System

Abstract

To provide a system according to an embodiment that proposes an appropriate destination or route on the basis of a user's ambiguous demand.SOLUTION: A system comprises a receiving unit, an analysis unit, a proposal unit, and a drop-in proposal unit. The receiving unit receives a user's unclear demand. The analysis unit analyzes the demand received by the receiving unit. The proposal unit proposes a destination or a route on the basis of the demand analyzed by the analysis unit. The drop-in proposal unit proposes a drop-in spot on the basis of the route proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem that it is difficult to suggest appropriate destinations and routes based on a user's vague requests.

[0005] The system according to the embodiment aims to propose appropriate destinations and routes based on the vague needs of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a detour proposal unit. The reception unit receives an unclear request from a user. The analysis unit analyzes the request received by the reception unit. The proposal unit proposes a destination or a route based on the request analyzed by the analysis unit. The detour proposal unit proposes a detour spot based on the route proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can suggest appropriate destinations and routes based on the vague desires of the user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​car navigation system according to the embodiment of the present invention is a system that suggests destinations and routes based on vague requests from a user. This AI car navigation system suggests destinations and routes based on vague conditions such as the user's mood, what they want to eat, and what they want to see. For example, the system suggests recommended driving routes and detour spots in response to user requests such as "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." First, the user inputs vague conditions into the car navigation system. For example, the user inputs requests such as "I want to relax today," "I want to eat delicious Japanese food," and "I want to interact with animals." This input can be done by voice input or text input. Next, the AI ​​analyzes the user's input and suggests appropriate destinations and routes. The AI ​​searches for places where you can relax, delicious Japanese restaurants, spots where you can interact with animals, etc. based on the user's mood and requests. For example, hot springs and parks are examples of "places where you can relax," and Japanese restaurants with a good reputation in the local area are examples of "delicious Japanese restaurants," and zoos and pet cafes are examples of "spots where you can interact with animals." Furthermore, the AI ​​calculates the optimal driving route from the user's current location for the proposed destinations and routes. At this time, the optimal route is proposed taking into consideration traffic conditions, weather, and the user's preferences. For example, a route that avoids traffic jams or a route with a beautiful view can be considered. The AI ​​also suggests detour spots according to the user's requests. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the AI ​​suggests Japanese restaurants that can be visited along the way. This allows the user to satisfy multiple requests in one drive. In this way, the AI ​​car navigation system of the present invention proposes optimal destinations and routes according to the user's vague requests, supporting a comfortable drive. The user can intuitively use the car navigation system without performing complicated operations, and can enjoy driving on holidays even more. This allows the AI ​​car navigation system to propose optimal destinations and routes based on the user's vague requests, supporting a comfortable drive.

[0029] The AI ​​car navigation system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a detour suggestion unit. The reception unit accepts a vague request from a user. The vague request from a user includes, but is not limited to, for example, "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can accept the request using voice input or text input. For example, when a user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. The analysis unit analyzes the request accepted by the reception unit. The analysis unit analyzes the user's request using, for example, natural language processing technology. For example, the analysis unit analyzes the user's request as text data and identifies a place where you can relax, a delicious Japanese restaurant, a spot where you can interact with animals, and the like. The suggestion unit proposes a destination and a route based on the request analyzed by the analysis unit. The suggestion unit suggests, for example, hot springs and parks as places to relax, Japanese restaurants that are well-known locally as delicious Japanese restaurants, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location. The suggestion unit suggests the optimal route, for example, taking into consideration traffic conditions, weather, and the user's preferences. The detour suggestion unit suggests detour spots based on the route proposed by the suggestion unit. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be stopped at on the way. As a result, the AI ​​car navigation system according to the embodiment can suggest optimal destinations and routes based on the user's vague requests and support a comfortable drive. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's requests and outputs destinations and routes.

[0030] The reception unit receives the user's vague request. The user's vague request includes, but is not limited to, "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can receive the request using voice input or text input. For example, if the user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. Specifically, in the case of voice input, the user's utterance is converted into text data using voice recognition technology, and the text data is analyzed. Voice recognition technology includes a process of extracting voice features and breaking them down into phonemes and words. This allows the user's utterance to be accurately understood. In the case of text input, the text data entered by the user is used for analysis as is. When receiving a user's request, the reception unit can also refer to the user's past requests and behavior history. This makes it possible to understand the user's preferences and tendencies and collect basic data for making more appropriate suggestions. For example, if a user who requested a "place where I can relax" in the past chooses a hot spring resort, the hot spring resort can be preferentially suggested if the user makes a similar request next time. Furthermore, the reception unit can provide real-time feedback when receiving a user's request. For example, if the user says "a place where I can relax" during voice input, the reception unit can ask an additional question such as "What kind of place are you looking for specifically?" to make the request more specific. This allows the reception unit to accurately understand the user's vague request and prepare to proceed to the next analysis step.

[0031] The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the user's request using, for example, natural language processing technology. Specifically, the analysis unit uses natural language processing technology to analyze the user's request as text data and identify places where you can relax, delicious Japanese restaurants, spots where you can interact with animals, and the like. Natural language processing technology includes morphological analysis, context analysis, semantic analysis, and the like. In morphological analysis, text data is divided into words and the part of speech of each word is identified. In context analysis, the relationship between words and the context are understood to grasp the intention of the request. In semantic analysis, the meaning of words and phrases is understood and the specific content of the request is identified. For example, if a user inputs "I want to relax today," the analysis unit extracts the keyword "relax" and identifies places and activities related to the keyword. The analysis unit can also utilize external databases and APIs when analyzing the user's request. For example, it is possible to obtain information on places where you can relax and delicious Japanese restaurants by using APIs that provide tourist information and restaurant information. Furthermore, the analysis unit can utilize past data and statistical information when analyzing the user's request. For example, more appropriate suggestions can be made by referring to the places and activities selected by users who had similar requests in the past. This allows the analysis unit to provide basic data for converting the user's vague requests into specific suggestions, and prepare to proceed to the next suggestion step.

[0032] The suggestion unit proposes a destination and a route based on the request analyzed by the analysis unit. For example, the suggestion unit proposes hot springs and parks as places to relax, Japanese restaurants that are well-known locally as delicious Japanese restaurants, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location. The suggestion unit proposes the optimal route, for example, taking into account traffic conditions, weather, and the user's preferences. Specifically, the suggestion unit acquires real-time traffic information and calculates the optimal route taking into account information such as congestion and accidents. Weather information is also taken into account, and safe routes are prioritized in bad weather. Regarding the user's preferences, the suggestion unit preferentially proposes places and routes that the user prefers based on past behavior history and evaluation data. The suggestion unit can analyze these data using AI and make optimal suggestions. For example, the suggestion unit can make suggestions using an AI model that takes the user's request as input and outputs destinations and routes. The AI ​​model can make more appropriate suggestions by learning past data and understanding the user's preferences and tendencies. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can evaluate the suggested locations and routes, and the next suggestion can be improved based on the evaluation data. This allows the suggestion unit to suggest optimal destinations and routes based on the user's vague requests, supporting a comfortable drive.

[0033] The detour suggestion unit suggests a detour spot based on the route proposed by the suggestion unit. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. Specifically, the detour suggestion unit collects information on restaurants, tourist spots, rest areas, etc. on the proposed route, and identifies a detour spot that meets the user's request. The detour suggestion unit makes suggestions so that the user can make a detour smoothly, taking into account real-time traffic information and business information. For example, the detour suggestion unit confirms that a reputable Japanese restaurant is open on the way to a place where the user can relax, and suggests the Japanese restaurant as a detour spot. In addition, the detour suggestion unit can suggest a more appropriate detour spot by taking into account the user's preferences and past behavior history. For example, for a user who has visited Japanese restaurants in the past, the detour suggestion unit preferentially suggests Japanese restaurants. Furthermore, the detour suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can evaluate the proposed detour spot, and the next suggestion can be improved based on the evaluation data. This allows the detour suggestion unit to suggest optimal detour spots based on the user's vague requests, and support a comfortable drive.

[0034] The analysis unit may include a data reference unit that references the user's past data. The data reference unit, for example, references the user's past travel history and search history. For example, the data reference unit references related data based on places the user has visited in the past and keywords searched for. The data reference unit may also reference the user's past evaluation data. For example, the data reference unit suggests related spots based on spots that the user has given high ratings in the past. This improves the accuracy of the analysis by referring to the user's past data. A part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit may perform data reference using an AI model that inputs the user's past data and outputs related data.

[0035] The suggestion unit may include a situation consideration unit that considers traffic conditions or weather. The situation consideration unit, for example, refers to real-time traffic information and weather forecast data. For example, the situation consideration unit proposes a route to avoid traffic congestion. The situation consideration unit may also propose a route according to the weather. For example, the situation consideration unit proposes an indoor spot when it is raining, and an outdoor spot when it is sunny. This allows a more appropriate route to be proposed by considering the traffic conditions and weather. A part or all of the above-mentioned processing in the situation consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the situation consideration unit may make a proposal using an AI model that inputs real-time traffic information and weather forecast data and outputs an optimal route.

[0036] The suggestion unit can search for a place where you can relax, a delicious Japanese restaurant, or a spot where you can interact with animals. For example, the suggestion unit searches for a park or a hot spring as a place where you can relax, a Japanese restaurant that is well-known locally as a delicious Japanese restaurant, and a zoo or a pet cafe as a spot where you can interact with animals. For example, the suggestion unit searches for related spots based on the user's request. The suggestion unit can also suggest the optimal spot based on the user's current location and past data. This makes it possible to search for and suggest spots according to the user's request. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can perform a search using an AI model that inputs the user's request and outputs related spots.

[0037] The detour suggestion unit can suggest detour spots based on the user's request. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on my way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. For example, the detour suggestion unit suggests a related detour spot based on the user's request. The detour suggestion unit can also suggest an optimal detour spot based on the user's current location and past data. This makes it possible to satisfy multiple requests by suggesting detour spots according to the user's request. A part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the detour suggestion unit can make suggestions using an AI model that inputs the user's request and outputs related detour spots.

[0038] The suggestion unit can calculate an efficient driving route from the user's current location. The suggestion unit, for example, calculates the shortest distance and the shortest time from the user's current location to the destination. For example, the suggestion unit calculates an optimal driving route based on real-time traffic information and past traffic data. The suggestion unit can also propose an optimal route taking into account the user's preferences and weather. This allows for efficient driving by calculating an optimal driving route from the user's current location. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can perform calculations using an AI model that inputs the user's current location and destination and outputs an optimal driving route.

[0039] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the user's past request history. This makes it possible to select the optimal reception method by analyzing the user's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can make the selection using an AI model that inputs the user's past request history and outputs the optimal reception method.

[0040] The reception unit can perform filtering based on the user's current mood and physical condition when receiving a request. For example, when the user is tired, the reception unit preferentially suggests a place where the user can relax. In addition, when the user is in good health, the reception unit can also suggest an active spot. Furthermore, when the user is not feeling well, the reception unit can also suggest a nearby spot. In this way, by filtering based on the user's mood and physical condition, more appropriate requests can be received. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's mood and physical condition data and performs filtering.

[0041] When receiving a request, the reception unit can preferentially receive a highly relevant request in consideration of the geographical location information of the user. For example, the reception unit preferentially suggests spots close to the user's current location. In addition, when the user is in a specific area, the reception unit can also suggest popular spots in that area. Furthermore, when the user is traveling, the reception unit can also preferentially suggest tourist attractions. In this way, by considering the geographical location information of the user, a highly relevant request can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive a request using an AI model that receives a highly relevant request with priority using the geographical location information of the user as an input.

[0042] The reception unit can analyze the user's social media activity when receiving a request and receive related requests. For example, if the user posts "I want to relax" on social media, the reception unit can suggest a place where the user can relax. In addition, if the user posts "I want to eat Japanese food", the reception unit can also suggest a Japanese restaurant. In addition, if the user posts "I want to interact with animals", the reception unit can also suggest a spot where the user can interact with animals. In this way, related requests can be received by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can receive the request using an AI model that receives related requests using the user's social media activity data as input.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past data when analyzing the request. The analysis unit, for example, refers to the user's past visit history and suggests related spots. The analysis unit can also refer to the user's past evaluation data and suggest highly rated spots. Furthermore, the analysis unit can also refer to the user's past movement patterns and suggest the optimal route. As a result, the accuracy of the analysis is improved by referring to the user's past data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that uses the user's past data as input and improves the accuracy of the analysis.

[0044] The analysis unit can apply different analysis algorithms based on the category of the request when analyzing the request. For example, the analysis unit applies an algorithm that emphasizes a natural environment or a quiet place to a request for a place to relax. The analysis unit can also apply an algorithm that emphasizes restaurant ratings and menus to a request for a meal. Furthermore, the analysis unit can apply an algorithm that emphasizes the type of animal or the rating of the facility to a request for a spot where you can interact with animals. This allows for more appropriate analysis by applying an analysis algorithm according to the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the category of the request and applies different analysis algorithms.

[0045] The analysis unit can set the priority of the analysis based on the time of submission of the request when analyzing the request. For example, the analysis unit prioritizes the analysis of breakfast spots for a request submitted by the user in the morning. The analysis unit can also prioritize the analysis of lunch spots for a request submitted by the user in the afternoon. Furthermore, the analysis unit can also prioritize the analysis of dinner spots for a request submitted by the user in the evening. This allows for more appropriate analysis by determining the priority of the analysis based on the time of submission of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the setting using an AI model that inputs the time of submission of the request and sets the priority of the analysis.

[0046] The analysis unit can set the order of analysis based on the relevance of the requests when analyzing the requests. For example, when the user's requests are "a place to relax" and "a delicious Japanese restaurant", the analysis unit prioritizes analysis of highly relevant spots. In addition, when the user's requests are "want to interact with animals" and "a place to relax", the analysis unit can also prioritize analysis of highly relevant spots. In addition, when the user's requests are "want to interact with animals" and "a delicious Japanese restaurant", the analysis unit can also prioritize analysis of highly relevant spots. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the requests. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the setting using an AI model that inputs the relevance of the requests and sets the order of analysis.

[0047] The suggestion unit can set the level of detail of the proposal based on the importance of the destination or route at the time of proposal. For example, in the case of an important destination, the suggestion unit provides detailed information. In addition, in the case of low importance, the suggestion unit can also provide concise information. Furthermore, in the case of multiple destinations, the suggestion unit can adjust the level of detail according to the importance. This allows for more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the destination or route. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can perform the setting using an AI model that inputs the importance of the destination or route and sets the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms based on the category of the destination or route when making a suggestion. For example, the suggestion unit can apply an algorithm that emphasizes natural environments and quiet places to suggest places where you can relax. The suggestion unit can also apply an algorithm that emphasizes restaurant ratings and menus to suggest meals. Furthermore, the suggestion unit can apply an algorithm that emphasizes animal types and facility ratings to suggest spots where you can interact with animals. This allows for more appropriate suggestions by applying a suggestion algorithm according to the category of the destination or route. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that uses the category of the destination or route as input and applies different suggestion algorithms.

[0049] The suggestion unit can set the priority of the proposal based on the submission time of the destination or route at the time of proposal. For example, the suggestion unit prioritizes the proposal of a breakfast spot for a request submitted by the user in the morning. The suggestion unit can also prioritize the proposal of a lunch spot for a request submitted by the user in the afternoon. Furthermore, the suggestion unit can also prioritize the proposal of a dinner spot for a request submitted by the user in the evening. This enables more appropriate proposals by determining the priority of the proposal based on the submission time of the destination or route. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can set the priority of the proposal using an AI model that inputs the submission time.

[0050] The suggestion unit can set the order of suggestions based on the relevance of the destination or route at the time of suggestion. For example, when the user's request is "a place to relax" and "a delicious Japanese restaurant", the suggestion unit preferentially suggests highly relevant spots. In addition, when the user's request is "want to interact with animals" and "a place to relax", the suggestion unit can also preferentially suggest highly relevant spots. In addition, when the user's request is "want to interact with animals" and "a delicious Japanese restaurant", the suggestion unit can also preferentially suggest highly relevant spots. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the destination or route. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can set the order of suggestions using an AI model that inputs the relevance of the destination and route and sets the order of suggestions.

[0051] When proposing a detour spot, the detour suggestion unit can provide an appropriate suggestion by referring to the user's past detour history. The detour suggestion unit can, for example, suggest related spots based on detour spots visited by the user in the past. The detour suggestion unit can also suggest highly rated spots from the user's past detour history. Furthermore, the detour suggestion unit can also suggest optimal spots by referring to the user's past detour patterns. In this way, the optimal detour spot can be suggested by referring to the user's past detour history. A part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the detour suggestion unit can make a suggestion using an AI model that inputs the user's past detour history and suggests the optimal detour spot.

[0052] The detour suggestion unit can provide an appropriate suggestion when proposing a detour spot, taking into consideration the geographical location information of the user. For example, the detour suggestion unit preferentially suggests detour spots close to the user's current location. In addition, when the user is in a specific area, the detour suggestion unit can also suggest popular detour spots in that area. Furthermore, when the user is traveling, the detour suggestion unit can also preferentially suggest tourist attractions. In this way, the optimal detour spot can be suggested by taking into consideration the geographical location information of the user. Part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the detour suggestion unit can make a suggestion using an AI model that inputs the geographical location information of the user and suggests the optimal detour spot.

[0053] The data reference unit can adjust the analysis algorithm by referring to past data when referring to data. The data reference unit, for example, refers to the user's past visit history and preferentially refers to data on related spots. The data reference unit can also refer to the user's past evaluation data and preferentially refer to data on highly rated spots. Furthermore, the data reference unit can also refer to the user's past movement patterns and preferentially refer to data on optimal routes. This allows the analysis algorithm to be optimized by referring to past data. Part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit can make adjustments using an AI model that uses past data as input and adjusts the analysis algorithm.

[0054] The data reference unit can set the weighting of data based on the time of request submission when referencing data. For example, the data reference unit places emphasis on data of breakfast spots for requests submitted by the user in the morning. The data reference unit can also place emphasis on data of lunch spots for requests submitted by the user in the afternoon. Furthermore, the data reference unit can also place emphasis on data of dinner spots for requests submitted by the user in the evening. This allows more appropriate data to be referenced by weighting data based on the time of request submission. Part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit can perform the setting using an AI model that inputs the time of request submission and sets the weighting of data.

[0055] The situation consideration unit can provide an appropriate proposal by referring to the user's past driving history when considering traffic conditions or weather. The situation consideration unit can, for example, refer to the user's past driving history and propose a route that avoids congestion. The situation consideration unit can also refer to the user's past driving history and propose a route according to the weather. Furthermore, the situation consideration unit can also refer to the user's past driving history and propose an optimal rest point. This makes it possible to make a more appropriate proposal by referring to the user's past driving history. A part or all of the above-mentioned processing in the situation consideration unit can be performed, for example, using AI or can be performed without using AI. For example, the situation consideration unit can make a proposal using an AI model that inputs the user's past driving history and provides an appropriate proposal.

[0056] The situation consideration unit can provide an appropriate proposal by considering the geographical location information of the user when considering the traffic conditions or weather. For example, the situation consideration unit preferentially proposes a route close to the user's current location. In addition, when the user is in a specific area, the situation consideration unit can also propose a route that considers the traffic conditions of the area. Furthermore, when the user is traveling, the situation consideration unit can also preferentially propose a route that includes tourist attractions. This makes it possible to provide a more appropriate proposal by considering the geographical location information of the user. A part or all of the above-mentioned processing in the situation consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the situation consideration unit can make a proposal using an AI model that inputs the geographical location information of the user and provides an appropriate proposal.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When accepting an ambiguous request from a user, the reception unit can automatically display requests that have been frequently input in the past as candidates by referring to the user's past request history. For example, if the user has frequently input "a place to relax" in the past, the reception unit will preferentially display that request. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used during a specific time period from the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history.

[0059] The analysis unit may include a data reference unit that references the user's past data. The data reference unit references, for example, the user's past travel history or search history. For example, the data reference unit references related data based on places the user has visited in the past or keywords the user has searched for. The data reference unit may also reference the user's past evaluation data. For example, the data reference unit suggests related spots based on spots that the user has given high ratings in the past. This improves the accuracy of the analysis by referring to the user's past data.

[0060] The suggestion unit may include a situation consideration unit that considers traffic conditions or weather. The situation consideration unit, for example, refers to real-time traffic information or weather forecast data. For example, the situation consideration unit proposes a route to avoid traffic congestion. The situation consideration unit may also propose a route according to the weather. For example, the situation consideration unit proposes indoor spots when it is raining and outdoor spots when it is sunny. In this way, a more appropriate route can be proposed by considering the traffic conditions and weather.

[0061] The suggestion unit can search for places to relax, restaurants serving delicious Japanese food, or spots where one can interact with animals. For example, the suggestion unit searches for parks and hot springs as places to relax, Japanese restaurants that are well-known locally as restaurants serving delicious Japanese food, and zoos and pet cafes as spots where one can interact with animals. For example, the suggestion unit searches for related spots based on the user's request. The suggestion unit can also suggest optimal spots based on the user's current location and past data. This makes it possible to search for and suggest spots that meet the user's request.

[0062] The detour suggestion unit can suggest detour spots based on the user's request. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. For example, the detour suggestion unit suggests a related detour spot based on the user's request. In addition, the detour suggestion unit can also suggest an optimal detour spot based on the user's current location and past data. In this way, multiple requests can be met by suggesting detour spots according to the user's requests.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives the user's vague request. The user's vague requests include "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can receive requests using voice input or text input. For example, if the user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. Step 2: The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the user's request using natural language processing technology. For example, the analysis unit analyzes the user's request as text data and identifies places to relax, restaurants serving delicious Japanese food, spots where you can interact with animals, etc. Step 3: The suggestion unit suggests destinations and routes based on the requests analyzed by the analysis unit. The suggestion unit suggests hot springs and parks as places to relax, Japanese restaurants that are well-known locally for their delicious Japanese food, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location and suggests the optimal route taking into account traffic conditions, weather, the user's preferences, etc. Step 4: The detour suggestion unit suggests detour spots based on the route suggested by the suggestion unit. In response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests Japanese restaurants that can be visited on the way.

[0065] (Example 2) The AI ​​car navigation system according to the embodiment of the present invention is a system that suggests destinations and routes based on vague requests from a user. This AI car navigation system suggests destinations and routes based on vague conditions such as the user's mood, what they want to eat, and what they want to see. For example, the system suggests recommended driving routes and detour spots in response to user requests such as "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." First, the user inputs vague conditions into the car navigation system. For example, the user inputs requests such as "I want to relax today," "I want to eat delicious Japanese food," and "I want to interact with animals." This input can be done by voice input or text input. Next, the AI ​​analyzes the user's input and suggests appropriate destinations and routes. The AI ​​searches for places where you can relax, delicious Japanese restaurants, spots where you can interact with animals, etc. based on the user's mood and requests. For example, hot springs and parks are examples of "places where you can relax," and Japanese restaurants with a good reputation in the local area are examples of "delicious Japanese restaurants," and zoos and pet cafes are examples of "spots where you can interact with animals." Furthermore, the AI ​​calculates the optimal driving route from the user's current location for the proposed destinations and routes. At this time, the optimal route is proposed taking into consideration traffic conditions, weather, and the user's preferences. For example, a route that avoids traffic jams or a route with a beautiful view can be considered. The AI ​​also suggests detour spots according to the user's requests. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the AI ​​suggests Japanese restaurants that can be visited along the way. This allows the user to satisfy multiple requests in one drive. In this way, the AI ​​car navigation system of the present invention proposes optimal destinations and routes according to the user's vague requests, supporting a comfortable drive. The user can intuitively use the car navigation system without performing complicated operations, and can enjoy driving on holidays even more. This allows the AI ​​car navigation system to propose optimal destinations and routes based on the user's vague requests, supporting a comfortable drive.

[0066] The AI ​​car navigation system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a detour suggestion unit. The reception unit accepts a vague request from a user. The vague request from a user includes, but is not limited to, for example, "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can accept the request using voice input or text input. For example, when a user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. The analysis unit analyzes the request accepted by the reception unit. The analysis unit analyzes the user's request using, for example, natural language processing technology. For example, the analysis unit analyzes the user's request as text data and identifies a place where you can relax, a delicious Japanese restaurant, a spot where you can interact with animals, and the like. The suggestion unit proposes a destination and a route based on the request analyzed by the analysis unit. The suggestion unit suggests, for example, hot springs and parks as places to relax, Japanese restaurants that are well-known locally as delicious Japanese restaurants, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location. The suggestion unit suggests the optimal route, for example, taking into consideration traffic conditions, weather, and the user's preferences. The detour suggestion unit suggests detour spots based on the route proposed by the suggestion unit. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be stopped at on the way. As a result, the AI ​​car navigation system according to the embodiment can suggest optimal destinations and routes based on the user's vague requests and support a comfortable drive. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's requests and outputs destinations and routes.

[0067] The reception unit receives the user's vague request. The user's vague request includes, but is not limited to, "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can receive the request using voice input or text input. For example, if the user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. Specifically, in the case of voice input, the user's utterance is converted into text data using voice recognition technology, and the text data is analyzed. Voice recognition technology includes a process of extracting voice features and breaking them down into phonemes and words. This allows the user's utterance to be accurately understood. In the case of text input, the text data entered by the user is used for analysis as is. When receiving a user's request, the reception unit can also refer to the user's past requests and behavior history. This makes it possible to understand the user's preferences and tendencies and collect basic data for making more appropriate suggestions. For example, if a user who requested a "place where I can relax" in the past chooses a hot spring resort, the hot spring resort can be preferentially suggested if the user makes a similar request next time. Furthermore, the reception unit can provide real-time feedback when receiving a user's request. For example, if the user says "a place where I can relax" during voice input, the reception unit can ask an additional question such as "What kind of place are you looking for specifically?" to make the request more specific. This allows the reception unit to accurately understand the user's vague request and prepare to proceed to the next analysis step.

[0068] The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the user's request using, for example, natural language processing technology. Specifically, the analysis unit uses natural language processing technology to analyze the user's request as text data and identify places where you can relax, delicious Japanese restaurants, spots where you can interact with animals, and the like. Natural language processing technology includes morphological analysis, context analysis, semantic analysis, and the like. In morphological analysis, text data is divided into words and the part of speech of each word is identified. In context analysis, the relationship between words and the context are understood to grasp the intention of the request. In semantic analysis, the meaning of words and phrases is understood and the specific content of the request is identified. For example, if a user inputs "I want to relax today," the analysis unit extracts the keyword "relax" and identifies places and activities related to the keyword. The analysis unit can also utilize external databases and APIs when analyzing the user's request. For example, it is possible to obtain information on places where you can relax and delicious Japanese restaurants by using APIs that provide tourist information and restaurant information. Furthermore, the analysis unit can utilize past data and statistical information when analyzing the user's request. For example, more appropriate suggestions can be made by referring to the places and activities selected by users who had similar requests in the past. This allows the analysis unit to provide basic data for converting the user's vague requests into specific suggestions, and prepare to proceed to the next suggestion step.

[0069] The suggestion unit proposes a destination and a route based on the request analyzed by the analysis unit. For example, the suggestion unit proposes hot springs and parks as places to relax, Japanese restaurants that are well-known locally as delicious Japanese restaurants, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location. The suggestion unit proposes the optimal route, for example, taking into account traffic conditions, weather, and the user's preferences. Specifically, the suggestion unit acquires real-time traffic information and calculates the optimal route taking into account information such as congestion and accidents. Weather information is also taken into account, and safe routes are prioritized in bad weather. Regarding the user's preferences, the suggestion unit preferentially proposes places and routes that the user prefers based on past behavior history and evaluation data. The suggestion unit can analyze these data using AI and make optimal suggestions. For example, the suggestion unit can make suggestions using an AI model that takes the user's request as input and outputs destinations and routes. The AI ​​model can make more appropriate suggestions by learning past data and understanding the user's preferences and tendencies. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can evaluate the suggested locations and routes, and the next suggestion can be improved based on the evaluation data. This allows the suggestion unit to suggest optimal destinations and routes based on the user's vague requests, supporting a comfortable drive.

[0070] The detour suggestion unit suggests a detour spot based on the route proposed by the suggestion unit. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. Specifically, the detour suggestion unit collects information on restaurants, tourist spots, rest areas, etc. on the proposed route, and identifies a detour spot that meets the user's request. The detour suggestion unit makes suggestions so that the user can make a detour smoothly, taking into account real-time traffic information and business information. For example, the detour suggestion unit confirms that a reputable Japanese restaurant is open on the way to a place where the user can relax, and suggests the Japanese restaurant as a detour spot. In addition, the detour suggestion unit can suggest a more appropriate detour spot by taking into account the user's preferences and past behavior history. For example, for a user who has visited Japanese restaurants in the past, the detour suggestion unit preferentially suggests Japanese restaurants. Furthermore, the detour suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the user can evaluate the proposed detour spot, and the next suggestion can be improved based on the evaluation data. This allows the detour suggestion unit to suggest optimal detour spots based on the user's vague requests, and support a comfortable drive.

[0071] The analysis unit may include a data reference unit that references the user's past data. The data reference unit, for example, references the user's past travel history and search history. For example, the data reference unit references related data based on places the user has visited in the past and keywords searched for. The data reference unit may also reference the user's past evaluation data. For example, the data reference unit suggests related spots based on spots that the user has given high ratings in the past. This improves the accuracy of the analysis by referring to the user's past data. A part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit may perform data reference using an AI model that inputs the user's past data and outputs related data.

[0072] The suggestion unit may include a situation consideration unit that considers traffic conditions or weather. The situation consideration unit, for example, refers to real-time traffic information and weather forecast data. For example, the situation consideration unit proposes a route to avoid traffic congestion. The situation consideration unit may also propose a route according to the weather. For example, the situation consideration unit proposes an indoor spot when it is raining, and an outdoor spot when it is sunny. This allows a more appropriate route to be proposed by considering the traffic conditions and weather. A part or all of the above-mentioned processing in the situation consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the situation consideration unit may make a proposal using an AI model that inputs real-time traffic information and weather forecast data and outputs an optimal route.

[0073] The suggestion unit can search for a place where you can relax, a delicious Japanese restaurant, or a spot where you can interact with animals. For example, the suggestion unit searches for a park or a hot spring as a place where you can relax, a Japanese restaurant that is well-known locally as a delicious Japanese restaurant, and a zoo or a pet cafe as a spot where you can interact with animals. For example, the suggestion unit searches for related spots based on the user's request. The suggestion unit can also suggest the optimal spot based on the user's current location and past data. This makes it possible to search for and suggest spots according to the user's request. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can perform a search using an AI model that inputs the user's request and outputs related spots.

[0074] The detour suggestion unit can suggest detour spots based on the user's request. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on my way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. For example, the detour suggestion unit suggests a related detour spot based on the user's request. The detour suggestion unit can also suggest an optimal detour spot based on the user's current location and past data. This makes it possible to satisfy multiple requests by suggesting detour spots according to the user's request. A part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the detour suggestion unit can make suggestions using an AI model that inputs the user's request and outputs related detour spots.

[0075] The suggestion unit can calculate an efficient driving route from the user's current location. The suggestion unit, for example, calculates the shortest distance and the shortest time from the user's current location to the destination. For example, the suggestion unit calculates an optimal driving route based on real-time traffic information and past traffic data. The suggestion unit can also propose an optimal route taking into account the user's preferences and weather. This allows for efficient driving by calculating an optimal driving route from the user's current location. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can perform calculations using an AI model that inputs the user's current location and destination and outputs an optimal driving route.

[0076] The reception unit can estimate the user's emotion and adjust the reception method of an ambiguous request based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly input an ambiguous request. This allows a more appropriate request to be received by adjusting the reception method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can make adjustments using an AI model that uses the user's emotion data as input and adjusts the reception method.

[0077] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period from the user's past request history. This makes it possible to select the optimal reception method by analyzing the user's past request history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can make the selection using an AI model that inputs the user's past request history and outputs the optimal reception method.

[0078] The reception unit can perform filtering based on the user's current mood and physical condition when receiving a request. For example, when the user is tired, the reception unit preferentially suggests a place where the user can relax. In addition, when the user is in good health, the reception unit can also suggest an active spot. Furthermore, when the user is not feeling well, the reception unit can also suggest a nearby spot. In this way, by filtering based on the user's mood and physical condition, more appropriate requests can be received. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's mood and physical condition data and performs filtering.

[0079] The reception unit can estimate the user's emotion and determine the priority of the requests to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially suggests a place where the user can relax. In addition, when the user is having fun, the reception unit can also preferentially suggest an entertainment spot. Furthermore, when the user is in a hurry, the reception unit can also preferentially suggest a nearby spot. This enables more appropriate suggestions by determining the priority of requests based on the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can make a decision using an AI model that inputs the user's emotion data and determines the priority of requests.

[0080] When receiving a request, the reception unit can preferentially receive a highly relevant request in consideration of the geographical location information of the user. For example, the reception unit preferentially suggests spots close to the user's current location. In addition, when the user is in a specific area, the reception unit can also suggest popular spots in that area. Furthermore, when the user is traveling, the reception unit can also preferentially suggest tourist attractions. In this way, by considering the geographical location information of the user, a highly relevant request can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive a request using an AI model that receives a highly relevant request with priority using the geographical location information of the user as an input.

[0081] The reception unit can analyze the user's social media activity when receiving a request and receive related requests. For example, if the user posts "I want to relax" on social media, the reception unit can suggest a place where the user can relax. In addition, if the user posts "I want to eat Japanese food", the reception unit can also suggest a Japanese restaurant. In addition, if the user posts "I want to interact with animals", the reception unit can also suggest a spot where the user can interact with animals. In this way, related requests can be received by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can receive the request using an AI model that receives related requests using the user's social media activity data as input.

[0082] The analysis unit can estimate the user's emotion and adjust the analysis method of the request based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis and provides multiple suggestions. In addition, when the user is in a hurry, the analysis unit can also perform a quick analysis and provide optimal suggestions. Furthermore, when the user is excited, the analysis unit can also provide visually attractive suggestions. This enables more appropriate analysis by adjusting the analysis method based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can make adjustments using an AI model that inputs the user's emotion data and adjusts the analysis method.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past data when analyzing the request. The analysis unit, for example, refers to the user's past visit history and suggests related spots. The analysis unit can also refer to the user's past evaluation data and suggest highly rated spots. Furthermore, the analysis unit can also refer to the user's past movement patterns and suggest the optimal route. As a result, the accuracy of the analysis is improved by referring to the user's past data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that uses the user's past data as input and improves the accuracy of the analysis.

[0084] The analysis unit can apply different analysis algorithms based on the category of the request when analyzing the request. For example, the analysis unit applies an algorithm that emphasizes a natural environment or a quiet place to a request for a place to relax. The analysis unit can also apply an algorithm that emphasizes restaurant ratings and menus to a request for a meal. Furthermore, the analysis unit can apply an algorithm that emphasizes the type of animal or the rating of the facility to a request for a spot where you can interact with animals. This allows for more appropriate analysis by applying an analysis algorithm according to the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the category of the request and applies different analysis algorithms.

[0085] The analysis unit can estimate the user's emotion and determine the priority of the analysis based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit prioritizes the analysis of a place where the user can relax. In addition, when the user is having fun, the analysis unit can also prioritize the analysis of an entertainment spot. Furthermore, when the user is in a hurry, the analysis unit can also prioritize the analysis of a nearby spot. This enables more appropriate analysis by determining the priority of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can make a decision using an AI model that inputs the user's emotion data and determines the priority of the analysis.

[0086] The analysis unit can set the priority of the analysis based on the time of submission of the request when analyzing the request. For example, the analysis unit prioritizes the analysis of breakfast spots for a request submitted by the user in the morning. The analysis unit can also prioritize the analysis of lunch spots for a request submitted by the user in the afternoon. Furthermore, the analysis unit can also prioritize the analysis of dinner spots for a request submitted by the user in the evening. This allows for more appropriate analysis by determining the priority of the analysis based on the time of submission of the request. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the setting using an AI model that inputs the time of submission of the request and sets the priority of the analysis.

[0087] The analysis unit can set the order of analysis based on the relevance of the requests when analyzing the requests. For example, when the user's requests are "a place to relax" and "a delicious Japanese restaurant", the analysis unit prioritizes analysis of highly relevant spots. In addition, when the user's requests are "want to interact with animals" and "a place to relax", the analysis unit can also prioritize analysis of highly relevant spots. In addition, when the user's requests are "want to interact with animals" and "a delicious Japanese restaurant", the analysis unit can also prioritize analysis of highly relevant spots. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the requests. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the setting using an AI model that inputs the relevance of the requests and sets the order of analysis.

[0088] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is relaxed, the suggestion unit makes the suggestion in a relaxed expression. Also, when the user is in a hurry, the suggestion unit can make the suggestion in a concise and quick expression. Furthermore, when the user is excited, the suggestion unit can make the suggestion in a visually attractive expression. This enables a more appropriate suggestion by adjusting the way of expressing the suggestion based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can make the adjustment using an AI model that inputs the emotion data of the user and adjusts the way of expressing the suggestion.

[0089] The suggestion unit can set the level of detail of the proposal based on the importance of the destination or route at the time of proposal. For example, in the case of an important destination, the suggestion unit provides detailed information. In addition, in the case of low importance, the suggestion unit can also provide concise information. Furthermore, in the case of multiple destinations, the suggestion unit can adjust the level of detail according to the importance. This allows for more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the destination or route. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can perform the setting using an AI model that inputs the importance of the destination or route and sets the level of detail of the proposal.

[0090] The suggestion unit can apply different suggestion algorithms based on the category of the destination or route when making a suggestion. For example, the suggestion unit can apply an algorithm that emphasizes natural environments and quiet places to suggest places where you can relax. The suggestion unit can also apply an algorithm that emphasizes restaurant ratings and menus to suggest meals. Furthermore, the suggestion unit can apply an algorithm that emphasizes animal types and facility ratings to suggest spots where you can interact with animals. This allows for more appropriate suggestions by applying a suggestion algorithm according to the category of the destination or route. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that uses the category of the destination or route as input and applies different suggestion algorithms.

[0091] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is in a hurry, the suggestion unit makes a short and to the point suggestion. Also, when the user is relaxed, the suggestion unit can make a longer suggestion including a detailed explanation. Furthermore, when the user is excited, the suggestion unit can make a visually stimulating suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can make adjustments using an AI model that inputs the user's emotion data and adjusts the length of the suggestion.

[0092] The suggestion unit can set the priority of the proposal based on the submission time of the destination or route at the time of proposal. For example, the suggestion unit prioritizes the proposal of a breakfast spot for a request submitted by the user in the morning. The suggestion unit can also prioritize the proposal of a lunch spot for a request submitted by the user in the afternoon. Furthermore, the suggestion unit can also prioritize the proposal of a dinner spot for a request submitted by the user in the evening. This enables more appropriate proposals by determining the priority of the proposal based on the submission time of the destination or route. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can set the priority of the proposal using an AI model that inputs the submission time.

[0093] The suggestion unit can set the order of suggestions based on the relevance of the destination or route at the time of suggestion. For example, when the user's request is "a place to relax" and "a delicious Japanese restaurant", the suggestion unit preferentially suggests highly relevant spots. In addition, when the user's request is "want to interact with animals" and "a place to relax", the suggestion unit can also preferentially suggest highly relevant spots. In addition, when the user's request is "want to interact with animals" and "a delicious Japanese restaurant", the suggestion unit can also preferentially suggest highly relevant spots. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the destination or route. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can set the order of suggestions using an AI model that inputs the relevance of the destination and route and sets the order of suggestions.

[0094] The detour suggestion unit can estimate the user's emotion and adjust the method of suggesting detour spots based on the estimated emotion of the user. For example, when the user is relaxed, the detour suggestion unit can suggest detour spots that proceed at a leisurely pace. In addition, when the user is in a hurry, the detour suggestion unit can also suggest spots that can be quickly visited. Furthermore, when the user is excited, the detour suggestion unit can also suggest visually attractive spots. This allows for more appropriate suggestions by adjusting the method of suggesting detour spots based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detour suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detour suggestion unit can make adjustments using an AI model that inputs the user's emotion data and adjusts the method of suggesting detour spots.

[0095] When proposing a detour spot, the detour suggestion unit can provide an appropriate suggestion by referring to the user's past detour history. The detour suggestion unit can, for example, suggest related spots based on detour spots visited by the user in the past. The detour suggestion unit can also suggest highly rated spots from the user's past detour history. Furthermore, the detour suggestion unit can also suggest optimal spots by referring to the user's past detour patterns. In this way, the optimal detour spot can be suggested by referring to the user's past detour history. A part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the detour suggestion unit can make a suggestion using an AI model that inputs the user's past detour history and suggests the optimal detour spot.

[0096] The detour suggestion unit can estimate the user's emotion and determine the priority order of detour spots based on the estimated emotion of the user. For example, when the user is feeling stressed, the detour suggestion unit preferentially suggests detour spots where the user can relax. In addition, when the user is having fun, the detour suggestion unit can also preferentially suggest entertainment spots. Furthermore, when the user is in a hurry, the detour suggestion unit can also preferentially suggest nearby detour spots. This enables more appropriate suggestions by determining the priority order of detour spots based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detour suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detour suggestion unit can make a decision using an AI model that inputs the user's emotion data and determines the priority order of detour spots.

[0097] The detour suggestion unit can provide an appropriate suggestion when proposing a detour spot, taking into consideration the geographical location information of the user. For example, the detour suggestion unit preferentially suggests detour spots close to the user's current location. In addition, when the user is in a specific area, the detour suggestion unit can also suggest popular detour spots in that area. Furthermore, when the user is traveling, the detour suggestion unit can also preferentially suggest tourist attractions. In this way, the optimal detour spot can be suggested by taking into consideration the geographical location information of the user. Part or all of the above-mentioned processing in the detour suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the detour suggestion unit can make a suggestion using an AI model that inputs the geographical location information of the user and suggests the optimal detour spot.

[0098] The data reference unit can estimate the user's emotion and select data to be referenced based on the estimated user's emotion. For example, when the user is relaxed, the data reference unit preferentially refers to data of places where the user can relax. In addition, when the user is in a hurry, the data reference unit can also preferentially refer to data of spots that can be accessed quickly. Furthermore, when the user is excited, the data reference unit can also preferentially refer to data of visually attractive spots. In this way, more appropriate data can be referenced by selecting data to be referenced based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data reference unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data reference unit can make the selection using an AI model that inputs the user's emotion data and selects the data to be referenced.

[0099] The data reference unit can adjust the analysis algorithm by referring to past data when referring to data. The data reference unit, for example, refers to the user's past visit history and preferentially refers to data on related spots. The data reference unit can also refer to the user's past evaluation data and preferentially refer to data on highly rated spots. Furthermore, the data reference unit can also refer to the user's past movement patterns and preferentially refer to data on optimal routes. This allows the analysis algorithm to be optimized by referring to past data. Part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit can make adjustments using an AI model that uses past data as input and adjusts the analysis algorithm.

[0100] The data reference unit can estimate the user's emotion and adjust the frequency of data reference based on the estimated user's emotion. For example, when the user is relaxed, the data reference unit can set the frequency of data reference low and make suggestions at a leisurely pace. Also, when the user is in a hurry, the data reference unit can set the frequency of data reference high and make suggestions quickly. Furthermore, when the user is excited, the data reference unit can set the frequency of data reference to a medium level and make visually attractive suggestions. This allows for more appropriate suggestions by adjusting the frequency of data reference based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data reference unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data reference unit can make adjustments using an AI model that uses the user's emotion data as input and adjusts the frequency of data reference.

[0101] The data reference unit can set the weighting of data based on the time of request submission when referencing data. For example, the data reference unit places emphasis on data of breakfast spots for requests submitted by the user in the morning. The data reference unit can also place emphasis on data of lunch spots for requests submitted by the user in the afternoon. Furthermore, the data reference unit can also place emphasis on data of dinner spots for requests submitted by the user in the evening. This allows more appropriate data to be referenced by weighting data based on the time of request submission. Part or all of the above-mentioned processing in the data reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the data reference unit can perform the setting using an AI model that inputs the time of request submission and sets the weighting of data.

[0102] The situation consideration unit can estimate the user's emotion and adjust the method of considering the traffic situation and the weather based on the estimated user's emotion. For example, when the user is relaxed, the situation consideration unit preferentially suggests a route with a good view. In addition, when the user is in a hurry, the situation consideration unit can also preferentially suggest the shortest route. Furthermore, when the user is excited, the situation consideration unit can also preferentially suggest a visually attractive route. This allows for more appropriate suggestions by adjusting the method of considering the traffic situation and the weather based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the situation consideration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the situation consideration unit can make adjustments using an AI model that inputs the user's emotion data and adjusts the method of considering the traffic situation and the weather.

[0103] The situation consideration unit can provide an appropriate proposal by referring to the user's past driving history when considering traffic conditions or weather. The situation consideration unit can, for example, refer to the user's past driving history and propose a route that avoids congestion. The situation consideration unit can also refer to the user's past driving history and propose a route according to the weather. Furthermore, the situation consideration unit can also refer to the user's past driving history and propose an optimal rest point. This makes it possible to make a more appropriate proposal by referring to the user's past driving history. A part or all of the above-mentioned processing in the situation consideration unit can be performed, for example, using AI or can be performed without using AI. For example, the situation consideration unit can make a proposal using an AI model that inputs the user's past driving history and provides an appropriate proposal.

[0104] The situation consideration unit can estimate the user's emotion and determine the priority of traffic conditions and weather based on the estimated user's emotion. For example, when the user is feeling stressed, the situation consideration unit preferentially suggests a route that avoids congestion. In addition, when the user is enjoying himself, the situation consideration unit can also preferentially suggest a route with good scenery. Furthermore, when the user is in a hurry, the situation consideration unit can also preferentially suggest the shortest route. This enables more appropriate suggestions by determining the priority of traffic conditions and weather based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the situation consideration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the situation consideration unit can make a decision using an AI model that inputs the user's emotion data and determines the priority of traffic conditions and weather.

[0105] The situation consideration unit can provide an appropriate proposal by considering the geographical location information of the user when considering the traffic conditions or weather. For example, the situation consideration unit preferentially proposes a route close to the user's current location. In addition, when the user is in a specific area, the situation consideration unit can also propose a route that considers the traffic conditions of the area. Furthermore, when the user is traveling, the situation consideration unit can also preferentially propose a route that includes tourist attractions. This makes it possible to provide a more appropriate proposal by considering the geographical location information of the user. A part or all of the above-mentioned processing in the situation consideration unit may be performed, for example, using AI, or may be performed without using AI. For example, the situation consideration unit can make a proposal using an AI model that inputs the geographical location information of the user and provides an appropriate proposal.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting an ambiguous request from a user, the reception unit can automatically display requests that have been frequently input in the past as candidates by referring to the user's past request history. For example, if the user has frequently input "a place to relax" in the past, the reception unit will preferentially display that request. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used during a specific time period from the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history.

[0108] The analysis unit may include a data reference unit that references the user's past data. The data reference unit references, for example, the user's past travel history or search history. For example, the data reference unit references related data based on places the user has visited in the past or keywords the user has searched for. The data reference unit may also reference the user's past evaluation data. For example, the data reference unit suggests related spots based on spots that the user has given high ratings in the past. This improves the accuracy of the analysis by referring to the user's past data.

[0109] The suggestion unit may include a situation consideration unit that considers traffic conditions or weather. The situation consideration unit, for example, refers to real-time traffic information or weather forecast data. For example, the situation consideration unit proposes a route to avoid traffic congestion. The situation consideration unit may also propose a route according to the weather. For example, the situation consideration unit proposes indoor spots when it is raining and outdoor spots when it is sunny. In this way, a more appropriate route can be proposed by considering the traffic conditions and weather.

[0110] The suggestion unit can search for places to relax, restaurants serving delicious Japanese food, or spots where one can interact with animals. For example, the suggestion unit searches for parks and hot springs as places to relax, Japanese restaurants that are well-known locally as restaurants serving delicious Japanese food, and zoos and pet cafes as spots where one can interact with animals. For example, the suggestion unit searches for related spots based on the user's request. The suggestion unit can also suggest optimal spots based on the user's current location and past data. This makes it possible to search for and suggest spots that meet the user's request.

[0111] The detour suggestion unit can suggest detour spots based on the user's request. For example, in response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests a Japanese restaurant that can be visited on the way. For example, the detour suggestion unit suggests a related detour spot based on the user's request. In addition, the detour suggestion unit can also suggest an optimal detour spot based on the user's current location and past data. In this way, multiple requests can be met by suggesting detour spots according to the user's requests.

[0112] The reception unit can estimate the user's emotion and adjust the reception method of an ambiguous request based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly input an ambiguous request. In this way, by adjusting the reception method according to the user's emotion, a more appropriate request can be received.

[0113] The analysis unit can estimate the user's emotions and adjust the method of analyzing the request based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis and provides multiple suggestions. Also, when the user is in a hurry, the analysis unit can perform a quick analysis and provide optimal suggestions. Furthermore, when the user is excited, the analysis unit can provide visually appealing suggestions. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions.

[0114] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is relaxed, the suggestion unit can make the suggestion in a relaxed manner. When the user is in a hurry, the suggestion unit can also make the suggestion in a concise and quick manner. Furthermore, when the user is excited, the suggestion unit can also make the suggestion in a visually attractive manner. This allows for more appropriate suggestions to be made by adjusting the way of expressing the suggestion based on the emotion of the user.

[0115] The detour suggestion unit can estimate the user's emotions and adjust the method of suggesting detour spots based on the estimated user's emotions. For example, when the user is relaxed, the detour suggestion unit can suggest detour spots that proceed at a leisurely pace. Also, when the user is in a hurry, the detour suggestion unit can also suggest spots that can be quickly visited. Furthermore, when the user is excited, the detour suggestion unit can also suggest visually attractive spots. This allows more appropriate suggestions to be made by adjusting the method of suggesting detour spots based on the user's emotions.

[0116] The situation consideration unit can estimate the user's emotions and adjust the method of considering traffic conditions and weather based on the estimated user's emotions. For example, when the user is relaxed, the situation consideration unit preferentially suggests a route with a good view. Also, when the user is in a hurry, the situation consideration unit can also preferentially suggest the shortest route. Furthermore, when the user is excited, the situation consideration unit can also preferentially suggest a visually attractive route. This allows more appropriate suggestions to be made by adjusting the method of considering traffic conditions and weather based on the user's emotions.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The reception unit receives the user's vague request. The user's vague requests include "I want to go to a place where I can relax," "I want to eat Japanese food for lunch," and "I want to be soothed by animals." The reception unit can receive requests using voice input or text input. For example, if the user voice-inputs "I want to relax today," the reception unit analyzes the voice and accepts it as a request. Step 2: The analysis unit analyzes the request received by the reception unit. The analysis unit analyzes the user's request using natural language processing technology. For example, the analysis unit analyzes the user's request as text data and identifies places to relax, restaurants serving delicious Japanese food, spots where you can interact with animals, etc. Step 3: The suggestion unit suggests destinations and routes based on the requests analyzed by the analysis unit. The suggestion unit suggests hot springs and parks as places to relax, Japanese restaurants that are well-known locally for their delicious Japanese food, and zoos and pet cafes as spots where you can interact with animals. The suggestion unit calculates the optimal driving route from the user's current location and suggests the optimal route taking into account traffic conditions, weather, the user's preferences, etc. Step 4: The detour suggestion unit suggests detour spots based on the route suggested by the suggestion unit. In response to a request such as "I want to stop by a delicious Japanese restaurant on the way to a place where I can relax," the detour suggestion unit suggests Japanese restaurants that can be visited on the way.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and detour suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and accepts voice input and text input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using natural language processing technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a destination and a route based on the user's request. The detour suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests a detour spot based on the proposed route. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and detour suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts voice input and text input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using natural language processing technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a destination and a route based on the user's request. The detour suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests a detour spot based on the proposed route. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and detour suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and accepts voice input and text input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using natural language processing technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a destination and a route based on the user's request. The detour suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314 and suggests a detour spot based on the proposed route. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and detour suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts voice input and text input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using natural language processing technology. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a destination and a route based on the user's request. The detour suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests a detour spot based on the proposed route. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] 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.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0182] 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.

[0183] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A reception unit that receives an unclear request from a user; an analysis unit that analyzes the request received by the reception unit; a suggestion unit that suggests a destination or a route based on the request analyzed by the analysis unit; A detour suggestion unit that suggests detour spots based on the route suggested by the suggestion unit. A system characterized by: (Appendix 2) The analysis unit is Equipped with a data reference section that references the user's past data 2. The system of claim 1, (Appendix 3) The suggestion unit, Equipped with a situation consideration unit that considers traffic conditions or weather 2. The system of claim 1, (Appendix 4) The suggestion unit, Search for a relaxing place, a delicious Japanese restaurant, or a spot where you can interact with animals. 2. The system of claim 1, (Appendix 5) The detour suggestion unit, Suggesting detour spots based on user requests 2. The system of claim 1, (Appendix 6) The suggestion unit, Calculate efficient driving routes from the user's current location 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the method of accepting ambiguous requests based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past request history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is When receiving requests, filter them based on the user's current mood or physical condition 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize requests based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting requests, the system takes into account the user's geographic location information to prioritize relevant requests. 2. The system of claim 1, (Appendix 12) The reception unit is When you make a request, we analyze your social media activity to make the request more relevant to you. 2. The system of claim 1, (Appendix 13) The analysis unit is To estimate a user's emotion and adjust a method of analyzing a request based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing requests, the accuracy of the analysis can be improved by referring to the user's past data. 2. The system of claim 1, (Appendix 15) The analysis unit is During request analysis, apply different analysis algorithms based on the request category 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring user emotions and determining analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During request analysis, set analysis priorities based on when the request was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing requests, set the order of analysis based on the relevance of requests 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, set the detail level of the suggestions based on the importance of the destination or route 2. The system of claim 1, (Appendix 21) The suggestion unit, During suggestions, apply different suggestion algorithms based on destination or route category 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, During suggestions, prioritize suggestions based on when a destination or route was submitted 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, order suggestions based on destination or route relevance 2. The system of claim 1, (Appendix 25) The detour suggestion unit, Estimate the user's emotions and adjust the method of suggesting detour spots based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The detour suggestion unit, When suggesting detour spots, the system provides appropriate suggestions by referring to the user's past detour history. 2. The system of claim 1, (Appendix 27) The detour suggestion unit, Estimate the user's emotions and prioritize detour spots based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The detour suggestion unit, When suggesting detour spots, the system provides appropriate suggestions by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 29) The data reference unit is Infer the user's emotions and select the data to be referenced based on the inferred user emotions. 3. The system of claim 2, (Appendix 30) The data reference unit is When referencing data, refer to past data to adjust analysis algorithms 3. The system of claim 2, (Appendix 31) The data reference unit is Infer user sentiment and adjust the frequency of data reference based on the inferred user sentiment 3. The system of claim 2, (Appendix 32) The data reference unit is When referencing data, weight the data based on when the request was submitted 3. The system of claim 2, (Appendix 33) The situation consideration unit, Estimate the user's emotions and adjust the way traffic conditions and weather are taken into account based on the estimated user's emotions. 4. The system of claim 3. (Appendix 34) The situation consideration unit, Refer to the user's past driving history to provide appropriate suggestions when considering traffic or weather conditions 4. The system of claim 3. (Appendix 35) The situation consideration unit, Estimate user emotions and prioritize traffic and weather conditions based on the estimated user emotions 4. The system of claim 3. (Appendix 36) The situation consideration unit, Taking into account the user's geographic location when considering traffic or weather conditions to provide appropriate suggestions 4. The system of claim 3. [Explanation of symbols]

[0191] 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 an unclear request from a user; an analysis unit that analyzes the request received by the reception unit; a suggestion unit that suggests a destination or a route based on the request analyzed by the analysis unit; A detour suggestion unit that suggests detour spots based on the route suggested by the suggestion unit. A system characterized by:

2. The analysis unit is Equipped with a data reference section that references the user's past data 2. The system of claim 1 .

3. The suggestion unit, Equipped with a situation consideration part that considers traffic conditions or weather 2. The system of claim 1 .

4. The suggestion unit, Search for a relaxing place, a delicious Japanese restaurant, or a spot where you can interact with animals.

2. The system of claim 1 .

5. The detour suggestion unit, Suggesting detour spots based on user requests 2. The system of claim 1 .

6. The suggestion unit, Calculate efficient driving routes from the user's current location 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the method of accepting ambiguous requests based on the estimated user emotions 2. The system of claim 1 .

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

9. The reception unit is When receiving requests, filter them based on the user's current mood or physical condition 2. The system of claim 1 .

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