system

Abstract

The system according to this embodiment aims to quickly suggest the most suitable restaurant based on the user's preferences and budget. [Solution] The system according to the embodiment comprises a reception unit, a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The reception unit receives user input conditions. The data collection unit collects data based on the conditions entered by the reception unit. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the most suitable restaurant based on the analysis results obtained by the analysis unit. The feedback unit reflects the user's evaluation of the restaurant proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly propose a restaurant according to the user's preferences and budget, and there is a problem that it takes time.

[0005] The system according to the embodiment aims to quickly propose an optimal restaurant according to the user's preferences and budget.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The reception unit receives user input conditions. The data collection unit collects data based on the conditions entered by the reception unit. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the most suitable restaurant based on the analysis results obtained by the analysis unit. The feedback unit reflects the user's evaluation of the restaurant proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly suggest the most suitable restaurant based on the user's preferences and budget. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that quickly suggests restaurants that meet the user's preferences and budget, saving time and improving satisfaction. This AI agent system suggests the most suitable restaurants when the user inputs their desired conditions. For example, the user inputs information such as what they want to eat (Japanese food, Italian food, etc.), budget (under 3,000 yen, etc.), number of people (2-4 people, etc.), and location (Shinjuku, Shibuya, etc.). This information is input into the AI ​​agent. Next, the AI ​​agent collects data from gourmet websites and analyzes it using a machine learning algorithm. For example, if the user inputs "I want to eat Japanese food in Shinjuku for under 3,000 yen," the AI ​​agent collects data on Japanese restaurants in the Shinjuku area and selects restaurants that fit the budget. After that, the AI ​​agent lists 3 to 5 of the most suitable restaurants based on the user's conditions and displays the rating and characteristics of each restaurant. For example, it suggests restaurants with high ratings or restaurants that specialize in a particular dish. Furthermore, by allowing the user to rate the suggested restaurants, the AI ​​agent can make more accurate suggestions in the future. For example, if the user rates a restaurant as "good," this will be reflected in future suggestions. This system enables quick and accurate restaurant recommendations to a variety of users, including busy business people, tourists, and food lovers. For example, it can quickly suggest nearby restaurants to business people who want to eat a quick lunch, and suggest restaurants near tourist attractions where tourists can enjoy local cuisine to those unfamiliar with the area. It can also offer food lovers opportunities to enjoy culinary adventures by suggesting restaurants that incorporate new dishes and trends. This AI-powered restaurant recommendation system is expected to meet user needs and bring innovation to the restaurant industry. Specifically, it is expected to improve user satisfaction, reduce search time, and boost restaurant customer acquisition. For example, by suggesting restaurants that perfectly match the user's criteria, user satisfaction will increase, and manual search time will be significantly reduced, enabling more efficient selection. In addition, the recommendation system is expected to boost customer acquisition for restaurants, leading to increased sales.This allows the AI ​​agent system to quickly suggest restaurants that match the user's preferences and budget, saving time and improving satisfaction.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a suggestion unit, and a feedback unit. The reception unit takes user conditions as input. User conditions include, but are not limited to, examples of food desired, budget, number of people, and location. For example, the reception unit can input Japanese food, Italian food, Chinese food, etc., as food desired by the user. The reception unit can also input a budget ranging from 1,000 yen to 5,000 yen. Furthermore, the reception unit can input the number of people as 1 person, 2 people, group, etc. For location, it can input station name, address, area, etc. The data collection unit collects data based on the conditions input by the reception unit. The data collection unit collects data from, for example, gourmet websites. The data collection unit can also collect data from gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. The analysis unit analyzes the data collected by the data collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. The analysis unit can use a combination of multiple machine learning algorithms to improve the accuracy of data analysis. The suggestion unit proposes the most suitable restaurants based on the analysis results obtained by the analysis unit. For example, the suggestion unit lists 3 to 5 suitable restaurants based on the user's conditions and displays the ratings and characteristics of each restaurant. The suggestion unit can list restaurants based on criteria such as rating scores, review content, and distance. The suggestion unit can use a combination of multiple criteria to propose the most suitable restaurants for the user. The feedback unit reflects the user's evaluation of the restaurants proposed by the suggestion unit. For example, the feedback unit allows the user to input star ratings and comments on the proposed restaurants. The feedback unit collects user evaluations and reflects them in future suggestions. As a result, the AI ​​agent system according to this embodiment can improve user satisfaction by proposing the most suitable restaurants based on the user's conditions and reflecting evaluations.

[0030] The reception desk takes user input. User input criteria include, but are not limited to, the type of food the user wants to eat, budget, number of people, and location. For example, the reception desk can accept input such as Japanese, Italian, or Chinese food as the type of food the user wants to eat. The reception desk can accept input such as a budget range of 1,000 to 5,000 yen. Furthermore, the reception desk can accept input such as 1 person, 2 people, or group as the number of people. For location, the reception desk can accept input such as station name, address, or area. The reception desk provides a user-friendly interface to accurately receive the user's input criteria. For example, dropdown menus and checkboxes are used to allow users to easily select criteria. It also incorporates a voice input function, allowing users to input criteria by voice. This allows users to operate intuitively and reduces input errors. Furthermore, the reception desk can remember the user's past input history and present it as a reference the next time they input. This saves users the trouble of entering the same criteria every time. For example, a user who previously selected Japanese food will see Japanese food as a suggestion the next time they input. Furthermore, the reception desk checks user input in real time and displays appropriate alerts if there is missing or inconsistent information. This allows users to enter accurate conditions, improving the system's accuracy.

[0031] The data collection unit collects data based on the conditions entered by the reception unit. The data collection unit collects data from sources such as gourmet websites. It can also collect data from gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. Specifically, the data collection unit uses web scraping technology to automatically extract necessary information from each data source. For example, from gourmet websites, it collects information such as store name, address, phone number, business hours, menu, price range, rating, and review content. Similar information is collected from gourmet information data sources and integrated into the database. The data collection unit performs data cleansing to prevent data duplication and inconsistencies. For example, if the same store appears in multiple data sources, duplication is eliminated to maintain the most up-to-date and accurate information. Furthermore, the data collection unit calculates the reliability of each data source to evaluate data reliability and prioritizes the use of highly reliable information. This allows the data collection unit to provide users with highly reliable information. In addition, the data collection unit updates data in real time, always maintaining the latest information. For example, if a new store opens or information about an existing store changes, the database is updated immediately. This allows the data collection unit to always provide the latest information and respond quickly to user needs.

[0032] The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. To improve the accuracy of data analysis, the analysis unit can combine multiple machine learning algorithms. Specifically, the analysis unit first preprocesses the collected data, including imputing missing values ​​and removing outliers. Next, it applies machine learning algorithms to the preprocessed data to identify the restaurant best suited to the user's criteria. For example, it might use a decision tree algorithm to classify restaurants based on user criteria, and a random forest algorithm to integrate the results of multiple decision trees to obtain the most reliable result. It can also use neural networks to learn complex patterns and relationships, enabling more accurate predictions. Furthermore, the analysis unit incorporates past user evaluations and feedback as training data to continuously improve the model's accuracy. This allows the analysis unit to identify the restaurant best suited to the user's criteria with high accuracy.

[0033] The suggestion department proposes the most suitable restaurants based on the analysis results obtained by the analysis department. For example, the suggestion department will list 3 to 5 optimal restaurants based on the user's criteria and display the ratings and characteristics of each restaurant. The suggestion department can list restaurants based on criteria such as rating scores, review content, and distance. The suggestion department can use multiple criteria in combination to propose the most suitable restaurant for the user. Specifically, the suggestion department selects the restaurant that best suits the user's conditions based on the list of restaurants provided by the analysis department. For example, it will prioritize listing restaurants with high rating scores and good reviews. It will also select easily accessible restaurants considering the distance from the user's current location. Furthermore, the suggestion department will display detailed information such as the characteristics, menu, and price range of each restaurant to make it easier for the user to make a selection. The suggestion department can also provide personalized suggestions by considering the user's preferences and past selection history. This allows the suggestion department to quickly and accurately propose the most suitable restaurant for the user.

[0034] The Feedback Department reflects user evaluations of restaurants suggested by the Suggestion Department. For example, users can enter star ratings and comments for suggested restaurants. The Feedback Department collects user evaluations and incorporates them into future suggestions. Specifically, after a user visits a suggested restaurant, the Feedback Department provides a function to enter star ratings and comments through an evaluation form. User evaluations are reflected in the restaurant's rating and review content, and are used as reference for future suggestions. For example, restaurants highly rated by users will be prioritized for other users. Furthermore, user comments provide important information for evaluating the restaurant's characteristics and service quality. The Feedback Department not only collects user evaluations but also analyzes them and provides feedback to the Suggestion and Analysis Departments. This improves the overall accuracy and reliability of the system. Additionally, the Feedback Department records user evaluation history and learns user preferences and tendencies, allowing for more personalized suggestions in the future. This allows the Feedback Department to improve user satisfaction and enhance the system's value.

[0035] The reception desk allows users to input information such as what they want to eat, their budget, the number of people, and the location. For example, the reception desk can allow users to input Japanese food, Italian food, Chinese food, etc., as the type of food they want. The reception desk can allow users to input a budget ranging from 1,000 yen to 5,000 yen. The reception desk can allow users to input the number of people, such as 1 person, 2 people, or a group. The reception desk can allow users to input the location, such as the station name, address, or area. This allows users to input their desired conditions in detail. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the conditions entered by the user into a generating AI, which can then suggest the optimal input method based on those conditions.

[0036] The data collection unit can collect data from sources such as gourmet websites. For example, the data collection unit can collect restaurant data from gourmet websites. The data collection unit can also collect data from other gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. This allows for the collection of highly reliable data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from gourmet websites into a generating AI, which can then evaluate the reliability of the data.

[0037] The analysis unit can analyze the collected data using machine learning algorithms. For example, the analysis unit can analyze the collected data using machine learning algorithms such as decision trees, random forests, and neural networks. The analysis unit can use multiple machine learning algorithms in combination to improve the accuracy of the data analysis. This improves the accuracy of the data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then perform the data analysis.

[0038] The suggestion unit can list 3 to 5 optimal restaurants based on the user's criteria and display the rating and characteristics of each restaurant. For example, the suggestion unit can list 3 to 5 optimal restaurants based on the user's criteria and display the rating and characteristics of each restaurant. The suggestion unit can list restaurants based on criteria such as rating score, review content, and distance. The suggestion unit can use a combination of multiple criteria to suggest the best restaurant for the user. This allows the suggestion unit to recommend the most suitable restaurant for the user. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's criteria into a generating AI, which can then list the best restaurants.

[0039] The feedback unit can make more accurate suggestions in the future by reflecting user evaluations. For example, the feedback unit allows users to input star ratings and comments on suggested restaurants. The feedback unit collects user evaluations and reflects them in future suggestions. This improves the accuracy of suggestions by reflecting user evaluations. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user evaluations into a generating AI, which can then analyze the evaluations and reflect them in future suggestions.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest conditions to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI, which can then suggest the optimal input method.

[0041] The reception desk can automatically suggest input fields based on the user's current situation. For example, if the user opens the app at lunchtime, the reception desk can suggest restaurants suitable for lunch. If the user is in a specific location, the reception desk can prioritize displaying restaurants close to that location. If the user uses the app at night, the reception desk can suggest restaurants suitable for dinner. This allows the reception desk to suggest input fields that are appropriate to the user's current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into a generating AI, which can then suggest the most suitable input fields.

[0042] The reception desk can prioritize displaying input fields that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk can prioritize displaying restaurants related to that location. If the user is traveling, the reception desk can suggest restaurants near tourist attractions. If the user is at home, the reception desk can prioritize displaying restaurants near their home. This allows the reception desk to display input fields that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into a generating AI, which can then prioritize displaying input fields that are highly relevant.

[0043] The reception desk can analyze a user's social media activity and suggest relevant input fields. For example, if a user mentions a particular dish on social media, the reception desk can suggest restaurants related to that dish. If a user mentions a particular place on social media, the reception desk can suggest restaurants related to that place. If a user mentions a particular event on social media, the reception desk can suggest restaurants related to that event. This allows the system to suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then suggest relevant input fields.

[0044] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. The data collection unit can collect data at the optimal timing based on the user's past data collection history. The data collection unit can analyze the user's past data collection history and select the most efficient collection method. This allows the optimal collection method to be selected based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0045] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can collect data based on the type of cuisine the user is currently interested in. The data collection unit can collect data based on the area the user is currently interested in. The data collection unit can collect data based on the budget the user is currently interested in. This allows the data to be filtered based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's current areas of interest data into a generating AI, which can then filter the data.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. If the user is traveling, the data collection unit can collect data on restaurants near tourist attractions. If the user is at home, the data collection unit can prioritize the collection of data on restaurants near their home. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0047] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if a user mentions a particular dish on social media, the data collection unit can collect data related to that dish. If a user mentions a particular place on social media, the data collection unit can collect data related to that place. If a user mentions a particular event on social media, the data collection unit can collect data related to that event. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0048] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit performs the current analysis based on past analysis data. The analysis unit can improve the accuracy of its analysis by referring to past analysis data. The analysis unit can select the optimal analysis method using past analysis data. This improves the accuracy of the analysis based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI, which can then select the optimal method for improving the accuracy of the analysis.

[0049] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can perform analysis based on the user's age and gender. The analysis unit can perform analysis based on the user's hobbies and interests. The analysis unit can perform analysis based on the user's past behavioral history. This allows the analysis to be performed based on the user's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI, and the generating AI can perform analysis based on the attribute information.

[0050] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform the optimal analysis based on the geographical distribution of the data. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution. The analysis unit can select the optimal analysis method using the geographical distribution. This improves the accuracy of the analysis based on the geographical distribution of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the data into a generating AI, and the generating AI can select the optimal analysis method based on the geographical distribution.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs the current analysis based on relevant literature. The analysis unit can improve the accuracy of its analysis by referring to relevant literature. The analysis unit can select the optimal analysis method using relevant literature. This improves the accuracy of the analysis based on relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into a generating AI, which can then select the optimal method for improving the accuracy of the analysis based on the literature.

[0052] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the restaurants. For example, it can provide detailed suggestions to highly-rated restaurants and simplified suggestions to less-rated restaurants. The suggestion unit can also prioritize suggesting highly important restaurants based on user preferences. This allows for a level of detail in suggestions that matches the importance of the restaurants. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input restaurant rating data into a generating AI, which can then adjust the level of detail in its suggestions based on importance.

[0053] The suggestion unit can apply different suggestion algorithms depending on the restaurant category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm specialized for Japanese cuisine to a Japanese restaurant, an Italian restaurant to an Italian restaurant, and a cafe to a cafe. This allows for the application of the most suitable suggestion algorithm for each restaurant category. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input restaurant category data into a generating AI, which can then apply a suggestion algorithm appropriate to the category.

[0054] The suggestion unit can determine the priority of suggestions based on restaurant ratings when making suggestions. For example, the suggestion unit can prioritize suggesting restaurants with high ratings. The suggestion unit can postpone suggesting restaurants with low ratings. The suggestion unit can also prioritize suggesting restaurants with high ratings based on user preferences. This allows for the provision of suggestion priorities based on restaurant ratings. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input restaurant rating data into a generating AI, which can then determine the priority of suggestions based on the ratings.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the restaurants when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant restaurants based on the user's preferences. The suggestion unit can prioritize suggesting highly relevant restaurants based on the user's past ratings. The suggestion unit can prioritize suggesting highly relevant restaurants based on the user's current situation. This allows for the provision of a suggestion order based on the relevance of the restaurants. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input restaurant relevance data into a generating AI, which can then adjust the order of suggestions based on relevance.

[0056] The feedback unit can improve the accuracy of feedback collection by referring to past feedback data during the feedback collection process. For example, the feedback unit collects current feedback based on past feedback data. The feedback unit can improve the accuracy of collection by referring to past feedback data. The feedback unit can select the optimal collection method using past feedback data. This improves the accuracy of collection based on past feedback data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback data into a generating AI, which can then select the optimal method for improving the accuracy of collection.

[0057] The feedback unit can select the optimal feedback method when collecting feedback, taking into account the user's geographical location information. For example, if the user is in a specific location, the feedback unit can prioritize collecting feedback related to that location. If the user is traveling, the feedback unit can collect feedback on restaurants near tourist attractions. If the user is at home, the feedback unit can prioritize collecting feedback on restaurants near their home. This allows the feedback unit to provide the optimal feedback method based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, which can then select the optimal feedback method.

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

[0059] The reception desk can analyze a user's past search history and automatically suggest search criteria based on those criteria. For example, if a user has previously searched for Japanese food, Japanese food will be prioritized in their next search. Furthermore, if a user frequently sets a specific budget range, that budget range can be set as the default. Additionally, if a user frequently searches a particular area, that area can be prioritized in search results. This enables faster and more efficient searches based on the user's past search history.

[0060] The data collection unit can acquire the user's current location information in real time and collect optimal restaurant data based on that location. For example, if the user is in Shinjuku, it will prioritize collecting restaurant data around Shinjuku. Furthermore, if the user is on the move, it can collect data based on the area they are moving to. Additionally, if the user is attending a specific event, it can collect restaurant data around the event venue. This allows for the provision of more appropriate restaurant data based on the user's current location.

[0061] The analytics department can refer to users' past rating data and prioritize suggesting restaurants with high ratings. For example, it can analyze the characteristics of restaurants that users have previously given high ratings to and suggest restaurants with similar characteristics. Furthermore, if a user has given high ratings to a specific cuisine genre, it can prioritize suggesting restaurants in that genre. Additionally, if a user has given high ratings to a specific area, it can prioritize suggesting restaurants in that area. This allows for more satisfying suggestions based on users' past rating data.

[0062] The reception desk can analyze users' social media activity and suggest relevant input fields. For example, if a user mentions a specific dish on social media, it can suggest restaurants related to that dish. Similarly, if a user mentions a specific location on social media, it can suggest restaurants related to that location. Furthermore, if a user mentions a specific event on social media, it can suggest restaurants related to that event. This allows the system to suggest relevant input fields based on the user's social media activity.

[0063] The analysis department can perform analyses while considering user attribute information. For example, it can perform analyses based on the user's age and gender. It can also perform analyses based on the user's hobbies and interests. Furthermore, it can perform analyses based on the user's past behavioral history. This allows for more accurate analyses based on user attribute information.

[0064] The feedback system can prioritize collecting highly relevant feedback by considering the user's geographical location. For example, if the user is in a specific location, it will prioritize collecting feedback related to that location. If the user is traveling, it can collect feedback on restaurants near tourist attractions. Furthermore, if the user is at home, it can prioritize collecting feedback on restaurants near their home. This allows for the collection of more relevant feedback based on the user's geographical location.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters the user's criteria. These criteria include the type of food the user wants to eat, budget, number of people, and location. For example, the user can specify Japanese, Italian, or Chinese food, and a budget ranging from 1,000 to 5,000 yen. They can specify the number of people (1 person, 2 people, group, etc.) and the location (station name, address, area, etc.). Step 2: The collection unit collects data based on the conditions entered by the reception unit. The collection unit collects data from gourmet information data sources. Multiple data sources can be used to collect reliable data. Step 3: The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. Multiple machine learning algorithms can be used in combination to improve the accuracy of data analysis. Step 4: The suggestion department proposes the most suitable restaurants based on the analysis results obtained by the analysis department. The suggestion department lists 3 to 5 of the most suitable restaurants based on the user's criteria and displays the ratings and characteristics of each restaurant. Restaurants can be listed based on criteria such as rating score, review content, and distance. Step 5: The Feedback Department incorporates user ratings for the restaurants suggested by the Suggestion Department. Users can provide star ratings and comments for the suggested restaurants. The Feedback Department collects user ratings and incorporates them into future suggestions.

[0067] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that quickly suggests restaurants that meet the user's preferences and budget, saving time and improving satisfaction. This AI agent system suggests the most suitable restaurants when the user inputs their desired conditions. For example, the user inputs information such as what they want to eat (Japanese food, Italian food, etc.), budget (under 3,000 yen, etc.), number of people (2-4 people, etc.), and location (Shinjuku, Shibuya, etc.). This information is input into the AI ​​agent. Next, the AI ​​agent collects data from gourmet websites and analyzes it using a machine learning algorithm. For example, if the user inputs "I want to eat Japanese food in Shinjuku for under 3,000 yen," the AI ​​agent collects data on Japanese restaurants in the Shinjuku area and selects restaurants that fit the budget. After that, the AI ​​agent lists 3 to 5 of the most suitable restaurants based on the user's conditions and displays the rating and characteristics of each restaurant. For example, it suggests restaurants with high ratings or restaurants that specialize in a particular dish. Furthermore, by allowing the user to rate the suggested restaurants, the AI ​​agent can make more accurate suggestions in the future. For example, if the user rates a restaurant as "good," this will be reflected in future suggestions. This system enables quick and accurate restaurant recommendations to a variety of users, including busy business people, tourists, and food lovers. For example, it can quickly suggest nearby restaurants to business people who want to eat a quick lunch, and suggest restaurants near tourist attractions where tourists can enjoy local cuisine to those unfamiliar with the area. It can also offer food lovers opportunities to enjoy culinary adventures by suggesting restaurants that incorporate new dishes and trends. This AI-powered restaurant recommendation system is expected to meet user needs and bring innovation to the restaurant industry. Specifically, it is expected to improve user satisfaction, reduce search time, and boost restaurant customer acquisition. For example, by suggesting restaurants that perfectly match the user's criteria, user satisfaction will increase, and manual search time will be significantly reduced, enabling more efficient selection. In addition, the recommendation system is expected to boost customer acquisition for restaurants, leading to increased sales.This allows the AI ​​agent system to quickly suggest restaurants that match the user's preferences and budget, saving time and improving satisfaction.

[0068] The AI ​​agent system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a suggestion unit, and a feedback unit. The reception unit takes user conditions as input. User conditions include, but are not limited to, examples of food desired, budget, number of people, and location. For example, the reception unit can input Japanese food, Italian food, Chinese food, etc., as food desired by the user. The reception unit can also input a budget ranging from 1,000 yen to 5,000 yen. Furthermore, the reception unit can input the number of people as 1 person, 2 people, group, etc. For location, it can input station name, address, area, etc. The data collection unit collects data based on the conditions input by the reception unit. The data collection unit collects data from, for example, gourmet websites. The data collection unit can also collect data from gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. The analysis unit analyzes the data collected by the data collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. The analysis unit can use a combination of multiple machine learning algorithms to improve the accuracy of data analysis. The suggestion unit proposes the most suitable restaurants based on the analysis results obtained by the analysis unit. For example, the suggestion unit lists 3 to 5 suitable restaurants based on the user's conditions and displays the ratings and characteristics of each restaurant. The suggestion unit can list restaurants based on criteria such as rating scores, review content, and distance. The suggestion unit can use a combination of multiple criteria to propose the most suitable restaurants for the user. The feedback unit reflects the user's evaluation of the restaurants proposed by the suggestion unit. For example, the feedback unit allows the user to input star ratings and comments on the proposed restaurants. The feedback unit collects user evaluations and reflects them in future suggestions. As a result, the AI ​​agent system according to this embodiment can improve user satisfaction by proposing the most suitable restaurants based on the user's conditions and reflecting evaluations.

[0069] The reception desk takes user input. User input criteria include, but are not limited to, the type of food the user wants to eat, budget, number of people, and location. For example, the reception desk can accept input such as Japanese, Italian, or Chinese food as the type of food the user wants to eat. The reception desk can accept input such as a budget range of 1,000 to 5,000 yen. Furthermore, the reception desk can accept input such as 1 person, 2 people, or group as the number of people. For location, the reception desk can accept input such as station name, address, or area. The reception desk provides a user-friendly interface to accurately receive the user's input criteria. For example, dropdown menus and checkboxes are used to allow users to easily select criteria. It also incorporates a voice input function, allowing users to input criteria by voice. This allows users to operate intuitively and reduces input errors. Furthermore, the reception desk can remember the user's past input history and present it as a reference the next time they input. This saves users the trouble of entering the same criteria every time. For example, a user who previously selected Japanese food will see Japanese food as a suggestion the next time they input. Furthermore, the reception desk checks user input in real time and displays appropriate alerts if there is missing or inconsistent information. This allows users to enter accurate conditions, improving the system's accuracy.

[0070] The data collection unit collects data based on the conditions entered by the reception unit. The data collection unit collects data from sources such as gourmet websites. It can also collect data from gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. Specifically, the data collection unit uses web scraping technology to automatically extract necessary information from each data source. For example, from gourmet websites, it collects information such as store name, address, phone number, business hours, menu, price range, rating, and review content. Similar information is collected from gourmet information data sources and integrated into the database. The data collection unit performs data cleansing to prevent data duplication and inconsistencies. For example, if the same store appears in multiple data sources, duplication is eliminated to maintain the most up-to-date and accurate information. Furthermore, the data collection unit calculates the reliability of each data source to evaluate data reliability and prioritizes the use of highly reliable information. This allows the data collection unit to provide users with highly reliable information. In addition, the data collection unit updates data in real time, always maintaining the latest information. For example, if a new store opens or information about an existing store changes, the database is updated immediately. This allows the data collection unit to always provide the latest information and respond quickly to user needs.

[0071] The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. To improve the accuracy of data analysis, the analysis unit can combine multiple machine learning algorithms. Specifically, the analysis unit first preprocesses the collected data, including imputing missing values ​​and removing outliers. Next, it applies machine learning algorithms to the preprocessed data to identify the restaurant best suited to the user's criteria. For example, it might use a decision tree algorithm to classify restaurants based on user criteria, and a random forest algorithm to integrate the results of multiple decision trees to obtain the most reliable result. It can also use neural networks to learn complex patterns and relationships, enabling more accurate predictions. Furthermore, the analysis unit incorporates past user evaluations and feedback as training data to continuously improve the model's accuracy. This allows the analysis unit to identify the restaurant best suited to the user's criteria with high accuracy.

[0072] The suggestion department proposes the most suitable restaurants based on the analysis results obtained by the analysis department. For example, the suggestion department will list 3 to 5 optimal restaurants based on the user's criteria and display the ratings and characteristics of each restaurant. The suggestion department can list restaurants based on criteria such as rating scores, review content, and distance. The suggestion department can use multiple criteria in combination to propose the most suitable restaurant for the user. Specifically, the suggestion department selects the restaurant that best suits the user's conditions based on the list of restaurants provided by the analysis department. For example, it will prioritize listing restaurants with high rating scores and good reviews. It will also select easily accessible restaurants considering the distance from the user's current location. Furthermore, the suggestion department will display detailed information such as the characteristics, menu, and price range of each restaurant to make it easier for the user to make a selection. The suggestion department can also provide personalized suggestions by considering the user's preferences and past selection history. This allows the suggestion department to quickly and accurately propose the most suitable restaurant for the user.

[0073] The Feedback Department reflects user evaluations of restaurants suggested by the Suggestion Department. For example, users can enter star ratings and comments for suggested restaurants. The Feedback Department collects user evaluations and incorporates them into future suggestions. Specifically, after a user visits a suggested restaurant, the Feedback Department provides a function to enter star ratings and comments through an evaluation form. User evaluations are reflected in the restaurant's rating and review content, and are used as reference for future suggestions. For example, restaurants highly rated by users will be prioritized for other users. Furthermore, user comments provide important information for evaluating the restaurant's characteristics and service quality. The Feedback Department not only collects user evaluations but also analyzes them and provides feedback to the Suggestion and Analysis Departments. This improves the overall accuracy and reliability of the system. Additionally, the Feedback Department records user evaluation history and learns user preferences and tendencies, allowing for more personalized suggestions in the future. This allows the Feedback Department to improve user satisfaction and enhance the system's value.

[0074] The reception desk allows users to input information such as what they want to eat, their budget, the number of people, and the location. For example, the reception desk can allow users to input Japanese food, Italian food, Chinese food, etc., as the type of food they want. The reception desk can allow users to input a budget ranging from 1,000 yen to 5,000 yen. The reception desk can allow users to input the number of people, such as 1 person, 2 people, or a group. The reception desk can allow users to input the location, such as the station name, address, or area. This allows users to input their desired conditions in detail. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the conditions entered by the user into a generating AI, which can then suggest the optimal input method based on those conditions.

[0075] The data collection unit can collect data from sources such as gourmet websites. For example, the data collection unit can collect restaurant data from gourmet websites. The data collection unit can also collect data from other gourmet information data sources. The data collection unit can utilize multiple data sources to collect highly reliable data. This allows for the collection of highly reliable data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from gourmet websites into a generating AI, which can then evaluate the reliability of the data.

[0076] The analysis unit can analyze the collected data using machine learning algorithms. For example, the analysis unit can analyze the collected data using machine learning algorithms such as decision trees, random forests, and neural networks. The analysis unit can use multiple machine learning algorithms in combination to improve the accuracy of the data analysis. This improves the accuracy of the data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then perform the data analysis.

[0077] The suggestion unit can list 3 to 5 optimal restaurants based on the user's criteria and display the rating and characteristics of each restaurant. For example, the suggestion unit can list 3 to 5 optimal restaurants based on the user's criteria and display the rating and characteristics of each restaurant. The suggestion unit can list restaurants based on criteria such as rating score, review content, and distance. The suggestion unit can use a combination of multiple criteria to suggest the best restaurant for the user. This allows the suggestion unit to recommend the most suitable restaurant for the user. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's criteria into a generating AI, which can then list the best restaurants.

[0078] The feedback unit can make more accurate suggestions in the future by reflecting user evaluations. For example, the feedback unit allows users to input star ratings and comments on suggested restaurants. The feedback unit collects user evaluations and reflects them in future suggestions. This improves the accuracy of suggestions by reflecting user evaluations. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user evaluations into a generating AI, which can then analyze the evaluations and reflect them in future suggestions.

[0079] The reception unit can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input to allow for quick information entry. This allows for an interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI, which can then adjust the display of the interface based on the emotions.

[0080] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest conditions to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history into a generating AI, which can then suggest the optimal input method.

[0081] The reception desk can automatically suggest input fields based on the user's current situation. For example, if the user opens the app at lunchtime, the reception desk can suggest restaurants suitable for lunch. If the user is in a specific location, the reception desk can prioritize displaying restaurants close to that location. If the user uses the app at night, the reception desk can suggest restaurants suitable for dinner. This allows the reception desk to suggest input fields that are appropriate to the user's current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation data into a generating AI, which can then suggest the most suitable input fields.

[0082] The reception desk can estimate the user's emotions and prioritize input fields based on those emotions. For example, if the user is stressed, the reception desk may prioritize important input fields and simplify the input process. If the user is relaxed, the reception desk may provide detailed input fields and suggest customizable input methods. If the user is in a hurry, the reception desk may prioritize voice input to allow for quick information entry. This allows for prioritizing input fields according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI, which can then prioritize input fields based on the emotion.

[0083] The reception desk can prioritize displaying input fields that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk can prioritize displaying restaurants related to that location. If the user is traveling, the reception desk can suggest restaurants near tourist attractions. If the user is at home, the reception desk can prioritize displaying restaurants near their home. This allows the reception desk to display input fields that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into a generating AI, which can then prioritize displaying input fields that are highly relevant.

[0084] The reception desk can analyze a user's social media activity and suggest relevant input fields. For example, if a user mentions a particular dish on social media, the reception desk can suggest restaurants related to that dish. If a user mentions a particular place on social media, the reception desk can suggest restaurants related to that place. If a user mentions a particular event on social media, the reception desk can suggest restaurants related to that event. This allows the system to suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then suggest relevant input fields.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect data quickly. If the user is stressed, the data collection unit can delay data collection to reduce the user's burden. If the user is in a hurry, the data collection unit can collect data quickly and provide suggestions earlier. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of data collection based on the emotions.

[0086] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. The data collection unit can collect data at the optimal timing based on the user's past data collection history. The data collection unit can analyze the user's past data collection history and select the most efficient collection method. This allows the optimal collection method to be selected based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0087] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can collect data based on the type of cuisine the user is currently interested in. The data collection unit can collect data based on the area the user is currently interested in. The data collection unit can collect data based on the budget the user is currently interested in. This allows the data to be filtered based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's current areas of interest data into a generating AI, which can then filter the data.

[0088] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can collect detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the prioritization of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of data to collect based on the emotions.

[0089] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. If the user is traveling, the data collection unit can collect data on restaurants near tourist attractions. If the user is at home, the data collection unit can prioritize the collection of data on restaurants near their home. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0090] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if a user mentions a particular dish on social media, the data collection unit can collect data related to that dish. If a user mentions a particular place on social media, the data collection unit can collect data related to that place. If a user mentions a particular event on social media, the data collection unit can collect data related to that event. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can perform a simplified analysis. If the user is in a hurry, the analysis unit can perform a rapid analysis. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis algorithm based on the emotions.

[0092] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit performs the current analysis based on past analysis data. The analysis unit can improve the accuracy of its analysis by referring to past analysis data. The analysis unit can select the optimal analysis method using past analysis data. This improves the accuracy of the analysis based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI, which can then select the optimal method for improving the accuracy of the analysis.

[0093] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can perform analysis based on the user's age and gender. The analysis unit can perform analysis based on the user's hobbies and interests. The analysis unit can perform analysis based on the user's past behavioral history. This allows the analysis to be performed based on the user's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI, and the generating AI can perform analysis based on the attribute information.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for a display method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the analysis results based on the emotions.

[0095] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform the optimal analysis based on the geographical distribution of the data. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution. The analysis unit can select the optimal analysis method using the geographical distribution. This improves the accuracy of the analysis based on the geographical distribution of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the data into a generating AI, and the generating AI can select the optimal analysis method based on the geographical distribution.

[0096] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs the current analysis based on relevant literature. The analysis unit can improve the accuracy of its analysis by referring to relevant literature. The analysis unit can select the optimal analysis method using relevant literature. This improves the accuracy of the analysis based on relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into a generating AI, which can then select the optimal method for improving the accuracy of the analysis based on the literature.

[0097] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide simplified suggestions. If the user is in a hurry, the suggestion unit can provide suggestions quickly. This allows for the presentation of suggestions to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents suggestions based on the emotion.

[0098] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the restaurants. For example, it can provide detailed suggestions to highly-rated restaurants and simplified suggestions to less-rated restaurants. The suggestion unit can also prioritize suggesting highly important restaurants based on user preferences. This allows for a level of detail in suggestions that matches the importance of the restaurants. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input restaurant rating data into a generating AI, which can then adjust the level of detail in its suggestions based on importance.

[0099] The suggestion unit can apply different suggestion algorithms depending on the restaurant category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm specialized for Japanese cuisine to a Japanese restaurant, an Italian restaurant to an Italian restaurant, and a cafe to a cafe. This allows for the application of the most suitable suggestion algorithm for each restaurant category. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input restaurant category data into a generating AI, which can then apply a suggestion algorithm appropriate to the category.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide simplified suggestions. If the user is in a hurry, the suggestion unit can provide quick suggestions. This allows for suggestion lengths that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions based on the emotions.

[0101] The suggestion unit can determine the priority of suggestions based on restaurant ratings when making suggestions. For example, the suggestion unit can prioritize suggesting restaurants with high ratings. The suggestion unit can postpone suggesting restaurants with low ratings. The suggestion unit can also prioritize suggesting restaurants with high ratings based on user preferences. This allows for the provision of suggestion priorities based on restaurant ratings. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input restaurant rating data into a generating AI, which can then determine the priority of suggestions based on the ratings.

[0102] The suggestion unit can adjust the order of suggestions based on the relevance of the restaurants when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant restaurants based on the user's preferences. The suggestion unit can prioritize suggesting highly relevant restaurants based on the user's past ratings. The suggestion unit can prioritize suggesting highly relevant restaurants based on the user's current situation. This allows for the provision of a suggestion order based on the relevance of the restaurants. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input restaurant relevance data into a generating AI, which can then adjust the order of suggestions based on relevance.

[0103] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback unit may request detailed feedback. If the user is stressed, the feedback unit may request simplified feedback. If the user is in a hurry, the feedback unit can collect feedback quickly. This provides a feedback collection method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can then adjust the feedback collection method based on the emotions.

[0104] The feedback unit can improve the accuracy of feedback collection by referring to past feedback data during the feedback collection process. For example, the feedback unit collects current feedback based on past feedback data. The feedback unit can improve the accuracy of collection by referring to past feedback data. The feedback unit can select the optimal collection method using past feedback data. This improves the accuracy of collection based on past feedback data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past feedback data into a generating AI, which can then select the optimal method for improving the accuracy of collection.

[0105] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit can prioritize collecting important feedback. If the user is relaxed, the feedback unit can collect detailed feedback. If the user is in a hurry, the feedback unit can prioritize collecting feedback that can be collected quickly. This allows for prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can then prioritize feedback based on the emotions.

[0106] The feedback unit can select the optimal feedback method when collecting feedback, taking into account the user's geographical location information. For example, if the user is in a specific location, the feedback unit can prioritize collecting feedback related to that location. If the user is traveling, the feedback unit can collect feedback on restaurants near tourist attractions. If the user is at home, the feedback unit can prioritize collecting feedback on restaurants near their home. This allows the feedback unit to provide the optimal feedback method based on the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, which can then select the optimal feedback method.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception desk can analyze a user's past search history and automatically suggest search criteria based on those criteria. For example, if a user has previously searched for Japanese food, Japanese food will be prioritized in their next search. Furthermore, if a user frequently sets a specific budget range, that budget range can be set as the default. Additionally, if a user frequently searches a particular area, that area can be prioritized in search results. This enables faster and more efficient searches based on the user's past search history.

[0109] The data collection unit can acquire the user's current location information in real time and collect optimal restaurant data based on that location. For example, if the user is in Shinjuku, it will prioritize collecting restaurant data around Shinjuku. Furthermore, if the user is on the move, it can collect data based on the area they are moving to. Additionally, if the user is attending a specific event, it can collect restaurant data around the event venue. This allows for the provision of more appropriate restaurant data based on the user's current location.

[0110] The analytics department can refer to users' past rating data and prioritize suggesting restaurants with high ratings. For example, it can analyze the characteristics of restaurants that users have previously given high ratings to and suggest restaurants with similar characteristics. Furthermore, if a user has given high ratings to a specific cuisine genre, it can prioritize suggesting restaurants in that genre. Additionally, if a user has given high ratings to a specific area, it can prioritize suggesting restaurants in that area. This allows for more satisfying suggestions based on users' past rating data.

[0111] The recommendation system can adjust its suggestions based on the user's current mood. For example, if the user is relaxed, it can suggest restaurants featuring new dishes or current trends. If the user is stressed, it can suggest restaurants with a calm atmosphere. Furthermore, if the user is in a hurry, it can suggest restaurants that can serve meals quickly. This allows the system to suggest the most suitable restaurant for the user's current mood.

[0112] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, it can request detailed feedback. If the user is stressed, it can request simplified feedback. Furthermore, if the user is in a hurry, it can collect feedback quickly. This allows for a feedback collection method that is tailored to the user's emotions.

[0113] The reception desk can analyze users' social media activity and suggest relevant input fields. For example, if a user mentions a specific dish on social media, it can suggest restaurants related to that dish. Similarly, if a user mentions a specific location on social media, it can suggest restaurants related to that location. Furthermore, if a user mentions a specific event on social media, it can suggest restaurants related to that event. This allows the system to suggest relevant input fields based on the user's social media activity.

[0114] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those estimates. For example, if the user is relaxed, data collection can be performed quickly. Conversely, if the user is stressed, data collection can be delayed to reduce the user's burden. Furthermore, if the user is in a hurry, data collection can be performed quickly to provide suggestions earlier. This allows the timing of data collection to be adjusted according to the user's emotions.

[0115] The analysis department can perform analyses while considering user attribute information. For example, it can perform analyses based on the user's age and gender. It can also perform analyses based on the user's hobbies and interests. Furthermore, it can perform analyses based on the user's past behavioral history. This allows for more accurate analyses based on user attribute information.

[0116] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those estimates. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide simplified suggestions. Furthermore, if the user is in a hurry, it can provide suggestions quickly. This allows the system to provide suggestions in a way that is appropriate to the user's emotions.

[0117] The feedback system can prioritize collecting highly relevant feedback by considering the user's geographical location. For example, if the user is in a specific location, it will prioritize collecting feedback related to that location. If the user is traveling, it can collect feedback on restaurants near tourist attractions. Furthermore, if the user is at home, it can prioritize collecting feedback on restaurants near their home. This allows for the collection of more relevant feedback based on the user's geographical location.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk enters the user's criteria. These criteria include the type of food the user wants to eat, budget, number of people, and location. For example, the user can specify Japanese, Italian, or Chinese food, and a budget ranging from 1,000 to 5,000 yen. They can specify the number of people (1 person, 2 people, group, etc.) and the location (station name, address, area, etc.). Step 2: The collection unit collects data based on the conditions entered by the reception unit. The collection unit collects data from gourmet information data sources. Multiple data sources can be used to collect reliable data. Step 3: The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. The analysis unit can use machine learning algorithms such as decision trees, random forests, and neural networks. Multiple machine learning algorithms can be used in combination to improve the accuracy of data analysis. Step 4: The suggestion department proposes the most suitable restaurants based on the analysis results obtained by the analysis department. The suggestion department lists 3 to 5 of the most suitable restaurants based on the user's criteria and displays the ratings and characteristics of each restaurant. Restaurants can be listed based on criteria such as rating score, review content, and distance. Step 5: The Feedback Department incorporates user ratings for the restaurants suggested by the Suggestion Department. Users can provide star ratings and comments for the suggested restaurants. The Feedback Department collects user ratings and incorporates them into future suggestions.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and feedback unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and takes user conditions as input. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects data from gourmet websites, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable restaurant. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and collects user evaluations and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which inputs user conditions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects data from gourmet websites, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the data using a machine learning algorithm. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable restaurant. The feedback unit is implemented by the control unit 46A of the smart glasses 214, which collects user evaluations and reflects them in future proposals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which inputs user conditions. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects data from gourmet websites, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the data using a machine learning algorithm. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable restaurant. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314, which collects user evaluations and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the 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.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes user conditions as input. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects data from gourmet websites, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the data using a machine learning algorithm. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable restaurant. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and collects user evaluations and reflects them in the next proposal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) A reception area where users enter their conditions, A collection unit that collects data based on the conditions entered by the reception unit, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable restaurant, The system includes a feedback unit that reflects user evaluations of restaurants proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The user enters information such as what they want to eat, their budget, the number of people, and the location. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect data from gourmet websites and other sources. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected data is analyzed using machine learning algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the user's criteria, the system lists 3-5 of the most suitable restaurants and displays the ratings and features of each establishment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is We will incorporate user feedback to make future suggestions more accurate. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system automatically suggests input fields based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system prioritizes displaying relevant input fields based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, we refer to past analysis data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When performing analysis, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When performing analysis, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the restaurant category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, we prioritize them based on our evaluation of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When collecting feedback, we improve the accuracy of the collection by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When collecting feedback, the optimal feedback method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their conditions, A collection unit that collects data based on the conditions entered by the reception unit, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable restaurant, The system includes a feedback unit that reflects user evaluations of restaurants proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is The user enters information such as what they want to eat, their budget, the number of people, and the location. The system according to feature 1.

3. The aforementioned collection unit is We collect data from sites like Gurunavi. The system according to feature 1.

4. The aforementioned analysis unit is The collected data is analyzed using machine learning algorithms. The system according to feature 1.

5. The aforementioned proposal section is, Based on the user's criteria, the system lists 3 to 5 of the most suitable restaurants and displays the ratings and characteristics of each establishment. The system according to feature 1.

6. The aforementioned feedback unit is We will incorporate user feedback to make future suggestions more accurate. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is The system automatically suggests input fields based on the user's current status. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

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