System

Abstract

An object of a system according to an embodiment is to complete all operations by a voice operation in a fully autonomous vehicle.SOLUTION: A system includes a voice operation part, a destination setting part, a music selection part, and a video content selection part. The voice operation part receives a voice operation. The destination setting part analyzes the instruction received by the voice operation part and sets the destination. The music selection part analyzes the instruction received by the voice operation part and selects music. The video content selector analyzes the instruction received by the audio operation part, and selects the video content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to complete all operations in fully autonomous vehicles using voice control.

[0005] The system according to the embodiment aims to enable all operations in fully autonomous vehicles to be completed by voice control. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice operation unit, a destination setting unit, a music selection unit, and a video content selection unit. The voice operation unit accepts voice operations. The destination setting unit analyzes the instructions accepted by the voice operation unit and sets a destination. The music selection unit analyzes the instructions accepted by the voice operation unit and selects music. The video content selection unit analyzes the instructions accepted by the voice operation unit and selects video content. [Effects of the Invention]

[0007] The system according to the embodiment allows all operations to be completed by voice control in a fully autonomous vehicle. [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 next-generation voice concierge service according to an embodiment of the present invention is a system that allows a user to complete all operations in a fully automated driving vehicle using only voice commands. This allows the user to perform all operations using only voice commands, providing a comfortable driving experience.

[0029] A next-generation voice concierge service according to an embodiment includes a voice operation unit, a destination setting unit, a music selection unit, and a video content selection unit. The voice operation unit accepts a user's voice instruction. For example, if the user instructs, "My next destination is my office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instruction and transmits the instruction to an appropriate element. The destination setting unit analyzes the instruction accepted by the voice operation unit and sets a destination. For example, the destination setting unit calculates an optimal route based on the user's instruction and reflects the calculated route in the navigation system. The music selection unit analyzes the instruction accepted by the voice operation unit and selects music. For example, the music selection unit selects and plays music optimally suited to the user's mood and preferences. The video content selection unit analyzes the instruction accepted by the voice operation unit and selects video content. For example, the video content selection unit selects and plays video content optimally suited to the user's mood and preferences. This allows the next-generation voice concierge service to perform all operations using only voice operations.

[0030] The destination setting unit can analyze the user's past movement history and predict and suggest the next destination. For example, the destination setting unit collects data on places the user has visited in the past and identifies frequently visited places. For example, the destination setting unit analyzes the gym visited every Monday or the monthly meeting place to predict the next destination. The destination setting unit also predicts places to visit on specific days of the week or at specific times of the day based on the past movement history. For example, it learns patterns such as the office on weekday mornings and the shopping mall on weekend afternoons. The destination setting unit also analyzes the user's past movement history and predicts destinations related to specific events or seasons. For example, it suggests resorts visited every summer or ski resorts to go to in winter. In this way, the next destination can be predicted and suggested based on the user's past movement history.

[0031] The destination setting unit can link with the user's schedule information and automatically set a destination based on the schedule. The destination setting unit, for example, links with the user's calendar app and automatically sets a destination based on the schedule. For example, if a meeting is scheduled, the meeting location is automatically set. The destination setting unit also analyzes the schedule information and sets a destination based on a specific event or appointment. For example, a destination is set based on a doctor's appointment or a lunch plan with a friend. The destination setting unit also updates the user's schedule information in real time and automatically resets the destination if there is a change. For example, if the meeting time is changed, the destination is adjusted to match the new time. This allows the destination to be automatically set based on the user's schedule information.

[0032] The music selection unit can monitor the user's heart rate and body temperature and select music according to their physical condition. For example, the music selection unit monitors the user's heart rate and selects music that has the effect of lowering the heart rate if the user wants to relax. For example, it plays classical music or nature sounds. The music selection unit also monitors the user's body temperature and selects music that has a cooling effect if the user's body temperature is high. For example, it plays music with a slow tempo. The music selection unit also simultaneously monitors the user's heart rate and body temperature and selects music according to the user's overall physical condition. For example, it selects music that helps the user relax after exercising. This allows the optimal music to be selected based on the user's heart rate and body temperature.

[0033] The music selection unit can analyze the user's past music playback history and suggest music that suits the time of day and the weather. For example, the music selection unit analyzes the user's past music playback history and suggests music that the user likes to listen to at a specific time of day. For example, it selects uplifting music in the morning and relaxing music in the evening. The music selection unit also suggests music that suits the weather based on the user's music playback history. For example, it selects calming music on rainy days and cheerful music on sunny days. The music selection unit also analyzes the user's past music playback history and suggests music related to specific events or seasons. For example, it selects Christmas songs at Christmas and summer songs in summer. In this way, it is possible to suggest music that suits the time of day and the weather based on the user's past music playback history.

[0034] The video content selection unit can analyze the user's viewing history and suggest video content based on preferences for genres and actors. For example, the video content selection unit can analyze the user's past viewing history and suggest video content based on preferences for specific genres and actors. For example, it can select action movies or movies starring specific actors. The video content selection unit can also suggest video content related to a specific series or franchise based on the user's viewing history. For example, it can select sequels or spin-offs of a series. The video content selection unit can also analyze the user's viewing history and suggest video content based on a specific theme or storyline. For example, it can select adventure or romance movies. This makes it possible to suggest video content that matches preferences for genres and actors based on the user's viewing history.

[0035] The video content selection unit can link with the user's schedule information to suggest short videos and episodes that fit the viewing time. For example, the video content selection unit can link with the user's calendar app to suggest short videos and episodes that fit the viewing time. For example, it can select a short film or drama episode for a 30-minute free time. The video content selection unit can also analyze the schedule information to suggest video content that is easy to watch during a specific time period. For example, it can select short videos or news clips for commuting time. The video content selection unit can also update the user's schedule information in real time, and if there is a change, it can automatically re-suggest video content that fits the viewing time. For example, it can suggest new video content if the schedule ends early. This makes it possible to suggest short videos and episodes that fit the viewing time based on the user's schedule information.

[0036] The voice operation unit can analyze the user's past operation history and prioritize the display of frequently used functions. For example, we will develop a system that analyzes the user's past operation history and prioritizes the display of frequently used functions. For example, adjusting the air conditioner temperature or playing music. The voice operation unit will also display frequently used functions according to specific time periods or situations based on the past operation history. For example, it will prioritize the display of navigation during the morning commute. The voice operation unit will also build a system that updates the user's operation history in real time and automatically adjusts the display content if there are any changes. For example, it will respond when a new function becomes frequently used. This will make it possible to prioritize the display of frequently used functions based on the user's past operation history.

[0037] In addition to hands-free operation, the voice control unit can strengthen collaboration with smartphones and smartwatches. For example, the voice control unit can collaborate with the user's smartphone or smartwatch to build a system for operating the vehicle. For example, adjusting the air conditioner temperature using a smartphone app. The voice control unit can also use sensors in the smartphone or smartwatch to monitor the user's condition and automate vehicle operations. For example, adjusting the air conditioner temperature based on heart rate data from the smartwatch. The voice control unit can also strengthen collaboration with smartphones and smartwatches to develop a system that allows for more intuitive vehicle operation. For example, opening windows using gestures on the smartwatch. This will enable hands-free operation as well as strengthen collaboration with smartphones and smartwatches.

[0038] The voice operation unit can introduce a multi-user voice operation system so that all passengers can operate the vehicle. The voice operation unit, for example, builds a multi-user system that allows all passengers to operate the vehicle by voice. For example, each passenger can adjust the air conditioning temperature from their own seat. The voice operation unit also introduces a multi-user voice operation system and sets different voice commands for each passenger. For example, different voice commands are used for the driver's seat and the passenger seat. The voice operation unit also develops a system that recognizes the voices of all passengers and performs operations according to each passenger's preferences. For example, music is played according to each passenger's musical preferences. This allows the introduction of a multi-user voice operation system so that all passengers can operate the vehicle.

[0039] The voice operation unit can analyze not only the user's voice and facial expression, but also biometric data such as heart rate and body temperature. For example, the voice operation unit not only analyzes the user's voice and facial expression, but also collects biometric data such as heart rate and body temperature to build a system that comprehensively analyzes the user's mood. For example, a high heart rate may be determined to indicate stress. The voice operation unit also monitors biometric data in real time to analyze the user's mood. For example, a high body temperature may be determined to indicate stress. The voice operation unit also develops a system that comprehensively analyzes the user's voice, facial expression, and biometric data to determine the user's mood. For example, a high heart rate may be determined to indicate stress even if the user's voice is calm. This allows the system to analyze not only the user's voice and facial expression, but also biometric data such as heart rate and body temperature.

[0040] The voice operation unit can analyze the user's past behavioral data over the long term and learn mood patterns. The voice operation unit, for example, collects the user's past behavioral data over the long term and builds a system that learns mood patterns. For example, it identifies patterns in which a user is likely to feel stressed on specific days of the week or at specific times of the day. The voice operation unit also learns mood patterns according to specific events or situations based on the past behavioral data. For example, it identifies patterns in which a user is likely to feel stressed before or after a meeting. The voice operation unit also develops a system that analyzes the user's behavioral data over the long term and predicts mood fluctuations. For example, it learns mood fluctuations according to specific seasons or weather. This makes it possible to analyze the user's past behavioral data over the long term and learn mood patterns.

[0041] In addition to analyzing mood, the voice operation unit can also analyze the user's health condition and stress level. For example, the voice operation unit will build a system that analyzes the user's health condition and stress level in addition to analyzing the user's mood. For example, it will collect and comprehensively analyze biometric data such as heart rate, body temperature, and blood pressure. The voice operation unit will also monitor the user's health condition in real time and analyze the stress level. For example, it will determine that a user is feeling stressed if their heart rate is high. The voice operation unit will also develop a system that comprehensively analyzes the user's mood, health condition, and stress level and makes comprehensive suggestions. For example, if the user is feeling stressed, it will suggest relaxing music or videos. This will allow the system to analyze the user's health condition and stress level in addition to analyzing mood.

[0042] The voice operation unit can analyze the moods of all passengers and provide an environment in which everyone can feel comfortable. For example, the voice operation unit can analyze the moods of all passengers from their voices and facial expressions, and build a system that provides an environment in which everyone can feel comfortable. For example, it can select music that everyone can relax to. The voice operation unit can also analyze the moods of passengers in real time and adjust the in-car environment based on the results. For example, it can adjust the temperature of the air conditioner and the brightness of the lights. The voice operation unit can also comprehensively analyze the moods of all passengers and develop a system that makes suggestions to help everyone feel comfortable. For example, it can select video content that everyone can enjoy. This allows the moods of all passengers to be analyzed and an environment in which everyone can feel comfortable to be provided.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The voice operation unit accepts voice instructions from the user. For example, if the user instructs, "My next destination is my office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instructions and transmits the instructions to an appropriate element. The destination setting unit analyzes the instructions accepted by the voice operation unit and sets a destination. For example, the destination setting unit calculates an optimal route based on the user's instructions and reflects the route in the navigation system. The music selection unit analyzes the instructions accepted by the voice operation unit and selects music. For example, the music selection unit selects and plays music optimally suited to the user's mood and preferences. The video content selection unit analyzes the instructions accepted by the voice operation unit and selects video content. For example, the video content selection unit selects and plays video content optimally suited to the user's mood and preferences. This allows the next-generation voice concierge service to complete all operations using only voice operations.

[0045] The destination setting unit can analyze the user's past movement history and predict and suggest the next destination. For example, data on places the user has visited in the past is collected to identify frequently visited places. For example, the destination setting unit can analyze the gym the user visits every Monday or the monthly meeting place to predict the next destination. The destination setting unit can also predict places to visit on specific days of the week or at specific times of the day based on the past movement history. For example, the destination setting unit can learn patterns such as the office on weekday mornings and the shopping mall on weekend afternoons. The destination setting unit can also analyze the user's past movement history and predict destinations related to specific events or seasons. For example, the destination setting unit can suggest resorts visited every summer or ski resorts to visit in winter. This makes it possible to predict and suggest the next destination based on the user's past movement history.

[0046] The destination setting unit can link with the user's schedule information and automatically set a destination based on the schedule. For example, it can link with the user's calendar app and automatically set a destination based on the schedule. For example, if a meeting is scheduled, the meeting location can be automatically set. The destination setting unit can also analyze the schedule information and set a destination based on a specific event or appointment. For example, it can set a destination based on a doctor's appointment or a planned lunch with a friend. The destination setting unit can also update the user's schedule information in real time and automatically reset the destination if there is a change. For example, if the meeting time is changed, the destination can be adjusted to match the new time. This allows the destination to be automatically set based on the user's schedule information.

[0047] The music selection unit can monitor the user's heart rate and body temperature and select music according to their physical condition. For example, it can monitor the user's heart rate and select music that has the effect of lowering the heart rate if the user wants to relax. For example, it can play classical music or nature sounds. The music selection unit can also monitor the user's body temperature and select music that has a cooling effect if the user's body temperature is high. For example, it can play music with a slow tempo. The music selection unit can also simultaneously monitor the user's heart rate and body temperature and select music according to their overall physical condition. For example, it can select music that helps them relax after exercising. This allows the optimal music to be selected based on the user's heart rate and body temperature.

[0048] The music selection unit can analyze the user's past music playback history and suggest music that suits the time of day and the weather. For example, the music selection unit can analyze the user's past music playback history and suggest music that the user likes to listen to at a specific time of day. For example, it can select uplifting music in the morning and relaxing music in the evening. The music selection unit can also suggest music that suits the weather based on the user's music playback history. For example, it can select calming music on rainy days and cheerful music on sunny days. The music selection unit can also analyze the user's past music playback history and suggest music related to specific events or seasons. For example, it can select Christmas songs at Christmas and summer songs in summer. This makes it possible to suggest music that suits the time of day and the weather based on the user's past music playback history.

[0049] The video content selection unit can analyze the user's viewing history and suggest video content based on preferences for genres and actors. For example, the video content selection unit can analyze the user's past viewing history and suggest video content based on preferences for specific genres and actors. For example, it can select action movies or movies starring specific actors. The video content selection unit can also suggest video content related to a specific series or franchise based on the user's viewing history. For example, it can select sequels or spin-offs of a series. The video content selection unit can also analyze the user's viewing history and suggest video content based on a specific theme or storyline. For example, it can select adventure or romance movies. This makes it possible to suggest video content that matches preferences for genres and actors based on the user's viewing history.

[0050] The video content selection unit can link with the user's schedule information to suggest short videos and episodes that fit the viewing time. For example, it can link with the user's calendar app to suggest short videos and episodes that fit the viewing time. For example, it can select a short film or drama episode for a 30-minute free time. The video content selection unit can also analyze the schedule information to suggest video content that is easy to watch during specific time periods. For example, it can select short videos or news clips for commuting time. The video content selection unit can also update the user's schedule information in real time, and if there are any changes, it can automatically re-suggest video content that fits the viewing time. For example, it can suggest new video content if the schedule ends early. This makes it possible to suggest short videos and episodes that fit the viewing time based on the user's schedule information.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The voice operation unit accepts a user's voice instruction. For example, if the user says, "My next destination is the office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instruction and sends the instruction to the appropriate element. Step 2: The destination setting unit analyzes the instructions received by the voice operation unit and sets the destination. For example, the destination setting unit calculates the optimal route based on the user's instructions and reflects this in the navigation system. Step 3: The music selection unit analyzes the instruction received by the voice operation unit and selects music. For example, the music selection unit selects and plays music that is optimal for the user's mood and preferences. Step 4: The video content selection unit analyzes the instruction received by the voice operation unit and selects video content. For example, the video content selection unit selects and plays the most suitable video content according to the user's mood and preferences.

[0053] (Example 2) A next-generation voice concierge service according to an embodiment of the present invention is a system that allows a user to complete all operations in a fully automated driving vehicle using only voice commands. This allows the user to perform all operations using only voice commands, providing a comfortable driving experience.

[0054] A next-generation voice concierge service according to an embodiment includes a voice operation unit, a destination setting unit, a music selection unit, and a video content selection unit. The voice operation unit accepts a user's voice instruction. For example, if the user instructs, "My next destination is my office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instruction and transmits the instruction to an appropriate element. The destination setting unit analyzes the instruction accepted by the voice operation unit and sets a destination. For example, the destination setting unit calculates an optimal route based on the user's instruction and reflects the calculated route in the navigation system. The music selection unit analyzes the instruction accepted by the voice operation unit and selects music. For example, the music selection unit selects and plays music optimally suited to the user's mood and preferences. The video content selection unit analyzes the instruction accepted by the voice operation unit and selects video content. For example, the video content selection unit selects and plays video content optimally suited to the user's mood and preferences. This allows the next-generation voice concierge service to perform all operations using only voice operations.

[0055] The destination setting unit can analyze the user's past movement history and predict and suggest the next destination. For example, the destination setting unit collects data on places the user has visited in the past and identifies frequently visited places. For example, the destination setting unit analyzes the gym visited every Monday or the monthly meeting place to predict the next destination. The destination setting unit also predicts places to visit on specific days of the week or at specific times of the day based on the past movement history. For example, it learns patterns such as the office on weekday mornings and the shopping mall on weekend afternoons. The destination setting unit also analyzes the user's past movement history and predicts destinations related to specific events or seasons. For example, it suggests resorts visited every summer or ski resorts to go to in winter. In this way, the next destination can be predicted and suggested based on the user's past movement history.

[0056] The destination setting unit can link with the user's schedule information and automatically set a destination based on the schedule. The destination setting unit, for example, links with the user's calendar app and automatically sets a destination based on the schedule. For example, if a meeting is scheduled, the meeting location is automatically set. The destination setting unit also analyzes the schedule information and sets a destination based on a specific event or appointment. For example, a destination is set based on a doctor's appointment or a lunch plan with a friend. The destination setting unit also updates the user's schedule information in real time and automatically resets the destination if there is a change. For example, if the meeting time is changed, the destination is adjusted to match the new time. This allows the destination to be automatically set based on the user's schedule information.

[0057] The destination setting unit can use the emotion estimation function to suggest a relaxing route or a scenic route according to the user's mood. For example, the destination setting unit analyzes the user's voice and facial expression, and if it detects that the user is in a relaxing mood, it suggests a scenic route. For example, it selects a route along the seaside or through a park. Furthermore, if the destination setting unit uses the emotion estimation function to suggest a route that avoids traffic congestion when the user is feeling stressed. For example, it selects back roads or highways to avoid congestion. Furthermore, the destination setting unit suggests a scenic route while playing relaxing music or natural sounds according to the user's mood. For example, it selects a route along a mountain path or a river. In this way, it is possible to suggest a relaxing route or a scenic route according to the user's mood.

[0058] The music selection unit can monitor the user's heart rate and body temperature and select music according to their physical condition. For example, the music selection unit monitors the user's heart rate and selects music that has the effect of lowering the heart rate if the user wants to relax. For example, it plays classical music or nature sounds. The music selection unit also monitors the user's body temperature and selects music that has a cooling effect if the user's body temperature is high. For example, it plays music with a slow tempo. The music selection unit also simultaneously monitors the user's heart rate and body temperature and selects music according to the user's overall physical condition. For example, it selects music that helps the user relax after exercising. This allows the optimal music to be selected based on the user's heart rate and body temperature.

[0059] The music selection unit can analyze the user's past music playback history and suggest music that suits the time of day and the weather. For example, the music selection unit analyzes the user's past music playback history and suggests music that the user likes to listen to at a specific time of day. For example, it selects uplifting music in the morning and relaxing music in the evening. The music selection unit also suggests music that suits the weather based on the user's music playback history. For example, it selects calming music on rainy days and cheerful music on sunny days. The music selection unit also analyzes the user's past music playback history and suggests music related to specific events or seasons. For example, it selects Christmas songs at Christmas and summer songs in summer. In this way, it is possible to suggest music that suits the time of day and the weather based on the user's past music playback history.

[0060] The music selection unit can use the emotion estimation function to automatically generate a playlist that matches the user's mood. The music selection unit, for example, analyzes the user's voice and facial expressions to automatically generate a playlist that matches the user's current mood. For example, if the user wants to relax, the music selection unit creates a playlist that includes relaxing music. The music selection unit also uses the emotion estimation function to automatically generate a playlist that includes uplifting music if the user is feeling down. For example, the music selection unit selects upbeat music or songs with positive lyrics. The music selection unit also automatically generates a playlist based on a specific theme according to the user's mood. For example, if the user is traveling, the music selection unit creates a playlist that includes travel-related music. In this way, a playlist that matches the user's mood can be automatically generated.

[0061] The video content selection unit can analyze the user's viewing history and suggest video content based on preferences for genres and actors. For example, the video content selection unit can analyze the user's past viewing history and suggest video content based on preferences for specific genres and actors. For example, it can select action movies or movies starring specific actors. The video content selection unit can also suggest video content related to a specific series or franchise based on the user's viewing history. For example, it can select sequels or spin-offs of a series. The video content selection unit can also analyze the user's viewing history and suggest video content based on a specific theme or storyline. For example, it can select adventure or romance movies. This makes it possible to suggest video content that matches preferences for genres and actors based on the user's viewing history.

[0062] The video content selection unit can link with the user's schedule information to suggest short videos and episodes that fit the viewing time. For example, the video content selection unit can link with the user's calendar app to suggest short videos and episodes that fit the viewing time. For example, it can select a short film or drama episode for a 30-minute free time. The video content selection unit can also analyze the schedule information to suggest video content that is easy to watch during a specific time period. For example, it can select short videos or news clips for commuting time. The video content selection unit can also update the user's schedule information in real time, and if there is a change, it can automatically re-suggest video content that fits the viewing time. For example, it can suggest new video content if the schedule ends early. This makes it possible to suggest short videos and episodes that fit the viewing time based on the user's schedule information.

[0063] The video content selection unit can use the emotion estimation function to automatically select video content that matches the user's mood. The video content selection unit, for example, analyzes the user's voice and facial expression and automatically selects video content that matches the user's current mood. For example, if the user wants to relax, the video content selection unit selects a comedy movie. The video content selection unit also uses the emotion estimation function to automatically select uplifting video content if the user is feeling down. For example, the video content selection unit selects an action movie or a movie with a positive story. The video content selection unit also automatically selects video content based on a specific theme according to the user's mood. For example, if the user is traveling, the video content selection unit selects a documentary related to travel. This allows the automatic selection of video content that matches the user's mood.

[0064] The voice operation unit can analyze the user's past operation history and prioritize the display of frequently used functions. For example, we will develop a system that analyzes the user's past operation history and prioritizes the display of frequently used functions. For example, adjusting the air conditioner temperature or playing music. The voice operation unit will also display frequently used functions according to specific time periods or situations based on the past operation history. For example, it will prioritize the display of navigation during the morning commute. The voice operation unit will also build a system that updates the user's operation history in real time and automatically adjusts the display content if there are any changes. For example, it will respond when a new function becomes frequently used. This will make it possible to prioritize the display of frequently used functions based on the user's past operation history.

[0065] The voice operation unit can use the emotion estimation function to simplify operations when the user is feeling stressed. For example, the voice operation unit analyzes the user's voice and facial expressions, and introduces a system that simplifies operations when it detects that the user is feeling stressed. For example, it replaces complicated operations with simple voice commands. Furthermore, the voice operation unit can use the emotion estimation function to simplify operations when it detects that the user wants to relax. For example, it can provide a macro function that performs multiple operations with one touch. Furthermore, the voice operation unit can build a system that simplifies the operation interface depending on the user's mood. For example, when the user is feeling stressed, only the most necessary functions are displayed. This can simplify operations when the user is feeling stressed.

[0066] In addition to hands-free operation, the voice control unit can strengthen collaboration with smartphones and smartwatches. For example, the voice control unit can collaborate with the user's smartphone or smartwatch to build a system for operating the vehicle. For example, adjusting the air conditioner temperature using a smartphone app. The voice control unit can also use sensors in the smartphone or smartwatch to monitor the user's condition and automate vehicle operations. For example, adjusting the air conditioner temperature based on heart rate data from the smartwatch. The voice control unit can also strengthen collaboration with smartphones and smartwatches to develop a system that allows for more intuitive vehicle operation. For example, opening windows using gestures on the smartwatch. This will enable hands-free operation as well as strengthen collaboration with smartphones and smartwatches.

[0067] The voice operation unit can introduce a multi-user voice operation system so that all passengers can operate the vehicle. The voice operation unit, for example, builds a multi-user system that allows all passengers to operate the vehicle by voice. For example, each passenger can adjust the air conditioning temperature from their own seat. The voice operation unit also introduces a multi-user voice operation system and sets different voice commands for each passenger. For example, different voice commands are used for the driver's seat and the passenger seat. The voice operation unit also develops a system that recognizes the voices of all passengers and performs operations according to each passenger's preferences. For example, music is played according to each passenger's musical preferences. This allows the introduction of a multi-user voice operation system so that all passengers can operate the vehicle.

[0068] The voice operation unit can use the emotion estimation function to simplify the operation interface when the user wants to relax. For example, the voice operation unit analyzes the user's voice and facial expression, and introduces a system that simplifies the operation interface when it detects that the user wants to relax. For example, it displays only the most necessary functions. The voice operation unit also uses the emotion estimation function to simplify the operation interface when the user is feeling stressed. For example, it replaces complex operations with simple voice commands. The voice operation unit also builds a system that dynamically adjusts the operation interface according to the user's mood. For example, if the user wants to relax, it reduces the number of operation buttons. This makes it possible to simplify the operation interface when the user wants to relax.

[0069] The voice operation unit can analyze not only the user's voice and facial expression, but also biometric data such as heart rate and body temperature. For example, the voice operation unit not only analyzes the user's voice and facial expression, but also collects biometric data such as heart rate and body temperature to build a system that comprehensively analyzes the user's mood. For example, a high heart rate may be determined to indicate stress. The voice operation unit also monitors biometric data in real time to analyze the user's mood. For example, a high body temperature may be determined to indicate stress. The voice operation unit also develops a system that comprehensively analyzes the user's voice, facial expression, and biometric data to determine the user's mood. For example, a high heart rate may be determined to indicate stress even if the user's voice is calm. This allows the system to analyze not only the user's voice and facial expression, but also biometric data such as heart rate and body temperature.

[0070] The voice operation unit can analyze the user's past behavioral data over the long term and learn mood patterns. The voice operation unit, for example, collects the user's past behavioral data over the long term and builds a system that learns mood patterns. For example, it identifies patterns in which a user is likely to feel stressed on specific days of the week or at specific times of the day. The voice operation unit also learns mood patterns according to specific events or situations based on the past behavioral data. For example, it identifies patterns in which a user is likely to feel stressed before or after a meeting. The voice operation unit also develops a system that analyzes the user's behavioral data over the long term and predicts mood fluctuations. For example, it learns mood fluctuations according to specific seasons or weather. This makes it possible to analyze the user's past behavioral data over the long term and learn mood patterns.

[0071] The voice operation unit can use the emotion estimation function to make customized suggestions according to the user's mood. The voice operation unit, for example, uses the emotion estimation function to build a system that makes customized suggestions according to the user's mood. For example, if the user wants to relax, it will suggest relaxing music or videos. The voice operation unit also analyzes the user's mood in real time and makes customized suggestions based on the results. For example, if the user is feeling stressed, it will suggest a route that has a relaxing effect. The voice operation unit also develops a system that makes customized suggestions based on the user's preferences and interests based on the emotion estimation data. For example, it will suggest movies or music of a specific genre according to the user's mood. This makes it possible to make customized suggestions according to the user's mood.

[0072] In addition to analyzing mood, the voice operation unit can also analyze the user's health condition and stress level. For example, the voice operation unit will build a system that analyzes the user's health condition and stress level in addition to analyzing the user's mood. For example, it will collect and comprehensively analyze biometric data such as heart rate, body temperature, and blood pressure. The voice operation unit will also monitor the user's health condition in real time and analyze the stress level. For example, it will determine that a user is feeling stressed if their heart rate is high. The voice operation unit will also develop a system that comprehensively analyzes the user's mood, health condition, and stress level and makes comprehensive suggestions. For example, if the user is feeling stressed, it will suggest relaxing music or videos. This will allow the system to analyze the user's health condition and stress level in addition to analyzing mood.

[0073] The voice operation unit can analyze the moods of all passengers and provide an environment in which everyone can feel comfortable. For example, the voice operation unit can analyze the moods of all passengers from their voices and facial expressions, and build a system that provides an environment in which everyone can feel comfortable. For example, it can select music that everyone can relax to. The voice operation unit can also analyze the moods of passengers in real time and adjust the in-car environment based on the results. For example, it can adjust the temperature of the air conditioner and the brightness of the lights. The voice operation unit can also comprehensively analyze the moods of all passengers and develop a system that makes suggestions to help everyone feel comfortable. For example, it can select video content that everyone can enjoy. This allows the moods of all passengers to be analyzed and an environment in which everyone can feel comfortable to be provided.

[0074] The voice operation unit can use the emotion estimation function to adjust the lighting and fragrance inside the car when the user wants to relax. For example, the voice operation unit analyzes the user's voice and facial expression, and if it detects that the user wants to relax, it introduces a system that adjusts the lighting and fragrance inside the car. For example, it changes the lighting to warm colors and emits a fragrance that has a relaxing effect. The voice operation unit also uses the emotion estimation function to adjust the lighting and fragrance inside the car when the user is feeling stressed. For example, it changes the lighting to soft light and uses an aroma that has a relaxing effect. The voice operation unit also builds a system that dynamically adjusts the environment inside the car according to the user's mood. For example, if the user wants to relax, it adjusts the brightness of the lighting and emits a fragrance that has a relaxing effect. In this way, the lighting and fragrance inside the car can be adjusted when the user wants to relax.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The voice operation unit accepts voice instructions from the user. For example, if the user instructs, "My next destination is my office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instructions and transmits the instructions to an appropriate element. The destination setting unit analyzes the instructions accepted by the voice operation unit and sets a destination. For example, the destination setting unit calculates an optimal route based on the user's instructions and reflects the route in the navigation system. The music selection unit analyzes the instructions accepted by the voice operation unit and selects music. For example, the music selection unit selects and plays music optimally suited to the user's mood and preferences. The video content selection unit analyzes the instructions accepted by the voice operation unit and selects video content. For example, the video content selection unit selects and plays video content optimally suited to the user's mood and preferences. This allows the next-generation voice concierge service to complete all operations using only voice operations.

[0077] The destination setting unit can analyze the user's past movement history and predict and suggest the next destination. For example, data on places the user has visited in the past is collected to identify frequently visited places. For example, the destination setting unit can analyze the gym the user visits every Monday or the monthly meeting place to predict the next destination. The destination setting unit can also predict places to visit on specific days of the week or at specific times of the day based on the past movement history. For example, the destination setting unit can learn patterns such as the office on weekday mornings and the shopping mall on weekend afternoons. The destination setting unit can also analyze the user's past movement history and predict destinations related to specific events or seasons. For example, the destination setting unit can suggest resorts visited every summer or ski resorts to visit in winter. This makes it possible to predict and suggest the next destination based on the user's past movement history.

[0078] The destination setting unit can link with the user's schedule information and automatically set a destination based on the schedule. For example, it can link with the user's calendar app and automatically set a destination based on the schedule. For example, if a meeting is scheduled, the meeting location can be automatically set. The destination setting unit can also analyze the schedule information and set a destination based on a specific event or appointment. For example, it can set a destination based on a doctor's appointment or a planned lunch with a friend. The destination setting unit can also update the user's schedule information in real time and automatically reset the destination if there is a change. For example, if the meeting time is changed, the destination can be adjusted to match the new time. This allows the destination to be automatically set based on the user's schedule information.

[0079] The destination setting unit can use the emotion estimation function to suggest a relaxing route or a scenic route according to the user's mood. For example, if the destination setting unit analyzes the user's voice and facial expression and detects that the user is in a relaxing mood, it can suggest a scenic route. For example, it can select a route along the seaside or through a park. Furthermore, if the destination setting unit uses the emotion estimation function to suggest a route that avoids traffic congestion if the user is feeling stressed. For example, it can select back roads or expressways to avoid congestion. Furthermore, the destination setting unit can suggest a scenic route while playing relaxing music or natural sounds according to the user's mood. For example, it can select a route along a mountain path or a river. In this way, it can suggest a relaxing route or a scenic route according to the user's mood.

[0080] The music selection unit can monitor the user's heart rate and body temperature and select music according to their physical condition. For example, it can monitor the user's heart rate and select music that has the effect of lowering the heart rate if the user wants to relax. For example, it can play classical music or nature sounds. The music selection unit can also monitor the user's body temperature and select music that has a cooling effect if the user's body temperature is high. For example, it can play music with a slow tempo. The music selection unit can also simultaneously monitor the user's heart rate and body temperature and select music according to their overall physical condition. For example, it can select music that helps them relax after exercising. This allows the optimal music to be selected based on the user's heart rate and body temperature.

[0081] The music selection unit can analyze the user's past music playback history and suggest music that suits the time of day and the weather. For example, the music selection unit can analyze the user's past music playback history and suggest music that the user likes to listen to at a specific time of day. For example, it can select uplifting music in the morning and relaxing music in the evening. The music selection unit can also suggest music that suits the weather based on the user's music playback history. For example, it can select calming music on rainy days and cheerful music on sunny days. The music selection unit can also analyze the user's past music playback history and suggest music related to specific events or seasons. For example, it can select Christmas songs at Christmas and summer songs in summer. This makes it possible to suggest music that suits the time of day and the weather based on the user's past music playback history.

[0082] The music selection unit can use the emotion estimation function to automatically generate a playlist that matches the user's mood. For example, it can analyze the user's voice and facial expressions to automatically generate a playlist that matches the user's current mood. For example, if the user wants to relax, it can create a playlist that includes relaxing music. The music selection unit can also use the emotion estimation function to automatically generate a playlist that includes uplifting music if the user is feeling down. For example, it can select upbeat music or songs with positive lyrics. The music selection unit can also automatically generate a playlist based on a specific theme depending on the user's mood. For example, if the user is traveling, it can create a playlist that includes travel-related music. In this way, it is possible to automatically generate a playlist that matches the user's mood.

[0083] The video content selection unit can analyze the user's viewing history and suggest video content based on preferences for genres and actors. For example, the video content selection unit can analyze the user's past viewing history and suggest video content based on preferences for specific genres and actors. For example, it can select action movies or movies starring specific actors. The video content selection unit can also suggest video content related to a specific series or franchise based on the user's viewing history. For example, it can select sequels or spin-offs of a series. The video content selection unit can also analyze the user's viewing history and suggest video content based on a specific theme or storyline. For example, it can select adventure or romance movies. This makes it possible to suggest video content that matches preferences for genres and actors based on the user's viewing history.

[0084] The video content selection unit can link with the user's schedule information to suggest short videos and episodes that fit the viewing time. For example, it can link with the user's calendar app to suggest short videos and episodes that fit the viewing time. For example, it can select a short film or drama episode for a 30-minute free time. The video content selection unit can also analyze the schedule information to suggest video content that is easy to watch during specific time periods. For example, it can select short videos or news clips for commuting time. The video content selection unit can also update the user's schedule information in real time, and if there are any changes, it can automatically re-suggest video content that fits the viewing time. For example, it can suggest new video content if the schedule ends early. This makes it possible to suggest short videos and episodes that fit the viewing time based on the user's schedule information.

[0085] The video content selection unit can use the emotion estimation function to automatically select video content that matches the user's mood. For example, it analyzes the user's voice and facial expression and automatically selects video content that matches the user's current mood. For example, if the user wants to relax, it selects a comedy movie. The video content selection unit also uses the emotion estimation function to automatically select uplifting video content if the user is feeling down. For example, it selects an action movie or a movie with a positive story. The video content selection unit also automatically selects video content based on a specific theme depending on the user's mood. For example, if the user is traveling, it selects a documentary related to travel. This makes it possible to automatically select video content that matches the user's mood.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The voice operation unit accepts a user's voice instruction. For example, if the user says, "My next destination is the office," the voice operation unit accepts the instruction. The voice operation unit also analyzes the user's voice instruction and sends the instruction to the appropriate element. Step 2: The destination setting unit analyzes the instructions received by the voice operation unit and sets the destination. For example, the destination setting unit calculates the optimal route based on the user's instructions and reflects this in the navigation system. Step 3: The music selection unit analyzes the instruction received by the voice operation unit and selects music. For example, the music selection unit selects and plays music that is optimal for the user's mood and preferences. Step 4: The video content selection unit analyzes the instruction received by the voice operation unit and selects video content. For example, the video content selection unit selects and plays the most suitable video content according to the user's mood and preferences.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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. [Explanation of symbols]

[0155] 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 voice operation unit; a destination setting unit that analyzes an instruction received by the voice operation unit and sets a destination; a music selection unit that analyzes an instruction received by the voice operation unit and selects music; a video content selection unit that analyzes the instruction received by the voice operation unit and selects video content. A system characterized by:

2. The destination setting unit Analyze the user's past travel history to predict and suggest the next destination 2. The system of claim 1.

3. The destination setting unit Linking users' schedule information and automatically setting destinations based on their schedules 2. The system of claim 1.

4. The destination setting unit Suggesting relaxing and scenic routes based on the user's mood 2. The system of claim 1.

5. The music selection unit Monitors the user's heart rate and body temperature and selects music according to their physical condition 2. The system of claim 1.

6. The music selection unit Analyzes the user's past music playback history and suggests music according to the time of day and weather 2. The system of claim 1.

7. The music selection unit Automatically generate playlists that match the user's mood 2. The system of claim 1.

8. The video content selection unit Analyzes user viewing history and suggests video content based on genre and actor preferences 2. The system of claim 1.

Images