system

Abstract

The system according to the embodiment aims to reduce the need for gaze while driving and improve safety. According to an embodiment, the system includes a reception unit, an analysis unit, and an adjustment unit. The reception unit receives instructions in natural language from a driver. The analysis unit analyzes the instructions received by the reception unit and calculates a route. The adjustment unit adjusts navigation in real time based on the route calculated by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not reduce the need to look at navigation systems or smartphones while driving, posing safety issues.

[0005] The system according to the embodiment aims to reduce the need for gaze while driving and improve safety. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an adjustment unit. The reception unit receives instructions in natural language from a driver. The analysis unit analyzes the instructions received by the reception unit and calculates a route. The adjustment unit adjusts navigation in real time based on the route calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the need for gaze while driving and improve safety. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A car navigation system according to an embodiment of the present invention uses a generative AI to accept and analyze natural language instructions from the driver and adjust navigation in real time. This car navigation system allows the driver to input destination and route instructions in natural language, and the generative AI analyzes the instructions and calculates the optimal route. The generative AI understands what the driver says and provides appropriate navigation information. For example, the generative AI provides specific instructions such as, "Turn right at the next intersection, then go straight." Furthermore, the generative AI adjusts navigation in real time based on the driver's situation. For example, it recalculates the optimal route and issues instructions to the driver, taking into account traffic congestion and traffic accident information. This eliminates the need for the driver to focus on the navigation system or smartphone, allowing for safer driving. This mechanism allows the driver to receive navigation in a natural, conversational format, reducing the risk of car accidents by reducing attention. Furthermore, the high accuracy of the generative AI allows the driver to obtain reliable navigation information. For example, when a driver asks, "Where is the next gas station?", the generative AI instantly provides the location of the nearest gas station. In this way, incorporating generative AI into car navigation systems can reduce the driver's attention and reduce car accidents. This allows the car navigation system to accept and interpret the driver's natural language instructions and adjust navigation in real time.

[0029] A car navigation system according to an embodiment includes a reception unit, an analysis unit, and an adjustment unit. The reception unit receives instructions in natural language from a driver. Examples of natural language instructions from a driver include, but are not limited to, voice instructions and text instructions. The reception unit converts the driver's voice instructions into text data using, for example, speech recognition technology. The reception unit can also receive instructions entered by the driver in text format. For example, the driver can enter instructions in text format using a smartphone or tablet. The analysis unit analyzes the instructions received by the reception unit and calculates a route. The analysis unit understands the driver's speech using, for example, a generation AI and provides appropriate navigation information. The generation AI analyzes the driver's instructions using a text generation AI (e.g., LLM) and calculates an optimal route. The analysis unit can also adjust navigation in real time according to the driver's situation. For example, the generation AI recalculates an optimal route based on the driver's speech, taking into account traffic congestion information and traffic accident information. The adjustment unit adjusts navigation in real time based on the route calculated by the analysis unit. The adjustment unit provides specific instructions to the driver, for example. For example, the generation AI provides specific instructions such as "Turn right at the next intersection, then go straight." The adjustment unit can also adjust navigation according to the driver's situation. For example, it recalculates the optimal route taking into account traffic congestion information and traffic accident information, and issues instructions to the driver. As a result, the car navigation system according to the embodiment can accept and analyze instructions in natural language from the driver and adjust navigation in real time. This allows the driver to receive navigation in a natural conversational format, reducing the need for gaze and thereby reducing the risk of car accidents.

[0030] The analysis unit can understand the driver's utterances and provide appropriate navigation information. The analysis unit, for example, uses a generation AI to understand the driver's utterances. The generation AI analyzes the driver's utterances using a text generation AI (e.g., LLM) and provides appropriate navigation information. For example, the generation AI can understand a driver's instruction such as "Turn right at the next intersection, then go straight" and provide appropriate navigation information. The analysis unit can also provide information such as the distance to the destination, the required time, and traffic conditions based on the driver's utterances. For example, when the driver asks, "Where is the next gas station?", the generation AI can instantly provide the location of the nearest gas station. This allows for understanding the driver's utterances and providing more appropriate navigation information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the driver's utterances into the generation AI and have the generation AI understand the utterances.

[0031] The adjustment unit can recalculate an optimal route based on congestion information or traffic accident information and issue instructions to the driver. The adjustment unit recalculates the optimal route, for example, taking congestion information or traffic accident information into consideration. The adjustment unit acquires congestion information using, for example, a traffic sensor or real-time data. The adjustment unit can also acquire traffic accident information using police data or a traffic app. For example, the adjustment unit recalculates the optimal route based on congestion information acquired from a traffic sensor. The adjustment unit can also recalculate the optimal route based on traffic accident information acquired from police data. Furthermore, the adjustment unit can calculate a route that takes into consideration the shortest time, shortest distance, and traffic conditions to calculate the optimal route. For example, the adjustment unit calculates a route that reaches the destination in the shortest time. The adjustment unit can also calculate a route that reaches the destination in the shortest distance. This makes it possible to provide an optimal route by taking congestion information and traffic accident information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input congestion information and traffic accident information into the generation AI and cause the generation AI to recalculate the optimal route.

[0032] The reception unit allows the driver to input instructions regarding a destination and a route in natural language. For example, the reception unit allows the driver to input instructions regarding a destination and a route using voice input. The reception unit converts the driver's voice instructions into text data using, for example, voice recognition technology. The reception unit also allows the driver to input instructions in text format. For example, the driver can input instructions in text format using a smartphone or tablet. This simplifies operation by allowing the driver to input instructions in natural language. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the driver's voice instructions into a generation AI and have the generation AI convert the voice instructions into text data.

[0033] The analysis unit can adjust navigation in real time based on the driver's situation. The analysis unit, for example, analyzes the driver's situation and adjusts navigation in real time. The driver's situation includes, but is not limited to, behavior while driving and the in-vehicle environment. The analysis unit, for example, monitors the driver's behavior and analyzes the driver's situation. The analysis unit can also monitor the in-vehicle environment and analyze the driver's situation. For example, the analysis unit analyzes the driver's situation based on driver behavior data. The analysis unit can also analyze the driver's situation based on in-vehicle environmental data. This allows for more appropriate navigation by adjusting navigation according to the driver's situation. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input driver behavior data and in-vehicle environmental data into the generation AI and cause the generation AI to analyze the driver's situation.

[0034] The adjustment unit can provide specific instructions to the driver. For example, the adjustment unit provides specific instructions to the driver. Specific instructions include, but are not limited to, instructions to turn right at the next intersection or guidance to a specific landmark. For example, the adjustment unit provides specific instructions such as, "Turn right at the next intersection and then go straight." The adjustment unit can also provide specific instructions according to the driver's situation. For example, the adjustment unit recalculates an optimal route taking into account traffic congestion information and traffic accident information and provides specific instructions to the driver. This provides specific instructions to the driver, making driving safer. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input driver situation data into the generation AI and cause the generation AI to generate specific instructions.

[0035] The reception unit can analyze the driver's past instruction history and select the optimal reception method. The reception unit, for example, analyzes the driver's past instruction history and selects the optimal reception method. The past instruction history includes, for example, past navigation history and the driver's behavior patterns, but is not limited to these examples. The reception unit, for example, automatically displays destinations and routes frequently input by the driver in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the driver has used in the past. Furthermore, the reception unit can predict and suggest destinations and routes to be used during a specific time period based on the driver's past instruction history. This allows a more appropriate reception method to be selected by analyzing the past instruction history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the driver's past instruction history data into the generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit may perform filtering based on the driver's current driving situation and areas of interest when receiving an instruction. For example, the reception unit may perform filtering based on the driver's current driving situation and areas of interest when receiving an instruction. Examples of driving situations include, but are not limited to, vehicle speed, traffic conditions, and driver behavior. Examples of areas of interest include, but are not limited to, the driver's hobbies and past search history. For example, when the driver is driving on a highway, the reception unit may preferentially receive instructions related to the highway. Furthermore, when the driver is heading to a tourist spot, the reception unit may preferentially receive information related to the tourist spot. Furthermore, when the driver is commuting to work, the reception unit may preferentially receive instructions related to the commuting route. By performing filtering based on the driving situation and areas of interest, more appropriate instructions can be received. Some or all of the above-described processing in the reception unit may be performed using, or without using, a generation AI. For example, the reception unit may input the driver's driving situation data and area of ​​interest data into the generation AI and cause the generation AI to perform filtering.

[0037] The reception unit can prioritize receiving highly relevant instructions based on the driver's geographical location information when receiving instructions. For example, the reception unit prioritizes receiving highly relevant instructions based on the driver's geographical location information when receiving instructions. Geographical location information includes, but is not limited to, GPS data and map information. For example, when the driver is in a specific area, the reception unit prioritizes receiving instructions related to the area. Furthermore, when the driver is approaching a destination, the reception unit can prioritize receiving instructions related to the destination. Furthermore, when the driver is traveling a specific route, the reception unit can prioritize receiving instructions related to the route. In this way, by taking the geographical location information into consideration, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without using, a generation AI. For example, the reception unit can input the driver's geographical location information into the generation AI and determine the priority of highly relevant instructions.

[0038] The reception unit may analyze the driver's social media activity and receive related instructions when receiving instructions. For example, the reception unit may analyze the driver's social media activity and receive related instructions when receiving instructions. Social media activity may include, but is not limited to, posted content and like history. For example, the reception unit may preferentially receive instructions related to places the driver has shared on social media. The reception unit may also preferentially receive instructions related to places the driver follows on social media. Furthermore, the reception unit may also preferentially receive instructions related to places the driver has checked in to on social media. By analyzing social media activity, related instructions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the driver's social media activity data into the generation AI and cause the generation AI to receive related instructions.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the instruction during analysis. The importance of the instruction includes, but is not limited to, for example, the urgency and the driver's intention. For example, the analysis unit performs a detailed analysis for important instructions. The analysis unit can also perform a concise analysis for general instructions. Furthermore, the analysis unit can also perform a quick analysis for urgent instructions. By adjusting the level of detail of the analysis based on the importance of the instruction, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input instruction importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms based on the category of the instruction during analysis. For example, the analysis unit applies different analysis algorithms based on the category of the instruction during analysis. The categories of instructions include, but are not limited to, traffic information, tourist information, and emergency information. For example, the analysis unit applies a traffic analysis algorithm to instructions related to traffic information. The analysis unit can also apply a tourist analysis algorithm to instructions related to tourist information. Furthermore, the analysis unit can apply an emergency analysis algorithm to instructions related to emergency information. By applying different analysis algorithms depending on the category of the instruction, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of the instruction into the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the timing of instruction submission during analysis. For example, the analysis unit determines the priority of analysis based on the timing of instruction submission during analysis. The timing of instruction submission includes, but is not limited to, the time when the instruction was issued or the driver's schedule. For example, the analysis unit may give top priority to analysis of emergency instructions. The analysis unit may also give priority to analysis of important instructions. Furthermore, the analysis unit may also give normal priority to analysis of general instructions. By determining the priority of analysis based on the timing of instruction submission, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input instruction submission time data into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of instructions during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of instructions during analysis. Examples of relevance of instructions include, but are not limited to, the current driving situation and past behavioral history. For example, the analysis unit may give top priority to analyzing important instructions. The analysis unit may also prioritize analyzing highly relevant instructions. Furthermore, the analysis unit may also analyze less relevant instructions with normal priority. By adjusting the order of analysis based on the relevance of instructions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input instruction relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] During adjustment, the adjustment unit can analyze the driver's past driving behavior and select an optimal adjustment method. For example, during adjustment, the adjustment unit analyzes the driver's past driving behavior and selects an optimal adjustment method. Past driving behavior includes, but is not limited to, driving history and driving patterns. For example, the adjustment unit preferentially provides a navigation method that the driver has previously preferred. The adjustment unit can also suggest an optimal navigation method based on the driver's past driving behavior. Furthermore, the adjustment unit can analyze the driver's past driving behavior and provide the most efficient navigation method. By analyzing the past driving behavior, more appropriate navigation can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's past driving behavior data into the generation AI and cause the generation AI to select an optimal adjustment method.

[0044] The adjustment unit can customize the navigation means according to the driver's current driving situation during adjustment. For example, the adjustment unit customizes the navigation means based on the driver's current driving situation during adjustment. The current driving situation includes, but is not limited to, vehicle speed, traffic conditions, and driver behavior. For example, when the driver is driving on a highway, the adjustment unit can provide navigation suitable for the highway. Furthermore, when the driver is driving in an urban area, the adjustment unit can also provide navigation suitable for the urban area. Furthermore, when the driver is stuck in traffic, the adjustment unit can provide navigation that avoids traffic jams. This allows for more appropriate navigation to be provided by customizing the navigation based on the current driving situation. Some or all of the above-described processing in the adjustment unit may be performed using, or without using, a generation AI. For example, the adjustment unit can input data on the driver's current driving situation into the generation AI and cause the generation AI to customize the navigation means.

[0045] The adjustment unit can select an optimal navigation method based on the driver's geographical location information during adjustment. For example, the adjustment unit selects an optimal navigation method based on the driver's geographical location information during adjustment. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, when the driver is in a specific area, the adjustment unit can provide navigation appropriate for that area. Furthermore, when the driver is approaching a destination, the adjustment unit can also provide navigation appropriate for the destination. Furthermore, when the driver is traveling a specific route, the adjustment unit can provide navigation appropriate for that route. This allows for more appropriate navigation to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's geographical location information to the generation AI and cause the generation AI to select an optimal navigation method.

[0046] During adjustment, the adjustment unit can analyze the driver's social media activity and suggest a navigation method. For example, during adjustment, the adjustment unit analyzes the driver's social media activity and suggests a navigation method. Social media activity includes, for example, but is not limited to, posted content and like history. For example, the adjustment unit provides navigation related to places the driver has shared on social media. The adjustment unit can also provide navigation related to places the driver follows on social media. Furthermore, the adjustment unit can also provide navigation related to places the driver has checked in to on social media. This allows for more appropriate navigation to be provided by analyzing social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's social media activity data into the generation AI and cause the generation AI to suggest a navigation method.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze the driver's past driving history and customize navigation based on the driver's preferences and habits. For example, if the driver has preferred a particular route in the past, the analysis unit can preferentially suggest that route. Also, if the driver has a habit of going to a specific location at a certain time of day, the analysis unit can provide navigation tailored to that time of day. Furthermore, it can take into account routes the driver has avoided in the past and suggest alternative routes. This allows the system to provide more personalized navigation based on the driver's past driving history.

[0049] The reception unit can analyze the acoustic environment inside the driver's vehicle and adjust the instruction reception method based on the acoustic environment. For example, if the vehicle is noisy, the reception unit can prioritize receiving voice input. Alternatively, if the vehicle is quiet, the reception unit can prioritize receiving text input. Furthermore, if the acoustic environment inside the vehicle changes, the reception unit can automatically switch the reception method. This makes it possible to provide a more appropriate instruction reception method depending on the acoustic environment inside the vehicle.

[0050] The adjustment unit can analyze the driver's past navigation history and select the optimal navigation method. For example, if the driver has preferred a particular route in the past, the adjustment unit can preferentially suggest that route. It can also consider routes that the driver has avoided in the past and suggest alternative routes. Furthermore, if the driver has a habit of going to a particular location at a particular time of day, the adjustment unit can provide navigation tailored to that time of day. This makes it possible to provide more personalized navigation based on the driver's past navigation history.

[0051] The analysis unit can monitor the driver's current driving situation and adjust navigation based on the driving situation. For example, if the driver is driving on a highway, the analysis unit can provide navigation suitable for the highway. Also, if the driver is driving in a city, the analysis unit can provide navigation suitable for the city. Furthermore, if the driver is stuck in traffic, the analysis unit can provide navigation that avoids the traffic jam. This makes it possible to provide more appropriate navigation based on the driving situation.

[0052] The reception unit can adjust the method of receiving instructions based on the driver's current driving situation and areas of interest. For example, when the driver is driving on a highway, instructions related to the highway can be received with priority. Also, when the driver is heading to a tourist spot, information related to the tourist spot can be received with priority. Furthermore, when the driver is commuting, instructions related to the commuting route can be received with priority. This makes it possible to receive more appropriate instructions based on the driving situation and areas of interest.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives natural language instructions from the driver. The natural language instructions from the driver include voice instructions and text instructions. The reception unit converts the driver's voice instructions into text data using voice recognition technology. The driver can also input instructions in text format using a smartphone or tablet. Step 2: The analysis unit analyzes the instructions received by the reception unit and calculates a route. The analysis unit uses the generation AI to understand the driver's speech and provide appropriate navigation information. The generation AI uses text generation AI (e.g., LLM) to analyze the driver's instructions and calculate the optimal route. The analysis unit can also adjust navigation in real time according to the driver's situation. Step 3: The adjustment unit adjusts the navigation in real time based on the route calculated by the analysis unit. The adjustment unit provides specific instructions to the driver. For example, the generation AI provides specific instructions such as "Turn right at the next intersection, then go straight." The adjustment unit can also adjust the navigation according to the driver's situation. For example, it can recalculate the optimal route taking into account traffic congestion and traffic accident information and issue instructions to the driver.

[0055] (Example 2) A car navigation system according to an embodiment of the present invention uses a generative AI to accept and analyze natural language instructions from the driver and adjust navigation in real time. This car navigation system allows the driver to input destination and route instructions in natural language, and the generative AI analyzes the instructions and calculates the optimal route. The generative AI understands what the driver says and provides appropriate navigation information. For example, the generative AI provides specific instructions such as, "Turn right at the next intersection, then go straight." Furthermore, the generative AI adjusts navigation in real time based on the driver's situation. For example, it recalculates the optimal route and issues instructions to the driver, taking into account traffic congestion and traffic accident information. This eliminates the need for the driver to focus on the navigation system or smartphone, allowing for safer driving. This mechanism allows the driver to receive navigation in a natural, conversational format, reducing the risk of car accidents by reducing attention. Furthermore, the high accuracy of the generative AI allows the driver to obtain reliable navigation information. For example, when a driver asks, "Where is the next gas station?", the generative AI instantly provides the location of the nearest gas station. In this way, incorporating generative AI into car navigation systems can reduce the driver's attention and reduce car accidents. This allows the car navigation system to accept and interpret the driver's natural language instructions and adjust navigation in real time.

[0056] A car navigation system according to an embodiment includes a reception unit, an analysis unit, and an adjustment unit. The reception unit receives instructions in natural language from a driver. Examples of natural language instructions from a driver include, but are not limited to, voice instructions and text instructions. The reception unit converts the driver's voice instructions into text data using, for example, speech recognition technology. The reception unit can also receive instructions entered by the driver in text format. For example, the driver can enter instructions in text format using a smartphone or tablet. The analysis unit analyzes the instructions received by the reception unit and calculates a route. The analysis unit understands the driver's speech using, for example, a generation AI and provides appropriate navigation information. The generation AI analyzes the driver's instructions using a text generation AI (e.g., LLM) and calculates an optimal route. The analysis unit can also adjust navigation in real time according to the driver's situation. For example, the generation AI recalculates an optimal route based on the driver's speech, taking into account traffic congestion information and traffic accident information. The adjustment unit adjusts navigation in real time based on the route calculated by the analysis unit. The adjustment unit provides specific instructions to the driver, for example. For example, the generation AI provides specific instructions such as "Turn right at the next intersection, then go straight." The adjustment unit can also adjust navigation according to the driver's situation. For example, it recalculates the optimal route taking into account traffic congestion information and traffic accident information, and issues instructions to the driver. As a result, the car navigation system according to the embodiment can accept and analyze instructions in natural language from the driver and adjust navigation in real time. This allows the driver to receive navigation in a natural conversational format, reducing the need for gaze and thereby reducing the risk of car accidents.

[0057] The analysis unit can understand the driver's utterances and provide appropriate navigation information. The analysis unit, for example, uses a generation AI to understand the driver's utterances. The generation AI analyzes the driver's utterances using a text generation AI (e.g., LLM) and provides appropriate navigation information. For example, the generation AI can understand a driver's instruction such as "Turn right at the next intersection, then go straight" and provide appropriate navigation information. The analysis unit can also provide information such as the distance to the destination, the required time, and traffic conditions based on the driver's utterances. For example, when the driver asks, "Where is the next gas station?", the generation AI can instantly provide the location of the nearest gas station. This allows for understanding the driver's utterances and providing more appropriate navigation information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the driver's utterances into the generation AI and have the generation AI understand the utterances.

[0058] The adjustment unit can recalculate an optimal route based on congestion information or traffic accident information and issue instructions to the driver. The adjustment unit recalculates the optimal route, for example, taking congestion information or traffic accident information into consideration. The adjustment unit acquires congestion information using, for example, a traffic sensor or real-time data. The adjustment unit can also acquire traffic accident information using police data or a traffic app. For example, the adjustment unit recalculates the optimal route based on congestion information acquired from a traffic sensor. The adjustment unit can also recalculate the optimal route based on traffic accident information acquired from police data. Furthermore, the adjustment unit can calculate a route that takes into consideration the shortest time, shortest distance, and traffic conditions to calculate the optimal route. For example, the adjustment unit calculates a route that reaches the destination in the shortest time. The adjustment unit can also calculate a route that reaches the destination in the shortest distance. This makes it possible to provide an optimal route by taking congestion information and traffic accident information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input congestion information and traffic accident information into the generation AI and cause the generation AI to recalculate the optimal route.

[0059] The reception unit allows the driver to input instructions regarding a destination and a route in natural language. For example, the reception unit allows the driver to input instructions regarding a destination and a route using voice input. The reception unit converts the driver's voice instructions into text data using, for example, voice recognition technology. The reception unit also allows the driver to input instructions in text format. For example, the driver can input instructions in text format using a smartphone or tablet. This simplifies operation by allowing the driver to input instructions in natural language. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the driver's voice instructions into a generation AI and have the generation AI convert the voice instructions into text data.

[0060] The analysis unit can adjust navigation in real time based on the driver's situation. The analysis unit, for example, analyzes the driver's situation and adjusts navigation in real time. The driver's situation includes, but is not limited to, behavior while driving and the in-vehicle environment. The analysis unit, for example, monitors the driver's behavior and analyzes the driver's situation. The analysis unit can also monitor the in-vehicle environment and analyze the driver's situation. For example, the analysis unit analyzes the driver's situation based on driver behavior data. The analysis unit can also analyze the driver's situation based on in-vehicle environmental data. This allows for more appropriate navigation by adjusting navigation according to the driver's situation. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input driver behavior data and in-vehicle environmental data into the generation AI and cause the generation AI to analyze the driver's situation.

[0061] The adjustment unit can provide specific instructions to the driver. For example, the adjustment unit provides specific instructions to the driver. Specific instructions include, but are not limited to, instructions to turn right at the next intersection or guidance to a specific landmark. For example, the adjustment unit provides specific instructions such as, "Turn right at the next intersection and then go straight." The adjustment unit can also provide specific instructions according to the driver's situation. For example, the adjustment unit recalculates an optimal route taking into account traffic congestion information and traffic accident information and provides specific instructions to the driver. This provides specific instructions to the driver, making driving safer. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input driver situation data into the generation AI and cause the generation AI to generate specific instructions.

[0062] The reception unit can analyze the driver's emotions and adjust the instruction reception method based on the analyzed driver's emotions. The reception unit, for example, analyzes the driver's emotions and adjusts the instruction reception method based on the analyzed driver's emotions. Examples of the driver's emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the driver is stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, when the driver is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the driver is in a hurry, the reception unit prioritizes voice input and enables the driver to quickly enter destination and route instructions. This allows the instruction reception method to be adjusted according to the driver's emotions, thereby enabling more appropriate instructions to be received. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the driver's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0063] The reception unit can analyze the driver's past instruction history and select the optimal reception method. The reception unit, for example, analyzes the driver's past instruction history and selects the optimal reception method. The past instruction history includes, for example, past navigation history and the driver's behavior patterns, but is not limited to these examples. The reception unit, for example, automatically displays destinations and routes frequently input by the driver in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the driver has used in the past. Furthermore, the reception unit can predict and suggest destinations and routes to be used during a specific time period based on the driver's past instruction history. This allows a more appropriate reception method to be selected by analyzing the past instruction history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the driver's past instruction history data into the generation AI and have the generation AI select the optimal reception method.

[0064] The reception unit may perform filtering based on the driver's current driving situation and areas of interest when receiving an instruction. For example, the reception unit may perform filtering based on the driver's current driving situation and areas of interest when receiving an instruction. Examples of driving situations include, but are not limited to, vehicle speed, traffic conditions, and driver behavior. Examples of areas of interest include, but are not limited to, the driver's hobbies and past search history. For example, when the driver is driving on a highway, the reception unit may preferentially receive instructions related to the highway. Furthermore, when the driver is heading to a tourist spot, the reception unit may preferentially receive information related to the tourist spot. Furthermore, when the driver is commuting to work, the reception unit may preferentially receive instructions related to the commuting route. By performing filtering based on the driving situation and areas of interest, more appropriate instructions can be received. Some or all of the above-described processing in the reception unit may be performed using, or without using, a generation AI. For example, the reception unit may input the driver's driving situation data and area of ​​interest data into the generation AI and cause the generation AI to perform filtering.

[0065] The reception unit can estimate the driver's emotion and determine the priority of instructions to be received based on the estimated driver's emotion. The reception unit, for example, estimates the driver's emotion and determines the priority of instructions to be received based on the estimated driver's emotion. The driver's emotion includes, but is not limited to, tension, relaxation, hurry, etc. For example, when the driver is tension, the reception unit can prioritize receiving important instructions. Furthermore, when the driver is relaxed, the reception unit can prioritize receiving detailed instructions. Furthermore, when the driver is hurrying, the reception unit can prioritize receiving instructions that require a quick response. In this way, by determining the priority of instructions according to the driver's emotion, more appropriate instructions can be received. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the driver's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The reception unit can prioritize receiving highly relevant instructions based on the driver's geographical location information when receiving instructions. For example, the reception unit prioritizes receiving highly relevant instructions based on the driver's geographical location information when receiving instructions. Geographical location information includes, but is not limited to, GPS data and map information. For example, when the driver is in a specific area, the reception unit prioritizes receiving instructions related to the area. Furthermore, when the driver is approaching a destination, the reception unit can prioritize receiving instructions related to the destination. Furthermore, when the driver is traveling a specific route, the reception unit can prioritize receiving instructions related to the route. In this way, by taking the geographical location information into consideration, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without using, a generation AI. For example, the reception unit can input the driver's geographical location information into the generation AI and determine the priority of highly relevant instructions.

[0067] The reception unit may analyze the driver's social media activity and receive related instructions when receiving instructions. For example, the reception unit may analyze the driver's social media activity and receive related instructions when receiving instructions. Social media activity may include, but is not limited to, posted content and like history. For example, the reception unit may preferentially receive instructions related to places the driver has shared on social media. The reception unit may also preferentially receive instructions related to places the driver follows on social media. Furthermore, the reception unit may also preferentially receive instructions related to places the driver has checked in to on social media. By analyzing social media activity, related instructions can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the driver's social media activity data into the generation AI and cause the generation AI to receive related instructions.

[0068] The analysis unit can analyze the driver's emotions and adjust the way the analysis is presented based on the analyzed driver's emotions. The analysis unit, for example, analyzes the driver's emotions and adjusts the way the analysis is presented based on the analyzed driver's emotions. Examples of the driver's emotions include, but are not limited to, relaxed, nervous, and in a hurry. For example, the analysis unit can provide a detailed analysis result when the driver is relaxed. Furthermore, the analysis unit can provide a concise and concise analysis result when the driver is nervous. Furthermore, the analysis unit can provide a quickly understandable analysis result when the driver is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the driver's emotional data into the generation AI and have the generation AI perform emotion analysis.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the instruction during analysis. The importance of the instruction includes, but is not limited to, for example, the urgency and the driver's intention. For example, the analysis unit performs a detailed analysis for important instructions. The analysis unit can also perform a concise analysis for general instructions. Furthermore, the analysis unit can also perform a quick analysis for urgent instructions. By adjusting the level of detail of the analysis based on the importance of the instruction, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without using, the generation AI. For example, the analysis unit can input instruction importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms based on the category of the instruction during analysis. For example, the analysis unit applies different analysis algorithms based on the category of the instruction during analysis. The categories of instructions include, but are not limited to, traffic information, tourist information, and emergency information. For example, the analysis unit applies a traffic analysis algorithm to instructions related to traffic information. The analysis unit can also apply a tourist analysis algorithm to instructions related to tourist information. Furthermore, the analysis unit can apply an emergency analysis algorithm to instructions related to emergency information. By applying different analysis algorithms depending on the category of the instruction, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of the instruction into the generation AI and cause the generation AI to apply different analysis algorithms.

[0071] The analysis unit can estimate the driver's emotions and adjust the length of the analysis based on the estimated driver's emotions. The analysis unit, for example, estimates the driver's emotions and adjusts the length of the analysis based on the estimated driver's emotions. Examples of the driver's emotions include, but are not limited to, relaxed, nervous, and in a hurry. For example, the analysis unit performs a detailed analysis when the driver is relaxed. Furthermore, the analysis unit can perform a concise analysis when the driver is nervous. Furthermore, the analysis unit can perform a quick and understandable analysis when the driver is in a hurry. This allows for adjusting the length of the analysis according to the driver's emotions to provide more appropriate analysis results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit can input the driver's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] The analysis unit can determine the priority of analysis based on the timing of instruction submission during analysis. For example, the analysis unit determines the priority of analysis based on the timing of instruction submission during analysis. The timing of instruction submission includes, but is not limited to, the time when the instruction was issued or the driver's schedule. For example, the analysis unit may give top priority to analysis of emergency instructions. The analysis unit may also give priority to analysis of important instructions. Furthermore, the analysis unit may also give normal priority to analysis of general instructions. By determining the priority of analysis based on the timing of instruction submission, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input instruction submission time data into the generation AI and have the generation AI determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of instructions during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of instructions during analysis. Examples of relevance of instructions include, but are not limited to, the current driving situation and past behavioral history. For example, the analysis unit may give top priority to analyzing important instructions. The analysis unit may also prioritize analyzing highly relevant instructions. Furthermore, the analysis unit may also analyze less relevant instructions with normal priority. By adjusting the order of analysis based on the relevance of instructions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input instruction relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0074] The adjustment unit can estimate the driver's emotions and adjust the navigation adjustment method based on the estimated driver's emotions. The adjustment unit, for example, estimates the driver's emotions and adjusts the navigation adjustment method based on the estimated driver's emotions. The driver's emotions include, but are not limited to, tension, relaxation, and hurry. For example, when the driver is tensioned, the adjustment unit can provide simple and highly visible navigation. Furthermore, when the driver is relaxed, the adjustment unit can also provide detailed navigation. Furthermore, when the driver is hurrying, the adjustment unit can also provide navigation that is quickly understandable. This allows for adjusting the navigation adjustment method according to the driver's emotions to provide more appropriate navigation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the driver's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] During adjustment, the adjustment unit can analyze the driver's past driving behavior and select an optimal adjustment method. For example, during adjustment, the adjustment unit analyzes the driver's past driving behavior and selects an optimal adjustment method. Past driving behavior includes, but is not limited to, driving history and driving patterns. For example, the adjustment unit preferentially provides a navigation method that the driver has previously preferred. The adjustment unit can also suggest an optimal navigation method based on the driver's past driving behavior. Furthermore, the adjustment unit can analyze the driver's past driving behavior and provide the most efficient navigation method. By analyzing the past driving behavior, more appropriate navigation can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's past driving behavior data into the generation AI and cause the generation AI to select an optimal adjustment method.

[0076] The adjustment unit can customize the navigation means according to the driver's current driving situation during adjustment. For example, the adjustment unit customizes the navigation means based on the driver's current driving situation during adjustment. The current driving situation includes, but is not limited to, vehicle speed, traffic conditions, and driver behavior. For example, when the driver is driving on a highway, the adjustment unit can provide navigation suitable for the highway. Furthermore, when the driver is driving in an urban area, the adjustment unit can also provide navigation suitable for the urban area. Furthermore, when the driver is stuck in traffic, the adjustment unit can provide navigation that avoids traffic jams. This allows for more appropriate navigation to be provided by customizing the navigation based on the current driving situation. Some or all of the above-described processing in the adjustment unit may be performed using, or without using, a generation AI. For example, the adjustment unit can input data on the driver's current driving situation into the generation AI and cause the generation AI to customize the navigation means.

[0077] The adjustment unit can estimate the driver's emotions and determine navigation priorities based on the estimated driver's emotions. The adjustment unit, for example, estimates the driver's emotions and determines navigation priorities based on the estimated driver's emotions. Driver emotions include, but are not limited to, tension, relaxation, and hurry. For example, when the driver is tensioned, the adjustment unit can prioritize providing important navigation. Furthermore, when the driver is relaxed, the adjustment unit can prioritize providing detailed navigation. Furthermore, when the driver is hurrying, the adjustment unit can prioritize providing navigation that requires a quick response. In this way, by determining navigation priorities according to the driver's emotions, more appropriate navigation can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input the driver's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The adjustment unit can select an optimal navigation method based on the driver's geographical location information during adjustment. For example, the adjustment unit selects an optimal navigation method based on the driver's geographical location information during adjustment. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, when the driver is in a specific area, the adjustment unit can provide navigation appropriate for that area. Furthermore, when the driver is approaching a destination, the adjustment unit can also provide navigation appropriate for the destination. Furthermore, when the driver is traveling a specific route, the adjustment unit can provide navigation appropriate for that route. This allows for more appropriate navigation to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's geographical location information to the generation AI and cause the generation AI to select an optimal navigation method.

[0079] During adjustment, the adjustment unit can analyze the driver's social media activity and suggest a navigation method. For example, during adjustment, the adjustment unit analyzes the driver's social media activity and suggests a navigation method. Social media activity includes, for example, but is not limited to, posted content and like history. For example, the adjustment unit provides navigation related to places the driver has shared on social media. The adjustment unit can also provide navigation related to places the driver follows on social media. Furthermore, the adjustment unit can also provide navigation related to places the driver has checked in to on social media. This allows for more appropriate navigation to be provided by analyzing social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the driver's social media activity data into the generation AI and cause the generation AI to suggest a navigation method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice instructions or text instructions from the driver using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's instructions using a generation AI to calculate an optimal route. The adjustment unit is realized by the control unit 46A of the smart device 14 and adjusts navigation in real time based on the route calculated by the analysis unit and provides specific instructions to the driver. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and adjustment 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 receives the driver's voice instructions using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's instructions using a generation AI to calculate an optimal route. The adjustment unit is realized by the control unit 46A of the smart glasses 214 and adjusts navigation in real time based on the route calculated by the analysis unit and provides specific instructions to the driver. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and adjustment 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 receives voice instructions from the driver using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the driver's instructions using a generation AI and calculates an optimal route. The adjustment unit is realized by the control unit 46A of the headset-type terminal 314, and adjusts navigation in real time based on the route calculated by the analysis unit and provides specific instructions to the driver. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice instructions from the driver using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the driver's instructions using a generation AI and calculates an optimal route. The adjustment unit is realized by the control unit 46A of the robot 414, and adjusts navigation in real time based on the route calculated by the analysis unit and provides specific instructions to the driver.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the tone and speed of the driver's voice to estimate the driver's level of urgency. For example, if the driver sounds rushed, the reception unit can prioritize the shortest route to respond quickly. If the driver sounds calm, the reception unit can also provide detailed navigation information. Furthermore, if the driver's voice is unstable, the reception unit can provide simple, easy-to-understand instructions. This makes it possible to provide more appropriate navigation information based on the driver's tone and speed of voice.

[0082] The analysis unit can analyze the driver's past driving history and customize navigation based on the driver's preferences and habits. For example, if the driver has preferred a particular route in the past, the analysis unit can preferentially suggest that route. Also, if the driver has a habit of going to a specific location at a certain time of day, the analysis unit can provide navigation tailored to that time of day. Furthermore, it can take into account routes the driver has avoided in the past and suggest alternative routes. This allows the system to provide more personalized navigation based on the driver's past driving history.

[0083] The adjustment unit can monitor the driver's current health condition and adjust navigation based on the driver's health condition. For example, if the driver's heart rate is high, the adjustment unit can suggest a relaxing route. Also, if the driver is tired, the adjustment unit can suggest rest spots. Furthermore, if the driver is in good health, the adjustment unit can prioritize the shortest route. This allows for more appropriate navigation to be provided according to the driver's health condition.

[0084] The reception unit can analyze the acoustic environment inside the driver's vehicle and adjust the instruction reception method based on the acoustic environment. For example, if the vehicle is noisy, the reception unit can prioritize receiving voice input. Alternatively, if the vehicle is quiet, the reception unit can prioritize receiving text input. Furthermore, if the acoustic environment inside the vehicle changes, the reception unit can automatically switch the reception method. This makes it possible to provide a more appropriate instruction reception method depending on the acoustic environment inside the vehicle.

[0085] The analysis unit can estimate the driver's emotions and adjust the way navigation is presented based on the estimated emotions. For example, if the driver is feeling stressed, the analysis unit can provide simple and easy-to-understand navigation. If the driver is relaxed, the analysis unit can provide detailed navigation information. Furthermore, if the driver is in a hurry, the analysis unit can provide quick and easy-to-understand navigation. This makes it possible to provide more appropriate navigation according to the driver's emotions.

[0086] The adjustment unit can analyze the driver's past navigation history and select the optimal navigation method. For example, if the driver has preferred a particular route in the past, the adjustment unit can preferentially suggest that route. It can also consider routes that the driver has avoided in the past and suggest alternative routes. Furthermore, if the driver has a habit of going to a particular location at a particular time of day, the adjustment unit can provide navigation tailored to that time of day. This makes it possible to provide more personalized navigation based on the driver's past navigation history.

[0087] The reception unit can estimate the driver's emotions and adjust the method of receiving instructions based on the estimated emotions. For example, if the driver is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the driver is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the driver is in a hurry, voice input can be prioritized to allow the driver to quickly enter destination and route instructions. This allows the reception method of instructions to be adjusted according to the driver's emotions, making it possible to receive more appropriate instructions.

[0088] The analysis unit can monitor the driver's current driving situation and adjust navigation based on the driving situation. For example, if the driver is driving on a highway, the analysis unit can provide navigation suitable for the highway. Also, if the driver is driving in a city, the analysis unit can provide navigation suitable for the city. Furthermore, if the driver is stuck in traffic, the analysis unit can provide navigation that avoids the traffic jam. This makes it possible to provide more appropriate navigation based on the driving situation.

[0089] The adjustment unit can estimate the driver's emotions and determine the priority of navigation based on the estimated emotions. For example, if the driver is nervous, important navigation can be provided with priority. Also, if the driver is relaxed, detailed navigation can be provided with priority. Furthermore, if the driver is in a hurry, navigation that requires a quick response can be provided with priority. In this way, by determining the priority of navigation according to the driver's emotions, more appropriate navigation can be provided.

[0090] The reception unit can adjust the method of receiving instructions based on the driver's current driving situation and areas of interest. For example, when the driver is driving on a highway, instructions related to the highway can be received with priority. Also, when the driver is heading to a tourist spot, information related to the tourist spot can be received with priority. Furthermore, when the driver is commuting, instructions related to the commuting route can be received with priority. This makes it possible to receive more appropriate instructions based on the driving situation and areas of interest.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives natural language instructions from the driver. The natural language instructions from the driver include voice instructions and text instructions. The reception unit converts the driver's voice instructions into text data using voice recognition technology. The driver can also input instructions in text format using a smartphone or tablet. Step 2: The analysis unit analyzes the instructions received by the reception unit and calculates a route. The analysis unit uses the generation AI to understand the driver's speech and provide appropriate navigation information. The generation AI uses text generation AI (e.g., LLM) to analyze the driver's instructions and calculate the optimal route. The analysis unit can also adjust navigation in real time according to the driver's situation. Step 3: The adjustment unit adjusts the navigation in real time based on the route calculated by the analysis unit. The adjustment unit provides specific instructions to the driver. For example, the generation AI provides specific instructions such as "Turn right at the next intersection, then go straight." The adjustment unit can also adjust the navigation according to the driver's situation. For example, it can recalculate the optimal route taking into account traffic congestion and traffic accident information and issue instructions to the driver.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] 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.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 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 instructions in natural language from the driver; an analysis unit that analyzes the instruction received by the reception unit and calculates a route; an adjustment unit that adjusts navigation in real time based on the route calculated by the analysis unit; A system characterized by:

2. The analysis unit Understanding what the driver is saying and providing appropriate navigation information 2. The system of claim 1.

3. The adjustment unit Recalculate the optimal route based on traffic congestion or traffic accident information and provide instructions to the driver 2. The system of claim 1.

4. The reception unit Drivers input destination and route instructions in natural language 2. The system of claim 1.

5. The analysis unit Adjust navigation in real time based on driver conditions 2. The system of claim 1.

6. The adjustment unit Provide specific instructions to drivers 2. The system of claim 1.

7. The reception unit Analyze the driver's emotions and adjust the way instructions are received based on the analyzed driver's emotions 2. The system of claim 1.

8. The reception unit Analyze the driver's past instruction history and select the optimal reception method 2. The system of claim 1.

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