system

Abstract

The system according to the embodiment aims to enable travelers to efficiently find and navigate to their desired destinations. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a navigation unit. The reception unit receives requests from a user. The analysis unit analyzes the requests received by the reception unit and proposes candidate locations. The selection unit selects a destination from the candidate locations proposed by the analysis unit. The navigation unit performs navigation based on the destination selected by the selection 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 make it difficult for travelers to efficiently find their desired destinations in unfamiliar locations, and there is room for improvement.

[0005] The system according to the embodiment aims to enable travelers to efficiently find and navigate to their desired destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a navigation unit. The reception unit receives requests from a user. The analysis unit analyzes the requests received by the reception unit and proposes candidate locations. The selection unit selects a destination from the candidate locations proposed by the analysis unit. The navigation unit performs navigation based on the destination selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to efficiently find and navigate to their desired destinations. [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 navigation system according to an embodiment of the present invention is a system that, when a traveler traveling by car or on foot has only a vague idea of ​​the shops (accommodations, restaurants, shopping, etc.) they want to visit in an unfamiliar place, determines their destination through conversational consultation while driving or walking, and then navigates them to their destination. While driving or walking, the user consults the system in a conversational format about the shops they want to visit. The system then analyzes the user's preferences and suggests appropriate candidate locations. When the user selects a destination from the suggested candidate locations, the system immediately begins navigation and takes the user to their destination. First, the user consults the system in a conversational format about the shops they want to visit. For example, the user might ask the system a question such as, "I want to eat some delicious ramen. Do you have any recommendations?" The system then considers the user's current location, past history, preferences, and so on to suggest appropriate candidate locations. Next, the system analyzes the user's preferences and suggests appropriate candidate locations. For example, the system might suggest multiple candidate locations, such as, "There are three delicious ramen restaurants nearby. Restaurant A is famous for its tonkotsu ramen, Restaurant B has a great soy sauce ramen, and Restaurant C is popular for its shio ramen." At this time, the system selects the most suitable candidate location, taking into consideration factors such as the user's preferences, distance from the current location, and past ratings. When the user selects a destination from the suggested candidate locations, the system immediately begins navigation and takes the user to the destination. For example, if the user selects "I want to go to Store B," the system begins navigation and guides the user to Store B. At this time, the system calculates the route in real time and provides the optimal route. This system allows users to easily find the shop they want to go to, even in unfamiliar places, and reach their destination smoothly. For example, if they suddenly feel like eating while traveling, they can simply consult the system, which will suggest an appropriate candidate location and navigate them to the destination. Similarly, even when walking, they can consult the system to find their destination and navigate them.This allows the navigation system to smoothly reach the desired shop even in unfamiliar areas.

[0029] A navigation system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a navigation unit. The reception unit receives a user's request. For example, the user can communicate a request such as "I want to eat delicious ramen" to the system. The reception unit can take into consideration the user's current location, past history, preferences, and the like when receiving the user's request. For example, the reception unit can acquire the user's current location using a GPS module. The reception unit can also refer to a database that stores the user's past history and preferences. The analysis unit analyzes the request received by the reception unit and suggests appropriate candidate locations. For example, the analysis unit can analyze the user's request and suggest nearby delicious ramen restaurants. The analysis unit can analyze the content of the user's conversation and suggest appropriate candidate locations. For example, the analysis unit can analyze the content of the user's conversation using voice recognition technology or natural language processing technology. The selection unit selects a destination from the candidate locations suggested by the analysis unit. For example, the user can select "Shop B" from the suggested ramen restaurants. The selection unit can determine a destination based on the user's selection. The navigation unit performs navigation based on the destination selected by the selection unit. For example, the navigation unit can guide the user to "Store B." The navigation unit can provide an optimal route using an algorithm that calculates routes in real time. For example, the navigation unit can calculate a route using Dijkstra's algorithm or the A* algorithm. This allows the navigation system according to the embodiment to smoothly reach a desired shop even in unfamiliar areas. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

[0030] A navigation system includes a GPS module for acquiring a user's current location. The GPS module is used to acquire the user's current location. For example, GPS modules are available in built-in and external types, with varying degrees of accuracy. Built-in GPS modules are often incorporated into smartphones and car navigation systems. External GPS modules are sometimes used via USB or Bluetooth® connections. The GPS module receives signals from satellites and determines the user's current location. For example, the GPS module receives signals from multiple satellites and calculates the user's position based on the time difference between the signals' arrival times. This allows the user's current location to be acquired with high accuracy. Some or all of the above-described processing in the GPS module may be performed using, for example, AI, or may be performed without AI. For example, the GPS module may determine the current location using an AI model that inputs satellite signals and outputs the user's current location.

[0031] The analysis unit can reference a database that stores the user's past history and preferences. The database is used to store the user's past history and preferences. For example, the database stores the places the user has visited in the past and the searches the user has performed. The past history includes visit history and search history. The visit history is information about places the user has visited in the past, and the search history is information about the searches the user has performed in the past. Preferences include the user's favorite shops and foods. Preference information is obtained from survey results, past selection history, and the like. The database stores this information and the analysis unit can refer to it when analyzing the user's wishes. For example, the analysis unit can obtain the user's past history and preferences from the database and suggest appropriate candidate locations based on the information. This allows the analysis unit to suggest more appropriate candidate locations taking the user's past history and preferences into consideration. Some or all of the above-described processing in the database may be performed using, for example, AI, or may be performed without AI. For example, the database can suggest candidate locations using an AI model that inputs the user's past history and preferences and outputs appropriate candidate locations.

[0032] The navigation unit can use an algorithm for calculating routes in real time. Algorithms for calculating routes in real time are used to provide an optimal route from a user's current location to a destination. Examples include Dijkstra's algorithm and the A* algorithm. Dijkstra's algorithm is an algorithm based on graph theory and is used to calculate the shortest route. The A* algorithm is an improved version of Dijkstra's algorithm and uses a heuristic function to improve search efficiency. Calculating a route in real time requires taking into account information about the current location, the destination, traffic conditions, and so on. For example, the navigation unit calculates the optimal route based on the distance from the current location to the destination, the required travel time, traffic congestion information, and so on. This allows the user to reach the destination via the optimal route. Some or all of the above-described processing in the algorithm for calculating routes in real time may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can calculate a route in real time using an AI model that inputs data about the current location, the destination, and traffic conditions and outputs the optimal route.

[0033] The analysis unit can analyze the content of the user's conversation and suggest appropriate candidate locations. The analysis unit uses speech recognition technology and natural language processing technology to analyze the content of the user's conversation. For example, speech recognition technology is used to convert the user's speech into text. Natural language processing technology is used to analyze text data and understand its meaning. The analysis unit can analyze the content of the user's conversation and understand the user's preferences. For example, if the user says, "I want to eat delicious ramen," the analysis unit can analyze that content and suggest ramen restaurants. When analyzing the content of the user's conversation, the analysis unit can also take into account the user's current location, past history, and preferences. This allows the analysis of the user's conversation to suggest more appropriate candidate locations. 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 perform speech recognition using an AI model that receives the user's speech data as input and outputs text data. The analysis unit can also perform natural language processing using an AI model that receives the text data as input and outputs the user's preferences.

[0034] The navigation unit can navigate to a destination based on a user's selection. The navigation unit uses an algorithm that calculates a route in real time to navigate to a destination based on the user's selection. For example, the navigation unit can calculate an optimal route based on a destination selected by the user. When navigating based on the user's selection, the navigation unit can also take into account the current location and traffic conditions. For example, the navigation unit can calculate an optimal route based on the distance and travel time from the current location to the destination, traffic congestion information, and the like. This allows the user to smoothly reach their desired destination. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

[0035] The navigation system includes a reception unit that analyzes the user's past request history and selects the optimal reception method. The reception unit analyzes the user's past request history and selects the optimal reception method. For example, the reception unit can automatically display requests frequently entered by the user in the past as candidates. It can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest requests to be used during a specific time period based on the user's past request history. The past request history includes visit history and search history. Visit history is information about places the user has visited in the past, and search history is information about what the user has searched for in the past. By analyzing the past request history, it is possible to provide the user with the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past request history and outputs the optimal reception method.

[0036] The navigation system includes a reception unit that performs filtering based on the user's current situation and areas of interest when receiving a request. The reception unit performs filtering based on the user's current situation and areas of interest when receiving a request. For example, if the user is currently located, candidate locations based on the user's surrounding areas of interest can be preferentially displayed. Also, if the user has a specific area of ​​interest, candidate locations related to that area can be preferentially suggested. Furthermore, optimal candidate locations can be filtered and displayed based on the user's current situation (time of day, weather, etc.). The current situation includes the user's current location, time of day, weather, etc. The area of ​​interest includes themes and categories in which the user is interested. In this way, filtering based on the user's current situation and areas of interest can suggest more appropriate candidate locations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that inputs data on the user's current situation and areas of interest and outputs optimal candidate locations.

[0037] The navigation system includes a reception unit that, when receiving a request, prioritizes receiving highly relevant requests by taking into account the user's geographical location information. When receiving a request, the reception unit prioritizes receiving highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize receiving requests for locations close to the user's current location. Furthermore, when the user is in a specific area, the reception unit can prioritize receiving requests within that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving requests along the user's route. Geographical location information includes GPS and Wi-Fi location information. Highly relevant requests include distance, ratings, popularity, and the like. Thus, by taking the user's geographical location information into account, highly relevant requests can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive requests using an AI model that inputs the user's geographical location information and outputs highly relevant requests.

[0038] The navigation system includes a reception unit that analyzes a user's social media activity and receives related requests when receiving a request. The reception unit analyzes the user's social media activity and receives related requests when receiving a request. For example, the reception unit can receive requests based on places and events shared by the user on social media. The reception unit can also receive requests based on places visited by the user's social media followers. The reception unit can also receive requests based on places the user has "liked" on social media. Social media activity includes the content of posts and the number of "likes." This allows the reception unit to analyze the user's social media activity and receive related requests. 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 receive requests using an AI model that inputs data on the user's social media activity and outputs related requests.

[0039] The navigation system includes an analysis unit that adjusts the accuracy of the analysis based on the desired importance during analysis. The analysis unit adjusts the accuracy of the analysis based on the desired importance during analysis. For example, if the desire has a high importance, the analysis unit can perform a detailed analysis. If the desire has a medium importance, the analysis unit can perform an analysis with a moderate level of detail. If the desire has a low importance, the analysis unit can perform a concise analysis. The desired importance includes the user's rating, urgency, etc. In this way, by adjusting the level of detail of the analysis based on the desired importance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the accuracy of the analysis using an AI model that inputs the desired importance and outputs the accuracy of the analysis.

[0040] The navigation system includes an analysis unit that applies different analysis algorithms depending on the desired category during analysis. The analysis unit applies different analysis algorithms depending on the desired category during analysis. For example, an analysis algorithm for food ratings and menus can be applied to restaurant-related preferences. An analysis algorithm for room ratings and facilities can be applied to accommodation-related preferences. An analysis algorithm for product ratings and prices can be applied to shopping-related preferences. Desired categories include tourist spots, restaurants, shops, etc. Analysis algorithms include clustering and regression analysis. In this way, by applying different analysis algorithms depending on the desired category, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the desired category and outputs an appropriate analysis algorithm.

[0041] The navigation system includes an analysis unit that determines the priority of analysis based on the desired submission time during analysis. The analysis unit determines the priority of analysis based on the desired submission time during analysis. For example, if the request was submitted recently, the analysis unit can prioritize the analysis. Also, if the request was submitted in the past, the analysis unit can postpone the analysis. Furthermore, if the request was submitted during a specific time period, the priority can be determined based on that time period. The desired submission time includes the submission date and time and the submission order, etc. In this way, by determining the priority of analysis based on the desired submission time, more appropriate analysis results can be provided. 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 determine the priority using an AI model that inputs the desired submission time and outputs the analysis priority.

[0042] The navigation system includes an analysis unit that adjusts the order of analysis based on desired relevance during analysis. The analysis unit adjusts the order of analysis based on desired relevance during analysis. For example, if the desired relevance is high, the analysis unit can prioritize analysis. If the desired relevance is medium, the analysis unit can perform analysis in a moderate order. Furthermore, if the desired relevance is low, the analysis unit can postpone analysis. Desired relevance includes distance, rating, popularity, etc. In this way, by adjusting the order of analysis based on the desired relevance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order using an AI model that inputs the desired relevance and outputs the order of analysis.

[0043] The navigation system includes a selection unit that adjusts the accuracy of selection based on the importance of a candidate location during selection. The selection unit adjusts the accuracy of selection based on the importance of the candidate location during selection. For example, if a candidate location has high importance, the selection unit can provide detailed information. If a candidate location has medium importance, the selection unit can provide information with moderate detail. If a candidate location has low importance, the selection unit can provide concise information. The importance of a candidate location includes a user's rating, urgency, etc. Thus, by adjusting the level of selection detail based on the importance of the candidate location, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can adjust the accuracy using an AI model that inputs the importance of a candidate location and outputs the accuracy of the selection.

[0044] The navigation system includes a selection unit that applies different selection algorithms depending on the category of the candidate location during selection. The selection unit applies different selection algorithms depending on the category of the candidate location during selection. For example, a food rating and menu selection algorithm can be applied to restaurant-related candidate locations. A room rating and facility selection algorithm can be applied to accommodation-related candidate locations. A product rating and price selection algorithm can be applied to shopping-related candidate locations. Candidate location categories include tourist destinations, restaurants, shops, etc. Selection algorithms include clustering and regression analysis. By applying different selection algorithms depending on the category of the candidate location, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can apply an algorithm using an AI model that inputs the category of the candidate location and outputs an appropriate selection algorithm.

[0045] The navigation system includes a selection unit that, at the time of selection, determines a selection priority based on the submission date of the candidate locations. The selection unit, at the time of selection, determines the selection priority based on the submission date of the candidate locations. For example, if a candidate location was submitted recently, the selection unit can select it preferentially. Also, if a candidate location was submitted in the past, the selection unit can select it later. Furthermore, if a candidate location was submitted during a specific time period, the priority can be determined based on that time period. The submission date of the candidate locations includes the submission date and time, the submission order, etc. In this way, by determining the selection priority based on the submission date of the candidate locations, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can determine the priority using an AI model that inputs the submission date of the candidate locations and outputs the selection priority.

[0046] The navigation system includes a selection unit that adjusts the order of selection based on the relevance of candidate locations during selection. The selection unit adjusts the order of selection based on the relevance of candidate locations during selection. For example, if a candidate location has high relevance, the selection unit can select it preferentially. If a candidate location has medium relevance, the selection unit can select it in a moderate order. Furthermore, if a candidate location has low relevance, the selection unit can select it later. The relevance of candidate locations includes distance, rating, popularity, etc. Thus, by adjusting the order of selection based on the relevance of candidate locations, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can adjust the order using an AI model that inputs the relevance of candidate locations and outputs the order of selection.

[0047] The navigation system includes a navigation unit that, during navigation, selects an optimal navigation method by referring to the user's past navigation history. The navigation unit selects the optimal navigation method by referring to the user's past navigation history. For example, the navigation unit can suggest an optimal navigation method based on routes the user has used in the past. Furthermore, the navigation unit can suggest a navigation method that avoids congestion based on the user's past navigation history. Furthermore, the navigation unit can analyze the user's past navigation history and suggest the most efficient navigation method. The past navigation history includes visit history, search history, etc. Thus, by referring to the user's past navigation history, a more appropriate navigation method can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can select a method using an AI model that inputs the user's past navigation history and outputs the optimal navigation method.

[0048] The navigation system includes a navigation unit that customizes a navigation method based on a user's current situation during navigation. The navigation unit customizes a navigation method based on a user's current situation during navigation. For example, if a user is traveling on foot, the navigation unit can provide a navigation method for pedestrians. If a user is traveling by car, the navigation unit can provide a navigation method for vehicles. If a user is traveling by bicycle, the navigation unit can provide a navigation method for bicycles. The current situation includes a current location, a time of day, weather, etc. The navigation method includes walking, car, bicycle, etc. In this way, by customizing the navigation method based on the user's current situation, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can customize the navigation method using an AI model that inputs the user's current situation and outputs a navigation method.

[0049] The navigation system includes a navigation unit that selects an optimal navigation method by taking into account the user's geographical location information. The navigation unit selects the optimal navigation method by taking into account the user's geographical location information. For example, the navigation unit can prioritize navigation to a location close to the user's current location. Furthermore, if the user is in a specific area, the navigation unit can prioritize navigation within that area. Furthermore, if the user is moving, the navigation unit can prioritize navigation along the user's route. The geographical location information includes GPS and Wi-Fi location information. The optimal navigation method includes real-time traffic information and the shortest route. This allows for providing a more appropriate navigation method by taking the user's geographical location information into account. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can select a method using an AI model that inputs the user's geographical location information and outputs the optimal navigation method.

[0050] The navigation system includes a navigation unit that analyzes a user's social media activity and suggests a navigation method during navigation. The navigation unit analyzes the user's social media activity and suggests a navigation method during navigation. For example, navigation can be performed based on places and events shared by the user on social media. Navigation can also be performed based on places visited by the user's social media followers. Navigation can also be performed based on places the user has "liked" on social media. Social media activity includes the content of posts and the number of "likes." Navigation methods include walking, driving, cycling, etc. This allows for the analysis of the user's social media activity to provide a more appropriate navigation method. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can suggest a method using an AI model that inputs data on the user's social media activity and outputs a navigation method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] Navigation systems can obtain not only the user's current location, but also the user's moving speed and direction. For example, if the user is driving on a highway, the system can prioritize suggesting service areas and parking areas along the highway. If the user is traveling on foot, the system can suggest candidate locations within walking distance. Furthermore, if the user is traveling by bicycle, the system can suggest candidate locations with bicycle-only lanes and bicycle parking areas. This makes it possible to suggest the most suitable candidate locations according to the user's travel situation.

[0053] The navigation system can refer not only to the user's past history, but also to the history of the user's friends and family. For example, it can prioritize ramen restaurants that the user's friends have highly rated. It can also suggest cafes that the user's family frequently visits. It can also suggest places that the user has "liked" on social media. This allows the system to utilize the user's social network to suggest more personalized candidate locations.

[0054] Navigation systems not only calculate routes in real time, but also select the type of route based on the user's preferences. For example, if the user wants to enjoy the scenery, the system can suggest a scenic route. If the user is in a hurry, the system can suggest a route that will get them there in the shortest time. Furthermore, if the user wants to avoid traffic jams, the system can suggest a route with less traffic congestion. This allows the system to provide the optimal route according to the user's preferences.

[0055] The navigation unit can take into account the user's past selection history when navigating to a destination based on the user's selection. For example, it can prioritize and suggest routes that the user has previously selected. It can also exclude routes that the user has avoided in the past. It can also suggest routes that the user has previously given high ratings. This makes it possible to provide more personalized navigation by utilizing the user's past selection history.

[0056] The navigation system can select the optimal reception method by taking into consideration not only the user's past reception request history but also the user's current situation and environment. For example, if the user is driving a car, it can prioritize voice input. Also, if the user is using public transportation, it can prioritize text input. Furthermore, if the user is in a quiet place, it can provide detailed input options. This makes it possible to provide the optimal reception method according to the user's current situation and environment.

[0057] When receiving a request, the reception unit not only filters based on the user's current situation and areas of interest, but also takes into account the user's past behavioral patterns. For example, it can prioritize and suggest places that the user has visited in the past during a specific time period. It can also suggest places that the user has visited in the past under specific weather conditions. It can also suggest places that the user has visited in the past during a specific event period. This makes it possible to utilize the user's past behavioral patterns to suggest more appropriate candidate locations.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives a request from the user. For example, the user can communicate a request such as "I want to eat delicious ramen" to the system. When receiving the user's request, the reception unit can take into consideration the user's current location, past history, preferences, etc. For example, the reception unit can obtain the user's current location using a GPS module. The reception unit can also refer to a database that stores the user's past history and preferences. Step 2: The analysis unit analyzes the requests received by the reception unit and suggests suitable candidate locations. For example, the analysis unit can analyze the user's requests and suggest a nearby delicious ramen restaurant. The analysis unit can analyze the content of the user's conversation and suggest suitable candidate locations. For example, the analysis unit can analyze the content of the user's conversation using voice recognition technology or natural language processing technology. Step 3: The selection unit selects a destination from the candidate locations proposed by the analysis unit. For example, the user can select "Store B" from the ramen restaurants proposed. The selection unit can determine the destination based on the user's selection. Step 4: The navigation unit performs navigation based on the destination selected by the selection unit. For example, the navigation unit can guide the user to "Store B." The navigation unit can provide an optimal route using an algorithm that calculates routes in real time. For example, the navigation unit can calculate a route using Dijkstra's algorithm or the A* algorithm. This allows the navigation system according to the embodiment to smoothly reach a desired shop even in unfamiliar areas. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

[0060] (Example 2) A navigation system according to an embodiment of the present invention is a system that, when a traveler traveling by car or on foot has only a vague idea of ​​the shops (accommodations, restaurants, shopping, etc.) they want to visit in an unfamiliar place, determines their destination through conversational consultation while driving or walking, and then navigates them to their destination. While driving or walking, the user consults the system in a conversational format about the shops they want to visit. The system then analyzes the user's preferences and suggests appropriate candidate locations. When the user selects a destination from the suggested candidate locations, the system immediately begins navigation and takes the user to their destination. First, the user consults the system in a conversational format about the shops they want to visit. For example, the user might ask the system a question such as, "I want to eat some delicious ramen. Do you have any recommendations?" The system then considers the user's current location, past history, preferences, and so on to suggest appropriate candidate locations. Next, the system analyzes the user's preferences and suggests appropriate candidate locations. For example, the system might suggest multiple candidate locations, such as, "There are three delicious ramen restaurants nearby. Restaurant A is famous for its tonkotsu ramen, Restaurant B has a great soy sauce ramen, and Restaurant C is popular for its shio ramen." At this time, the system selects the most suitable candidate location, taking into consideration factors such as the user's preferences, distance from the current location, and past ratings. When the user selects a destination from the suggested candidate locations, the system immediately begins navigation and takes the user to the destination. For example, if the user selects "I want to go to Store B," the system begins navigation and guides the user to Store B. At this time, the system calculates the route in real time and provides the optimal route. This system allows users to easily find the shop they want to go to, even in unfamiliar places, and reach their destination smoothly. For example, if they suddenly feel like eating while traveling, they can simply consult the system, which will suggest an appropriate candidate location and navigate them to the destination. Similarly, even when walking, they can consult the system to find their destination and navigate them.This allows the navigation system to smoothly reach the desired shop even in unfamiliar areas.

[0061] A navigation system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a navigation unit. The reception unit receives a user's request. For example, the user can communicate a request such as "I want to eat delicious ramen" to the system. The reception unit can take into consideration the user's current location, past history, preferences, and the like when receiving the user's request. For example, the reception unit can acquire the user's current location using a GPS module. The reception unit can also refer to a database that stores the user's past history and preferences. The analysis unit analyzes the request received by the reception unit and suggests appropriate candidate locations. For example, the analysis unit can analyze the user's request and suggest nearby delicious ramen restaurants. The analysis unit can analyze the content of the user's conversation and suggest appropriate candidate locations. For example, the analysis unit can analyze the content of the user's conversation using voice recognition technology or natural language processing technology. The selection unit selects a destination from the candidate locations suggested by the analysis unit. For example, the user can select "Shop B" from the suggested ramen restaurants. The selection unit can determine a destination based on the user's selection. The navigation unit performs navigation based on the destination selected by the selection unit. For example, the navigation unit can guide the user to "Store B." The navigation unit can provide an optimal route using an algorithm that calculates routes in real time. For example, the navigation unit can calculate a route using Dijkstra's algorithm or the A* algorithm. This allows the navigation system according to the embodiment to smoothly reach a desired shop even in unfamiliar areas. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

[0062] A navigation system includes a GPS module for acquiring a user's current location. The GPS module is used to acquire the user's current location. For example, GPS modules are available in built-in and external types, with varying degrees of accuracy. Built-in GPS modules are often incorporated into smartphones and car navigation systems. External GPS modules are sometimes used via USB or Bluetooth connections. The GPS module receives signals from satellites and determines the user's current location. For example, the GPS module receives signals from multiple satellites and calculates the user's position based on the time difference between the signals' arrival times. This allows the user's current location to be acquired with high accuracy. Some or all of the above-described processing in the GPS module may be performed using, for example, AI, or may be performed without AI. For example, the GPS module may determine the current location using an AI model that inputs satellite signals and outputs the user's current location.

[0063] The analysis unit can reference a database that stores the user's past history and preferences. The database is used to store the user's past history and preferences. For example, the database stores the places the user has visited in the past and the searches the user has performed. The past history includes visit history and search history. The visit history is information about places the user has visited in the past, and the search history is information about the searches the user has performed in the past. Preferences include the user's favorite shops and foods. Preference information is obtained from survey results, past selection history, and the like. The database stores this information and the analysis unit can refer to it when analyzing the user's wishes. For example, the analysis unit can obtain the user's past history and preferences from the database and suggest appropriate candidate locations based on the information. This allows the analysis unit to suggest more appropriate candidate locations taking the user's past history and preferences into consideration. Some or all of the above-described processing in the database may be performed using, for example, AI, or may be performed without AI. For example, the database can suggest candidate locations using an AI model that inputs the user's past history and preferences and outputs appropriate candidate locations.

[0064] The navigation unit can use an algorithm for calculating routes in real time. Algorithms for calculating routes in real time are used to provide an optimal route from a user's current location to a destination. Examples include Dijkstra's algorithm and the A* algorithm. Dijkstra's algorithm is an algorithm based on graph theory and is used to calculate the shortest route. The A* algorithm is an improved version of Dijkstra's algorithm and uses a heuristic function to improve search efficiency. Calculating a route in real time requires taking into account information about the current location, the destination, traffic conditions, and so on. For example, the navigation unit calculates the optimal route based on the distance from the current location to the destination, the required travel time, traffic congestion information, and so on. This allows the user to reach the destination via the optimal route. Some or all of the above-described processing in the algorithm for calculating routes in real time may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can calculate a route in real time using an AI model that inputs data about the current location, the destination, and traffic conditions and outputs the optimal route.

[0065] The analysis unit can analyze the content of the user's conversation and suggest appropriate candidate locations. The analysis unit uses speech recognition technology and natural language processing technology to analyze the content of the user's conversation. For example, speech recognition technology is used to convert the user's speech into text. Natural language processing technology is used to analyze text data and understand its meaning. The analysis unit can analyze the content of the user's conversation and understand the user's preferences. For example, if the user says, "I want to eat delicious ramen," the analysis unit can analyze that content and suggest ramen restaurants. When analyzing the content of the user's conversation, the analysis unit can also take into account the user's current location, past history, and preferences. This allows the analysis of the user's conversation to suggest more appropriate candidate locations. 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 perform speech recognition using an AI model that receives the user's speech data as input and outputs text data. The analysis unit can also perform natural language processing using an AI model that receives the text data as input and outputs the user's preferences.

[0066] The navigation unit can navigate to a destination based on a user's selection. The navigation unit uses an algorithm that calculates a route in real time to navigate to a destination based on the user's selection. For example, the navigation unit can calculate an optimal route based on a destination selected by the user. When navigating based on the user's selection, the navigation unit can also take into account the current location and traffic conditions. For example, the navigation unit can calculate an optimal route based on the distance and travel time from the current location to the destination, traffic congestion information, and the like. This allows the user to smoothly reach their desired destination. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

[0067] The navigation system includes a reception unit that estimates a user's emotions and adjusts the desired reception method based on the estimated user emotions. The reception unit estimates the user's emotions and adjusts the desired reception method based on the estimated user emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input their preferences. Emotion estimation is achieved 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. This allows the desired reception method to be adjusted according to the user's emotions, thereby providing a more appropriate interface. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can adjust the desired reception method using an AI model that inputs the user's emotion data and outputs the optimal reception method.

[0068] The navigation system includes a reception unit that analyzes the user's past request history and selects the optimal reception method. The reception unit analyzes the user's past request history and selects the optimal reception method. For example, the reception unit can automatically display requests frequently entered by the user in the past as candidates. It can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest a request to be used during a specific time period based on the user's past request history. The past request history includes a visit history and a search history. The visit history is information about places the user has visited in the past, and the search history is information about what the user has searched for in the past. By analyzing the past request history, it is possible to provide the user with the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can select a reception method using an AI model that inputs the user's past request history and outputs the optimal reception method.

[0069] The navigation system includes a reception unit that performs filtering based on the user's current situation and areas of interest when receiving a request. The reception unit performs filtering based on the user's current situation and areas of interest when receiving a request. For example, if the user is currently located, candidate locations based on the user's surrounding areas of interest can be preferentially displayed. Also, if the user has a specific area of ​​interest, candidate locations related to that area can be preferentially suggested. Furthermore, optimal candidate locations can be filtered and displayed based on the user's current situation (time of day, weather, etc.). The current situation includes the user's current location, time of day, weather, etc. The area of ​​interest includes themes and categories in which the user is interested. In this way, filtering based on the user's current situation and areas of interest can suggest more appropriate candidate locations. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that inputs data on the user's current situation and areas of interest and outputs optimal candidate locations.

[0070] The navigation system includes a reception unit that estimates a user's emotions and determines the priority of requests to be received based on the estimated user emotions. The reception unit estimates the user's emotions and determines the priority of requests to be received based on the estimated user emotions. For example, if the user is excited, entertainment-related requests can be received with priority. Also, if the user is tired, relaxation requests can be received with priority. Furthermore, if the user is hungry, restaurant-related requests can be received with priority. Emotion estimation is achieved 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. This allows the priority of requests to be determined based on the user's emotions, thereby enabling more appropriate requests to be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority using an AI model that inputs user emotion data and outputs a priority order of requests.

[0071] The navigation system includes a reception unit that, when receiving a request, prioritizes receiving highly relevant requests by taking into account the user's geographical location information. When receiving a request, the reception unit prioritizes receiving highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize receiving requests for locations close to the user's current location. Furthermore, when the user is in a specific area, the reception unit can prioritize receiving requests within that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving requests along the user's route. Geographical location information includes GPS and Wi-Fi location information. Highly relevant requests include distance, ratings, popularity, and the like. Thus, by taking the user's geographical location information into account, highly relevant requests can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive requests using an AI model that inputs the user's geographical location information and outputs highly relevant requests.

[0072] The navigation system includes a reception unit that analyzes a user's social media activity and receives related requests when receiving a request. The reception unit analyzes the user's social media activity and receives related requests when receiving a request. For example, the reception unit can receive requests based on places and events shared by the user on social media. The reception unit can also receive requests based on places visited by the user's social media followers. The reception unit can also receive requests based on places the user has "liked" on social media. Social media activity includes the content of posts and the number of "likes." This allows the reception unit to analyze the user's social media activity and receive related requests. 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 receive requests using an AI model that inputs data on the user's social media activity and outputs related requests.

[0073] The navigation system includes an analysis unit that estimates a user's emotions and adjusts the presentation of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for adjusting the presentation of the analysis based on the user's emotions, thereby providing more appropriate analysis results. 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 may adjust the presentation using an AI model that receives user emotion data as input and outputs a presentation of the analysis.

[0074] The navigation system includes an analysis unit that adjusts the accuracy of the analysis based on the desired importance during analysis. The analysis unit adjusts the accuracy of the analysis based on the desired importance during analysis. For example, if the desire has a high importance, the analysis unit can perform a detailed analysis. If the desire has a medium importance, the analysis unit can perform an analysis with a moderate level of detail. If the desire has a low importance, the analysis unit can perform a concise analysis. The desired importance includes the user's rating, urgency, etc. In this way, by adjusting the level of detail of the analysis based on the desired importance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the accuracy of the analysis using an AI model that inputs the desired importance and outputs the accuracy of the analysis.

[0075] The navigation system includes an analysis unit that applies different analysis algorithms depending on the desired category during analysis. The analysis unit applies different analysis algorithms depending on the desired category during analysis. For example, an analysis algorithm for food ratings and menus can be applied to restaurant-related preferences. An analysis algorithm for room ratings and facilities can be applied to accommodation-related preferences. An analysis algorithm for product ratings and prices can be applied to shopping-related preferences. Desired categories include tourist spots, restaurants, shops, etc. Analysis algorithms include clustering and regression analysis. In this way, by applying different analysis algorithms depending on the desired category, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply an analysis algorithm using an AI model that inputs the desired category and outputs an appropriate analysis algorithm.

[0076] The navigation system includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for adjusting the length of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may adjust the length of the analysis using an AI model that inputs user emotion data and outputs the length of the analysis.

[0077] The navigation system includes an analysis unit that determines the priority of analysis based on the desired submission time during analysis. The analysis unit determines the priority of analysis based on the desired submission time during analysis. For example, if the request was submitted recently, the analysis unit can prioritize the analysis. Also, if the request was submitted in the past, the analysis unit can postpone the analysis. Furthermore, if the request was submitted during a specific time period, the priority can be determined based on that time period. The desired submission time includes the submission date and time and the submission order, etc. In this way, by determining the priority of analysis based on the desired submission time, more appropriate analysis results can be provided. 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 determine the priority using an AI model that inputs the desired submission time and outputs the analysis priority.

[0078] The navigation system includes an analysis unit that adjusts the order of analysis based on desired relevance during analysis. The analysis unit adjusts the order of analysis based on desired relevance during analysis. For example, if the desired relevance is high, the analysis unit can prioritize analysis. If the desired relevance is medium, the analysis unit can perform analysis in a moderate order. Furthermore, if the desired relevance is low, the analysis unit can postpone analysis. Desired relevance includes distance, rating, popularity, etc. In this way, by adjusting the order of analysis based on the desired relevance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the order using an AI model that inputs the desired relevance and outputs the order of analysis.

[0079] The navigation system includes a selection unit that estimates a user's emotion and adjusts a selection method based on the estimated user's emotion. The selection unit estimates the user's emotion and adjusts the selection method based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can provide detailed selection options. If the user is in a hurry, the selection unit can provide concise selection options. Furthermore, if the user is excited, the selection unit can provide visually appealing selection options. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the selection method to be adjusted according to the user's emotion, thereby providing more appropriate selection options. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may adjust the method using an AI model that inputs user emotion data and outputs a selection method.

[0080] The navigation system includes a selection unit that adjusts the accuracy of selection based on the importance of a candidate location during selection. The selection unit adjusts the accuracy of selection based on the importance of the candidate location during selection. For example, if a candidate location has high importance, the selection unit can provide detailed information. If a candidate location has medium importance, the selection unit can provide information with moderate detail. If a candidate location has low importance, the selection unit can provide concise information. The importance of a candidate location includes a user's rating, urgency, etc. Thus, by adjusting the level of selection detail based on the importance of the candidate location, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can adjust the accuracy using an AI model that inputs the importance of a candidate location and outputs the accuracy of the selection.

[0081] The navigation system includes a selection unit that applies different selection algorithms depending on the category of the candidate location during selection. The selection unit applies different selection algorithms depending on the category of the candidate location during selection. For example, a food rating and menu selection algorithm can be applied to restaurant-related candidate locations. A room rating and facility selection algorithm can be applied to accommodation-related candidate locations. A product rating and price selection algorithm can be applied to shopping-related candidate locations. Candidate location categories include tourist destinations, restaurants, shops, etc. Selection algorithms include clustering and regression analysis. By applying different selection algorithms depending on the category of the candidate location, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can apply an algorithm using an AI model that inputs the category of the candidate location and outputs an appropriate selection algorithm.

[0082] The navigation system includes a selection unit that estimates a user's emotions and determines a selection priority based on the estimated user emotions. The selection unit estimates the user's emotions and determines a selection priority based on the estimated user emotions. For example, if the user is excited, the selection unit may prioritize entertainment-related candidate locations. Furthermore, if the user is tired, the selection unit may prioritize relaxation-related candidate locations. Furthermore, if the user is hungry, the selection unit may prioritize restaurant-related candidate locations. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for determining the selection priority based on the user's emotions, thereby providing more appropriate selection options. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit may determine the priority using an AI model that inputs user emotion data and outputs a selection priority.

[0083] The navigation system includes a selection unit that, at the time of selection, determines a selection priority based on the submission date of the candidate locations. The selection unit, at the time of selection, determines the selection priority based on the submission date of the candidate locations. For example, if a candidate location was submitted recently, the selection unit can select it preferentially. Also, if a candidate location was submitted in the past, the selection unit can select it later. Furthermore, if a candidate location was submitted during a specific time period, the priority can be determined based on that time period. The submission date of the candidate locations includes the submission date and time, the submission order, etc. In this way, by determining the selection priority based on the submission date of the candidate locations, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can determine the priority using an AI model that inputs the submission date of the candidate locations and outputs the selection priority.

[0084] The navigation system includes a selection unit that adjusts the order of selection based on the relevance of candidate locations during selection. The selection unit adjusts the order of selection based on the relevance of candidate locations during selection. For example, if a candidate location has high relevance, the selection unit can select it preferentially. If a candidate location has medium relevance, the selection unit can select it in a moderate order. Furthermore, if a candidate location has low relevance, the selection unit can select it later. The relevance of candidate locations includes distance, rating, popularity, etc. Thus, by adjusting the order of selection based on the relevance of candidate locations, more appropriate selection options can be provided. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can adjust the order using an AI model that inputs the relevance of candidate locations and outputs the order of selection.

[0085] The navigation system includes a navigation unit that estimates a user's emotions and adjusts a navigation method based on the estimated user emotions. The navigation unit estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. For example, if the user is nervous, the navigation unit can provide a simple, highly visible navigation method. If the user is relaxed, the navigation unit can provide a navigation method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a navigation method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for more appropriate navigation by adjusting the navigation method according to the user's emotions. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can adjust the method using an AI model that inputs user emotion data and outputs a navigation method.

[0086] The navigation system includes a navigation unit that, during navigation, selects an optimal navigation method by referring to the user's past navigation history. The navigation unit selects the optimal navigation method by referring to the user's past navigation history. For example, the navigation unit can suggest an optimal navigation method based on routes the user has used in the past. Furthermore, the navigation unit can suggest a navigation method that avoids congestion based on the user's past navigation history. Furthermore, the navigation unit can analyze the user's past navigation history and suggest the most efficient navigation method. The past navigation history includes visit history, search history, etc. Thus, by referring to the user's past navigation history, a more appropriate navigation method can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can select a method using an AI model that inputs the user's past navigation history and outputs the optimal navigation method.

[0087] The navigation system includes a navigation unit that customizes a navigation method based on a user's current situation during navigation. The navigation unit customizes a navigation method based on a user's current situation during navigation. For example, if a user is traveling on foot, the navigation unit can provide a navigation method for pedestrians. If a user is traveling by car, the navigation unit can provide a navigation method for vehicles. If a user is traveling by bicycle, the navigation unit can provide a navigation method for bicycles. The current situation includes a current location, a time of day, weather, etc. The navigation method includes walking, car, bicycle, etc. In this way, by customizing the navigation method based on the user's current situation, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can customize the navigation method using an AI model that inputs the user's current situation and outputs a navigation method.

[0088] The navigation system includes a navigation unit that estimates a user's emotions and determines navigation priorities based on the estimated user emotions. The navigation unit estimates the user's emotions and determines navigation priorities based on the estimated user emotions. For example, if the user is excited, the navigation unit can prioritize entertainment-related navigation. If the user is tired, the navigation unit can prioritize navigation to places where the user can relax. If the user is hungry, the navigation unit can prioritize navigation to restaurants. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for more appropriate navigation by determining navigation priorities based on the user's emotions. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or without AI. For example, the navigation unit may determine priorities using an AI model that inputs user emotion data and outputs navigation priorities.

[0089] The navigation system includes a navigation unit that selects an optimal navigation method by taking into account the user's geographical location information. The navigation unit selects the optimal navigation method by taking into account the user's geographical location information. For example, the navigation unit can prioritize navigation to a location close to the user's current location. Furthermore, if the user is in a specific area, the navigation unit can prioritize navigation within that area. Furthermore, if the user is moving, the navigation unit can prioritize navigation along the user's route. The geographical location information includes GPS and Wi-Fi location information. The optimal navigation method includes real-time traffic information and the shortest route. This allows for providing a more appropriate navigation method by taking the user's geographical location information into account. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can select a method using an AI model that inputs the user's geographical location information and outputs the optimal navigation method.

[0090] The navigation system includes a navigation unit that analyzes a user's social media activity and suggests a navigation method during navigation. The navigation unit analyzes the user's social media activity and suggests a navigation method during navigation. For example, navigation can be performed based on places and events shared by the user on social media. Navigation can also be performed based on places visited by the user's social media followers. Navigation can also be performed based on places the user has "liked" on social media. Social media activity includes the content of posts and the number of "likes." Navigation methods include walking, driving, cycling, etc. This allows for the analysis of the user's social media activity to provide a more appropriate navigation method. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without AI. For example, the navigation unit can suggest a method using an AI model that inputs data on the user's social media activity and outputs a navigation method. === Hard Collateral 1-1 === Each element of the navigation system described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the user's request. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's request and proposes appropriate candidate locations. The selection unit is realized by the control unit 46A of the smart device 14 and allows the user to select a destination from the proposed candidate locations. The navigation unit is realized by the identification processing unit 290 of the data processing device 12 and performs navigation based on the selected destination. A GPS module is often built into the smart device 14 and acquires the user's current location. === Hard Collateral 1-2 === Each element of the navigation system described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the user's request. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's request and proposes appropriate candidate locations. The selection unit is realized by the control unit 46A of the smart glasses 214 and allows the user to select a destination from the proposed candidate locations. The navigation unit is realized by the identification processing unit 290 of the data processing device 12 and performs navigation based on the selected destination. A GPS module is often built into the smart glasses 214 and acquires the user's current location. === Hard Collateral 1-3 === Each element of the navigation system described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and receives the user's wishes. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's wishes and proposes appropriate candidate locations. The selection unit is realized by the control unit 46A of the headset terminal 314 and allows the user to select a destination from the proposed candidate locations. The navigation unit is realized by the identification processing unit 290 of the data processing device 12 and performs navigation based on the selected destination. A GPS module is often built into the headset terminal 314 and acquires the user's current location. === Hard Collateral 1-4 === Each element of the navigation system described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the user's wishes. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the user's wishes and proposes appropriate candidate locations. The selection unit is realized, for example, by the control unit 46A of the robot 414 and allows the user to select a destination from the proposed candidate locations. The navigation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and performs navigation based on the selected destination. A GPS module is often built into the robot 414 and acquires the user's current location.

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

[0092] The navigation system can estimate the user's emotions and adjust the number of candidate locations to suggest based on the estimated emotions. For example, if the user is stressed, the system can suggest a smaller number of candidate locations to reduce the burden of selection. If the user is relaxed, the system can suggest a larger number of candidate locations to expand the options. Furthermore, if the user is excited, the system can prioritize suggesting visually appealing candidate locations. This allows the system to provide a more appropriate navigation experience by adjusting the suggestion method according to the user's emotions.

[0093] Navigation systems can obtain not only the user's current location, but also the user's moving speed and direction. For example, if the user is driving on a highway, the system can prioritize suggesting service areas and parking areas along the highway. If the user is traveling on foot, the system can suggest candidate locations within walking distance. Furthermore, if the user is traveling by bicycle, the system can suggest candidate locations with bicycle-only lanes and bicycle parking areas. This makes it possible to suggest the most suitable candidate locations according to the user's travel situation.

[0094] The navigation system can refer not only to the user's past history, but also to the history of the user's friends and family. For example, it can prioritize ramen restaurants that the user's friends have highly rated. It can also suggest cafes that the user's family frequently visits. It can also suggest places that the user has "liked" on social media. This allows the system to utilize the user's social network to suggest more personalized candidate locations.

[0095] Navigation systems not only calculate routes in real time, but also select the type of route based on the user's preferences. For example, if the user wants to enjoy the scenery, the system can suggest a scenic route. If the user is in a hurry, the system can suggest a route that will get them there in the shortest time. Furthermore, if the user wants to avoid traffic jams, the system can suggest a route with less traffic congestion. This allows the system to provide the optimal route according to the user's preferences.

[0096] When analyzing the content of a user's conversation, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the emotions. For example, if the user is excited, the analysis unit can perform a detailed analysis and suggest multiple candidate locations. If the user is tired, the analysis unit can perform a concise analysis and suggest a small number of candidate locations. Furthermore, if the user is relaxed, the analysis unit can perform a balanced analysis and suggest an appropriate number of candidate locations. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate candidate locations can be suggested.

[0097] The navigation unit can take into account the user's past selection history when navigating to a destination based on the user's selection. For example, it can prioritize and suggest routes that the user has previously selected. It can also exclude routes that the user has avoided in the past. It can also suggest routes that the user has previously given high ratings. This makes it possible to provide more personalized navigation by utilizing the user's past selection history.

[0098] The reception unit can estimate the user's emotion and adjust the interface design based on the estimated emotion. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. If the user is relaxed, a customizable interface can be provided. If the user is excited, a visually appealing interface can be provided. In this way, by adjusting the interface design according to the user's emotion, a more comfortable user experience can be provided.

[0099] The navigation system can select the optimal reception method by taking into consideration not only the user's past reception request history but also the user's current situation and environment. For example, if the user is driving a car, it can prioritize voice input. Also, if the user is using public transportation, it can prioritize text input. Furthermore, if the user is in a quiet place, it can provide detailed input options. This makes it possible to provide the optimal reception method according to the user's current situation and environment.

[0100] When receiving a request, the reception unit not only filters based on the user's current situation and areas of interest, but also takes into account the user's past behavioral patterns. For example, it can prioritize and suggest places that the user has visited in the past during a specific time period. It can also suggest places that the user has visited in the past under specific weather conditions. It can also suggest places that the user has visited in the past during a specific event period. This makes it possible to utilize the user's past behavioral patterns to suggest more appropriate candidate locations.

[0101] The reception unit can estimate the user's emotions and determine the desired priority based on the estimated emotions, as well as track changes in the user's emotions in real time and dynamically adjust the priority. For example, if the user is initially relaxed but begins to feel stressed midway through the trip, the system can reevaluate the priority and prioritize suggesting candidate locations that will help reduce stress. Also, if the user is excited, the system can prioritize suggesting candidate locations related to entertainment. This allows the system to dynamically adjust the priority according to changes in the user's emotions, thereby suggesting more appropriate candidate locations.

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

[0103] Step 1: The reception unit receives a request from the user. For example, the user can communicate a request such as "I want to eat delicious ramen" to the system. When receiving the user's request, the reception unit can take into consideration the user's current location, past history, preferences, etc. For example, the reception unit can obtain the user's current location using a GPS module. The reception unit can also refer to a database that stores the user's past history and preferences. Step 2: The analysis unit analyzes the requests received by the reception unit and suggests suitable candidate locations. For example, the analysis unit can analyze the user's requests and suggest a nearby delicious ramen restaurant. The analysis unit can analyze the content of the user's conversation and suggest suitable candidate locations. For example, the analysis unit can analyze the content of the user's conversation using voice recognition technology or natural language processing technology. Step 3: The selection unit selects a destination from the candidate locations proposed by the analysis unit. For example, the user can select "Store B" from the ramen restaurants proposed. The selection unit can determine the destination based on the user's selection. Step 4: The navigation unit performs navigation based on the destination selected by the selection unit. For example, the navigation unit can guide the user to "Store B." The navigation unit can provide an optimal route using an algorithm that calculates routes in real time. For example, the navigation unit can calculate a route using Dijkstra's algorithm or the A* algorithm. This allows the navigation system according to the embodiment to smoothly reach a desired shop even in unfamiliar areas. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can perform navigation using an AI model that inputs a destination selected by the user and outputs an optimal route.

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

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

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

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

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

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

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

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

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

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

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

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

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

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

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

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

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0175] [Explanation of symbols]

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

Claims

1. a reception unit that receives requests from users; an analysis unit that analyzes the requests received by the reception unit and proposes candidate locations; a selection unit that selects a destination from among the candidate sites proposed by the analysis unit; a navigation unit that performs navigation based on the destination selected by the selection unit. A system characterized by:

2. The reception unit Equipped with a GPS module to obtain the user's current location The system of claim 1 .

3. The analysis unit Referencing a database that stores the user's past history and preferences The system of claim 1 .

4. The navigation unit Uses real-time route calculation algorithms The system of claim 1 .

5. The analysis unit Analyzing user conversations and suggesting suitable locations The system of claim 1 .

6. The navigation unit Navigate to a destination based on the user's selection The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the desired reception method based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past request history and select the request method The system of claim 1 .

9. The reception unit Filter requests based on the user's current situation and interests The system of claim 1 .

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