system

Abstract

The system according to this embodiment aims to transform the user's dreams into concrete goals and support their realization. [Solution] The system according to the embodiment comprises a dialogue unit, a design unit, and a support unit. The dialogue unit verbalizes the user's dreams into concrete goals. The design unit identifies the necessary skills and knowledge based on the goals verbalized by the dialogue unit and designs the optimal way to spend a day. The support unit assists in executing the schedule designed by the design unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to convert an abstract dream into a concrete goal and make a specific action plan for realizing it.

[0005] The system according to the embodiment aims to convert the user's dream into a concrete goal and support its realization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, a design unit, and a support unit. The dialogue unit verbalizes the user's dreams into concrete goals. The design unit identifies the necessary skills and knowledge based on the goals verbalized by the dialogue unit and designs an optimal way of spending the day. The support unit assists in executing the schedule designed by the design unit. [Effects of the Invention]

[0007] The system according to this embodiment can transform a user's dreams into concrete goals and support their realization. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dream realization support system according to an embodiment of the present invention is a system that verbalizes a user's dream into concrete goals and designs the skills, knowledge, and optimal daily routine necessary to realize those dreams. The dream realization support system verbalizes the user's dream into concrete goals through dialogue, identifies the skills and knowledge necessary to achieve those goals, and designs the optimal daily routine. Furthermore, the dream realization support system provides persistent support by assisting with execution through alarms and voice encouragement, and if execution is not possible, it changes the schedule in real time or boosts motivation. In particular, it provides a time management strategy for people who find it difficult to manage their time on their own, enabling them to steadily build up their efforts day by day. For example, the dream realization support system allows the user to converse with AI and verbalize an abstract dream into concrete goals. For example, a user who dreams of "becoming a voice actor" can, through dialogue with AI, convert their dream into a concrete goal such as "joining a voice acting agency and aiming for an annual income of XX million yen." Next, the dream realization support system identifies the skills and knowledge necessary to achieve that goal and designs the optimal daily routine. For example, since becoming a voice actor requires vocal and articulation practice, the Dream Realization Support System creates a schedule for acquiring these skills. Furthermore, the Dream Realization Support System assists the user in taking action with alarms and voice encouragement. For instance, an alarm sounds when it's time to practice, and the Dream Realization Support System provides voice encouragement such as, "It's time to practice now. Let's do our best!" If the user fails to practice, the Dream Realization Support System adjusts the schedule in real time and reschedules practice for the next available time. In addition, to maintain the user's motivation, the Dream Realization Support System updates the goal image in line with daily efforts and provides encouraging messages. For example, to a user who feels like they can't do it anymore, it sends a message such as, "Your dream is so wonderful. Let's work hard together until you achieve your dream!" In this way, the Dream Realization Support System persistently supports and stands by the user until their dream comes true. In particular, it provides time management strategies for people who have difficulty with self-management or time management on their own, helping them to steadily build up their efforts day by day and supporting them in realizing their dreams.This allows the dream realization support system to transform the user's dreams into concrete goals and support their realization.

[0029] The dream realization support system according to this embodiment comprises a dialogue unit, a design unit, and a support unit. The dialogue unit verbalizes the user's dream into concrete goals. For example, if the user has a dream of "becoming a voice actor," the dialogue unit will convert it into a concrete goal such as "joining a voice acting agency and aiming for an annual income of XX million yen" through dialogue. The dialogue unit can verbalize the user's dream into concrete goals using generative AI. For example, the generative AI analyzes the user's input and proposes appropriate goals. The design unit identifies the necessary skills and knowledge based on the goals verbalized by the dialogue unit and designs an optimal daily schedule. For example, since vocalization and articulation practice are necessary to become a voice actor, the design unit creates a schedule for acquiring these skills. The design unit can design an optimal schedule based on the user's goals using generative AI. For example, the generative AI considers the user's goals and current skill level and proposes an optimal practice schedule. The support unit assists the user in executing the schedule designed by the design unit. The support unit assists the user in executing the schedule, for example, with alarms and voice encouragement. The support unit can assist the user in their actions using generative AI. For example, the support unit can sound an alarm when it's time to practice and encourage the user with a voice saying, "It's time to practice now. Let's do our best!" If the user is unable to perform their task, the support unit can change the schedule in real time and reschedule the practice to the next available time. The support unit can also change the user's schedule in real time using generative AI. For example, the support unit can monitor the user's progress and suggest the next available time if they are unable to perform their task. In this way, the dream realization support system according to the embodiment can transform the user's dream into a concrete goal and support its realization.

[0030] The dialogue unit translates the user's dreams into concrete goals. For example, if a user dreams of becoming a voice actor, the dialogue unit will translate this into a specific goal such as "join a voice acting agency and aim for an annual income of XX million yen." The dialogue unit can translate the user's dreams into concrete goals using generative AI. For example, the generative AI analyzes the user's input and suggests appropriate goals. Specifically, the generative AI uses natural language processing technology to analyze the user's input and understand the user's intentions and desires. For example, if a user inputs "I want to become a voice actor," the generative AI understands the context and suggests specific steps and goals to achieve that goal. Based on past data and success stories, the generative AI can set the optimal goals for the user. Furthermore, through dialogue with the user, the generative AI evaluates the feasibility and realism of the goals and modifies them as needed. For example, if a user wishes to join a voice acting agency within one year, the generative AI evaluates whether that goal is realistic and can modify it as needed, such as "join a voice acting agency within two years." This allows the dialogue unit to translate the user's dreams into concrete and realistic goals, and to support the user in taking effective action towards those goals.

[0031] The design department identifies the necessary skills and knowledge based on the goals articulated by the dialogue department and designs an optimal daily schedule. For example, the design department creates a schedule for acquiring skills such as vocalization and articulation practice, which are necessary to become a voice actor. The design department can use generative AI to design an optimal schedule based on the user's goals. For example, the generative AI considers the user's goals and current skill level to propose an optimal practice schedule. Specifically, the generative AI evaluates the user's current skill level and identifies the skills and knowledge necessary to achieve the goals. For example, it determines that the user needs vocalization practice, articulation practice, and acting practice to become a voice actor. The generative AI creates a schedule for effectively acquiring these skills and proposes it to the user. Furthermore, the generative AI designs a manageable schedule considering the user's lifestyle and other commitments. For example, if the user works on weekdays, the generative AI sets practice times for weekends or evenings. The generative AI can also monitor the user's progress in real time and adjust the schedule as needed. For example, if a user is unable to practice as scheduled, the generating AI will reschedule the practice to the next available time, helping the user to continue working towards their goal. This allows the design team to provide the user with an optimal schedule for achieving their goals and to help them acquire skills effectively.

[0032] The support team assists users in executing the schedule designed by the design team. The support team provides assistance, for example, through alarms and voice encouragement. The support team can use generative AI to assist users. For example, it might sound an alarm when it's time to practice and provide voice encouragement such as, "It's time to practice! Let's do our best!" If a user fails to complete their practice, the support team will modify the schedule in real time and reschedule the practice for the next available time. The support team can use generative AI to modify the user's schedule in real time. For example, it will monitor the user's progress and suggest the next available time if they fail to complete their practice. Specifically, the generative AI monitors the user's progress in real time to ensure they are practicing as scheduled. If a user fails to practice, the generative AI analyzes the reason and suggests the next available time. For example, if a user misses practice due to work commitments, the generative AI will reschedule it for their next break or the weekend. The support team also provides regular encouragement messages and progress reports to maintain user motivation. For example, if a user is making good progress towards their goal, the AI ​​generator will send a message such as, "Great progress! Keep up the good work!" Furthermore, when a user achieves their goal, the support team shares in their sense of accomplishment and helps them set their next goal. In this way, the support team can help users continue to work towards their goals and ultimately support their achievement.

[0033] The support unit can assist users in their efforts through alarms and voice encouragement. For example, when it's time to practice, the support unit will sound an alarm and provide voice encouragement such as, "It's time to practice! Let's do our best!" The support unit can also use generative AI to assist users in their efforts. For example, the support unit can set alarms based on the user's schedule and sound them when it's time to practice. The support unit can also monitor the user's progress and provide voice encouragement if they are falling behind. For example, the support unit can send an encouraging message such as, "You're a little behind, but you can still make it! Let's do our best!" In this way, by supporting users in their efforts through alarms and voice encouragement, the system can promote the achievement of goals.

[0034] The support unit can change the schedule in real time if an execution fails. For example, if a user is unable to perform a practice session, the support unit will reschedule the practice session to the next available time. The support unit can also change the user's schedule in real time using generative AI. For example, the support unit monitors the user's execution status and suggests the next available time if an execution fails. The support unit can also automatically adjust the user's schedule and incorporate tasks that could not be completed into the next schedule. For example, the support unit analyzes the user's schedule and reschedules tasks to the optimal time. This allows for flexible responses by changing the schedule in real time if an execution fails.

[0035] The support team can provide encouraging messages to maintain user motivation. For example, if a user feels like they "can't do it anymore," the support team can send a message such as, "Your dream is so wonderful. Let's work hard together until you achieve your dream!" The support team can also use generative AI to provide encouraging messages to maintain user motivation. For example, the support team can monitor the user's progress and send an encouraging message if their motivation is low. The support team can also update the user's goal image in line with their daily efforts and provide encouraging messages. For example, depending on the user's progress, the support team can send a message such as, "You've come so far. Just a little further!" In this way, the support team can help users achieve their goals by providing encouraging messages to maintain their motivation.

[0036] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can conduct a dialogue in a similar style to the dialogue style the user has preferred in the past. The dialogue unit can use generative AI to analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can analyze the content and frequency of the user's past dialogues and propose the optimal dialogue style. The dialogue unit can also design a dialogue to avoid points where the user has felt difficulty in past dialogues. For example, the dialogue unit can prioritize the use of specific keywords from the user's past dialogue history. Furthermore, the dialogue unit can adjust the tone and pace of the dialogue based on the user's past dialogue history. For example, the dialogue unit can recreate a dialogue style in which the user felt relaxed in the past and conduct the dialogue. In this way, the optimal dialogue method can be selected by analyzing the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using generative AI, or it may be performed without using generative AI.

[0037] The dialogue unit can customize the content of questions during a conversation based on the user's current lifestyle and areas of interest. For example, the dialogue unit can ask questions related to a project the user is currently working on. The dialogue unit can use generative AI to customize the content of questions based on the user's current lifestyle and areas of interest. For example, the dialogue unit can ask questions related to setting relevant goals based on the user's daily routine and hobbies. The dialogue unit can also ask questions at appropriate times in accordance with the user's daily routine. For example, the dialogue unit can ask questions during times when the user is relaxed to elicit detailed information. Furthermore, the dialogue unit can customize the content of the conversation based on the user's areas of interest. For example, the dialogue unit can ask questions related to topics the user is interested in and proceed with the conversation. By customizing the content of questions based on the user's lifestyle and areas of interest, a more effective conversation becomes possible. Some or all of the above processing in the dialogue unit may be performed using generative AI, or it may be performed without using generative AI.

[0038] The dialogue unit can prioritize topics of high relevance during a conversation, taking into account the user's geographical location. For example, the dialogue unit might bring up events or activities related to the user's current location. The dialogue unit can use generative AI to prioritize topics of high relevance, taking into account the user's geographical location. For example, the dialogue unit might provide region-specific information based on the user's location. The dialogue unit can also provide information about nearby resources and facilities based on the user's geographical location. For example, the dialogue unit might provide news or event information related to the user's current location. Furthermore, the dialogue unit can customize the content of the conversation based on the user's geographical location. For example, the dialogue unit might prioritize providing information about areas the user is interested in. This allows the dialogue unit to provide topics of high relevance by taking into account the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using generative AI, or not.

[0039] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can bring up topics that the user has recently shown interest in on social media. The dialogue unit can use generative AI to analyze the user's social media activity and provide relevant topics. For example, the dialogue unit can analyze the content of the user's social media posts and ask relevant goal-setting questions. The dialogue unit can also provide relevant information based on the activity of the user's social media followers and friends. For example, the dialogue unit can provide relevant topics based on the latest posts from accounts that the user follows. Furthermore, the dialogue unit can customize the content of the conversation based on the user's social media activity. For example, the dialogue unit can prioritize providing information related to topics that the user is interested in. This allows the dialogue unit to provide relevant topics by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using generative AI or not.

[0040] The design department can select the optimal learning method by referring to the user's past learning history when identifying necessary skills and knowledge. For example, the design department can prioritize suggesting learning methods that have been effective for the user in the past. The design department can use generative AI to refer to the user's past learning history and select the optimal learning method. For example, the design department can analyze the user's past learning content and learning results and suggest the optimal learning style. The design department can also customize learning methods for specific skills based on the user's past learning history. For example, the design department can suggest an optimal learning schedule based on the user's learning history. Furthermore, the design department can analyze the user's learning history and adjust the learning method according to the learning progress. For example, the design department can create a learning plan based on learning methods that have been effective for the user in the past. This allows the design department to select the optimal learning method by referring to the user's past learning history. Some or all of the above processes in the design department may be performed using generative AI, or they may not be performed using generative AI.

[0041] The design department can customize schedules based on the user's current lifestyle when designing the optimal way to spend a day. For example, if the user has a habit of waking up early, the design department will place important tasks in the morning. The design department can use generative AI to customize schedules based on the user's lifestyle. For example, the design department can analyze the user's lifestyle and activity patterns and propose an optimal schedule. The design department can also place tasks requiring concentration in the evening if the user is a night owl. For example, the design department can appropriately schedule breaks according to the user's lifestyle. Furthermore, the design department can adjust the flexibility of the schedule based on the user's lifestyle. For example, the design department can adjust the time allocation of the schedule based on the user's lifestyle. This allows for more effective schedule management by customizing schedules based on the user's lifestyle. Some or all of the above processes in the design department may be performed using generative AI, or they may not.

[0042] The design department can suggest optimal activity locations by considering the user's geographical location when designing schedules. For example, if the user is at home, the design department will prioritize tasks that can be done at home. The design department can use generative AI to suggest optimal activity locations by considering the user's geographical location. For example, based on the user's location, the design department will suggest tasks that can be done at a nearby cafe or library. The design department can also suggest optimal activity locations based on the user's location. For example, if the user is out, the design department will suggest tasks that can be done at a nearby cafe or library. Furthermore, the design department can adjust the flexibility of the schedule based on the user's geographical location. For example, based on the user's location, the design department will suggest optimal activity locations. In this way, the design department can suggest optimal activity locations by considering the user's geographical location. Some or all of the above processing in the design department may be performed using generative AI, or it may be performed without using generative AI.

[0043] The design department can analyze users' social media activity and suggest relevant activities when designing schedules. For example, the design department can incorporate events that users have shown interest in on social media into the schedule. The design department can use generative AI to analyze users' social media activity and suggest relevant activities. For example, the design department can analyze the content of users' social media posts and suggest relevant activities. The design department can also suggest relevant activities based on the activities of users' social media followers and friends. For example, the design department can incorporate events that users have shown interest in on social media into the schedule. Furthermore, the design department can adjust the flexibility of the schedule based on users' social media activity. For example, the design department can suggest relevant activities based on users' social media activity. In this way, relevant activities can be suggested by analyzing users' social media activity. Some or all of the above processes in the design department may be performed using generative AI or not.

[0044] The support unit can select the optimal schedule change method by referring to the user's past execution history if execution fails. For example, the support unit may prioritize suggesting schedule change methods that the user has successfully used in the past. The support unit can use generative AI to refer to the user's past execution history and select the optimal schedule change method. For example, the support unit may analyze the user's past execution content and results to suggest the optimal schedule change method. The support unit can also customize a specific schedule change method based on the user's past execution history. For example, the support unit may suggest the optimal schedule change method based on the user's execution history. Furthermore, the support unit may analyze the user's execution history and adjust the schedule change method according to the progress of the execution. For example, the support unit may create a schedule change plan based on schedule change methods that the user has successfully used in the past. This allows the support unit to select the optimal schedule change method by referring to the user's past execution history. Some or all of the above processes in the support unit may be performed using generative AI, or they may not be performed using generative AI.

[0045] The support unit can adjust the timing of alarms and voice encouragement based on the user's current activity level. For example, the support unit can adjust the timing of alarms if the user is concentrating. The support unit can use generative AI to adjust the timing based on the user's current activity level. For example, the support unit can analyze the user's activities and progress and set alarms at the optimal time. The support unit can also adjust the timing of encouragement messages if the user is taking a break. For example, the support unit can send encouragement messages at the optimal time based on the user's activity level. Furthermore, the support unit can adjust the frequency of alarms and encouragement messages based on the user's activity level. For example, the support unit can monitor the user's activity level in real time and send alarms and encouragement messages at the appropriate time. This allows for more effective support by adjusting the timing based on the user's current activity level. Some or all of the above processes in the support unit may be performed using generative AI or not.

[0046] The support unit can make optimal schedule changes considering the user's geographical location if execution is not possible. For example, if the user is out, the support unit can suggest tasks that can be done at a nearby cafe or library. The support unit can use generative AI to make optimal schedule changes considering the user's geographical location. For example, the support unit suggests optimal schedule changes based on the user's location. The support unit can also prioritize tasks that can be done at home if the user is at home. For example, the support unit makes optimal schedule changes based on the user's location. Furthermore, the support unit can adjust the flexibility of the schedule based on the user's geographical location. For example, the support unit suggests optimal schedule changes based on the user's location. This makes it possible to make optimal schedule changes by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using generative AI, or it may be performed without using generative AI.

[0047] The support unit can analyze the user's social media activity and provide relevant support messages when providing alarms or voice encouragement. For example, the support unit can send support messages related to topics the user has shown interest in on social media. The support unit can use generative AI to analyze the user's social media activity and provide relevant support messages. For example, the support unit can analyze the content of the user's social media posts and send relevant support messages. The support unit can also send relevant support messages based on the activity of the user's social media followers and friends. For example, the support unit can send support messages related to topics the user has shown interest in on social media. Furthermore, the support unit can customize the content of support messages based on the user's social media activity. For example, the support unit can send relevant support messages based on the user's social media activity. This allows the support unit to provide relevant support messages by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using generative AI or not.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The dream realization support system also includes a feedback unit. This unit can periodically evaluate the user's progress and provide feedback. For example, it can weekly evaluate the user's progress toward their set goals and report their level of achievement. It can also suggest the next steps based on the user's progress. For instance, if the user has achieved part of their goal, it can suggest the skills and knowledge they should focus on next. Furthermore, the feedback unit can provide advice to maintain motivation based on the user's progress. For example, if the user is not satisfied with their progress, it can suggest areas for improvement or new approaches. In this way, the feedback unit can support goal achievement by regularly evaluating the user's progress and providing appropriate feedback.

[0050] The dream realization support system also includes a community section. This community section can provide a platform for users to interact and support each other. For example, it can match users with similar goals, facilitating information exchange and mutual encouragement. It can also provide a forum for users to share their experiences and knowledge. For instance, users can post their success stories and failures and exchange opinions with other users. Furthermore, the community section can regularly host online events and workshops, providing users with opportunities to improve their skills. For example, users can acquire new knowledge through webinars and group discussions featuring experts. In this way, the community section can support goal achievement by promoting interaction among users and providing an environment where they can support each other.

[0051] The dream realization support system also includes a rewards section. The rewards section can provide rewards to users when they achieve their goals. For example, the rewards section can award badges or points when users achieve goals they have set. The rewards section can also provide a system where users can receive benefits using the points they have accumulated. For example, they can use points to purchase online courses or books. Furthermore, the rewards section can provide regular challenges to maintain user motivation. For example, it can set up challenges where users can earn additional points by achieving goals within a specific period. In this way, the rewards section can provide incentives for users to achieve their goals and maintain their motivation.

[0052] The dream realization support system also includes a reminder function. This reminder function can send reminders based on a user-set schedule. For example, it can send a notification at the start time of a task set by the user. It can also check whether the user has completed the task and send another reminder if it hasn't. For example, if the user fails to complete the task, it can send another notification at the next available time. Furthermore, the reminder function can adjust the frequency of reminders according to the user's progress. For example, if the user is progressing well with the task, the frequency of reminders can be reduced. This allows the reminder function to support the user in staying on schedule and achieving their goals.

[0053] The dream realization support system also includes an analytics department. The analytics department analyzes user activity data and provides insights that help improve performance. For example, it analyzes users' task completion rates and time management patterns and suggests areas for improvement. It can also visualize user progress and clarify the path to achieving goals. For example, it can visually display user progress using graphs and charts. Furthermore, the analytics department can provide personalized advice based on user activity data. For example, if a user is spending too much time on a particular task, it can suggest more efficient methods. In this way, the analytics department can support goal achievement by analyzing user activity data and providing insights that help improve performance.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The dialogue unit translates the user's dream into concrete goals. For example, if the user dreams of becoming a voice actor, the dialogue unit will translate this into a specific goal such as "join a voice acting agency and aim for an annual income of XX million yen." The dialogue unit uses generative AI to analyze the user's input and suggest appropriate goals. Step 2: The design team identifies the necessary skills and knowledge based on the goals articulated by the dialogue team and designs an optimal daily schedule. For example, since becoming a voice actor requires vocal and articulation practice, they create a schedule for acquiring these skills. The design team uses generative AI to propose an optimal practice schedule, taking into account the user's goals and current skill level. Step 3: The support team assists in executing the schedule designed by the design team. For example, they can support user execution with alarms and voice encouragement. The support team can use generative AI to assist user execution. For example, they can sound an alarm when it's time to practice and provide voice encouragement such as, "It's time to practice now. Let's do our best!" If the user fails to complete the task, the support team can modify the schedule in real time and reschedule the practice to the next available time.

[0056] (Example of form 2) The dream realization support system according to an embodiment of the present invention is a system that verbalizes a user's dream into concrete goals and designs the skills, knowledge, and optimal daily routine necessary to realize those dreams. The dream realization support system verbalizes the user's dream into concrete goals through dialogue, identifies the skills and knowledge necessary to achieve those goals, and designs the optimal daily routine. Furthermore, the dream realization support system provides persistent support by assisting with execution through alarms and voice encouragement, and if execution is not possible, it changes the schedule in real time or boosts motivation. In particular, it provides a time management strategy for people who find it difficult to manage their time on their own, enabling them to steadily build up their efforts day by day. For example, the dream realization support system allows the user to converse with AI and verbalize an abstract dream into concrete goals. For example, a user who dreams of "becoming a voice actor" can, through dialogue with AI, convert their dream into a concrete goal such as "joining a voice acting agency and aiming for an annual income of XX million yen." Next, the dream realization support system identifies the skills and knowledge necessary to achieve that goal and designs the optimal daily routine. For example, since becoming a voice actor requires vocal and articulation practice, the Dream Realization Support System creates a schedule for acquiring these skills. Furthermore, the Dream Realization Support System assists the user in taking action with alarms and voice encouragement. For instance, an alarm sounds when it's time to practice, and the Dream Realization Support System provides voice encouragement such as, "It's time to practice now. Let's do our best!" If the user fails to practice, the Dream Realization Support System adjusts the schedule in real time and reschedules practice for the next available time. In addition, to maintain the user's motivation, the Dream Realization Support System updates the goal image in line with daily efforts and provides encouraging messages. For example, to a user who feels like they can't do it anymore, it sends a message such as, "Your dream is so wonderful. Let's work hard together until you achieve your dream!" In this way, the Dream Realization Support System persistently supports and stands by the user until their dream comes true. In particular, it provides time management strategies for people who have difficulty with self-management or time management on their own, helping them to steadily build up their efforts day by day and supporting them in realizing their dreams.This allows the dream realization support system to transform the user's dreams into concrete goals and support their realization.

[0057] The dream realization support system according to this embodiment comprises a dialogue unit, a design unit, and a support unit. The dialogue unit verbalizes the user's dream into concrete goals. For example, if the user has a dream of "becoming a voice actor," the dialogue unit will convert it into a concrete goal such as "joining a voice acting agency and aiming for an annual income of XX million yen" through dialogue. The dialogue unit can verbalize the user's dream into concrete goals using generative AI. For example, the generative AI analyzes the user's input and proposes appropriate goals. The design unit identifies the necessary skills and knowledge based on the goals verbalized by the dialogue unit and designs an optimal daily schedule. For example, since vocalization and articulation practice are necessary to become a voice actor, the design unit creates a schedule for acquiring these skills. The design unit can design an optimal schedule based on the user's goals using generative AI. For example, the generative AI considers the user's goals and current skill level and proposes an optimal practice schedule. The support unit assists the user in executing the schedule designed by the design unit. The support unit assists the user in executing the schedule, for example, with alarms and voice encouragement. The support unit can assist the user in their actions using generative AI. For example, the support unit can sound an alarm when it's time to practice and encourage the user with a voice saying, "It's time to practice now. Let's do our best!" If the user is unable to perform their task, the support unit can change the schedule in real time and reschedule the practice to the next available time. The support unit can also change the user's schedule in real time using generative AI. For example, the support unit can monitor the user's progress and suggest the next available time if they are unable to perform their task. In this way, the dream realization support system according to the embodiment can transform the user's dream into a concrete goal and support its realization.

[0058] The dialogue unit translates the user's dreams into concrete goals. For example, if a user dreams of becoming a voice actor, the dialogue unit will translate this into a specific goal such as "join a voice acting agency and aim for an annual income of XX million yen." The dialogue unit can translate the user's dreams into concrete goals using generative AI. For example, the generative AI analyzes the user's input and suggests appropriate goals. Specifically, the generative AI uses natural language processing technology to analyze the user's input and understand the user's intentions and desires. For example, if a user inputs "I want to become a voice actor," the generative AI understands the context and suggests specific steps and goals to achieve that goal. Based on past data and success stories, the generative AI can set the optimal goals for the user. Furthermore, through dialogue with the user, the generative AI evaluates the feasibility and realism of the goals and modifies them as needed. For example, if a user wishes to join a voice acting agency within one year, the generative AI evaluates whether that goal is realistic and can modify it as needed, such as "join a voice acting agency within two years." This allows the dialogue unit to translate the user's dreams into concrete and realistic goals, and to support the user in taking effective action towards those goals.

[0059] The design department identifies the necessary skills and knowledge based on the goals articulated by the dialogue department and designs an optimal daily schedule. For example, the design department creates a schedule for acquiring skills such as vocalization and articulation practice, which are necessary to become a voice actor. The design department can use generative AI to design an optimal schedule based on the user's goals. For example, the generative AI considers the user's goals and current skill level to propose an optimal practice schedule. Specifically, the generative AI evaluates the user's current skill level and identifies the skills and knowledge necessary to achieve the goals. For example, it determines that the user needs vocalization practice, articulation practice, and acting practice to become a voice actor. The generative AI creates a schedule for effectively acquiring these skills and proposes it to the user. Furthermore, the generative AI designs a manageable schedule considering the user's lifestyle and other commitments. For example, if the user works on weekdays, the generative AI sets practice times for weekends or evenings. The generative AI can also monitor the user's progress in real time and adjust the schedule as needed. For example, if a user is unable to practice as scheduled, the generating AI will reschedule the practice to the next available time, helping the user to continue working towards their goal. This allows the design team to provide the user with an optimal schedule for achieving their goals and to help them acquire skills effectively.

[0060] The support team assists users in executing the schedule designed by the design team. The support team provides assistance, for example, through alarms and voice encouragement. The support team can use generative AI to assist users. For example, it might sound an alarm when it's time to practice and provide voice encouragement such as, "It's time to practice! Let's do our best!" If a user fails to complete their practice, the support team will modify the schedule in real time and reschedule the practice for the next available time. The support team can use generative AI to modify the user's schedule in real time. For example, it will monitor the user's progress and suggest the next available time if they fail to complete their practice. Specifically, the generative AI monitors the user's progress in real time to ensure they are practicing as scheduled. If a user fails to practice, the generative AI analyzes the reason and suggests the next available time. For example, if a user misses practice due to work commitments, the generative AI will reschedule it for their next break or the weekend. The support team also provides regular encouragement messages and progress reports to maintain user motivation. For example, if a user is making good progress towards their goal, the AI ​​generator will send a message such as, "Great progress! Keep up the good work!" Furthermore, when a user achieves their goal, the support team shares in their sense of accomplishment and helps them set their next goal. In this way, the support team can help users continue to work towards their goals and ultimately support their achievement.

[0061] The support unit can assist users in their efforts through alarms and voice encouragement. For example, when it's time to practice, the support unit will sound an alarm and provide voice encouragement such as, "It's time to practice! Let's do our best!" The support unit can also use generative AI to assist users in their efforts. For example, the support unit can set alarms based on the user's schedule and sound them when it's time to practice. The support unit can also monitor the user's progress and provide voice encouragement if they are falling behind. For example, the support unit can send an encouraging message such as, "You're a little behind, but you can still make it! Let's do our best!" In this way, by supporting users in their efforts through alarms and voice encouragement, the system can promote the achievement of goals.

[0062] The support unit can change the schedule in real time if an execution fails. For example, if a user is unable to perform a practice session, the support unit will reschedule the practice session to the next available time. The support unit can also change the user's schedule in real time using generative AI. For example, the support unit monitors the user's execution status and suggests the next available time if an execution fails. The support unit can also automatically adjust the user's schedule and incorporate tasks that could not be completed into the next schedule. For example, the support unit analyzes the user's schedule and reschedules tasks to the optimal time. This allows for flexible responses by changing the schedule in real time if an execution fails.

[0063] The support team can provide encouraging messages to maintain user motivation. For example, if a user feels like they "can't do it anymore," the support team can send a message such as, "Your dream is so wonderful. Let's work hard together until you achieve your dream!" The support team can also use generative AI to provide encouraging messages to maintain user motivation. For example, the support team can monitor the user's progress and send an encouraging message if their motivation is low. The support team can also update the user's goal image in line with their daily efforts and provide encouraging messages. For example, depending on the user's progress, the support team can send a message such as, "You've come so far. Just a little further!" In this way, the support team can help users achieve their goals by providing encouraging messages to maintain their motivation.

[0064] The dialogue unit can estimate the user's emotions and adjust the pace of the conversation based on those emotions. For example, if the user is anxious, the dialogue unit will speed up the conversation to quickly articulate the goal. The dialogue unit can use generative AI to estimate the user's emotions and adjust the pace of the conversation. For example, the dialogue unit can analyze the user's facial expressions and voice to estimate their emotions. The dialogue unit can also slow down the conversation to elicit more detailed information if the user is relaxed. For example, the dialogue unit adjusts the intervals and speaking speed of the conversation according to the user's emotions. Furthermore, if the user is feeling anxious, the dialogue unit can adjust the pace of the conversation to provide reassurance. For example, the dialogue unit monitors the user's emotions in real time and selects an appropriate conversation speed. By adjusting the pace of the conversation according to the user's emotions, more effective conversations become possible. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0065] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can conduct a dialogue in a similar style to the dialogue style the user has preferred in the past. The dialogue unit can use generative AI to analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can analyze the content and frequency of the user's past dialogues and propose the optimal dialogue style. The dialogue unit can also design a dialogue to avoid points where the user has felt difficulty in past dialogues. For example, the dialogue unit can prioritize the use of specific keywords from the user's past dialogue history. Furthermore, the dialogue unit can adjust the tone and pace of the dialogue based on the user's past dialogue history. For example, the dialogue unit can recreate a dialogue style in which the user felt relaxed in the past and conduct the dialogue. In this way, the optimal dialogue method can be selected by analyzing the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using generative AI, or it may be performed without using generative AI.

[0066] The dialogue unit can customize the content of questions during a conversation based on the user's current lifestyle and areas of interest. For example, the dialogue unit can ask questions related to a project the user is currently working on. The dialogue unit can use generative AI to customize the content of questions based on the user's current lifestyle and areas of interest. For example, the dialogue unit can ask questions related to setting relevant goals based on the user's daily routine and hobbies. The dialogue unit can also ask questions at appropriate times in accordance with the user's daily routine. For example, the dialogue unit can ask questions during times when the user is relaxed to elicit detailed information. Furthermore, the dialogue unit can customize the content of the conversation based on the user's areas of interest. For example, the dialogue unit can ask questions related to topics the user is interested in and proceed with the conversation. By customizing the content of questions based on the user's lifestyle and areas of interest, a more effective conversation becomes possible. Some or all of the above processing in the dialogue unit may be performed using generative AI, or it may be performed without using generative AI.

[0067] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on those emotions. For example, if the user is nervous, the dialogue unit will proceed with the dialogue in a calm tone. The dialogue unit can use generative AI to estimate the user's emotions and adjust the tone of the dialogue. For example, the dialogue unit can analyze the user's facial expressions and voice to estimate their emotions. The dialogue unit can also proceed with the dialogue in a cheerful tone if the user is enjoying themselves. For example, the dialogue unit can adjust the pitch and volume of its voice according to the user's emotions. Furthermore, if the user is tired, the dialogue unit can proceed with the dialogue in a gentle tone. For example, the dialogue unit can monitor the user's emotions in real time and select an appropriate dialogue tone. This allows for more effective dialogue by adjusting the tone of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The dialogue unit can prioritize topics of high relevance during a conversation, taking into account the user's geographical location. For example, the dialogue unit might bring up events or activities related to the user's current location. The dialogue unit can use generative AI to prioritize topics of high relevance, taking into account the user's geographical location. For example, the dialogue unit might provide region-specific information based on the user's location. The dialogue unit can also provide information about nearby resources and facilities based on the user's geographical location. For example, the dialogue unit might provide news or event information related to the user's current location. Furthermore, the dialogue unit can customize the content of the conversation based on the user's geographical location. For example, the dialogue unit might prioritize providing information about areas the user is interested in. This allows the dialogue unit to provide topics of high relevance by taking into account the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using generative AI, or not.

[0069] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant topics. For example, the dialogue unit can bring up topics that the user has recently shown interest in on social media. The dialogue unit can use generative AI to analyze the user's social media activity and provide relevant topics. For example, the dialogue unit can analyze the content of the user's social media posts and ask relevant goal-setting questions. The dialogue unit can also provide relevant information based on the activity of the user's social media followers and friends. For example, the dialogue unit can provide relevant topics based on the latest posts from accounts that the user follows. Furthermore, the dialogue unit can customize the content of the conversation based on the user's social media activity. For example, the dialogue unit can prioritize providing information related to topics that the user is interested in. This allows the dialogue unit to provide relevant topics by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using generative AI or not.

[0070] The design department can estimate the user's emotions and adjust the schedule flexibility based on those emotions. For example, if the user is stressed, the design department can allow for more flexibility in the schedule. The design department can use generative AI to estimate the user's emotions and adjust the schedule flexibility. For example, the design department can analyze the user's facial expressions and voice to estimate their emotions. The design department can also set a strict schedule if the user is relaxed. For example, the design department can adjust the time allocation of the schedule according to the user's emotions. Furthermore, if the user is in a hurry, the design department can prioritize important tasks in the schedule. For example, the design department can monitor the user's emotions in real time and suggest an appropriate schedule. This allows for more effective schedule management by adjusting the schedule flexibility according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The design department can select the optimal learning method by referring to the user's past learning history when identifying necessary skills and knowledge. For example, the design department can prioritize suggesting learning methods that have been effective for the user in the past. The design department can use generative AI to refer to the user's past learning history and select the optimal learning method. For example, the design department can analyze the user's past learning content and learning results and suggest the optimal learning style. The design department can also customize learning methods for specific skills based on the user's past learning history. For example, the design department can suggest an optimal learning schedule based on the user's learning history. Furthermore, the design department can analyze the user's learning history and adjust the learning method according to the learning progress. For example, the design department can create a learning plan based on learning methods that have been effective for the user in the past. This allows the design department to select the optimal learning method by referring to the user's past learning history. Some or all of the above processes in the design department may be performed using generative AI, or they may not be performed using generative AI.

[0072] The design department can customize schedules based on the user's current lifestyle when designing the optimal way to spend a day. For example, if the user has a habit of waking up early, the design department will place important tasks in the morning. The design department can use generative AI to customize schedules based on the user's lifestyle. For example, the design department can analyze the user's lifestyle and activity patterns and propose an optimal schedule. The design department can also place tasks requiring concentration in the evening if the user is a night owl. For example, the design department can appropriately schedule breaks according to the user's lifestyle. Furthermore, the design department can adjust the flexibility of the schedule based on the user's lifestyle. For example, the design department can adjust the time allocation of the schedule based on the user's lifestyle. This allows for more effective schedule management by customizing schedules based on the user's lifestyle. Some or all of the above processes in the design department may be performed using generative AI, or they may not.

[0073] The design department can estimate the user's emotions and prioritize the schedule based on those emotions. For example, if the user is stressed, the design department will prioritize relaxing tasks. The design department can use generative AI to estimate the user's emotions and prioritize the schedule. For example, the design department can analyze the user's facial expressions and voice to estimate their emotions. The design department can also prioritize challenging tasks if the user is highly motivated. For example, the design department can adjust the importance and urgency of tasks according to the user's emotions. Furthermore, the design department can prioritize lighter tasks if the user is tired. For example, the design department can monitor the user's emotions in real time and suggest appropriate task priorities. This allows for more effective schedule management by prioritizing the schedule according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The design department can suggest optimal activity locations by considering the user's geographical location when designing schedules. For example, if the user is at home, the design department will prioritize tasks that can be done at home. The design department can use generative AI to suggest optimal activity locations by considering the user's geographical location. For example, based on the user's location, the design department will suggest tasks that can be done at a nearby cafe or library. The design department can also suggest optimal activity locations based on the user's location. For example, if the user is out, the design department will suggest tasks that can be done at a nearby cafe or library. Furthermore, the design department can adjust the flexibility of the schedule based on the user's geographical location. For example, based on the user's location, the design department will suggest optimal activity locations. In this way, the design department can suggest optimal activity locations by considering the user's geographical location. Some or all of the above processing in the design department may be performed using generative AI, or it may be performed without using generative AI.

[0075] The design department can analyze users' social media activity and suggest relevant activities when designing schedules. For example, the design department can incorporate events that users have shown interest in on social media into the schedule. The design department can use generative AI to analyze users' social media activity and suggest relevant activities. For example, the design department can analyze the content of users' social media posts and suggest relevant activities. The design department can also suggest relevant activities based on the activities of users' social media followers and friends. For example, the design department can incorporate events that users have shown interest in on social media into the schedule. Furthermore, the design department can adjust the flexibility of the schedule based on users' social media activity. For example, the design department can suggest relevant activities based on users' social media activity. In this way, relevant activities can be suggested by analyzing users' social media activity. Some or all of the above processes in the design department may be performed using generative AI or not.

[0076] The support unit can estimate the user's emotions and adjust the content of encouragement messages based on those emotions. For example, if the user is feeling down, the support unit will send an encouraging message. The support unit can use generative AI to estimate the user's emotions and adjust the content of encouragement messages. For example, the support unit can analyze the user's facial expressions and voice to estimate their emotions. The support unit can also send messages encouraging challenges if the user is highly motivated. For example, the support unit adjusts the message content according to the user's emotions. Furthermore, if the user is tired, the support unit can send messages encouraging rest. For example, the support unit monitors the user's emotions in real time and suggests appropriate encouragement messages. This allows for more effective support by adjusting the content of encouragement messages according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The support unit can select the optimal schedule change method by referring to the user's past execution history if execution fails. For example, the support unit may prioritize suggesting schedule change methods that the user has successfully used in the past. The support unit can use generative AI to refer to the user's past execution history and select the optimal schedule change method. For example, the support unit may analyze the user's past execution content and results to suggest the optimal schedule change method. The support unit can also customize a specific schedule change method based on the user's past execution history. For example, the support unit may suggest the optimal schedule change method based on the user's execution history. Furthermore, the support unit may analyze the user's execution history and adjust the schedule change method according to the progress of the execution. For example, the support unit may create a schedule change plan based on schedule change methods that the user has successfully used in the past. This allows the support unit to select the optimal schedule change method by referring to the user's past execution history. Some or all of the above processes in the support unit may be performed using generative AI, or they may not be performed using generative AI.

[0078] The support unit can adjust the timing of alarms and voice encouragement based on the user's current activity level. For example, the support unit can adjust the timing of alarms if the user is concentrating. The support unit can use generative AI to adjust the timing based on the user's current activity level. For example, the support unit can analyze the user's activities and progress and set alarms at the optimal time. The support unit can also adjust the timing of encouragement messages if the user is taking a break. For example, the support unit can send encouragement messages at the optimal time based on the user's activity level. Furthermore, the support unit can adjust the frequency of alarms and encouragement messages based on the user's activity level. For example, the support unit can monitor the user's activity level in real time and send alarms and encouragement messages at the appropriate time. This allows for more effective support by adjusting the timing based on the user's current activity level. Some or all of the above processes in the support unit may be performed using generative AI or not.

[0079] The support unit can estimate the user's emotions and adjust the frequency of encouraging messages based on those emotions. For example, if the user is feeling down, the support unit will send encouraging messages more frequently. The support unit can use generative AI to estimate the user's emotions and adjust the frequency of encouraging messages. For example, the support unit can analyze the user's facial expressions and voice to estimate their emotions. The support unit can also send encouraging messages at an appropriate frequency if the user is highly motivated. For example, the support unit adjusts the message sending interval according to the user's emotions. Furthermore, the support unit can reduce the frequency of encouraging messages if the user is tired. For example, the support unit monitors the user's emotions in real time and sends encouraging messages at an appropriate frequency. By adjusting the frequency of encouraging messages according to the user's emotions, more effective support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The support unit can make optimal schedule changes considering the user's geographical location if execution is not possible. For example, if the user is out, the support unit can suggest tasks that can be done at a nearby cafe or library. The support unit can use generative AI to make optimal schedule changes considering the user's geographical location. For example, the support unit suggests optimal schedule changes based on the user's location. The support unit can also prioritize tasks that can be done at home if the user is at home. For example, the support unit makes optimal schedule changes based on the user's location. Furthermore, the support unit can adjust the flexibility of the schedule based on the user's geographical location. For example, the support unit suggests optimal schedule changes based on the user's location. This makes it possible to make optimal schedule changes by considering the user's geographical location. Some or all of the above processing in the support unit may be performed using generative AI, or it may be performed without using generative AI.

[0081] The support unit can analyze the user's social media activity and provide relevant support messages when providing alarms or voice encouragement. For example, the support unit can send support messages related to topics the user has shown interest in on social media. The support unit can use generative AI to analyze the user's social media activity and provide relevant support messages. For example, the support unit can analyze the content of the user's social media posts and send relevant support messages. The support unit can also send relevant support messages based on the activity of the user's social media followers and friends. For example, the support unit can send support messages related to topics the user has shown interest in on social media. Furthermore, the support unit can customize the content of support messages based on the user's social media activity. For example, the support unit can send relevant support messages based on the user's social media activity. This allows the support unit to provide relevant support messages by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using generative AI or not.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The dream realization support system also includes a feedback unit. This unit can periodically evaluate the user's progress and provide feedback. For example, it can weekly evaluate the user's progress toward their set goals and report their level of achievement. It can also suggest the next steps based on the user's progress. For instance, if the user has achieved part of their goal, it can suggest the skills and knowledge they should focus on next. Furthermore, the feedback unit can provide advice to maintain motivation based on the user's progress. For example, if the user is not satisfied with their progress, it can suggest areas for improvement or new approaches. In this way, the feedback unit can support goal achievement by regularly evaluating the user's progress and providing appropriate feedback.

[0084] The dream realization support system also includes a community section. This community section can provide a platform for users to interact and support each other. For example, it can match users with similar goals, facilitating information exchange and mutual encouragement. It can also provide a forum for users to share their experiences and knowledge. For instance, users can post their success stories and failures and exchange opinions with other users. Furthermore, the community section can regularly host online events and workshops, providing users with opportunities to improve their skills. For example, users can acquire new knowledge through webinars and group discussions featuring experts. In this way, the community section can support goal achievement by promoting interaction among users and providing an environment where they can support each other.

[0085] The dream realization support system also includes a rewards section. The rewards section can provide rewards to users when they achieve their goals. For example, the rewards section can award badges or points when users achieve goals they have set. The rewards section can also provide a system where users can receive benefits using the points they have accumulated. For example, they can use points to purchase online courses or books. Furthermore, the rewards section can provide regular challenges to maintain user motivation. For example, it can set up challenges where users can earn additional points by achieving goals within a specific period. In this way, the rewards section can provide incentives for users to achieve their goals and maintain their motivation.

[0086] The dream realization support system also includes a reminder function. This reminder function can send reminders based on a user-set schedule. For example, it can send a notification at the start time of a task set by the user. It can also check whether the user has completed the task and send another reminder if it hasn't. For example, if the user fails to complete the task, it can send another notification at the next available time. Furthermore, the reminder function can adjust the frequency of reminders according to the user's progress. For example, if the user is progressing well with the task, the frequency of reminders can be reduced. This allows the reminder function to support the user in staying on schedule and achieving their goals.

[0087] The dream realization support system also includes an analytics department. The analytics department analyzes user activity data and provides insights that help improve performance. For example, it analyzes users' task completion rates and time management patterns and suggests areas for improvement. It can also visualize user progress and clarify the path to achieving goals. For example, it can visually display user progress using graphs and charts. Furthermore, the analytics department can provide personalized advice based on user activity data. For example, if a user is spending too much time on a particular task, it can suggest more efficient methods. In this way, the analytics department can support goal achievement by analyzing user activity data and providing insights that help improve performance.

[0088] The dialogue unit can estimate the user's emotions and customize the content of the conversation based on those emotions. For example, if the user is feeling down, it will engage in a conversation that includes many words of encouragement. The dialogue unit can use generative AI to estimate the user's emotions and customize the content of the conversation. For example, the dialogue unit can analyze the user's facial expressions and voice to estimate their emotions. The dialogue unit can also provide calm advice if the user is agitated. For example, the dialogue unit provides appropriate advice and information according to the user's emotions. Furthermore, if the user is feeling anxious, the dialogue unit can engage in conversation that provides reassurance. For example, the dialogue unit monitors the user's emotions in real time and selects appropriate conversation content. By customizing the content of the conversation according to the user's emotions, more effective conversations become possible.

[0089] The support system can estimate the user's emotions and adjust the tone of the encouragement message based on those emotions. For example, if the user is feeling down, it can send an encouraging message in a gentle tone. The support system can use generative AI to estimate the user's emotions and adjust the tone of the encouragement message. For example, it can analyze the user's facial expressions and voice to estimate their emotions. The support system can also send a message encouraging the user to take on challenges in a strong tone if the user is highly motivated. For example, it can send a message in an appropriate tone depending on the user's emotions. Furthermore, if the user is tired, it can send a message encouraging them to rest in a calm tone. For example, it can monitor the user's emotions in real time and send an encouraging message in an appropriate tone. This allows for more effective support by adjusting the tone of the encouragement message according to the user's emotions.

[0090] The design department can estimate the user's emotions and adjust the schedule's flexibility based on those emotions. For example, if the user is stressed, the schedule can be made more flexible. The design department can use generative AI to estimate the user's emotions and adjust the schedule's flexibility. For example, the design department can analyze the user's facial expressions and voice to estimate their emotions. The design department can also set a strict schedule if the user is relaxed. For example, the design department can adjust the time allocation of the schedule according to the user's emotions. Furthermore, if the user is in a hurry, the design department can prioritize important tasks in the schedule. For example, the design department can monitor the user's emotions in real time and suggest an appropriate schedule. This allows for more effective schedule management by adjusting the schedule's flexibility according to the user's emotions.

[0091] The support unit can estimate the user's emotions and adjust the frequency of encouraging messages based on those emotions. For example, if the user is feeling down, it will send encouraging messages more frequently. The support unit can use generative AI to estimate the user's emotions and adjust the frequency of encouraging messages. For example, the support unit can analyze the user's facial expressions and voice to estimate their emotions. The support unit can also send encouraging messages at an appropriate frequency if the user is highly motivated. For example, the support unit adjusts the message sending interval according to the user's emotions. Furthermore, the support unit can reduce the frequency of encouraging messages if the user is tired. For example, the support unit monitors the user's emotions in real time and sends encouraging messages at an appropriate frequency. By adjusting the frequency of encouraging messages according to the user's emotions, more effective support becomes possible.

[0092] The design department can estimate user emotions and prioritize schedules based on those emotions. For example, if a user is stressed, it will prioritize relaxing tasks. The design department can use generative AI to estimate user emotions and prioritize schedules. For example, it can analyze a user's facial expressions and voice to estimate their emotions. The design department can also prioritize challenging tasks if a user is highly motivated. For example, it can adjust the importance and urgency of tasks according to the user's emotions. Furthermore, if a user is tired, the design department can prioritize lighter tasks. For example, it can monitor user emotions in real time and suggest appropriate task priorities. This allows for more effective schedule management by prioritizing schedules according to the user's emotions.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The dialogue unit translates the user's dream into concrete goals. For example, if the user dreams of becoming a voice actor, the dialogue unit will translate this into a specific goal such as "join a voice acting agency and aim for an annual income of XX million yen." The dialogue unit uses generative AI to analyze the user's input and suggest appropriate goals. Step 2: The design team identifies the necessary skills and knowledge based on the goals articulated by the dialogue team and designs an optimal daily schedule. For example, since becoming a voice actor requires vocal and articulation practice, they create a schedule for acquiring these skills. The design team uses generative AI to propose an optimal practice schedule, taking into account the user's goals and current skill level. Step 3: The support team assists in executing the schedule designed by the design team. For example, they can support user execution with alarms and voice encouragement. The support team can use generative AI to assist user execution. For example, they can sound an alarm when it's time to practice and provide voice encouragement such as, "It's time to practice now. Let's do our best!" If the user fails to complete the task, the support team can modify the schedule in real time and reschedule the practice to the next available time.

[0095] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0098] Each of the multiple elements described above, including the dialogue unit, design unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart device 14, which translates the user's dreams into concrete goals. The design unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies the necessary skills and knowledge based on the user's goals and designs an optimal way to spend the day. The support unit is implemented by the control unit 46A of the smart device 14, which assists the user in their actions with alarms and voice encouragement. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 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.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the dialogue unit, design unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart glasses 214, which verbalizes the user's dreams into concrete goals. The design unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies the necessary skills and knowledge based on the user's goals and designs an optimal way to spend the day. The support unit is implemented by the control unit 46A of the smart glasses 214, which assists the user's actions with alarms and voice encouragement. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the dialogue unit, design unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the headset terminal 314, which verbalizes the user's dreams into concrete goals. The design unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies the necessary skills and knowledge based on the user's goals and designs an optimal way to spend the day. The support unit is implemented by the control unit 46A of the headset terminal 314, which assists the user's actions with alarms and voice encouragement. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the dialogue unit, design unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the robot 414, which verbalizes the user's dreams into concrete goals. The design unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies the necessary skills and knowledge based on the user's goals and designs an optimal way to spend the day. The support unit is implemented by the control unit 46A of the robot 414, which assists the user's execution with alarms and voice encouragement. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0148] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] 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.

[0158] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) A dialogue section that translates the user's dreams into concrete goals, Based on the goals articulated by the aforementioned dialogue department, the design department identifies the necessary skills and knowledge and designs the optimal way to spend a day. The system includes a support unit that assists in executing the schedule designed by the design unit. A system characterized by the following features. (Note 2) The aforementioned support unit, Alarms and voice encouragement help users take action. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit, If execution fails, the schedule will be changed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, Provide encouraging messages to maintain user motivation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the pace of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned dialogue unit, During the conversation, the questions are customized based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned dialogue unit, During conversations, the system prioritizes relevant topics by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned design department, It estimates the user's emotions and adjusts the flexibility of the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, When identifying the necessary skills and knowledge, the system selects the optimal learning method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, When designing the optimal way to spend your day, customize the schedule based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, When designing a schedule, we suggest the optimal activity locations by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, When designing a schedule, we analyze users' social media activity and suggest relevant activities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit, The system estimates the user's emotions and adjusts the content of the support message based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit, If execution fails, the system will refer to the user's past execution history to select the most suitable method for rescheduling. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit, When providing alarms or voice encouragement, the timing is adjusted based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit, It estimates the user's emotions and adjusts the frequency of supportive messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit, If execution fails, the system will adjust the schedule to the optimal one, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit, When providing alarms or voice encouragement, the system analyzes the user's social media activity and provides relevant encouraging messages. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A dialogue section that translates the user's dreams into concrete goals, Based on the goals articulated by the aforementioned dialogue department, the design department identifies the necessary skills and knowledge and designs the optimal way to spend a day. The system includes a support unit that assists in executing the schedule designed by the design unit. A system characterized by the following features.

2. The aforementioned support unit, Alarms and voice encouragement help users take action. The system according to feature 1.

3. The aforementioned support unit, If execution fails, the schedule will be changed in real time. The system according to feature 1.

4. The aforementioned support unit, Provide encouraging messages to maintain user motivation. The system according to feature 1.

5. The aforementioned dialogue unit, It estimates the user's emotions and adjusts the pace of the conversation based on those emotions. The system according to feature 1.

6. The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system according to feature 1.

7. The aforementioned dialogue unit, During the conversation, the questions are customized based on the user's current life situation and areas of interest. The system according to feature 1.

8. The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone of the conversation based on those emotions. The system according to feature 1.

9. The aforementioned dialogue unit, During conversations, the system prioritizes relevant topics by considering the user's geographical location. The system according to feature 1.

10. The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant topics. The system according to feature 1.

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