system

Abstract

The system according to the embodiment aims to guide a user to the most suitable restaurant based on the user's taste information. [Solution] A system according to an embodiment includes a collection unit, an input unit, an analysis unit, and a guidance unit. The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. The input unit inputs the user's impressions of the food they ate. The analysis unit analyzes the impression data collected by the input unit and identifies the user's taste information. The guidance unit provides guidance on recommended restaurants based on the taste information identified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided guidance to optimal restaurants based on a user's taste information, and there is room for improvement.

[0005] The system according to the embodiment aims to guide a user to the most suitable restaurant based on the user's taste information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an input unit, an analysis unit, and a guidance unit. The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. The input unit inputs the user's impressions of the food they ate. The analysis unit analyzes the impression data collected by the input unit and identifies the user's taste information. The guidance unit provides guidance to recommended restaurants based on the taste information identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can guide a user to the most suitable restaurant based on the user's taste information. [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 restaurant search system according to an embodiment of the present invention is a system that recommends restaurants based on a user's taste information. This restaurant search system collects information about restaurants the user has visited in the past and information obtained from search results, inputs the user's impressions of the food, and collects the impression data. The collected impression data is analyzed to identify the user's taste information. Based on the identified taste information, the system recommends restaurants suited to the user. For example, the system collects information about restaurants the user has visited in the past and information obtained from search results. In this process, detailed data such as the names, menus, and ratings of the restaurants the user has visited is collected. For example, the system collects the names of the restaurants the user has visited and the ratings of the menus, and stores them in a database. Next, the system inputs the user's impressions of the food, and collects the impression data. For example, the user may input an impression such as "This dish was very delicious." This impression data is entered into the system and stored in a database. The system analyzes the collected impression data to identify the user's taste information. For example, if the user frequently inputs an impression such as "I like spicy food," the system determines that the user likes spicy food. This analysis is performed by a generation AI. Finally, the system recommends restaurants suited to the user based on the identified taste information. For example, if it is determined that a user likes spicy food, the system will recommend restaurants that are known for their delicious spicy food. This recommendation of restaurants is also provided by the generation AI. This system allows users to easily find restaurants that suit their tastes. For example, a user who likes spicy food will be recommended restaurants that are known for their delicious spicy food, so they can choose a restaurant that suits them. This allows the restaurant search system to recommend restaurants based on the user's taste information, allowing them to choose a restaurant that suits them better.

[0029] A restaurant search system according to an embodiment includes a collection unit, a reception unit, an analysis unit, and a guidance unit. The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. The collection unit collects detailed data, such as the names, menus, and ratings of restaurants visited by the user. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database. The reception unit inputs user impressions of the food and collects the impression data. For example, the reception unit inputs a user's impression, such as "This dish was very delicious." This impression data is entered into the system and stored in the database. The analysis unit analyzes the collected impression data and identifies the user's taste information. For example, if the user frequently inputs an impression such as "I like spicy food," the system determines that the user likes spicy food. This analysis is performed by a generation AI. The guidance unit recommends restaurants that suit the user based on the identified taste information. For example, if the guidance unit determines that the user likes spicy food, the system recommends restaurants with a reputation for delicious spicy food. This guidance of recommended restaurants is also performed by the generation AI. As a result, the restaurant search system according to the embodiment can guide the user to recommended restaurants based on the user's taste information, allowing the user to select a restaurant that best suits them.

[0030] The collection unit can collect detailed data such as the names, menus, and ratings of restaurants visited by the user. Detailed data includes, but is not limited to, for example, the store name, menu, rating score, and visit date and time. The collection unit can, for example, collect the names of restaurants visited by the user and ratings of their menus and store them in a database. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database. By collecting detailed data about restaurants visited by the user, more accurate restaurant recommendations can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database.

[0031] The reception unit can input the user's impressions of the food and collect the impression data. Impression data includes, for example, text format, evaluation scores, photos, etc., but is not limited to these examples. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database. In this way, by collecting the user's impression data, it is possible to identify taste information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database.

[0032] The analysis unit can analyze the collected impression data and identify the user's taste information. Taste information includes, for example, favorite tastes, disliked tastes, allergy information, etc., but is not limited to these examples. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI. In this way, the user's taste information can be identified by analyzing the impression data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI.

[0033] The guidance unit can recommend restaurants based on the identified taste information. Recommended restaurants include, but are not limited to, restaurant ratings, menu contents, and location conditions, for example. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI. In this way, by recommending recommended restaurants based on the user's taste information, the user can select a restaurant that is more suitable for them. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI.

[0034] The collection unit can analyze the user's past visit history and select an appropriate collection method. For example, the collection unit prioritizes collecting information about restaurants that the user frequently visits. Also, if the user likes a particular dish, the collection unit can focus on collecting information about restaurants that serve that dish. Furthermore, the collection unit can prioritize collecting information about restaurants that the user has given high ratings to in the past. This enables more effective information collection by analyzing the past visit history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's past visit history and select an appropriate collection method.

[0035] When collecting restaurant information, the collection unit can filter the information based on the user's current dietary preferences and allergy information. For example, if the user likes spicy food, the collection unit can preferentially collect information about restaurants that serve spicy food. The collection unit can also collect information about restaurants that do not use ingredients to which the user is allergic, in order to avoid such ingredients. Furthermore, if the user is vegetarian, the collection unit can also collect information about restaurants that offer vegetarian menus. This allows for filtering information based on the user's preferences and allergy information, thereby providing more appropriate information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can perform filtering based on the user's current dietary preferences and allergy information.

[0036] When collecting restaurant information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information about restaurants close to the user's current location. The collection unit can also focus on collecting restaurant information in areas the user frequently visits. Furthermore, when the user is traveling, the collection unit can prioritize collecting restaurant information about restaurants in the user's travel destination. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting restaurant information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information.

[0037] The collection unit can analyze the user's social media activity and collect related information when collecting restaurant information. For example, the collection unit collects information about restaurants that the user has given high ratings on social media. The collection unit can also collect information about restaurants visited by influencers that the user follows. Furthermore, the collection unit can also collect information about restaurants that the user has shared on social media. This allows for analysis of social media activity to provide more relevant information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activity and collect related information when collecting restaurant information.

[0038] The reception unit can provide input assistance by referring to the user's past impression history when inputting impressions. For example, the reception unit can automatically display impressions previously input by the user for reference. The reception unit can also suggest impression expressions previously used by the user. Furthermore, the reception unit can automatically complete related impressions from the user's past impression history. This makes it possible to provide input assistance by referring to the past impression history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can provide input assistance by referring to the user's past impression history when inputting impressions.

[0039] The reception unit can customize the input content based on the user's current mood and physical condition when inputting impressions. For example, when the user is relaxed, the reception unit can provide a detailed impression input option. Furthermore, when the user is tired, the reception unit can provide a simple impression input option. Furthermore, when the user is not feeling well, the reception unit can provide a simple and intuitive impression input interface. This allows the input content to be customized according to the user's mood and physical condition, enabling more appropriate impression input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input content based on the user's current mood and physical condition when inputting impressions.

[0040] The reception unit can provide an appropriate input method by taking into account device information of the user when inputting impressions. For example, when the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a desktop, the reception unit can also prioritize keyboard input. In this way, by taking device information into account, an easier-to-use input method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when inputting impressions, the reception unit can provide an appropriate input method by taking into account device information of the user.

[0041] The reception unit can analyze the user's social media activity and input related impressions when the user inputs impressions. For example, the reception unit automatically inputs impressions shared by the user on social media. The reception unit can also input impressions based on impressions of influencers the user follows. Furthermore, the reception unit can also input impressions of restaurants that the user has given high ratings to on social media. In this way, by analyzing social media activity, more relevant impressions can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's social media activity and input related impressions when the user inputs impressions.

[0042] When analyzing the impression data, the analysis unit can improve the accuracy of the analysis by referring to the user's past taste information. The analysis unit, for example, analyzes taste tendencies based on impression data previously input by the user. The analysis unit can also improve the accuracy of the analysis by referring to data on dishes that the user has previously given high ratings. Furthermore, the analysis unit can analyze the user's past taste information and reflect it in the analysis results. In this way, by referring to the past taste information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing the impression data, the analysis unit can improve the accuracy of the analysis by referring to the user's past taste information.

[0043] When analyzing the impression data, the analysis unit can take into account the user's current food preferences and allergy information. For example, if the user likes spicy food, the analysis unit can reflect that information in the analysis. The analysis unit can also reflect information in the analysis to help the user avoid ingredients to which they are allergic. Furthermore, if the user is a vegetarian, the analysis unit can also reflect that information in the analysis. This allows for more appropriate analysis by taking into account the user's preferences and allergy information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, when analyzing the impression data, the analysis unit can take into account the user's current food preferences and allergy information.

[0044] The analysis unit can take into account the user's geographical location information when analyzing the impression data. For example, the analysis unit can prioritize analyzing impression data of restaurants close to the user's current location. The analysis unit can also focus on analyzing impression data of areas the user frequently visits. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing impression data of travel destinations. This makes it possible to perform more relevant analysis by taking into account the geographical location information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without using AI. For example, the analysis unit can take into account the user's geographical location information when analyzing the impression data.

[0045] When analyzing the opinion data, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes opinion data shared by the user on social media. The analysis unit can also analyze opinion data of influencers followed by the user. Furthermore, the analysis unit can analyze opinion data of restaurants that the user has given high ratings to on social media. This enables more relevant analysis by analyzing social media activity. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing the opinion data, the analysis unit can analyze the user's social media activity and analyze related data.

[0046] When providing recommended restaurants, the guidance unit can select the most suitable restaurant by referring to the user's past visit history. For example, the guidance unit can prioritize restaurants that the user has given high ratings to in the past. The guidance unit can also prioritize restaurants that the user frequently visits. Furthermore, the guidance unit can also newly recommend restaurants that the user has not visited before. This makes it possible to provide more appropriate restaurant guidance by referring to the user's past visit history. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing recommended restaurants, the guidance unit can select the most suitable restaurant by referring to the user's past visit history.

[0047] The guidance unit can provide guidance on recommended restaurants while taking into consideration the user's current dietary preferences and allergy information. For example, if the user likes spicy food, the guidance unit can provide guidance on restaurants that serve spicy food. The guidance unit can also provide guidance on restaurants that do not use ingredients to which the user is allergic, in order to avoid such ingredients. Furthermore, if the user is vegetarian, the guidance unit can also provide guidance on restaurants that serve vegetarian menus. This allows for more appropriate restaurant guidance by taking into consideration the user's preferences and allergy information. Some or all of the above-described processing by the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can provide guidance on recommended restaurants while taking into consideration the user's current dietary preferences and allergy information.

[0048] When providing recommended stores, the guidance unit can provide the most appropriate store by taking into consideration the user's geographical location information. For example, the guidance unit can provide priority guidance to restaurants close to the user's current location. The guidance unit can also provide priority guidance to restaurants in areas the user frequently visits. Furthermore, if the user is traveling, the guidance unit can also provide priority guidance to restaurants in the user's travel destination. This makes it possible to provide more appropriate store guidance by taking into consideration the geographical location information. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing recommended stores, the guidance unit can provide priority guidance to the most appropriate store by taking into consideration the user's geographical location information.

[0049] The guidance unit can analyze the user's social media activity and provide related restaurants when providing recommended restaurants. For example, the guidance unit can provide restaurants that the user has given high ratings on social media. The guidance unit can also provide restaurants that have been visited by influencers the user follows. Furthermore, the guidance unit can also provide restaurants that the user has shared on social media. This enables more appropriate restaurant guidance by analyzing social media activity. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can analyze the user's social media activity and provide related restaurants when providing recommended restaurants.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The restaurant search system can further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., blood pressure, heart rate, blood sugar level, etc.) and provides it to the analysis unit. Based on the collected health data, the analysis unit can identify restaurants that are suitable for the user's health condition. For example, if the user has high blood pressure, it can recommend restaurants that offer low-salt menus. Also, if the user has diabetes, it can recommend restaurants that offer low-carbohydrate menus. This makes it possible to provide restaurant guidance that suits the user's health condition.

[0052] The restaurant search system can further include a history management unit that records the user's dining history. The history management unit records detailed information about the dishes and restaurants the user has eaten in the past and provides it to the analysis unit. The analysis unit can analyze the user's dining habits based on the data provided by the history management unit and identify recommended restaurants. For example, if the user has frequently ordered a particular dish in the past, it can recommend restaurants that serve that dish. It can also suggest new restaurants that the user has not visited before. This makes it possible to provide more personalized restaurant guidance based on the user's dining history.

[0053] The restaurant search system can further include a learning unit that learns the user's food preferences. The learning unit continuously learns the user's food preferences based on the user's impression data and visit history, and provides the learning to the analysis unit. The analysis unit can identify restaurants that match the user's latest food preferences based on the data provided by the learning unit. For example, if the user has recently developed a preference for spicy food, the analysis unit can recommend restaurants that serve spicy food. Also, if the user wants to try a new dish, the analysis unit can suggest restaurants that serve that dish. This makes it possible to provide restaurant guidance that responds to changes in the user's food preferences.

[0054] The restaurant search system can further include an evaluation unit that evaluates the user's satisfaction with the meal. The evaluation unit evaluates the user's satisfaction with the meal at the restaurant visited and provides the data to the analysis unit. Based on the data provided by the evaluation unit, the analysis unit can identify restaurants with high user satisfaction and recommend them as recommended restaurants. For example, it can prioritize recommending restaurants that the user has given high ratings to in the past. Furthermore, to avoid restaurants with low user satisfaction, it can also select recommended restaurants taking that information into consideration. This makes it possible to recommend restaurants based on the user's satisfaction.

[0055] The restaurant search system can further include a frequency management unit that manages the frequency of a user's meal visits. The frequency management unit records how often a user visits a particular restaurant and provides that data to the analysis unit. Based on the data provided by the frequency management unit, the analysis unit can identify restaurants that the user frequently visits and recommend them as recommended restaurants. For example, it can prioritize recommendations of restaurants that the user visits once a week. It can also suggest new restaurants that the user has not visited for a long time. This makes it possible to recommend restaurants based on the user's visit frequency.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. For example, detailed data such as the names, menus, and ratings of restaurants visited by the user is collected and stored in a database. Step 2: The reception unit inputs the user's impressions of the food and collects the impression data. For example, the user may input an impression such as "This dish was very delicious," and the impression data entered into the system is saved in a database. Step 3: The analysis unit analyzes the collected impression data and identifies the user's taste information. For example, if a user frequently enters impressions such as "I like spicy food," the system will determine that the user likes spicy food. This analysis is performed by the generation AI. Step 4: The guidance section recommends restaurants that suit the user based on the identified taste information. For example, if the user is identified as liking spicy food, the system will recommend restaurants that are known for their delicious spicy food. This recommendation of restaurants is also done by the generation AI.

[0058] (Example 2) A restaurant search system according to an embodiment of the present invention is a system that recommends restaurants based on a user's taste information. This restaurant search system collects information about restaurants the user has visited in the past and information obtained from search results, inputs the user's impressions of the food, and collects the impression data. The collected impression data is analyzed to identify the user's taste information. Based on the identified taste information, the system recommends restaurants suited to the user. For example, the system collects information about restaurants the user has visited in the past and information obtained from search results. In this process, detailed data such as the names, menus, and ratings of the restaurants the user has visited is collected. For example, the system collects the names of the restaurants the user has visited and the ratings of the menus, and stores them in a database. Next, the system inputs the user's impressions of the food, and collects the impression data. For example, the user may input an impression such as "This dish was very delicious." This impression data is entered into the system and stored in a database. The system analyzes the collected impression data to identify the user's taste information. For example, if the user frequently inputs an impression such as "I like spicy food," the system determines that the user likes spicy food. This analysis is performed by a generation AI. Finally, the system recommends restaurants suited to the user based on the identified taste information. For example, if it is determined that a user likes spicy food, the system will recommend restaurants that are known for their delicious spicy food. This recommendation of restaurants is also provided by the generation AI. This system allows users to easily find restaurants that suit their tastes. For example, a user who likes spicy food will be recommended restaurants that are known for their delicious spicy food, so they can choose a restaurant that suits them. This allows the restaurant search system to recommend restaurants based on the user's taste information, allowing them to choose a restaurant that suits them better.

[0059] A restaurant search system according to an embodiment includes a collection unit, a reception unit, an analysis unit, and a guidance unit. The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. The collection unit collects detailed data, such as the names, menus, and ratings of restaurants visited by the user. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database. The reception unit inputs user impressions of the food and collects the impression data. For example, the reception unit inputs a user's impression, such as "This dish was very delicious." This impression data is entered into the system and stored in the database. The analysis unit analyzes the collected impression data and identifies the user's taste information. For example, if the user frequently inputs an impression such as "I like spicy food," the system determines that the user likes spicy food. This analysis is performed by a generation AI. The guidance unit recommends restaurants that suit the user based on the identified taste information. For example, if the guidance unit determines that the user likes spicy food, the system recommends restaurants with a reputation for delicious spicy food. This guidance of recommended restaurants is also performed by the generation AI. As a result, the restaurant search system according to the embodiment can guide the user to recommended restaurants based on the user's taste information, allowing the user to select a restaurant that best suits them.

[0060] The collection unit can collect detailed data such as the names, menus, and ratings of restaurants visited by the user. Detailed data includes, but is not limited to, for example, the store name, menu, rating score, and visit date and time. The collection unit can, for example, collect the names of restaurants visited by the user and ratings of their menus and store them in a database. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database. By collecting detailed data about restaurants visited by the user, more accurate restaurant recommendations can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect the names of restaurants visited by the user and ratings of their menus and store them in a database.

[0061] The reception unit can input the user's impressions of the food and collect the impression data. Impression data includes, for example, text format, evaluation scores, photos, etc., but is not limited to these examples. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database. In this way, by collecting the user's impression data, it is possible to identify taste information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's impression, such as "This dish was very delicious." This impression data is input to the system and stored in a database.

[0062] The analysis unit can analyze the collected impression data and identify the user's taste information. Taste information includes, for example, favorite tastes, disliked tastes, allergy information, etc., but is not limited to these examples. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI. In this way, the user's taste information can be identified by analyzing the impression data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, if the user frequently inputs an impression such as "I like spicy food," the analysis unit determines that the user likes spicy food. This analysis is performed by the generation AI.

[0063] The guidance unit can recommend restaurants based on the identified taste information. Recommended restaurants include, but are not limited to, restaurant ratings, menu contents, and location conditions, for example. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI. In this way, by recommending recommended restaurants based on the user's taste information, the user can select a restaurant that is more suitable for them. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, if the guidance unit identifies that the user likes spicy food, the system recommends restaurants that are reputed to serve delicious spicy food. This recommendation of recommended restaurants is also performed by the generation AI.

[0064] The collection unit can analyze the user's emotions and adjust the timing of collecting restaurant information based on the analyzed user's emotions. For example, if the user is relaxed, the collection unit can immediately collect restaurant information and provide the information at a time when the user is likely to be interested. Furthermore, if the user is busy, the collection unit can postpone collecting restaurant information and provide the information when the user is calm. Furthermore, if the user is feeling stressed, the collection unit can refrain from collecting restaurant information and provide the information after the user has relaxed. This allows the collection timing to be adjusted according to the user's emotions, making it possible to provide information at a more appropriate time. Emotion analysis is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can analyze the user's emotions and adjust the timing of collecting restaurant information based on the analyzed user's emotions.

[0065] The collection unit can analyze the user's past visit history and select an appropriate collection method. For example, the collection unit prioritizes collecting information about restaurants that the user frequently visits. Also, if the user likes a particular dish, the collection unit can focus on collecting information about restaurants that serve that dish. Furthermore, the collection unit can prioritize collecting information about restaurants that the user has given high ratings to in the past. This enables more effective information collection by analyzing the past visit history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's past visit history and select an appropriate collection method.

[0066] When collecting restaurant information, the collection unit can filter the information based on the user's current dietary preferences and allergy information. For example, if the user likes spicy food, the collection unit can preferentially collect information about restaurants that serve spicy food. The collection unit can also collect information about restaurants that do not use ingredients to which the user is allergic, in order to avoid such ingredients. Furthermore, if the user is vegetarian, the collection unit can also collect information about restaurants that offer vegetarian menus. This allows for filtering information based on the user's preferences and allergy information, thereby providing more appropriate information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can perform filtering based on the user's current dietary preferences and allergy information.

[0067] The collection unit can estimate the user's emotions and determine the priority of restaurant information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting restaurant information that is likely to interest the user. Furthermore, when the user is busy, the collection unit can prioritize collecting restaurant information that is important. Furthermore, when the user is stressed, the collection unit can prioritize collecting restaurant information that is relaxing. This allows more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can estimate the user's emotions and determine the priority of restaurant information to be collected based on the estimated user emotions.

[0068] When collecting restaurant information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information about restaurants close to the user's current location. The collection unit can also focus on collecting restaurant information in areas the user frequently visits. Furthermore, when the user is traveling, the collection unit can prioritize collecting restaurant information about restaurants in the user's travel destination. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting restaurant information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information.

[0069] The collection unit can analyze the user's social media activity and collect related information when collecting restaurant information. For example, the collection unit collects information about restaurants that the user has given high ratings on social media. The collection unit can also collect information about restaurants visited by influencers that the user follows. Furthermore, the collection unit can also collect information about restaurants that the user has shared on social media. This allows for analysis of social media activity to provide more relevant information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activity and collect related information when collecting restaurant information.

[0070] The reception unit can analyze the user's emotions and adjust the impression input interface based on the analyzed user emotions. For example, the reception unit can provide a detailed impression input option when the user is relaxed. The reception unit can also provide a simple impression input option when the user is busy. Furthermore, the reception unit can provide a simple and intuitive impression input interface when the user is stressed. This allows for easier impression input by adjusting the interface according to the user's emotions. The emotion analysis is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can analyze the user's emotions and adjust the impression input interface based on the analyzed user emotions.

[0071] The reception unit can provide input assistance by referring to the user's past impression history when inputting impressions. For example, the reception unit can automatically display impressions previously input by the user for reference. The reception unit can also suggest impression expressions previously used by the user. Furthermore, the reception unit can automatically complete related impressions from the user's past impression history. This makes it possible to provide input assistance by referring to the past impression history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can provide input assistance by referring to the user's past impression history when inputting impressions.

[0072] The reception unit can customize the input content based on the user's current mood and physical condition when inputting impressions. For example, when the user is relaxed, the reception unit can provide a detailed impression input option. Furthermore, when the user is tired, the reception unit can provide a simple impression input option. Furthermore, when the user is not feeling well, the reception unit can provide a simple and intuitive impression input interface. This allows the input content to be customized according to the user's mood and physical condition, enabling more appropriate impression input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input content based on the user's current mood and physical condition when inputting impressions.

[0073] The reception unit can estimate the user's emotions and determine the priority of impression input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize the user's impression input. Furthermore, if the user is busy, the reception unit can postpone the user's impression input. Furthermore, if the user is feeling stressed, the reception unit can refrain from inputting impressions and prompt the user to input impressions after the user has relaxed. This allows the user to input impressions at a more appropriate time by determining the priority of inputs according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can estimate the user's emotions and determine the priority of impression input based on the estimated user emotions.

[0074] The reception unit can provide an appropriate input method by taking into account device information of the user when inputting impressions. For example, when the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, when the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, when the user is using a desktop, the reception unit can also prioritize keyboard input. In this way, by taking device information into account, an easier-to-use input method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when inputting impressions, the reception unit can provide an appropriate input method by taking into account device information of the user.

[0075] The reception unit can analyze the user's social media activity and input related impressions when the user inputs impressions. For example, the reception unit automatically inputs impressions shared by the user on social media. The reception unit can also input impressions based on impressions of influencers the user follows. Furthermore, the reception unit can also input impressions of restaurants that the user has given high ratings to on social media. In this way, by analyzing social media activity, more relevant impressions can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's social media activity and input related impressions when the user inputs impressions.

[0076] The analysis unit can analyze the user's emotions and adjust the analysis method of the impression data based on the analyzed user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis to improve accuracy. Furthermore, when the user is busy, the analysis unit can perform a simplified analysis to quickly provide results. Furthermore, when the user is stressed, the analysis unit can perform a simple analysis to reduce the user's burden. This enables more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the user's emotions and adjust the analysis method of the impression data based on the analyzed user emotions.

[0077] When analyzing the impression data, the analysis unit can improve the accuracy of the analysis by referring to the user's past taste information. The analysis unit, for example, analyzes taste tendencies based on impression data previously input by the user. The analysis unit can also improve the accuracy of the analysis by referring to data on dishes that the user has previously given high ratings. Furthermore, the analysis unit can analyze the user's past taste information and reflect it in the analysis results. In this way, by referring to the past taste information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing the impression data, the analysis unit can improve the accuracy of the analysis by referring to the user's past taste information.

[0078] When analyzing the impression data, the analysis unit can take into account the user's current food preferences and allergy information. For example, if the user likes spicy food, the analysis unit can reflect that information in the analysis. The analysis unit can also reflect information in the analysis to help the user avoid ingredients to which they are allergic. Furthermore, if the user is a vegetarian, the analysis unit can also reflect that information in the analysis. This allows for more appropriate analysis by taking into account the user's preferences and allergy information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, when analyzing the impression data, the analysis unit can take into account the user's current food preferences and allergy information.

[0079] The analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display simplified analysis results when the user is busy. Furthermore, the analysis unit can display simple analysis results when the user is stressed. This allows for more appropriate information provision by adjusting the display method of the analysis results according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions.

[0080] The analysis unit can take into account the user's geographical location information when analyzing the impression data. For example, the analysis unit can prioritize analyzing impression data of restaurants close to the user's current location. The analysis unit can also focus on analyzing impression data of areas the user frequently visits. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing impression data of travel destinations. This makes it possible to perform more relevant analysis by taking into account the geographical location information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without using AI. For example, the analysis unit can take into account the user's geographical location information when analyzing the impression data.

[0081] When analyzing the opinion data, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes opinion data shared by the user on social media. The analysis unit can also analyze opinion data of influencers followed by the user. Furthermore, the analysis unit can analyze opinion data of restaurants that the user has given high ratings to on social media. This enables more relevant analysis by analyzing social media activity. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing the opinion data, the analysis unit can analyze the user's social media activity and analyze related data.

[0082] The guidance unit can analyze the user's emotions and adjust the recommended store guidance method based on the analyzed user's emotions. For example, when the user is relaxed, the guidance unit can provide detailed information about recommended stores. Furthermore, when the user is busy, the guidance unit can provide simplified information about recommended stores. Furthermore, when the user is stressed, the guidance unit can provide simple and intuitive information about recommended stores. This enables more appropriate store guidance by adjusting the guidance method according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guidance unit can be performed using, for example, AI, or without AI. For example, the guidance unit can analyze the user's emotions and adjust the recommended store guidance method based on the analyzed user's emotions.

[0083] When providing recommended restaurants, the guidance unit can select the most suitable restaurant by referring to the user's past visit history. For example, the guidance unit can prioritize restaurants that the user has given high ratings to in the past. The guidance unit can also prioritize restaurants that the user frequently visits. Furthermore, the guidance unit can also newly recommend restaurants that the user has not visited before. This makes it possible to provide more appropriate restaurant guidance by referring to the user's past visit history. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing recommended restaurants, the guidance unit can select the most suitable restaurant by referring to the user's past visit history.

[0084] The guidance unit can provide guidance on recommended restaurants while taking into consideration the user's current dietary preferences and allergy information. For example, if the user likes spicy food, the guidance unit can provide guidance on restaurants that serve spicy food. The guidance unit can also provide guidance on restaurants that do not use ingredients to which the user is allergic, in order to avoid such ingredients. Furthermore, if the user is vegetarian, the guidance unit can also provide guidance on restaurants that serve vegetarian menus. This allows for more appropriate restaurant guidance by taking into consideration the user's preferences and allergy information. Some or all of the above-described processing by the guidance unit can be performed using, for example, AI, or can be performed without using AI. For example, the guidance unit can provide guidance on recommended restaurants while taking into consideration the user's current dietary preferences and allergy information.

[0085] The guidance unit can analyze the user's emotions and determine the priority of recommended stores based on the analyzed user's emotions. For example, when the user is relaxed, the guidance unit can prioritize stores that the user is likely to be interested in. Furthermore, when the user is busy, the guidance unit can prioritize stores that are important to the user. Furthermore, when the user is stressed, the guidance unit can prioritize stores that are relaxing. This enables more appropriate store guidance by determining the priority according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can analyze the user's emotions and determine the priority of recommended stores based on the analyzed user's emotions.

[0086] When providing recommended stores, the guidance unit can provide the most appropriate store by taking into consideration the user's geographical location information. For example, the guidance unit can provide priority guidance to restaurants close to the user's current location. The guidance unit can also provide priority guidance to restaurants in areas the user frequently visits. Furthermore, if the user is traveling, the guidance unit can also provide priority guidance to restaurants in the user's travel destination. This makes it possible to provide more appropriate store guidance by taking into consideration the geographical location information. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing recommended stores, the guidance unit can provide priority guidance to the most appropriate store by taking into consideration the user's geographical location information.

[0087] The guidance unit can analyze the user's social media activity and provide related restaurants when providing recommended restaurants. For example, the guidance unit can provide restaurants that the user has given high ratings on social media. The guidance unit can also provide restaurants that have been visited by influencers the user follows. Furthermore, the guidance unit can also provide restaurants that the user has shared on social media. This enables more appropriate restaurant guidance by analyzing social media activity. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can analyze the user's social media activity and provide related restaurants when providing recommended restaurants. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about restaurants visited by the user using the camera 42 and communication I / F 44 of the smart device 14, and stores the information in the database 24 via the identification processing unit 290 of the data processing device 12. The reception unit inputs the user's impressions using the touch panel 38A and microphone 38B of the smart device 14, and transmits the input to the data processing device 12 via the control unit 46A. The analysis unit analyzes the impression data collected by the identification processing unit 290 of the data processing device 12 and identifies the user's taste information. The guidance unit provides guidance to recommended restaurants based on the taste information identified by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about restaurants visited by the user using the camera 42 and communication I / F 44 of the smart glasses 214, and stores the information in the database 24 via the identification processing unit 290 of the data processing device 12. The reception unit inputs the user's impressions using the microphone 238 of the smart glasses 214, and transmits the input to the data processing device 12 via the control unit 46A. The analysis unit analyzes the impression data collected by the identification processing unit 290 of the data processing device 12 and identifies the user's taste information. The guidance unit provides guidance to recommended restaurants based on the taste information identified by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects information about restaurants visited by the user using the camera 42 and communication I / F 44 of the headset terminal 314, and stores the information in the database 24 by the identification processing unit 290 of the data processing device 12. The reception unit inputs the user's impressions using the microphone 238 of the headset terminal 314, and transmits the input to the data processing device 12 by the control unit 46A. The analysis unit analyzes the impression data collected by the identification processing unit 290 of the data processing device 12 and identifies the user's taste information. The guidance unit provides guidance to recommended restaurants based on the taste information identified by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about restaurants visited by the user using the camera 42 and communication I / F 44 of the robot 414, and stores the information in the database 24 by the identification processing unit 290 of the data processing device 12. The reception unit inputs the user's impressions using the microphone 238 of the robot 414, and transmits the input to the data processing device 12 by the control unit 46A. The analysis unit analyzes the impression data collected by the identification processing unit 290 of the data processing device 12 and identifies the user's taste information. The guidance unit provides guidance to recommended restaurants based on the taste information identified by the identification processing unit 290 of the data processing device 12.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The restaurant search system can further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., blood pressure, heart rate, blood sugar level, etc.) and provides it to the analysis unit. Based on the collected health data, the analysis unit can identify restaurants that are suitable for the user's health condition. For example, if the user has high blood pressure, it can recommend restaurants that offer low-salt menus. Also, if the user has diabetes, it can recommend restaurants that offer low-carbohydrate menus. This makes it possible to provide restaurant guidance that suits the user's health condition.

[0090] The restaurant search system can further include a history management unit that records the user's dining history. The history management unit records detailed information about the dishes and restaurants the user has eaten in the past and provides it to the analysis unit. The analysis unit can analyze the user's dining habits based on the data provided by the history management unit and identify recommended restaurants. For example, if the user has frequently ordered a particular dish in the past, it can recommend restaurants that serve that dish. It can also suggest new restaurants that the user has not visited before. This makes it possible to provide more personalized restaurant guidance based on the user's dining history.

[0091] The restaurant search system can further include a learning unit that learns the user's food preferences. The learning unit continuously learns the user's food preferences based on the user's impression data and visit history, and provides the learning to the analysis unit. The analysis unit can identify restaurants that match the user's latest food preferences based on the data provided by the learning unit. For example, if the user has recently developed a preference for spicy food, the analysis unit can recommend restaurants that serve spicy food. Also, if the user wants to try a new dish, the analysis unit can suggest restaurants that serve that dish. This makes it possible to provide restaurant guidance that responds to changes in the user's food preferences.

[0092] The restaurant search system can further include an evaluation unit that evaluates the user's satisfaction with the meal. The evaluation unit evaluates the user's satisfaction with the meal at the restaurant visited and provides the data to the analysis unit. Based on the data provided by the evaluation unit, the analysis unit can identify restaurants with high user satisfaction and recommend them as recommended restaurants. For example, it can prioritize recommending restaurants that the user has given high ratings to in the past. Furthermore, to avoid restaurants with low user satisfaction, it can also select recommended restaurants taking that information into consideration. This makes it possible to recommend restaurants based on the user's satisfaction.

[0093] The restaurant search system can further include a frequency management unit that manages the frequency of a user's meal visits. The frequency management unit records how often a user visits a particular restaurant and provides that data to the analysis unit. Based on the data provided by the frequency management unit, the analysis unit can identify restaurants that the user frequently visits and recommend them as recommended restaurants. For example, it can prioritize recommendations of restaurants that the user visits once a week. It can also suggest new restaurants that the user has not visited for a long time. This makes it possible to recommend restaurants based on the user's visit frequency.

[0094] The restaurant search system can further analyze the user's emotions and adjust the restaurant rating based on the analyzed user's emotions. For example, if the user is relaxed, a detailed rating can be provided. If the user is busy, a simplified rating can be provided. Furthermore, if the user is stressed, a simple rating can be provided. This allows for more appropriate ratings by adjusting the rating method according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, a detailed rating can be provided.

[0095] The restaurant search system can further analyze the user's emotions and display restaurant reviews based on the analyzed user's emotions. For example, if the user is relaxed, a detailed review can be displayed. If the user is busy, a simplified review can be displayed. Furthermore, if the user is stressed, a simple review can be displayed. This allows for more appropriate information to be provided by adjusting the way reviews are displayed according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, a detailed review can be displayed.

[0096] The restaurant search system can further analyze the user's emotions and suggest restaurant menus based on the analyzed user's emotions. For example, if the user is relaxed, a detailed menu can be suggested. If the user is busy, a simplified menu can be suggested. Furthermore, if the user is stressed, a simple menu can be suggested. This allows for more appropriate suggestions by adjusting the menu suggestion method according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, a detailed menu can be suggested.

[0097] The restaurant search system can further analyze the user's emotions and provide restaurant coupons based on the analyzed user's emotions. For example, if the user is relaxed, a detailed coupon can be provided. If the user is busy, a simplified coupon can be provided. Furthermore, if the user is stressed, a simple coupon can be provided. This allows for more appropriate coupon provision by adjusting the coupon provision method according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, a detailed coupon can be provided.

[0098] The restaurant search system can further analyze the user's emotions and provide restaurant event information based on the analyzed user's emotions. For example, if the user is relaxed, detailed event information can be provided. If the user is busy, simplified event information can be provided. Furthermore, if the user is stressed, simple event information can be provided. This allows for more appropriate information provision by adjusting the way event information is provided according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, detailed event information can be provided.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects information about restaurants the user has visited in the past or information obtained from search results. For example, detailed data such as the names, menus, and ratings of restaurants visited by the user is collected and stored in a database. Step 2: The reception unit inputs the user's impressions of the food and collects the impression data. For example, the user may input an impression such as "This dish was very delicious," and the impression data entered into the system is saved in a database. Step 3: The analysis unit analyzes the collected impression data and identifies the user's taste information. For example, if a user frequently enters impressions such as "I like spicy food," the system will determine that the user likes spicy food. This analysis is performed by the generation AI. Step 4: The guidance section recommends restaurants that suit the user based on the identified taste information. For example, if the user is identified as liking spicy food, the system will recommend restaurants that are known for their delicious spicy food. This recommendation of restaurants is also done by the generation AI.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 collection unit that collects information about restaurants that the user has visited in the past or information obtained from search results; A reception unit for inputting user impressions of the food; an analysis unit that analyzes the impression data collected by the unit and identifies taste information of the user; a guidance unit that provides guidance on recommended stores based on the taste information identified by the analysis unit. A system characterized by:

2. The collecting unit Collect detailed data on the name or menu of the restaurant visited by the user, as well as the rating 2. The system of claim 1.

3. The reception unit Users input their impressions of the food they ate and collect the impression data.

2. The system of claim 1.

4. The analysis unit Analyze the collected feedback data and identify the user's taste information 2. The system of claim 1.

5. The guide unit is Recommend restaurants based on identified taste information 2. The system of claim 1.

6. The collecting unit Analyze user emotions and adjust the timing of collecting restaurant information based on the analyzed user emotions 2. The system of claim 1.

7. The collecting unit Analyze the user's past visit history and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit When collecting restaurant information, filter it based on the user's current dietary preferences or allergy information.

2. The system of claim 1.

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