System

Abstract

To enable a user to easily acquire the most inexpensive meal information of the day.SOLUTION: A system according to an embodiment includes a reception unit, a collection unit, and a suggestion unit. The reception unit receives an input of a current location of the user. The collection unit collects information on the Internet based on the current location input by the reception unit. The proposal unit analyzes the information collected by the collection unit and proposes the most economical meal of the day.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to efficiently obtain information on the most affordable meals of the day.

[0005] The system according to the embodiment aims to enable a user to easily obtain information on the most affordable meals of the day. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, and a suggestion unit. The reception unit inputs the user's current location. The collection unit collects information on the Internet based on the current location input by the reception unit. The suggestion unit analyzes the information collected by the collection unit and suggests the best value meal of the day. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to easily obtain information on the most affordable meals of the day. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A proposal system according to an embodiment of the present invention allows a user to input their current location, collects information on the Internet, and suggests the best value meal of the day. The proposal system inputs the user's current location, and a generation AI collects the information and suggests the best value meal of the day based on the analysis results. For example, the proposal system allows a user to input their current location. For example, the user simply inputs location information such as their home or workplace. This information is then input into the generation AI. The proposal system then uses the generation AI to collect information on the Internet. The generation AI then collects and analyzes information such as menus, prices, special offers, and user reviews and ratings of nearby restaurants. For example, the information is collected from each restaurant's website, review site, coupon site, etc. The proposal system then uses the generation AI to suggest the best value meal of the day based on the analysis results. For example, if a particular restaurant is offering a discount on a lunch set, the system provides this information to the user. The proposal system also takes into account the user's preferences and allergy information. For example, if the user is vegetarian, the system prioritizes vegetarian menus. This allows the proposal system to easily find great value meals that suit their preferences and requirements. For example, if a user requests "Find a great lunch deal near my home," the AI ​​will analyze information about nearby restaurants and suggest the best lunch deal of the day, allowing users to enjoy a satisfying meal while saving time and money.

[0029] A proposal system according to an embodiment includes a reception unit, a collection unit, and a proposal unit. The reception unit inputs a user's current location. The user's current location may include, but is not limited to, home, work, and current location information. For example, the reception unit may input a request from the user to find a good lunch deal near their home. The collection unit uses a generation AI to collect information from the Internet. The collection unit collects, for example, menus, prices, special offers, user reviews, and ratings of nearby restaurants. For example, the collection unit collects information from websites of each restaurant, review sites, coupon sites, and the like. The collection unit may also analyze the collected information using the generation AI. The proposal unit analyzes the information collected by the collection unit and proposes the best value meal of the day. For example, if a specific restaurant offers a discount on a lunch set, the proposal unit provides the user with information about that discount. The proposal unit also takes into account the user's preferences and allergy information. For example, if the user is vegetarian, the proposal unit prioritizes vegetarian menus. This allows the proposal system according to an embodiment to easily find a good value meal that matches their preferences and requirements. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input information collected by the collection unit into the generation AI and generate a suggestion based on the analysis results.

[0030] The collection unit can collect menus, prices, special offer information, and user reviews or ratings of nearby restaurants. The collection unit collects, for example, menus, prices, special offer information, and user reviews or ratings of nearby restaurants. For example, the collection unit collects information from websites of each restaurant, review sites, coupon sites, etc. The collection unit can also analyze the collected information using a generation AI. This allows for more accurate recommendations by collecting detailed information about nearby restaurants. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input information collected from websites of each restaurant into a generation AI and organize the information based on the analysis results.

[0031] The suggestion unit can make suggestions based on the user's preferences and allergy information based on the collected information. The suggestion unit can make suggestions based on the user's preferences and allergy information based on the collected information, for example. For example, if the user is vegetarian, the suggestion unit can preferentially suggest vegetarian menus. The suggestion unit can also suggest safe menus taking into account the user's allergy information. This makes it possible to make suggestions that take into account the user's individual preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the information collected by the collection unit into a generation AI and generate suggestions that take into account the user's preferences and allergy information.

[0032] The suggestion unit can prioritize suggesting discount information at a specific restaurant. For example, the suggestion unit prioritizes suggesting discount information at a specific restaurant. For example, if a lunch set at a specific restaurant is discounted, the suggestion unit provides that information to the user. Furthermore, by prioritizing suggesting discount information, the suggestion unit can provide the user with the most advantageous options. This allows the user to select a good-value meal based on the discount information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the discount information collected by the collection unit into the generation AI and prioritize suggesting the discount information.

[0033] The suggestion unit can provide reliable information based on user reviews and ratings. The suggestion unit provides reliable information based on user reviews and ratings, for example. For example, the suggestion unit analyzes user reviews and ratings and selects reliable information. Furthermore, the suggestion unit can improve user satisfaction by providing reliable information. This allows users to select meals based on reliable information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the reviews and ratings collected by the collection unit into a generation AI and select reliable information.

[0034] The reception unit can analyze the user's past location information input history and select the optimal input method. The reception unit, for example, analyzes the user's past location information input history and selects the optimal input method. For example, the reception unit automatically displays current locations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest the current location to be used during a specific time period based on the user's past input history. This improves the efficiency of the user's input work by providing the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI to select the optimal input method.

[0035] The reception unit can select the optimal input means in consideration of the user's device information when inputting the current location. For example, the reception unit selects the optimal input means in consideration of the user's device information when inputting the current location. For example, if the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, if the user is using a desktop, the reception unit can also prioritize keyboard input. Furthermore, if the user is using a smartwatch, the reception unit can also prioritize voice input. This improves the efficiency of input work by providing the optimal input means according to the user's device. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's device information into the generation AI and select the optimal input means.

[0036] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting the current location. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting the current location. For example, when the user inputs the current location by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the current location by text, the reception unit can also provide an autocomplete function. Furthermore, when the user inputs the current location by image, the reception unit can also support the input using image recognition technology. In this way, the optimal input means depending on the user's input method is provided, thereby making the input work more efficient. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method data into the generation AI and select the optimal input means.

[0037] The reception unit can prioritize inputting highly relevant location information in consideration of the user's geographical location information when inputting the current location. For example, the reception unit prioritizes inputting highly relevant location information in consideration of the user's geographical location information when inputting the current location. For example, if the user is in a specific area, the reception unit can prioritize displaying location information within that area. Furthermore, if the user is in a specific building, the reception unit can prioritize displaying location information within that building. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying location information within that city. This improves the efficiency of input work by providing optimal location information based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data to the generation AI and prioritize inputting highly relevant location information.

[0038] The reception unit can analyze the user's social media activity and input related location information when the user inputs the current location. For example, the reception unit can analyze the user's social media activity and input related location information when the user inputs the current location. For example, the reception unit can suggest a location where the user checked in on social media as the current location. The reception unit can also analyze the content of the user's social media posts and suggest related location information as the current location. The reception unit can also suggest related location information as the current location by referring to the activities of the user's friends on social media. This improves the efficiency of input work by providing optimal location information based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and input related location information.

[0039] The reception unit can adjust the input method by reflecting the user's past feedback when inputting the current location. For example, the reception unit adjusts the input method by reflecting the user's past feedback when inputting the current location. For example, the reception unit preferentially suggests input methods that the user has previously provided feedback on. The reception unit can also customize the optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. This improves the efficiency of input work by providing the optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and adjust the input method.

[0040] The collection unit can analyze the user's past search history and select an appropriate collection method when collecting information. For example, the collection unit can analyze the user's past search history and select an appropriate collection method when collecting information. For example, the collection unit can prioritize collecting related information based on keywords the user has previously searched for. The collection unit can also predict and suggest information to be collected during a specific time period based on the user's past search history. The collection unit can also analyze the user's past search history and select the most efficient collection method. This improves the efficiency of information collection by providing an optimal collection method based on the user's past search history. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past search history data into a generation AI and select an optimal collection method.

[0041] The collection unit can select information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit selects information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting relevant information based on the user's current living situation. The collection unit can also prioritize collecting relevant information based on the user's areas of interest. The collection unit can also analyze the user's current living situation and areas of interest and filter out optimal information. This improves the efficiency of information collection by providing optimal information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's living situation and area of ​​interest data into the generation AI and select information.

[0042] The collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.) when collecting information. For example, the collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.) when collecting information. For example, when the user collects information by voice, the collection unit can support collection using voice recognition technology. Furthermore, when the user collects information by text, the collection unit can also provide an autocomplete function. Furthermore, when the user collects information by image, the collection unit can also support collection using image recognition technology. This improves the efficiency of information collection by providing the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input user's input method data into a generation AI and select the optimal collection means.

[0043] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information within that area. Furthermore, when the user is in a specific building, the collection unit can prioritize collecting information within that building. Furthermore, when the user is in a specific city, the collection unit can prioritize collecting information within that city. This improves the efficiency of information collection by providing optimal information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's geographical location information data into a generation AI and prioritize collecting highly relevant information.

[0044] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information about places the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. This improves the efficiency of information collection by providing optimal information based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI to collect related information.

[0045] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially suggests collection methods that the user has previously provided feedback on. The collection unit can also customize the optimal collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. This improves the efficiency of information collection by providing the optimal collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and adjust the collection method.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the collected information when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the collected information when making a suggestion. For example, the suggestion unit preferentially suggests information with high importance in detail. The suggestion unit can also briefly suggest information with low importance. The suggestion unit can also gradually adjust the level of detail of the proposal according to the importance. This improves user satisfaction by providing an optimal level of detail of the proposal according to the importance of the collected information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input importance data of the collected information into the generation AI and adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of collected information when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of collected information when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm that emphasizes price and special offer information to restaurant information. The suggestion unit can also apply a suggestion algorithm that emphasizes reliability to user reviews and ratings. The suggestion unit can also apply a suggestion algorithm that emphasizes discount rates to special offer information. This improves user satisfaction by providing an optimal suggestion algorithm depending on the category of collected information. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input category data of the collected information into a generation AI and apply different suggestion algorithms.

[0048] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes similar suggestions based on suggestions that the user has previously accepted. The suggestion unit can also avoid similar suggestions based on suggestions that the user has previously rejected. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestion. This improves the accuracy of the suggestion by providing optimal suggestions based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI to improve the accuracy of the suggestion.

[0049] The suggestion unit can determine the priority of the suggestions based on the submission time of the collected information when making a suggestion. The suggestion unit can, for example, determine the priority of the suggestions based on the submission time of the collected information when making a suggestion. For example, the suggestion unit prioritizes the most recent information. The suggestion unit can also briefly propose older information. The suggestion unit can also gradually adjust the priority of the suggestions depending on the submission time. This improves user satisfaction by providing optimal suggestions based on the submission time of the collected information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input submission time data of the collected information into the generation AI and determine the priority of the suggestions.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the collected information when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the collected information when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also gradually adjust the order of suggestions according to the relevance. This improves user satisfaction by providing an optimal suggestion order based on the relevance of the collected information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input relevance data of the collected information into the generation AI and adjust the order of suggestions.

[0051] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can gradually adjust the use of technical terminology in the proposal according to the user's level of expertise. This improves user satisfaction by providing optimal suggestions according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and adjust the use of technical terminology in the proposal.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion system can also make suggestions taking into account the user's health condition. For example, the suggestion unit can collect the user's health data (e.g., blood sugar level, blood pressure, allergy information, etc.) and suggest meals according to the user's health condition. If the user has diabetes, low-carbohydrate menus can be suggested preferentially. Also, if the user has high blood pressure, low-salt menus can be suggested. This makes it possible to suggest optimal meals according to the user's health condition and support health management.

[0054] The suggestion system can also analyze the user's past meal history to improve the accuracy of suggestions. For example, the suggestion unit can suggest meals that suit the user's preferences based on the user's past menu choices and ratings. Menus that the user has given high ratings to in the past are given priority in the suggestions. Menus that the user has avoided in the past can also be excluded from the suggestions. This makes it possible to make optimal suggestions based on the user's past meal history, thereby improving satisfaction.

[0055] The recommendation system can also analyze the user's social media activity to suggest related meals. For example, the suggestion unit can suggest similar menus based on restaurants the user has checked in to on social media or photos of meals posted by the user. It can also suggest the same restaurants and menus based on meal information the user has shared with friends. This allows for optimal suggestions based on the user's social media activity, attracting the user's interest.

[0056] The recommendation system can also make suggestions by taking into account the user's current activity level. For example, if the user has just exercised, the suggestion unit can suggest nutritious meals. If the user is doing desk work, the suggestion unit can suggest light meals or snacks. If the user is traveling, the suggestion unit can suggest local specialties or restaurants in tourist spots. This allows the system to make optimal suggestions based on the user's current activity level and meet the user's needs.

[0057] The suggestion system can also adjust the way suggestions are displayed by taking into account the user's device information. For example, if the user is using a smartphone, the suggestion unit can display a mobile-friendly display. If the user is using a desktop, detailed information can be displayed. Also, if the user is using a smartwatch, concise information can be displayed. This enables the optimal suggestion display method according to the user's device, improving user convenience.

[0058] The suggestion system can also improve the accuracy of suggestions by reflecting the user's past feedback. For example, the suggestion unit can suggest meals that suit the user's preferences based on the suggestions provided in the user's past feedback. Suggestions that the user has previously rated highly can be given priority. Suggestions that the user has previously rated poorly can also be avoided. This allows for optimal suggestions based on the user's past feedback, thereby improving satisfaction.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit inputs the user's current location. The user's current location includes home, work, current location information, etc. For example, the user can input a request to find a good lunch deal near their home. Step 2: The collection unit uses the generation AI to collect information from the Internet. For example, it collects information such as the menus and prices of nearby restaurants, special offers, and user reviews and ratings. The collection unit collects information from each restaurant's website, review sites, coupon sites, etc. Step 3: The suggestion unit analyzes the information collected by the collection unit and suggests the best value meal of the day. For example, if a particular restaurant is offering a discount on a lunch set, the suggestion unit provides that information to the user. The suggestion unit can also consider the user's preferences and allergy information and prioritize suggestions for vegetarian menus.

[0061] (Example 2) A proposal system according to an embodiment of the present invention allows a user to input their current location, collects information on the Internet, and suggests the best value meal of the day. The proposal system inputs the user's current location, and a generation AI collects the information and suggests the best value meal of the day based on the analysis results. For example, the proposal system allows a user to input their current location. For example, the user simply inputs location information such as their home or workplace. This information is then input into the generation AI. The proposal system then uses the generation AI to collect information on the Internet. The generation AI then collects and analyzes information such as menus, prices, special offers, and user reviews and ratings of nearby restaurants. For example, the information is collected from each restaurant's website, review site, coupon site, etc. The proposal system then uses the generation AI to suggest the best value meal of the day based on the analysis results. For example, if a particular restaurant is offering a discount on a lunch set, the system provides this information to the user. The proposal system also takes into account the user's preferences and allergy information. For example, if the user is vegetarian, the system prioritizes vegetarian menus. This allows the proposal system to easily find great value meals that suit their preferences and requirements. For example, if a user requests "Find a great lunch deal near my home," the AI ​​will analyze information about nearby restaurants and suggest the best lunch deal of the day, allowing users to enjoy a satisfying meal while saving time and money.

[0062] A proposal system according to an embodiment includes a reception unit, a collection unit, and a proposal unit. The reception unit inputs a user's current location. The user's current location may include, but is not limited to, home, work, and current location information. For example, the reception unit may input a request from the user to find a good lunch deal near their home. The collection unit uses a generation AI to collect information from the Internet. The collection unit collects, for example, menus, prices, special offers, user reviews, and ratings of nearby restaurants. For example, the collection unit collects information from websites of each restaurant, review sites, coupon sites, and the like. The collection unit may also analyze the collected information using the generation AI. The proposal unit analyzes the information collected by the collection unit and proposes the best value meal of the day. For example, if a specific restaurant offers a discount on a lunch set, the proposal unit provides the user with information about that discount. The proposal unit also takes into account the user's preferences and allergy information. For example, if the user is vegetarian, the proposal unit prioritizes vegetarian menus. This allows the proposal system according to an embodiment to easily find a good value meal that matches their preferences and requirements. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input information collected by the collection unit into the generation AI and generate a suggestion based on the analysis results.

[0063] The collection unit can collect menus, prices, special offer information, and user reviews or ratings of nearby restaurants. The collection unit collects, for example, menus, prices, special offer information, and user reviews or ratings of nearby restaurants. For example, the collection unit collects information from websites of each restaurant, review sites, coupon sites, etc. The collection unit can also analyze the collected information using a generation AI. This allows for more accurate recommendations by collecting detailed information about nearby restaurants. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input information collected from websites of each restaurant into a generation AI and organize the information based on the analysis results.

[0064] The suggestion unit can make suggestions based on the user's preferences and allergy information based on the collected information. The suggestion unit can make suggestions based on the user's preferences and allergy information based on the collected information, for example. For example, if the user is vegetarian, the suggestion unit can preferentially suggest vegetarian menus. The suggestion unit can also suggest safe menus taking into account the user's allergy information. This makes it possible to make suggestions that take into account the user's individual preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the information collected by the collection unit into a generation AI and generate suggestions that take into account the user's preferences and allergy information.

[0065] The suggestion unit can prioritize suggesting discount information at a specific restaurant. For example, the suggestion unit prioritizes suggesting discount information at a specific restaurant. For example, if a lunch set at a specific restaurant is discounted, the suggestion unit provides that information to the user. Furthermore, by prioritizing suggesting discount information, the suggestion unit can provide the user with the most advantageous options. This allows the user to select a good-value meal based on the discount information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the discount information collected by the collection unit into the generation AI and prioritize suggesting the discount information.

[0066] The suggestion unit can provide reliable information based on user reviews and ratings. The suggestion unit provides reliable information based on user reviews and ratings, for example. For example, the suggestion unit analyzes user reviews and ratings and selects reliable information. Furthermore, the suggestion unit can improve user satisfaction by providing reliable information. This allows users to select meals based on reliable information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the reviews and ratings collected by the collection unit into a generation AI and select reliable information.

[0067] The reception unit can estimate the user's emotions and adjust the input method for the current location based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the input method for the current location based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick input of the current location. This improves user convenience by providing an optimal input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and adjust the input method based on the emotion.

[0068] The reception unit can analyze the user's past location information input history and select the optimal input method. The reception unit, for example, analyzes the user's past location information input history and selects the optimal input method. For example, the reception unit automatically displays current locations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest the current location to be used during a specific time period based on the user's past input history. This improves the efficiency of the user's input work by providing the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI to select the optimal input method.

[0069] The reception unit can select the optimal input means in consideration of the user's device information when inputting the current location. For example, the reception unit selects the optimal input means in consideration of the user's device information when inputting the current location. For example, if the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, if the user is using a desktop, the reception unit can also prioritize keyboard input. Furthermore, if the user is using a smartwatch, the reception unit can also prioritize voice input. This improves the efficiency of input work by providing the optimal input means according to the user's device. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's device information into the generation AI and select the optimal input means.

[0070] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting the current location. For example, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting the current location. For example, when the user inputs the current location by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the current location by text, the reception unit can also provide an autocomplete function. Furthermore, when the user inputs the current location by image, the reception unit can also support the input using image recognition technology. In this way, the optimal input means depending on the user's input method is provided, thereby making the input work more efficient. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method data into the generation AI and select the optimal input means.

[0071] The reception unit can estimate the user's emotions and determine the priority of the current location to be input based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of the current location to be input based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize displaying the easiest current location to input. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable current location. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the current location. This improves user convenience by providing optimal input priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and determine the priority of the current location to be input based on the emotion.

[0072] The reception unit can prioritize inputting highly relevant location information in consideration of the user's geographical location information when inputting the current location. For example, the reception unit prioritizes inputting highly relevant location information in consideration of the user's geographical location information when inputting the current location. For example, if the user is in a specific area, the reception unit can prioritize displaying location information within that area. Furthermore, if the user is in a specific building, the reception unit can prioritize displaying location information within that building. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying location information within that city. This improves the efficiency of input work by providing optimal location information based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data to the generation AI and prioritize inputting highly relevant location information.

[0073] The reception unit can analyze the user's social media activity and input related location information when the user inputs the current location. For example, the reception unit can analyze the user's social media activity and input related location information when the user inputs the current location. For example, the reception unit can suggest a location where the user checked in on social media as the current location. The reception unit can also analyze the content of the user's social media posts and suggest related location information as the current location. The reception unit can also suggest related location information as the current location by referring to the activities of the user's friends on social media. This improves the efficiency of input work by providing optimal location information based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and input related location information.

[0074] The reception unit can adjust the input method by reflecting the user's past feedback when inputting the current location. For example, the reception unit adjusts the input method by reflecting the user's past feedback when inputting the current location. For example, the reception unit preferentially suggests input methods that the user has previously provided feedback on. The reception unit can also customize the optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. This improves the efficiency of input work by providing the optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and adjust the input method.

[0075] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can collect information at a leisurely pace. Also, if the user is in a hurry, the collection unit can collect information quickly. Also, if the user is excited, the collection unit can prioritize collecting visually stimulating information. This improves the efficiency of information collection by providing the optimal information collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and adjust the timing of information collection based on the emotion.

[0076] The collection unit can analyze the user's past search history and select an appropriate collection method when collecting information. For example, the collection unit can analyze the user's past search history and select an appropriate collection method when collecting information. For example, the collection unit can prioritize collecting related information based on keywords the user has previously searched for. The collection unit can also predict and suggest information to be collected during a specific time period based on the user's past search history. The collection unit can also analyze the user's past search history and select the most efficient collection method. This improves the efficiency of information collection by providing an optimal collection method based on the user's past search history. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past search history data into a generation AI and select an optimal collection method.

[0077] The collection unit can select information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit selects information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit prioritizes collecting relevant information based on the user's current living situation. The collection unit can also prioritize collecting relevant information based on the user's areas of interest. The collection unit can also analyze the user's current living situation and areas of interest and filter out optimal information. This improves the efficiency of information collection by providing optimal information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's living situation and area of ​​interest data into the generation AI and select information.

[0078] The collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.) when collecting information. For example, the collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.) when collecting information. For example, when the user collects information by voice, the collection unit can support collection using voice recognition technology. Furthermore, when the user collects information by text, the collection unit can also provide an autocomplete function. Furthermore, when the user collects information by image, the collection unit can also support collection using image recognition technology. This improves the efficiency of information collection by providing the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input user's input method data into a generation AI and select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting information that is easiest to understand. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This improves the efficiency of information collection by providing an optimal information collection priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the priority of information to be collected based on the emotion.

[0080] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information within that area. Furthermore, when the user is in a specific building, the collection unit can prioritize collecting information within that building. Furthermore, when the user is in a specific city, the collection unit can prioritize collecting information within that city. This improves the efficiency of information collection by providing optimal information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's geographical location information data into a generation AI and prioritize collecting highly relevant information.

[0081] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect information about places the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. This improves the efficiency of information collection by providing optimal information based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI to collect related information.

[0082] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially suggests collection methods that the user has previously provided feedback on. The collection unit can also customize the optimal collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. This improves the efficiency of information collection by providing the optimal collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and adjust the collection method.

[0083] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This improves user satisfaction by providing an optimal suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and adjust the way the suggestions are presented based on the emotion.

[0084] The suggestion unit can adjust the level of detail of the proposal based on the importance of the collected information when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the collected information when making a suggestion. For example, the suggestion unit preferentially suggests information with high importance in detail. The suggestion unit can also briefly suggest information with low importance. The suggestion unit can also gradually adjust the level of detail of the proposal according to the importance. This improves user satisfaction by providing an optimal level of detail of the proposal according to the importance of the collected information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input importance data of the collected information into the generation AI and adjust the level of detail of the proposal.

[0085] The suggestion unit can apply different suggestion algorithms depending on the category of collected information when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of collected information when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm that emphasizes price and special offer information to restaurant information. The suggestion unit can also apply a suggestion algorithm that emphasizes reliability to user reviews and ratings. The suggestion unit can also apply a suggestion algorithm that emphasizes discount rates to special offer information. This improves user satisfaction by providing an optimal suggestion algorithm depending on the category of collected information. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input category data of the collected information into a generation AI and apply different suggestion algorithms.

[0086] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes similar suggestions based on suggestions that the user has previously accepted. The suggestion unit can also avoid similar suggestions based on suggestions that the user has previously rejected. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestion. This improves the accuracy of the suggestion by providing optimal suggestions based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI to improve the accuracy of the suggestion.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This improves user satisfaction by providing an optimal length for the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and adjust the length of the suggestions based on the emotions.

[0088] The suggestion unit can determine the priority of the suggestions based on the submission time of the collected information when making a suggestion. The suggestion unit can, for example, determine the priority of the suggestions based on the submission time of the collected information when making a suggestion. For example, the suggestion unit prioritizes the most recent information. The suggestion unit can also briefly propose older information. The suggestion unit can also gradually adjust the priority of the suggestions depending on the submission time. This improves user satisfaction by providing optimal suggestions based on the submission time of the collected information. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input submission time data of the collected information into the generation AI and determine the priority of the suggestions.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the collected information when making suggestions. The suggestion unit, for example, adjusts the order of suggestions based on the relevance of the collected information when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant information. The suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also gradually adjust the order of suggestions according to the relevance. This improves user satisfaction by providing an optimal suggestion order based on the relevance of the collected information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input relevance data of the collected information into the generation AI and adjust the order of suggestions.

[0090] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can gradually adjust the use of technical terminology in the proposal according to the user's level of expertise. This improves user satisfaction by providing optimal suggestions according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and adjust the use of technical terminology in the proposal. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's current location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the Internet using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information collected by the collection unit and suggests the best value meal of the day. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, collection unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's current location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the Internet using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information collected by the collection unit and suggests the best value meal of the day. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the user's current location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the Internet using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information collected by the collection unit and suggests the best value meal of the day. The suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's current location. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the Internet using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information collected by the collection unit and suggests the best value meal of the day. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The suggestion system can also make suggestions taking into account the user's health condition. For example, the suggestion unit can collect the user's health data (e.g., blood sugar level, blood pressure, allergy information, etc.) and suggest meals according to the user's health condition. If the user has diabetes, low-carbohydrate menus can be suggested preferentially. Also, if the user has high blood pressure, low-salt menus can be suggested. This makes it possible to suggest optimal meals according to the user's health condition and support health management.

[0093] The suggestion system can also analyze the user's past meal history to improve the accuracy of suggestions. For example, the suggestion unit can suggest meals that suit the user's preferences based on the user's past menu choices and ratings. Menus that the user has given high ratings to in the past are given priority in the suggestions. Menus that the user has avoided in the past can also be excluded from the suggestions. This makes it possible to make optimal suggestions based on the user's past meal history, thereby improving satisfaction.

[0094] The suggestion system can also estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a relaxing meal. If the user is relaxed, the suggestion unit can suggest an adventurous menu. If the user is in a hurry, the suggestion unit can suggest a menu that can be served quickly. This makes it possible to make optimal suggestions based on the user's emotions, thereby improving user satisfaction.

[0095] The recommendation system can also analyze the user's social media activity to suggest related meals. For example, the suggestion unit can suggest similar menus based on restaurants the user has checked in to on social media or photos of meals posted by the user. It can also suggest the same restaurants and menus based on meal information the user has shared with friends. This allows for optimal suggestions based on the user's social media activity, attracting the user's interest.

[0096] The recommendation system can also make suggestions by taking into account the user's current activity level. For example, if the user has just exercised, the suggestion unit can suggest nutritious meals. If the user is doing desk work, the suggestion unit can suggest light meals or snacks. If the user is traveling, the suggestion unit can suggest local specialties or restaurants in tourist spots. This allows the system to make optimal suggestions based on the user's current activity level and meet the user's needs.

[0097] The suggestion system can further estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make suggestions for times when the user can relax. If the user is relaxed, the suggestion unit can proactively make meal suggestions. Also, if the user is in a hurry, the suggestion unit can make suggestions quickly. This makes it possible to make optimal suggestions based on the user's emotions, improving user convenience.

[0098] The suggestion system can also adjust the way suggestions are displayed by taking into account the user's device information. For example, if the user is using a smartphone, the suggestion unit can display a mobile-friendly display. If the user is using a desktop, detailed information can be displayed. Also, if the user is using a smartwatch, concise information can be displayed. This enables the optimal suggestion display method according to the user's device, improving user convenience.

[0099] The suggestion system can also estimate the user's emotions and personalize the content of suggestions based on the estimated emotions. For example, if the user is sad, the suggestion unit can suggest meals to lift the user's spirits. If the user is happy, the suggestion unit can suggest special menus. Also, if the user is tired, the suggestion unit can suggest meals to replenish energy. This makes it possible to make optimal suggestions based on the user's emotions, thereby improving user satisfaction.

[0100] The suggestion system can also improve the accuracy of suggestions by reflecting the user's past feedback. For example, the suggestion unit can suggest meals that suit the user's preferences based on the suggestions provided in the user's past feedback. Suggestions that the user has previously rated highly can be given priority. Suggestions that the user has previously rated poorly can also be avoided. This allows for optimal suggestions based on the user's past feedback, thereby improving satisfaction.

[0101] The suggestion system can further estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion method. If the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This enables the system to provide the optimal suggestion method according to the user's emotions, thereby improving user satisfaction.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit inputs the user's current location. The user's current location includes home, work, current location information, etc. For example, the user can input a request to find a good lunch deal near their home. Step 2: The collection unit uses the generation AI to collect information from the Internet. For example, it collects information such as the menus and prices of nearby restaurants, special offers, and user reviews and ratings. The collection unit collects information from each restaurant's website, review sites, coupon sites, etc. Step 3: The suggestion unit analyzes the information collected by the collection unit and suggests the best value meal of the day. For example, if a particular restaurant is offering a discount on a lunch set, the suggestion unit provides that information to the user. The suggestion unit can also consider the user's preferences and allergy information and prioritize suggestions for vegetarian menus.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's current location; a collection unit that collects information on the Internet based on the current location input by the reception unit; a suggestion unit that analyzes the information collected by the collection unit and suggests the most affordable meal of the day; Equipped with A system characterized by:

2. The collecting unit Collecting menus, prices, special offers, and user reviews or ratings for nearby restaurants 2. The system of claim 1.

3. The proposal unit Based on the collected information, suggestions are made based on the user's preferences and allergies.

2. The system of claim 1.

4. The proposal unit Prioritize discount information for specific restaurants 2. The system of claim 1.

5. The proposal unit Providing reliable information based on user reviews and ratings 2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the way you input your current location based on the estimated user emotions 2. The system of claim 1.

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

8. The reception unit When entering the current location, select the appropriate input method taking into account the user's device information 2. The system of claim 1.

Images