above-mentioned processing in the cancellation unit may be performed, for example, using AI or may be performed without using AI.

[0132] The cancellation unit performs filtering based on the user's current situation and field of interest at the time of cancellation / transfer. The cancellation unit performs filtering based on the user's current situation and field of interest at the time of cancellation / transfer. The user's current situation and field of interest include, for example, location information, past behavior history, questionnaire results, etc., but are not limited to such examples. The cancellation unit, for example, identifies the user's current situation using location information. It is also possible to identify the user's field of interest by analyzing the past behavior history. For example, location information is collected using GPS data or IP address. The past behavior history is analyzed based on the user's remark history and behavior pattern. The questionnaire result identifies the field of interest based on the questions answered by the user. This allows the cancellation unit to filter cancellation / transfer methods based on the user's current situation and field of interest. For example, when a user inputs a current situation, related cancellation / transfer methods are preferentially provided based on the situation. Related cancellation / transfer methods are filtered and provided based on the user's field of interest. Related cancellation / transfer methods are preferentially provided based on the user's current location information. Some or all of the above-described processing in the cancellation unit may be performed using AI, for example, or may be performed without using AI.

[0133] The cancellation unit provides a method for estimating the user's emotion and determining the priority order of cancellation and transfer based on the estimated user's emotion. The cancellation unit estimates the user's emotion and determines the priority order of cancellation and transfer based on the estimated user's emotion. The user's emotion includes, for example, tension, relaxation, hurry, etc., but is not limited to such examples. The cancellation unit estimates the user's emotion, for example, using facial expression recognition technology. The user's emotion can also be estimated using voice analysis technology. For example, the facial expression recognition technology analyzes the user's facial expression and calculates an emotion score. The voice analysis technology analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the cancellation unit to determine the priority order of cancellation and transfer based on the user's emotion. For example, if the user is nervous, important cancellation and transfer methods are provided with priority. If the user is relaxed, detailed cancellation and transfer methods are provided. If the user is in a hurry, cancellation and transfer methods that require a quick response are provided with priority. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0134] The cancellation unit provides an appropriate method in consideration of the geographical location information of the user at the time of cancellation / transfer. The cancellation unit provides an optimal method in consideration of the geographical location information of the user at the time of cancellation / transfer. The geographical location information of the user includes, for example, GPS data, IP address, etc., but is not limited to such examples. The cancellation unit, for example, identifies the user's current location using GPS data. The cancellation unit can also obtain the user's location information using the IP address. For example, GPS data is obtained from the user's device in real time. The IP address identifies the user's location information through an Internet connection. This allows the cancellation unit to provide the optimal cancellation / transfer method based on the user's geographical location information. For example, the cancellation / transfer method that can be executed in a location close to the user's current location is preferentially provided. The optimal cancellation / transfer method is provided based on the user's geographical location information. If the user is on the move, a cancellation / transfer method that can be executed at the destination is provided. A part or all of the above-mentioned processing in the cancellation unit may be performed, for example, using AI, or may be performed without using AI.

[0135] The cancellation unit analyzes the user's social media activity at the time of cancellation and transfer, and provides a related cancellation and transfer method. The cancellation unit analyzes the user's social media activity at the time of cancellation and transfer, and provides a related cancellation and transfer method. The user's social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The cancellation unit, for example, uses a social media API to collect user activity data. The content of social media posts can also be analyzed using text mining technology. For example, the social media API obtains the user's post content and the number of likes. The text mining technology extracts important keywords and topics from the post content. This allows the cancellation unit to provide a related cancellation and transfer method based on the user's social media activity. For example, based on information shared by the user on social media, the cancellation unit provides a related cancellation and transfer method. Based on the activities of the user's social media followers and friends, the cancellation unit provides a related cancellation and transfer method. Based on the cancellation and transfer method in which the user showed interest on social media, the cancellation unit provides a related cancellation and transfer method. Some or all of the above-described processing in the cancellation unit may be performed using AI, for example, or may be performed without using AI.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The analysis unit can learn the user's behavioral patterns based on the content of the user's remarks and predict the next remark. For example, if the user frequently makes specific remarks at a specific time period, the analysis unit can learn the pattern and predict the next remark. Also, if the user makes a specific remark under a specific situation, the analysis unit can detect the situation and predict the next remark. Furthermore, the analysis unit can extract the user's interests and concerns from the content of the user's remarks and predict the next remark based on that. This allows the analysis unit to more effectively analyze the user's remarks and make appropriate suggestions.

[0138] When providing information on medical institutions, the providing unit can refer to the user's past medical history and suggest the most suitable medical institution. For example, if the user has received treatment at a specific hospital in the past, information on that hospital is provided preferentially. The providing unit can also suggest an appropriate medical department based on the user's past medical records. Furthermore, the providing unit can check the reservation status of a specific medical institution from the user's past medical history and suggest an available time for reservation. This allows the providing unit to quickly find an appropriate medical institution for the user.

[0139] When notifying the user's emergency contacts, the notification unit can select the optimal notification method in consideration of the user's current situation. For example, when the user is on the move, push notification can be given priority. Also, when the user is at home, text messages can be sent preferentially. Furthermore, the notification unit can determine the priority of emergency contacts based on the user's current situation and notify the most appropriate contact. This allows the user to take prompt and appropriate action.

[0140] When suggesting related products based on the user's purchase history, the shopping unit can adjust the content of the suggestions by taking into account the user's current situation and areas of interest. For example, when suggesting products related to products previously purchased by the user, products that can be purchased at nearby stores are preferentially suggested based on the user's current location information. Also, related product categories can be suggested based on the user's areas of interest. Furthermore, products in the optimal price range can be suggested based on the user's current situation. This allows the shopping unit to quickly purchase items that the user needs.

[0141] When providing a home countermeasure, the countermeasure unit can suggest an optimal countermeasure in consideration of the user's home environment and available resources. For example, when the user is at home, a home remedy can be suggested preferentially. Also, when the user is out, first aid can be suggested preferentially. Furthermore, the countermeasure unit can suggest a countermeasure that takes into consideration available resources based on the user's home environment. This allows the countermeasure unit to allow the user to take an appropriate countermeasure.

[0142] The reception unit can estimate the user's emotions and adjust the timing of receiving utterances based on the estimated user's emotions. For example, if the user is stressed, the system immediately receives the utterance and responds quickly. If the user is relaxed, the system receives the utterance with a slight delay and collects detailed information. If the user is in a hurry, the system prioritizes voice input and quickly receives the utterance. This allows the reception unit to adjust the timing of receiving utterances according to the user's emotions.

[0143] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, if the user is relaxed, a detailed analysis result is provided. If the user is in a hurry, a concise analysis result that focuses on the main points is provided. If the user is excited, a visually stimulating analysis result is provided. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotion.

[0144] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated user's emotion. For example, when the user is relaxed, a detailed suggestion is made. When the user is in a hurry, a brief suggestion is made. When the user is excited, a visually stimulating suggestion is made. This allows the suggestion unit to adjust the way of expressing the suggestion based on the user's emotion.

[0145] The providing unit can estimate the user's emotions and adjust the method of providing medical institution information based on the estimated user's emotions. For example, if the user is nervous, simple and highly visible information is provided. If the user is relaxed, detailed medical institution information is provided. If the user is in a hurry, medical institution information that focuses on the main points is provided. This allows the providing unit to adjust the method of providing medical institution information based on the user's emotions.

[0146] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible notification is provided. If the user is relaxed, a detailed notification is provided. If the user is in a hurry, a concise notification that focuses on the main points is provided. This allows the notification unit to adjust the notification method based on the user's emotions.

[0147] The process flow of the second embodiment will be briefly described below.

[0148] Step 1: The reception unit receives a user's utterance. The user's utterance may include voice, text, gestures, etc. The reception unit converts the user's utterance into text data using voice recognition technology. It can also accept text input and gesture input. For example, voice recognition technology analyzes the user's utterance in real time and saves it as text data. The user can input text using a keyboard or touch screen. The gesture input is detected by using a camera or sensor to detect the user's movements and recognize them as utterances. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and keyword extraction. For example, natural language processing technology analyzes the user's comments and understands their intentions. Sentiment analysis technology infers emotions from the user's comments and reflects them in the analysis. Keyword extraction technology extracts important keywords from the user's comments and uses them in the analysis. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions are made in the form of product suggestions, service suggestions, action suggestions, etc. For example, the suggestion unit suggests related products or services based on the user's comments. The suggestion unit can also suggest actions to support the user's behavior.

[0149] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0150] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0151] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, provision unit, notification unit, shopping unit, response unit, and cancellation 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 accepts a user's utterance using the microphone 38B or the touch panel 38A of the smart device 14, and converts it into text data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and performs natural language processing and sentiment analysis. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes an appropriate suggestion based on the analysis result. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides information on medical institutions. The notification unit is realized by the control unit 46A of the smart device 14, and notifies emergency contacts. The shopping unit is realized by the control unit 46A of the smart device 14, and makes suggestions for online shopping. The response unit is realized by the specific processing unit 290 of the data processing device 12, and provides a method for responding at home. The cancel unit is realized by the control unit 46A of the smart device 14, and cancels or reschedules school classes or extracurricular activities. The relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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, the RAM 30, and the 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.

[0156] 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.

[0157] 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 the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] 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 performed in a secure state.

[0160] Fig. 4 shows an example of 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0164] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0166] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, provision unit, notification unit, shopping unit, response unit, and cancellation 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 accepts a user's utterance using the microphone 238 of the smart glasses 214 and converts it into text data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and sentiment analysis. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes an appropriate suggestion based on the analysis result. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides information on medical institutions. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies emergency contacts. The shopping unit is realized by the control unit 46A of the smart glasses 214 and suggests online shopping. The response unit is realized by the specific processing unit 290 of the data processing device 12 and provides a method of response at home. The cancellation unit is realized by the control unit 46A of the smart glasses 214, and cancels and reschedules school classes and extracurricular activities. The relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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, the RAM 30, and the 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.

[0172] 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.

[0173] 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 the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0175] 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 performed in a secure state.

[0176] 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.

[0177] 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.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0180] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] 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 voice indicating a user input for 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.

[0182] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, provision unit, notification unit, shopping unit, response unit, and cancellation 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 accepts a user's utterance using the microphone 238 of the headset type terminal 314 and converts it into text data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and sentiment analysis. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes an appropriate suggestion based on the analysis result. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides information on medical institutions. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies emergency contacts. The shopping unit is realized by the control unit 46A of the headset type terminal 314 and makes suggestions for online shopping. The response unit is realized by the specific processing unit 290 of the data processing device 12 and provides a method of response at home. The cancel unit is realized by control unit 46A of headset type terminal 314, and cancels and reschedules school classes and extracurricular activities. The relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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, the RAM 30, and the 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.

[0188] 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.

[0189] 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 the voice according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0191] 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 performed in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0193] 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.

[0194] 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.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0196] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0197] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0198] 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 a voice indicating a user input for 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.

[0199] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0200] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0201] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, provision unit, notification unit, shopping unit, response unit, and cancellation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts a user's utterance using the microphone 238 of the robot 414 and converts it into text data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and sentiment analysis. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes an appropriate suggestion based on the analysis result. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides information on medical institutions. The notification unit is realized by the control unit 46A of the robot 414 and notifies emergency contacts. The shopping unit is realized by the control unit 46A of the robot 414 and makes suggestions for online shopping. The response unit is realized by the specific processing unit 290 of the data processing device 12 and provides a method of response at home. The canceling unit is realized by the control unit 46A of the robot 414, and cancels or reschedules school classes or extracurricular activities. The relationship between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0203] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0205] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0207] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0209] 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.

[0210] 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 Universal Serial Bus (USB) 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.

[0211] In addition, 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 upon request from the data processing device 12.

[0212] 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.

[0213] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0214] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0215] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0216] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0218] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0220] (Appendix 1) a reception unit for receiving a user's comment; an analysis unit that analyzes the information received by the reception unit; A proposal unit that makes a proposal based on the information analyzed by the analysis unit. A system characterized by: (Appendix 2) The suggestion unit, Equipping a department that provides information on medical institutions 2. The system of claim 1, (Appendix 3) The suggestion unit, Equipped with a notification section that notifies emergency contacts 2. The system of claim 1, (Appendix 4) The suggestion unit, Equipping a shopping department that offers online shopping suggestions 2. The system of claim 1, (Appendix 5) The suggestion unit, Equipping a department that provides coping methods at home 2. The system of claim 1, (Appendix 6) The suggestion unit, Equipping a cancellation department to handle cancellations and transfers of school and extracurricular activities 2. The system of claim 1, (Appendix 7) The reception unit is To provide a method for estimating a user's emotion and adjusting the timing of accepting a speech based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past speech history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is When comments are accepted, they are filtered based on the user's current situation and areas of interest. 2. The system of claim 1, (Appendix 10) The reception unit is To provide a method for estimating a user's emotion and determining the priority of utterances to be received based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting comments, the system takes into account the user's geographic location information and prioritizes comments that are highly relevant. 2. The system of claim 1, (Appendix 12) The reception unit is When a comment is received, the user's social media activity is analyzed and related comments are received. 2. The system of claim 1, (Appendix 13) The analysis unit is To provide a method for estimating a user's emotion and adjusting the presentation of an analysis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the statement 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of the statement. 2. The system of claim 1, (Appendix 16) The analysis unit is To provide a method for estimating a user's emotion and adjusting the length of the analysis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when comments were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of utterances 2. The system of claim 1, (Appendix 19) The suggestion unit, To provide a method for estimating a user's emotion and adjusting the presentation of suggestions based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the information 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of information. 2. The system of claim 1, (Appendix 22) The suggestion unit, Providing a method for estimating a user's emotion and adjusting the length of a suggestion based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The suggestion unit, At the time of submission, we will prioritize proposals based on when the information is submitted. 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the information 2. The system of claim 1, (Appendix 25) The providing unit is To provide a method for estimating a user's emotion and adjusting a method for providing medical institution information based on the estimated user's emotion. 3. The system of claim 2, (Appendix 26) The providing unit is When providing medical institution information, provide appropriate information by referring to the user's past medical history 3. The system of claim 2, (Appendix 27) The providing unit is When providing medical institution information, provide appropriate information taking into account the user's current health condition 3. The system of claim 2, (Appendix 28) The providing unit is To provide a method for estimating a user's emotion and determining the priority of medical institution information based on the estimated user's emotion. 3. The system of claim 2, (Appendix 29) The providing unit is When providing information about medical institutions, provide appropriate information by taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 30) The providing unit is When providing medical institution information, analyze the user's social media activity and provide related medical institution information 3. The system of claim 2, (Appendix 31) The notification unit is To provide a method for estimating a user's emotion and adjusting a notification method based on the estimated user's emotion. 4. The system of claim 3. (Appendix 32) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 4. The system of claim 3. (Appendix 33) The notification unit is Filter notifications based on the user's current situation and interests 4. The system of claim 3. (Appendix 34) The notification unit is To provide a method for estimating a user's emotion and determining the priority of notifications based on the estimated user's emotion. 4. The system of claim 3. (Appendix 35) The notification unit is When notifying users, provide an appropriate notification method taking into account the user's device information. 4. The system of claim 3. (Appendix 36) The notification unit is When a notification is sent, it will provide multilingual notifications according to the user's language settings. 4. The system of claim 3. (Appendix 37) The shopping section includes: To provide a method for estimating a user's emotion and adjusting a shopping suggestion method based on the estimated user's emotion. 5. The system of claim 4. (Appendix 38) The shopping section includes: When making shopping suggestions, the system provides optimal suggestions by referring to the user's past purchase history. 5. The system of claim 4. (Appendix 39) The shopping section includes: Filter shopping suggestions based on the user's current context and interests 5. The system of claim 4. (Appendix 40) The shopping section includes: To provide a method for estimating a user's emotions and prioritizing shopping suggestions based on the estimated user's emotions 5. The system of claim 4. (Appendix 41) The shopping section includes: When making shopping suggestions, the app takes into account the user's geographic location to provide more relevant suggestions. 5. The system of claim 4. (Appendix 42) The shopping section includes: When making shopping suggestions, analyze users' social media activity to suggest relevant products 5. The system of claim 4. (Appendix 43) The countermeasure unit includes: To provide a method for estimating a user's emotion and adjusting a method for providing a countermeasure based on the estimated user's emotion. 6. The system of claim 5, (Appendix 44) The countermeasure unit includes: When providing solutions, provide the most appropriate solution by referring to the user's past solution history 6. The system of claim 5, (Appendix 45) The countermeasure unit includes: Filtering action suggestions based on the user's current situation and interests 6. The system of claim 5, (Appendix 46) The countermeasure unit includes: To provide a method for estimating a user's emotion and determining a priority order of measures based on the estimated user's emotion. 6. The system of claim 5, (Appendix 47) The countermeasure unit includes: When providing solutions, provide appropriate solutions by taking into account the user's geographic location information. 6. The system of claim 5, (Appendix 48) The countermeasure unit includes: When providing solutions, analyze your social media activity and provide relevant solutions 6. The system of claim 5, (Appendix 49) The cancellation unit is To provide a method for estimating a user's sentiment and adjusting a cancellation / transfer method based on the estimated user's sentiment. 7. The system of claim 6, (Appendix 50) The cancellation unit is When canceling or transferring a reservation, provide the most appropriate method by referring to the user's past cancellation history 7. The system of claim 6, (Appendix 51) The cancellation unit is Filtering cancellations and rescheduling based on the user's current circumstances and interests 7. The system of claim 6, (Appendix 52) The cancellation unit is To provide a method for estimating a user's sentiment and determining the priority of cancellations and transfers based on the estimated user's sentiment. 7. The system of claim 6, (Appendix 53) The cancellation unit is When canceling or rescheduling, provide appropriate methods taking into account the user's geographic location 7. The system of claim 6, (Appendix 54) The cancellation unit is When you cancel or reschedule, analyze your social media activity and provide you with relevant cancellation or rescheduling options 7. The system of claim 6, [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving a user's comment; an analysis unit that analyzes the information received by the reception unit; A proposal unit that makes a proposal based on the information analyzed by the analysis unit. A system characterized by:

2. The suggestion unit, Equipping a department that provides information on medical institutions 2. The system of claim 1 .

3. The suggestion unit, Equipped with a notification section that notifies emergency contacts 2. The system of claim 1 .

4. The suggestion unit, Equipping a shopping department that offers online shopping suggestions 2. The system of claim 1 .

5. The suggestion unit, Equipping a department that provides coping methods at home 2. The system of claim 1 .

6. The suggestion unit, Equipping a cancellation department to cancel or reschedule after-school and extracurricular activities 2. The system of claim 1 .

7. The reception unit is To provide a method for estimating a user's emotion and adjusting the timing of accepting a speech based on the estimated user's emotion.

2. The system of claim 1 .

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

Images