System

Abstract

To provide a system that enables efficient step setting and scheduling for achieving goals.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives input of a user's goal. The analysis unit analyzes the goal input through the reception unit and sets steps. The generation unit creates a schedule based on the steps set by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, setting steps and scheduling for achieving goals was time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently set steps and perform scheduling for achieving a goal. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs a user's goal. The analysis unit analyzes the goal input by the reception unit and sets steps. The generation unit creates a schedule based on the steps set by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently set steps and perform scheduling for achieving a goal. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The goal achievement assistance system according to the embodiment of the present invention is a system that creates a schedule by calculating backwards the steps required to achieve a goal set by the user. The system inputs the goal that the user wants to achieve, and calculates backwards the steps required to achieve the goal. Furthermore, the system creates a schedule based on the set steps and presents it to the user. The system uses a generation AI to analyze the user's input and generates optimal steps and a schedule. For example, the user inputs a specific goal such as "I want to lose 5 kg in 3 months." This information is input to the generation AI. The generation AI then analyzes the input goal and calculates backwards the steps required to achieve the goal. For example, in the case of a weight loss goal, specific steps such as dietary management and exercise plans are set. Furthermore, the generation AI creates a schedule based on the set steps. For example, the schedule includes daily meal content and exercise time. This schedule is adjusted to suit the user's lifestyle. A feature of this system is its advantage over a human coach. The user can confide personal problems such as negligence and frustration to the generation AI, and can expect improvement. For example, if a user inputs "I don't feel like exercising today," the generative AI will analyze the reason and provide advice to increase motivation. In this way, the goal achievement assistance system effectively supports users by providing them with specific steps and schedules to achieve the goals they set and by dealing with personal issues. This allows the goal achievement assistance system to effectively support users in achieving their goals.

[0029] The goal achievement assistance system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, for example, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like, but is not limited to such examples. The reception unit allows the user to input a specific goal, such as "I want to lose 5 kg in 3 months." The analysis unit uses a generation AI to analyze the goal input by the reception unit and set steps for achieving the goal. The analysis is performed by, for example, data analysis, text analysis, behavior analysis, and the like, but is not limited to such examples. For example, the generation AI analyzes the input goal and sets specific steps such as dietary management and exercise plan. The generation unit uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created by, for example, a timetable, a Gantt chart, a calendar format, and the like, but is not limited to such examples. For example, the generation AI incorporates daily meal contents and exercise time into the schedule. This allows the goal achievement assistance system to effectively support the user's goal achievement. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can create a schedule using an AI model that inputs the steps set by the analyzing unit and outputs a schedule. This allows the goal achievement assistance system according to the embodiment to effectively support the user's goal achievement.

[0030] The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, but is not limited to, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like. The reception unit can input, for example, a specific goal such as "I want to lose 5 kg in 3 months". The reception unit provides an interface for the user to input the goal. This interface can take the form of, for example, a smartphone app, a web application, a desktop application, and the like. The user can input the goal by a method such as text input, voice input, or selection from options. Furthermore, the reception unit can provide a field for inputting details and background information of the goal when the user inputs the goal. For example, when the user inputs a weight loss goal, the user can input information such as the current weight, height, eating habits, and exercise habits. In this way, the reception unit can collect detailed information about the user's goal and provide basic data for the analysis unit and the generation unit to perform more accurate analysis and schedule creation. Furthermore, the reception unit can provide a function for the user to confirm and correct the goal after inputting the goal. For example, the user can confirm the goal inputted by the user and input corrections or additional information as necessary. This enables the reception unit to support the user in inputting goals accurately and in detail, thereby improving the accuracy and effectiveness of the entire system.

[0031] The analysis unit uses the generation AI to analyze the goal input by the reception unit and set steps to achieve the goal. The analysis is performed by, for example, data analysis, text analysis, behavior analysis, etc., but is not limited to such examples. For example, the generation AI analyzes the input goal and sets specific steps such as dietary management and exercise plan. The generation AI understands the goal input by the user using natural language processing technology and collects and analyzes data related to the goal. For example, if the user inputs "I want to lose 5 kg in 3 months," the generation AI proposes a meal plan and exercise plan that are effective for weight loss based on past data and statistical information. Specifically, the generation AI analyzes information such as the user's current weight, height, eating habits, and exercise habits, and calculates calorie intake and calories burned. In addition, the generation AI can propose a meal menu and exercise menu according to the user's lifestyle and preferences. For example, if the user is busy, it proposes an effective exercise menu in a short time, and provides healthy recipes that are easy to prepare for meals. In addition, the analysis unit can monitor the user's progress and modify the steps as necessary. For example, if the user is behind on their goal, the generation AI will suggest increasing the amount of exercise or reviewing their diet. Also, if the user achieves their goal, the generation AI can suggest setting a new goal. This allows the analysis unit to effectively support the user in achieving their goal and promote continuous improvement.

[0032] The generation unit uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created in a timetable, Gantt chart, calendar format, or other format, but is not limited to these examples. For example, the generation AI incorporates daily meal contents and exercise time into the schedule. The generation unit creates a flexible and realistic schedule in accordance with the user's lifestyle and daily schedule. For example, if the user is busy with work during the weekdays, it can propose a schedule for concentrating on exercising on the weekends. The generation unit can also customize the schedule taking into account the user's preferences and constraints. For example, if the user does not like a particular ingredient, it can propose a meal plan that avoids that ingredient. Furthermore, the generation unit can monitor the progress of the schedule in real time and modify the schedule as necessary. For example, if the user is unable to exercise as planned, the generation AI will suggest increasing the amount of exercise the next day. In addition, if the user achieves a goal, it can automatically create a schedule for a new goal. The generation unit also provides a function for sending schedule reminders and notifications to the user. For example, it is possible to use the notification function of a smartphone to remind the user of exercise times and meal times. This allows the user to continue taking actions toward achieving their goals without forgetting about the schedule. Furthermore, the generation unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the schedule. This allows the generation unit to effectively support the user in achieving their goals and improve the performance of the entire system.

[0033] The generation unit includes an adjustment unit that adjusts the schedule based on the user's life pattern. The adjustment unit adjusts the schedule based on the user's life pattern. The life pattern includes, for example, daily routines, activity times, rest times, etc., but is not limited to such examples. For example, the adjustment unit adjusts the schedule taking into consideration whether the user is a morning person or a night person. The adjustment unit can also adjust the schedule taking into consideration the user's work schedule and home situation. Furthermore, the adjustment unit can adjust the schedule taking into consideration the user's health condition and hobbies. For example, if the user is interested in health, the adjustment unit sets a health-related schedule with priority. This makes it possible to adjust the schedule to match the user's lifestyle. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's life pattern data to the generation AI and cause the generation AI to adjust the schedule.

[0034] The analysis unit includes a provision unit that provides advice to increase motivation based on the user's input. The provision unit provides advice to increase motivation based on the user's input. The advice includes, for example, an encouraging message, a presentation of the benefits of achieving a goal, and the like, but is not limited to such examples. For example, when the user inputs "I don't feel like exercising today," the provision unit analyzes the reason and provides advice to increase motivation. In addition, when the user is making progress toward achieving a goal, the provision unit can also provide a message praising the progress. Furthermore, when the user is likely to give up, the provision unit can analyze the cause and provide advice to try again. For example, when the user inputs "I'm tired today," the provision unit provides advice to take a rest. This provides advice to increase the user's motivation. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the user's input data to the generation AI and cause the generation AI to generate advice.

[0035] The reception unit analyzes the user's past goal setting history and proposes an appropriate input method. The reception unit analyzes the user's past goal setting history and proposes an optimal input method. The past goal setting history includes, for example, past goal achievement status, the type of goal set, and the like, but is not limited to such examples. For example, the reception unit preferentially proposes a goal setting method that the user has been successful in the past. The reception unit can also suggest the user to avoid a goal setting method that the user has failed in the past. Furthermore, the reception unit can also propose an optimal input method based on the user's past goal achievement rate. For example, the reception unit analyzes the number of goals that the user has achieved in the past and the period until the achievement, and proposes an optimal input method. As a result, an optimal input method based on the past goal setting history is proposed. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past goal setting history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0036] The reception unit performs filtering based on the user's current living situation and field of interest when the goal is input. The reception unit performs filtering based on the user's current living situation and field of interest when the goal is input. The living situation includes, for example, work schedule, home situation, health condition, etc., but is not limited to such examples. For example, if the user is busy, the reception unit preferentially suggests short-term goals. In addition, if the user is interested in health, the reception unit can also preferentially suggest health-related goals. Furthermore, if the user is interested in learning, the reception unit can also preferentially suggest learning-related goals. For example, if the user wants to learn a new skill, the reception unit proposes a goal related to the skill. This enables goal filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation and field of interest data to the generation AI and cause the generation AI to execute goal filtering.

[0037] The reception unit, when inputting a goal, preferentially inputs a highly relevant goal in consideration of the geographical location information of the user. The reception unit, when inputting a goal, preferentially inputs a highly relevant goal in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, location information services, etc., but is not limited to such examples. For example, when the user is in a specific area, the reception unit proposes a goal related to the area. In addition, when the user is traveling, the reception unit can also propose a goal related to the travel destination. Furthermore, when the user is at home, the reception unit can also propose a goal that can be achieved at home. For example, when the user inputs "traveling", the reception unit proposes a goal that can be achieved at the travel destination. This makes it possible to input a goal based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the suggestion of goal input.

[0038] The reception unit analyzes the user's social media activity at the time of goal input and proposes a related goal. The reception unit analyzes the user's social media activity at the time of goal input and proposes a related goal. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit proposes a goal related to a theme that the user frequently mentions on social media. The reception unit can also propose a goal related to the activity of an influencer that the user follows. Furthermore, the reception unit can also propose a goal based on a trend of an online community in which the user participates. For example, if the user is interested in "health", the reception unit proposes a health-related goal. This makes it possible to propose a goal based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the goal proposal.

[0039] The analysis unit sets appropriate steps by referring to past goal achievement data during goal analysis. The analysis unit sets optimal steps by referring to past goal achievement data during goal analysis. The goal achievement data includes, for example, the number of goals achieved, the period until achievement, and the degree of achievement, but is not limited to such examples. For example, the analysis unit sets new steps by referring to steps that the user has succeeded in in the past. The analysis unit can also be set to avoid steps that the user has failed in the past. Furthermore, the analysis unit can set optimal steps based on the user's past goal achievement rate. For example, the analysis unit analyzes the number of goals that the user has achieved in the past and the period until achievement, and sets optimal steps. This makes it possible to set optimal steps based on past goal achievement data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past goal achievement data into the generation AI and cause the generation AI to execute optimal step setting.

[0040] The analysis unit customizes the steps based on the user's current living situation at the time of goal analysis. The analysis unit customizes the steps based on the user's current living situation at the time of goal analysis. Living situations include, for example, work schedule, home situation, health condition, etc., but are not limited to such examples. For example, the analysis unit sets short-term steps when the user is busy. In addition, the analysis unit can also set health-related steps when the user is interested in health. Furthermore, the analysis unit can also set learning-related steps when the user is interested in learning. For example, when the user inputs "I want to learn a new skill," the analysis unit sets steps related to that skill. This enables step customization based on the user's living situation. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living situation data to the generation AI and cause the generation AI to execute step customization.

[0041] The analysis unit sets appropriate steps in consideration of the geographical location information of the user at the time of goal analysis. The analysis unit sets optimal steps in consideration of the geographical location information of the user at the time of goal analysis. The geographical location information includes, but is not limited to, for example, GPS data, address information, location information services, etc. For example, when the user is in a specific area, the analysis unit sets steps related to that area. In addition, when the user is traveling, the analysis unit can also set steps related to the travel destination. Furthermore, when the user is at home, the analysis unit can also set steps that can be achieved at home. For example, when the user inputs "traveling", the analysis unit sets steps that can be achieved at the travel destination. This makes it possible to set steps based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute step setting.

[0042] The analysis unit analyzes the user's social media activities during goal analysis to suggest steps. The analysis unit analyzes the user's social media activities during goal analysis to suggest steps. Social media activities include, but are not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit sets steps related to themes that the user frequently mentions on social media. The analysis unit can also set steps related to the activities of influencers that the user follows. Furthermore, the analysis unit can set steps based on trends in online communities in which the user participates. For example, if the user is interested in "health," the analysis unit sets health-related steps. This makes it possible to suggest steps based on social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data to the generation AI and cause the generation AI to execute step suggestions.

[0043] The generating unit creates an appropriate schedule by referring to past schedule data when generating a schedule. The generating unit creates an optimal schedule by referring to past schedule data when generating a schedule. The schedule data includes, for example, past schedule history, task completion status, and the like, but is not limited to such examples. For example, the generating unit creates a new schedule by referring to schedules that the user has been successful in the past. The generating unit can also create a schedule so as to avoid schedules that the user has failed in the past. Furthermore, the generating unit can create an optimal schedule based on the user's past schedule achievement rate. For example, the generating unit analyzes the number of schedules that the user has achieved in the past and the period until achievement, and creates an optimal schedule. This makes it possible to create an optimal schedule based on past schedule data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's past schedule data into the generating AI and cause the generating AI to create an optimal schedule.

[0044] The generating unit customizes the schedule based on the user's current living situation when generating the schedule. The generating unit customizes the schedule based on the user's current living situation when generating the schedule. Living situations include, for example, work schedules, home situations, health conditions, etc., but are not limited to such examples. For example, the generating unit creates a short-term schedule when the user is busy. In addition, the generating unit can also create a health-related schedule when the user is interested in health. Furthermore, the generating unit can also create a learning-related schedule when the user is interested in learning. For example, when the user inputs "I want to learn a new skill," the generating unit creates a schedule related to the skill. This enables schedule customization based on the user's living situation. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's living situation data to the generating AI and cause the generating AI to customize the schedule.

[0045] The generating unit creates an appropriate schedule in consideration of the geographical location information of the user when generating the schedule. The generating unit creates an optimal schedule in consideration of the geographical location information of the user when generating the schedule. The geographical location information includes, for example, GPS data, address information, location information services, etc., but is not limited to such examples. For example, when the user is in a specific area, the generating unit creates a schedule related to the area. In addition, when the user is traveling, the generating unit can also create a schedule related to the travel destination. Furthermore, when the user is at home, the generating unit can also create a schedule that can be achieved at home. For example, when the user inputs "traveling", the generating unit creates a schedule that can be achieved at the travel destination. This makes it possible to create a schedule based on the geographical location information. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the geographical location information data of the user to the generating AI and cause the generating AI to execute schedule creation.

[0046] The generation unit analyzes the user's social media activities when generating a schedule and proposes a schedule. The generation unit analyzes the user's social media activities when generating a schedule and proposes a schedule. Social media activities include, for example, the content of posts, the number of likes, the number of followers, and the like, but are not limited to such examples. For example, the generation unit creates a schedule related to a theme that the user frequently mentions on social media. The generation unit can also create a schedule related to the activities of influencers that the user follows. Furthermore, the generation unit can create a schedule based on trends in online communities in which the user participates. For example, if the user is interested in "health", the generation unit creates a health-related schedule. This makes it possible to propose a schedule based on social media activities. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a schedule proposal.

[0047] The adjustment unit performs appropriate adjustments when adjusting the schedule by referring to the user's past schedule history. The adjustment unit performs optimal adjustments when adjusting the schedule by referring to the user's past schedule history. The schedule history includes, for example, past schedules, task completion status, and the like, but is not limited to such examples. For example, the adjustment unit adjusts a new schedule by referring to schedules that the user has been successful in the past. The adjustment unit can also adjust to avoid schedules that the user has failed in the past. Furthermore, the adjustment unit can adjust an optimal schedule based on the user's past schedule achievement rate. For example, the adjustment unit analyzes the number of schedules that the user has achieved in the past and the period until achievement, and adjusts the optimal schedule. This makes it possible to adjust the optimal schedule based on the past schedule history. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past schedule history data to the generation AI and cause the generation AI to execute optimal schedule adjustment.

[0048] The adjustment unit performs appropriate adjustments in consideration of the device information of the user when adjusting the schedule. The adjustment unit performs optimal adjustments in consideration of the device information of the user when adjusting the schedule. The device information includes, for example, the type of device being used, the usage status of the device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the adjustment unit adjusts the schedule to match the screen size. In addition, when the user is using a tablet, the adjustment unit can also adjust a schedule optimized for a large screen. Furthermore, when the user is using a smartwatch, the adjustment unit can also adjust a simple and highly visible schedule. For example, when the user inputs "I am using a smartphone," the adjustment unit adjusts the schedule to match the screen size of the smartphone. This enables optimal schedule adjustment based on the device information. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the device information data of the user to the generation AI and cause the generation AI to execute the schedule adjustment.

[0049] The providing unit provides appropriate advice by referring to the user's past behavioral history when providing advice. The providing unit provides optimal advice by referring to the user's past behavioral history when providing advice. The behavioral history includes, for example, past behavioral patterns, behavior frequency, etc., but is not limited to such examples. For example, the providing unit provides new advice by referring to actions that the user has succeeded in in the past. The providing unit can also provide advice to avoid actions that the user has failed in in the past. Furthermore, the providing unit can also provide optimal advice based on the user's past behavior achievement rate. For example, the providing unit analyzes the number of actions that the user has achieved in the past and the period until achievement, and provides optimal advice. This makes it possible to provide optimal advice based on the past behavioral history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavioral history data to the generating AI and cause the generating AI to provide optimal advice.

[0050] The providing unit provides appropriate advice in consideration of the device information of the user when providing advice. The providing unit provides optimal advice in consideration of the device information of the user when providing advice. The device information includes, for example, the type of device being used, the usage status of the device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides advice tailored to the screen size. In addition, when the user is using a tablet, the providing unit can also provide advice optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide advice that is concise and highly visible. For example, when the user inputs "I am using a smartphone," the providing unit provides advice tailored to the screen size of the smartphone. This makes it possible to provide optimal advice based on the device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to execute advice provision.

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

[0052] The reception unit can also analyze the user's past goal achievement history and suggest goals that are easy to achieve. For example, the reception unit can analyze the types of goals the user has achieved in the past and the time it took to achieve them, and suggest similar goals. The reception unit can also suggest that the user avoid goals that he or she has failed to achieve in the past. Furthermore, the reception unit can suggest goals that are highly achievable based on the user's past goal achievement rate. This makes it possible to suggest optimal goals based on the user's past goal achievement history.

[0053] The adjustment unit can also acquire the user's health data and adjust the schedule based on the user's health condition. For example, the adjustment unit can analyze the user's heart rate and sleep data and incorporate appropriate amounts of exercise and rest times into the schedule. The adjustment unit can also acquire the user's dietary data and suggest a dietary schedule that takes nutritional balance into consideration. Furthermore, the adjustment unit can monitor the user's stress level and incorporate activities for reducing stress into the schedule. This makes it possible to adjust the schedule based on the user's health condition.

[0054] The reception unit can also analyze the user's past goal setting history and suggest an appropriate input method. For example, the reception unit can preferentially suggest goal setting methods that the user has been successful in the past. The reception unit can also suggest goal setting methods that the user has failed in the past to be avoided. Furthermore, the reception unit can suggest an optimal input method based on the user's past goal achievement rate. In this way, an optimal input method is suggested based on the user's past goal setting history.

[0055] The reception unit can also filter the goals based on the user's current living situation and areas of interest when inputting the goals. For example, if the user is busy, short-term goals can be preferentially suggested. If the user is interested in health, health-related goals can be preferentially suggested. If the user is interested in learning, learning-related goals can be preferentially suggested. This makes it possible to filter goals based on the user's living situation and areas of interest.

[0056] When inputting a goal, the reception unit can also preferentially input highly relevant goals in consideration of the user's geographical location information. For example, if the user is in a specific area, goals related to that area can be suggested. If the user is traveling, goals related to the travel destination can also be suggested. Furthermore, if the user is at home, goals that can be achieved at home can also be suggested. This makes it possible to input goals based on geographical location information.

[0057] When the goal is input, the reception unit can analyze the user's social media activity and suggest related goals. For example, the reception unit can suggest goals related to themes that the user frequently mentions on social media. The reception unit can also suggest goals related to the activities of influencers that the user follows. Furthermore, the reception unit can suggest goals based on trends in online communities in which the user participates. This makes it possible to suggest goals based on social media activity.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, for example, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like. Specifically, the user can input a goal such as "I want to lose 5 kg in 3 months." Step 2: The analysis unit uses the generation AI to analyze the goal entered by the reception unit and set steps to achieve the goal. The analysis is performed by methods such as data analysis, text analysis, and behavior analysis. For example, the generation AI analyzes the entered goal and sets specific steps such as dietary management and exercise plans. Step 3: The generator uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created in a timetable, Gantt chart, calendar format, or other format. For example, the generation AI incorporates daily meal plans and exercise times into the schedule.

[0060] (Example 2) The goal achievement assistance system according to the embodiment of the present invention is a system that creates a schedule by calculating backwards the steps required to achieve a goal set by the user. The system inputs the goal that the user wants to achieve, and calculates backwards the steps required to achieve the goal. Furthermore, the system creates a schedule based on the set steps and presents it to the user. The system uses a generation AI to analyze the user's input and generates optimal steps and a schedule. For example, the user inputs a specific goal such as "I want to lose 5 kg in 3 months." This information is input to the generation AI. The generation AI then analyzes the input goal and calculates backwards the steps required to achieve the goal. For example, in the case of a weight loss goal, specific steps such as dietary management and exercise plans are set. Furthermore, the generation AI creates a schedule based on the set steps. For example, the schedule includes daily meal content and exercise time. This schedule is adjusted to suit the user's lifestyle. A feature of this system is its advantage over a human coach. The user can confide personal problems such as negligence and frustration to the generation AI, and can expect improvement. For example, if a user inputs "I don't feel like exercising today," the generative AI will analyze the reason and provide advice to increase motivation. In this way, the goal achievement assistance system effectively supports users by providing them with specific steps and schedules to achieve the goals they set and by dealing with personal issues. This allows the goal achievement assistance system to effectively support users in achieving their goals.

[0061] The goal achievement assistance system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, for example, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like, but is not limited to such examples. The reception unit allows the user to input a specific goal, such as "I want to lose 5 kg in 3 months." The analysis unit uses a generation AI to analyze the goal input by the reception unit and set steps for achieving the goal. The analysis is performed by, for example, data analysis, text analysis, behavior analysis, and the like, but is not limited to such examples. For example, the generation AI analyzes the input goal and sets specific steps such as dietary management and exercise plan. The generation unit uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created by, for example, a timetable, a Gantt chart, a calendar format, and the like, but is not limited to such examples. For example, the generation AI incorporates daily meal contents and exercise time into the schedule. This allows the goal achievement assistance system to effectively support the user's goal achievement. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can create a schedule using an AI model that inputs the steps set by the analyzing unit and outputs a schedule. This allows the goal achievement assistance system according to the embodiment to effectively support the user's goal achievement.

[0062] The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, but is not limited to, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like. The reception unit can input, for example, a specific goal such as "I want to lose 5 kg in 3 months". The reception unit provides an interface for the user to input the goal. This interface can take the form of, for example, a smartphone app, a web application, a desktop application, and the like. The user can input the goal by a method such as text input, voice input, or selection from options. Furthermore, the reception unit can provide a field for inputting details and background information of the goal when the user inputs the goal. For example, when the user inputs a weight loss goal, the user can input information such as the current weight, height, eating habits, and exercise habits. In this way, the reception unit can collect detailed information about the user's goal and provide basic data for the analysis unit and the generation unit to perform more accurate analysis and schedule creation. Furthermore, the reception unit can provide a function for the user to confirm and correct the goal after inputting the goal. For example, the user can confirm the goal inputted by the user and input corrections or additional information as necessary. This enables the reception unit to support the user in inputting goals accurately and in detail, thereby improving the accuracy and effectiveness of the entire system.

[0063] The analysis unit uses the generation AI to analyze the goal input by the reception unit and set steps to achieve the goal. The analysis is performed by, for example, data analysis, text analysis, behavior analysis, etc., but is not limited to such examples. For example, the generation AI analyzes the input goal and sets specific steps such as dietary management and exercise plan. The generation AI understands the goal input by the user using natural language processing technology and collects and analyzes data related to the goal. For example, if the user inputs "I want to lose 5 kg in 3 months," the generation AI proposes a meal plan and exercise plan that are effective for weight loss based on past data and statistical information. Specifically, the generation AI analyzes information such as the user's current weight, height, eating habits, and exercise habits, and calculates calorie intake and calories burned. In addition, the generation AI can propose a meal menu and exercise menu according to the user's lifestyle and preferences. For example, if the user is busy, it proposes an effective exercise menu in a short time, and provides healthy recipes that are easy to prepare for meals. In addition, the analysis unit can monitor the user's progress and modify the steps as necessary. For example, if the user is behind on their goal, the generation AI will suggest increasing the amount of exercise or reviewing their diet. Also, if the user achieves their goal, the generation AI can suggest setting a new goal. This allows the analysis unit to effectively support the user in achieving their goal and promote continuous improvement.

[0064] The generation unit uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created in a timetable, Gantt chart, calendar format, or other format, but is not limited to these examples. For example, the generation AI incorporates daily meal contents and exercise time into the schedule. The generation unit creates a flexible and realistic schedule in accordance with the user's lifestyle and daily schedule. For example, if the user is busy with work during the weekdays, it can propose a schedule for concentrating on exercising on the weekends. The generation unit can also customize the schedule taking into account the user's preferences and constraints. For example, if the user does not like a particular ingredient, it can propose a meal plan that avoids that ingredient. Furthermore, the generation unit can monitor the progress of the schedule in real time and modify the schedule as necessary. For example, if the user is unable to exercise as planned, the generation AI will suggest increasing the amount of exercise the next day. In addition, if the user achieves a goal, it can automatically create a schedule for a new goal. The generation unit also provides a function for sending schedule reminders and notifications to the user. For example, it is possible to use the notification function of a smartphone to remind the user of exercise times and meal times. This allows the user to continue taking actions toward achieving their goals without forgetting about the schedule. Furthermore, the generation unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the schedule. This allows the generation unit to effectively support the user in achieving their goals and improve the performance of the entire system.

[0065] The generation unit includes an adjustment unit that adjusts the schedule based on the user's life pattern. The adjustment unit adjusts the schedule based on the user's life pattern. The life pattern includes, for example, daily routines, activity times, rest times, etc., but is not limited to such examples. For example, the adjustment unit adjusts the schedule taking into consideration whether the user is a morning person or a night person. The adjustment unit can also adjust the schedule taking into consideration the user's work schedule and home situation. Furthermore, the adjustment unit can adjust the schedule taking into consideration the user's health condition and hobbies. For example, if the user is interested in health, the adjustment unit sets a health-related schedule with priority. This makes it possible to adjust the schedule to match the user's lifestyle. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's life pattern data to the generation AI and cause the generation AI to adjust the schedule.

[0066] The analysis unit includes a provision unit that provides advice to increase motivation based on the user's input. The provision unit provides advice to increase motivation based on the user's input. The advice includes, for example, an encouraging message, a presentation of the benefits of achieving a goal, and the like, but is not limited to such examples. For example, when the user inputs "I don't feel like exercising today," the provision unit analyzes the reason and provides advice to increase motivation. In addition, when the user is making progress toward achieving a goal, the provision unit can also provide a message praising the progress. Furthermore, when the user is likely to give up, the provision unit can analyze the cause and provide advice to try again. For example, when the user inputs "I'm tired today," the provision unit provides advice to take a rest. This provides advice to increase the user's motivation. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the user's input data to the generation AI and cause the generation AI to generate advice.

[0067] The reception unit estimates the user's emotion and adjusts the timing of the goal input based on the estimated user's emotion. For example, when the user is stressed, the reception unit prompts the user to input a goal during a time period when the user can relax. For example, the reception unit prompts the user to input a goal during a time period when the user is relaxing after work. In addition, the reception unit can also prompt the user to input a goal at a timing when the user is concentrating. For example, when the user is concentrating in the morning, the reception unit prompts the user to input a goal during that time period. In addition, the reception unit can also prompt the user to input a goal after resting when the user is tired. For example, when the user is tired in the evening, the reception unit prompts the user to input a goal the next morning. This makes it possible to adjust the timing of the goal input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the target input.

[0068] The reception unit analyzes the user's past goal setting history and proposes an appropriate input method. The reception unit analyzes the user's past goal setting history and proposes an optimal input method. The past goal setting history includes, for example, past goal achievement status, the type of goal set, and the like, but is not limited to such examples. For example, the reception unit preferentially proposes a goal setting method that the user has been successful in the past. The reception unit can also suggest the user to avoid a goal setting method that the user has failed in the past. Furthermore, the reception unit can also propose an optimal input method based on the user's past goal achievement rate. For example, the reception unit analyzes the number of goals that the user has achieved in the past and the period until the achievement, and proposes an optimal input method. As a result, an optimal input method based on the past goal setting history is proposed. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past goal setting history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0069] The reception unit performs filtering based on the user's current living situation and field of interest when the goal is input. The reception unit performs filtering based on the user's current living situation and field of interest when the goal is input. The living situation includes, for example, work schedule, home situation, health condition, etc., but is not limited to such examples. For example, if the user is busy, the reception unit preferentially suggests short-term goals. In addition, if the user is interested in health, the reception unit can also preferentially suggest health-related goals. Furthermore, if the user is interested in learning, the reception unit can also preferentially suggest learning-related goals. For example, if the user wants to learn a new skill, the reception unit proposes a goal related to the skill. This enables goal filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation and field of interest data to the generation AI and cause the generation AI to execute goal filtering.

[0070] The reception unit estimates the user's emotion and determines the priority of the goals to be input based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the goals to be input based on the estimated user's emotion. For example, when the user is highly motivated, the reception unit preferentially suggests a goal with a high level of difficulty. For example, when the user inputs "I'm motivated today," the reception unit suggests a challenging goal. In addition, when the user is tired, the reception unit can preferentially suggest an easy goal. For example, when the user inputs "I'm tired today," the reception unit suggests an easy goal. In addition, when the user is relaxed, the reception unit can preferentially suggest a goal with a medium level of difficulty. For example, when the user inputs "I'm relaxed today," the reception unit suggests a balanced goal. This makes it possible to determine the priority of the goals according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of goals.

[0071] The reception unit, when inputting a goal, preferentially inputs a highly relevant goal in consideration of the geographical location information of the user. The reception unit, when inputting a goal, preferentially inputs a highly relevant goal in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, address information, location information services, etc., but is not limited to such examples. For example, when the user is in a specific area, the reception unit proposes a goal related to the area. In addition, when the user is traveling, the reception unit can also propose a goal related to the travel destination. Furthermore, when the user is at home, the reception unit can also propose a goal that can be achieved at home. For example, when the user inputs "traveling", the reception unit proposes a goal that can be achieved at the travel destination. This makes it possible to input a goal based on the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the suggestion of goal input.

[0072] The reception unit analyzes the user's social media activity at the time of goal input and proposes a related goal. The reception unit analyzes the user's social media activity at the time of goal input and proposes a related goal. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit proposes a goal related to a theme that the user frequently mentions on social media. The reception unit can also propose a goal related to the activity of an influencer that the user follows. Furthermore, the reception unit can also propose a goal based on a trend of an online community in which the user participates. For example, if the user is interested in "health", the reception unit proposes a health-related goal. This makes it possible to propose a goal based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the goal proposal.

[0073] The analysis unit estimates the user's emotion and adjusts the step setting method based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the step setting method based on the estimated user's emotion. For example, when the user is highly motivated, a challenging step is set. For example, when the user inputs "I'm motivated today," the analysis unit sets a step with a high level of difficulty. Also, when the user is tired, an easy step can be set. For example, when the user inputs "I'm tired today," the analysis unit sets an easy step. Also, when the user is relaxed, a balanced step can be set. For example, when the user inputs "I'm relaxed today," the analysis unit sets a step with a medium level of difficulty. This makes it possible to adjust the step setting according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and cause the generation AI to adjust the step settings.

[0074] The analysis unit sets appropriate steps by referring to past goal achievement data during goal analysis. The analysis unit sets optimal steps by referring to past goal achievement data during goal analysis. The goal achievement data includes, for example, the number of goals achieved, the period until achievement, and the degree of achievement, but is not limited to such examples. For example, the analysis unit sets new steps by referring to steps that the user has succeeded in in the past. The analysis unit can also be set to avoid steps that the user has failed in the past. Furthermore, the analysis unit can set optimal steps based on the user's past goal achievement rate. For example, the analysis unit analyzes the number of goals that the user has achieved in the past and the period until achievement, and sets optimal steps. This makes it possible to set optimal steps based on past goal achievement data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past goal achievement data into the generation AI and cause the generation AI to execute optimal step setting.

[0075] The analysis unit customizes the steps based on the user's current living situation at the time of goal analysis. The analysis unit customizes the steps based on the user's current living situation at the time of goal analysis. Living situations include, for example, work schedule, home situation, health condition, etc., but are not limited to such examples. For example, the analysis unit sets short-term steps when the user is busy. In addition, the analysis unit can also set health-related steps when the user is interested in health. Furthermore, the analysis unit can also set learning-related steps when the user is interested in learning. For example, when the user inputs "I want to learn a new skill," the analysis unit sets steps related to that skill. This enables step customization based on the user's living situation. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living situation data to the generation AI and cause the generation AI to execute step customization.

[0076] The analysis unit estimates the user's emotion and determines the priority of steps based on the estimated user's emotion. The analysis unit estimates the user's emotion and determines the priority of steps based on the estimated user's emotion. For example, when the user is highly motivated, the analysis unit prioritizes steps with high difficulty. For example, when the user inputs "I'm motivated today," the analysis unit sets a challenging step. Also, when the user is tired, it can also prioritize easy steps. For example, when the user inputs "I'm tired today," the analysis unit sets an easy step. Also, when the user is relaxed, it can also prioritize steps with medium difficulty. For example, when the user inputs "I'm relaxed today," the analysis unit sets a balanced step. This makes it possible to prioritize steps according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of steps.

[0077] The analysis unit sets appropriate steps in consideration of the geographical location information of the user at the time of goal analysis. The analysis unit sets optimal steps in consideration of the geographical location information of the user at the time of goal analysis. The geographical location information includes, but is not limited to, for example, GPS data, address information, location information services, etc. For example, when the user is in a specific area, the analysis unit sets steps related to that area. In addition, when the user is traveling, the analysis unit can also set steps related to the travel destination. Furthermore, when the user is at home, the analysis unit can also set steps that can be achieved at home. For example, when the user inputs "traveling", the analysis unit sets steps that can be achieved at the travel destination. This makes it possible to set steps based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute step setting.

[0078] The analysis unit analyzes the user's social media activities during goal analysis to suggest steps. The analysis unit analyzes the user's social media activities during goal analysis to suggest steps. Social media activities include, but are not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit sets steps related to themes that the user frequently mentions on social media. The analysis unit can also set steps related to the activities of influencers that the user follows. Furthermore, the analysis unit can set steps based on trends in online communities in which the user participates. For example, if the user is interested in "health," the analysis unit sets health-related steps. This makes it possible to suggest steps based on social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data to the generation AI and cause the generation AI to execute step suggestions.

[0079] The generation unit estimates the user's emotion and adjusts the schedule generation method based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the schedule generation method based on the estimated user's emotion. For example, when the user is highly motivated, a challenging schedule is generated. For example, when the user inputs "I'm motivated today," the generation unit generates a schedule with a high level of difficulty. In addition, when the user is tired, an easy schedule can also be generated. For example, when the user inputs "I'm tired today," the generation unit generates an easy schedule. In addition, when the user is relaxed, a balanced schedule can also be generated. For example, when the user inputs "I'm relaxed today," the generation unit generates a schedule with a medium level of difficulty. This makes it possible to adjust the schedule generation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data to the generation AI and cause the generation AI to adjust the schedule generation.

[0080] The generating unit creates an appropriate schedule by referring to past schedule data when generating a schedule. The generating unit creates an optimal schedule by referring to past schedule data when generating a schedule. The schedule data includes, for example, past schedule history, task completion status, and the like, but is not limited to such examples. For example, the generating unit creates a new schedule by referring to schedules that the user has been successful in the past. The generating unit can also create a schedule so as to avoid schedules that the user has failed in the past. Furthermore, the generating unit can create an optimal schedule based on the user's past schedule achievement rate. For example, the generating unit analyzes the number of schedules that the user has achieved in the past and the period until achievement, and creates an optimal schedule. This makes it possible to create an optimal schedule based on past schedule data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's past schedule data into the generating AI and cause the generating AI to create an optimal schedule.

[0081] The generating unit customizes the schedule based on the user's current living situation when generating the schedule. The generating unit customizes the schedule based on the user's current living situation when generating the schedule. Living situations include, for example, work schedules, home situations, health conditions, etc., but are not limited to such examples. For example, the generating unit creates a short-term schedule when the user is busy. In addition, the generating unit can also create a health-related schedule when the user is interested in health. Furthermore, the generating unit can also create a learning-related schedule when the user is interested in learning. For example, when the user inputs "I want to learn a new skill," the generating unit creates a schedule related to the skill. This enables schedule customization based on the user's living situation. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's living situation data to the generating AI and cause the generating AI to customize the schedule.

[0082] The generation unit estimates the user's emotion and determines the priority of the schedule based on the estimated user's emotion. The generation unit estimates the user's emotion and determines the priority of the schedule based on the estimated user's emotion. For example, when the user is highly motivated, the generation unit creates a schedule with high difficulty with priority. For example, when the user inputs "I'm motivated today," the generation unit creates a challenging schedule. In addition, when the user is tired, the generation unit can also create an easy schedule with priority. For example, when the user inputs "I'm tired today," the generation unit creates an easy schedule. In addition, when the user is relaxed, the generation unit can also create a schedule with medium difficulty with priority. For example, when the user inputs "I'm relaxed today," the generation unit creates a balanced schedule. This makes it possible to determine the priority of the schedule according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the schedule.

[0083] The generating unit creates an appropriate schedule in consideration of the geographical location information of the user when generating the schedule. The generating unit creates an optimal schedule in consideration of the geographical location information of the user when generating the schedule. The geographical location information includes, for example, GPS data, address information, location information services, etc., but is not limited to such examples. For example, when the user is in a specific area, the generating unit creates a schedule related to the area. In addition, when the user is traveling, the generating unit can also create a schedule related to the travel destination. Furthermore, when the user is at home, the generating unit can also create a schedule that can be achieved at home. For example, when the user inputs "traveling", the generating unit creates a schedule that can be achieved at the travel destination. This makes it possible to create a schedule based on the geographical location information. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the geographical location information data of the user to the generating AI and cause the generating AI to execute schedule creation.

[0084] The generation unit analyzes the user's social media activities when generating a schedule and proposes a schedule. The generation unit analyzes the user's social media activities when generating a schedule and proposes a schedule. Social media activities include, for example, the content of posts, the number of likes, the number of followers, and the like, but are not limited to such examples. For example, the generation unit creates a schedule related to a theme that the user frequently mentions on social media. The generation unit can also create a schedule related to the activities of influencers that the user follows. Furthermore, the generation unit can create a schedule based on trends in online communities in which the user participates. For example, if the user is interested in "health", the generation unit creates a health-related schedule. This makes it possible to propose a schedule based on social media activities. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a schedule proposal.

[0085] The adjustment unit estimates the user's emotion and changes the schedule adjustment method based on the estimated user's emotion. The adjustment unit estimates the user's emotion and changes the schedule adjustment method based on the estimated user's emotion. For example, when the user is highly motivated, the adjustment unit adjusts the schedule to a challenging schedule. For example, when the user inputs "I'm motivated today," the adjustment unit adjusts the schedule to a high difficulty schedule. In addition, when the user is tired, the adjustment unit can also adjust the schedule to an easy schedule. For example, when the user inputs "I'm tired today," the adjustment unit adjusts the schedule to an easy schedule. In addition, when the user is relaxed, the adjustment unit can also adjust the schedule to a balanced schedule. For example, when the user inputs "I'm relaxed today," the adjustment unit adjusts the schedule to a medium difficulty schedule. This makes it possible to adjust the schedule according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the schedule.

[0086] The adjustment unit performs appropriate adjustments when adjusting the schedule by referring to the user's past schedule history. The adjustment unit performs optimal adjustments when adjusting the schedule by referring to the user's past schedule history. The schedule history includes, for example, past schedules, task completion status, and the like, but is not limited to such examples. For example, the adjustment unit adjusts a new schedule by referring to schedules that the user has been successful in the past. The adjustment unit can also adjust to avoid schedules that the user has failed in the past. Furthermore, the adjustment unit can adjust an optimal schedule based on the user's past schedule achievement rate. For example, the adjustment unit analyzes the number of schedules that the user has achieved in the past and the period until achievement, and adjusts the optimal schedule. This makes it possible to adjust the optimal schedule based on the past schedule history. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past schedule history data to the generation AI and cause the generation AI to execute optimal schedule adjustment.

[0087] The adjustment unit estimates the user's emotion and determines the priority order of schedule adjustment based on the estimated user's emotion. The adjustment unit estimates the user's emotion and determines the priority order of schedule adjustment based on the estimated user's emotion. For example, when the user is highly motivated, the adjustment unit adjusts a schedule with high difficulty with priority. For example, when the user inputs "I'm motivated today," the adjustment unit adjusts a challenging schedule with priority. In addition, when the user is tired, the adjustment unit can also adjust an easy schedule with priority. For example, when the user inputs "I'm tired today," the adjustment unit adjusts an easy schedule with priority. In addition, when the user is relaxed, the adjustment unit can also adjust a schedule with medium difficulty with priority. For example, when the user inputs "I'm relaxed today," the adjustment unit adjusts a balanced schedule with priority. This makes it possible to determine the priority order of schedule adjustment according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit may input the user's emotion data to the generation AI and cause the generation AI to determine the adjustment priority order of the schedule.

[0088] The adjustment unit performs appropriate adjustments in consideration of the device information of the user when adjusting the schedule. The adjustment unit performs optimal adjustments in consideration of the device information of the user when adjusting the schedule. The device information includes, for example, the type of device being used, the usage status of the device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the adjustment unit adjusts the schedule to match the screen size. In addition, when the user is using a tablet, the adjustment unit can also adjust a schedule optimized for a large screen. Furthermore, when the user is using a smartwatch, the adjustment unit can also adjust a simple and highly visible schedule. For example, when the user inputs "I am using a smartphone," the adjustment unit adjusts the schedule to match the screen size of the smartphone. This enables optimal schedule adjustment based on the device information. Part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the device information data of the user to the generation AI and cause the generation AI to execute the schedule adjustment.

[0089] The providing unit estimates the user's emotion and adjusts the method of providing advice based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the method of providing advice based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides advice in a calm voice. For example, if the user inputs "nervous", the providing unit provides advice in a calm voice. Also, if the user is relaxed, the advice can be provided in a bright voice. For example, if the user inputs "relaxed", the providing unit provides advice in a bright voice. Also, if the user is in a hurry, the advice can be provided quickly and concisely. For example, if the user inputs "in a hurry", the providing unit provides quick and concise advice. This makes it possible to provide advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data to the generating AI and cause the generating AI to adjust the advice provided.

[0090] The providing unit provides appropriate advice by referring to the user's past behavioral history when providing advice. The providing unit provides optimal advice by referring to the user's past behavioral history when providing advice. The behavioral history includes, for example, past behavioral patterns, behavior frequency, etc., but is not limited to such examples. For example, the providing unit provides new advice by referring to actions that the user has succeeded in in the past. The providing unit can also provide advice to avoid actions that the user has failed in in the past. Furthermore, the providing unit can also provide optimal advice based on the user's past behavior achievement rate. For example, the providing unit analyzes the number of actions that the user has achieved in the past and the period until achievement, and provides optimal advice. This makes it possible to provide optimal advice based on the past behavioral history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavioral history data to the generating AI and cause the generating AI to provide optimal advice.

[0091] The providing unit estimates the user's emotion and determines the priority of advice based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of advice based on the estimated user's emotion. For example, when the user is highly motivated, the providing unit provides high-difficulty advice preferentially. For example, when the user inputs "I'm motivated today", the providing unit provides challenging advice preferentially. In addition, when the user is tired, it can also provide easy advice preferentially. For example, when the user inputs "I'm tired today", the providing unit provides easy advice preferentially. In addition, when the user is relaxed, it can also provide advice of medium difficulty preferentially. For example, when the user inputs "I'm relaxed today", the providing unit provides balanced advice preferentially. This makes it possible to determine the priority of advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to determine the priority order of advice.

[0092] The providing unit provides appropriate advice in consideration of the device information of the user when providing advice. The providing unit provides optimal advice in consideration of the device information of the user when providing advice. The device information includes, for example, the type of device being used, the usage status of the device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides advice tailored to the screen size. In addition, when the user is using a tablet, the providing unit can also provide advice optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide advice that is concise and highly visible. For example, when the user inputs "I am using a smartphone," the providing unit provides advice tailored to the screen size of the smartphone. This makes it possible to provide optimal advice based on the device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to execute advice provision.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can also analyze the user's past goal achievement history and suggest goals that are easy to achieve. For example, the reception unit can analyze the types of goals the user has achieved in the past and the time it took to achieve them, and suggest similar goals. The reception unit can also suggest that the user avoid goals that he or she has failed to achieve in the past. Furthermore, the reception unit can suggest goals that are highly achievable based on the user's past goal achievement rate. This makes it possible to suggest optimal goals based on the user's past goal achievement history.

[0095] The adjustment unit can also acquire the user's health data and adjust the schedule based on the user's health condition. For example, the adjustment unit can analyze the user's heart rate and sleep data and incorporate appropriate amounts of exercise and rest times into the schedule. The adjustment unit can also acquire the user's dietary data and suggest a dietary schedule that takes nutritional balance into consideration. Furthermore, the adjustment unit can monitor the user's stress level and incorporate activities for reducing stress into the schedule. This makes it possible to adjust the schedule based on the user's health condition.

[0096] The providing unit can also estimate the user's emotions and customize the content of advice based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide advice on how to relax or relieve stress. If the user's motivation is low, the providing unit can also provide encouraging messages or introduce success stories. Furthermore, if the user is feeling happy, the providing unit can provide positive advice to maintain that emotion. This makes it possible to provide advice according to the user's emotions.

[0097] The reception unit can also estimate the user's emotions and adjust the timing of goal input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input goals during a time when the user can relax. If the user is concentrating, the reception unit can also prompt the user to input goals at that time. Furthermore, if the user is tired, the reception unit can prompt the user to input goals after resting. This makes it possible to adjust the timing of goal input according to the user's emotions.

[0098] The reception unit can also analyze the user's past goal setting history and suggest an appropriate input method. For example, the reception unit can preferentially suggest goal setting methods that the user has been successful in the past. The reception unit can also suggest goal setting methods that the user has failed in the past to be avoided. Furthermore, the reception unit can suggest an optimal input method based on the user's past goal achievement rate. In this way, an optimal input method is suggested based on the user's past goal setting history.

[0099] The reception unit can also filter the goals based on the user's current living situation and areas of interest when inputting the goals. For example, if the user is busy, short-term goals can be preferentially suggested. If the user is interested in health, health-related goals can be preferentially suggested. If the user is interested in learning, learning-related goals can be preferentially suggested. This makes it possible to filter goals based on the user's living situation and areas of interest.

[0100] The reception unit can also estimate the user's emotions and determine the priority order of the goals to be input based on the estimated emotions. For example, if the user is highly motivated, goals with high difficulty can be preferentially suggested. Also, if the user is tired, goals with easy difficulty can be preferentially suggested. Furthermore, if the user is relaxed, goals with medium difficulty can be preferentially suggested. This makes it possible to determine the priority order of goals according to the user's emotions.

[0101] When inputting a goal, the reception unit can also preferentially input highly relevant goals in consideration of the user's geographical location information. For example, if the user is in a specific area, goals related to that area can be suggested. If the user is traveling, goals related to the travel destination can also be suggested. Furthermore, if the user is at home, goals that can be achieved at home can also be suggested. This makes it possible to input goals based on geographical location information.

[0102] When the goal is input, the reception unit can analyze the user's social media activity and suggest related goals. For example, the reception unit can suggest goals related to themes that the user frequently mentions on social media. The reception unit can also suggest goals related to the activities of influencers that the user follows. Furthermore, the reception unit can suggest goals based on trends in online communities in which the user participates. This makes it possible to suggest goals based on social media activity.

[0103] The analysis unit can also estimate the user's emotions and adjust the step setting method based on the estimated emotions. For example, if the user is highly motivated, challenging steps can be set. Also, if the user is tired, easy steps can be set. Furthermore, if the user is relaxed, balanced steps can be set. This makes it possible to adjust the step setting according to the user's emotions.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The reception unit inputs a goal that the user wants to achieve. The goal that the user wants to achieve includes, for example, a short-term goal, a long-term goal, a quantitative goal, a qualitative goal, and the like. Specifically, the user can input a goal such as "I want to lose 5 kg in 3 months." Step 2: The analysis unit uses the generation AI to analyze the goal entered by the reception unit and set steps to achieve the goal. The analysis is performed by methods such as data analysis, text analysis, and behavior analysis. For example, the generation AI analyzes the entered goal and sets specific steps such as dietary management and exercise plans. Step 3: The generator uses the generation AI to create a schedule based on the steps set by the analysis unit. The schedule is created in a timetable, Gantt chart, calendar format, or other format. For example, the generation AI incorporates daily meal plans and exercise times into the schedule.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0108] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, adjustment unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs a goal that he or she wants to achieve. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the goal using a generation AI and sets steps. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a schedule based on the set steps. The adjustment unit is realized by the control unit 46A of the smart device 14, and adjusts the schedule based on the user's life pattern. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice to increase motivation based on the user's input. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0117] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, adjustment unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs a goal that he or she wants to achieve. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the goal using a generation AI and sets steps. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a schedule based on the set steps. The adjustment unit is realized by the control unit 46A of the smart glasses 214, and adjusts the schedule based on the user's life pattern. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice to increase motivation based on the user's input. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, adjustment unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs a goal that he or she wants to achieve. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the goal using a generation AI and sets steps. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a schedule based on the set steps. The adjustment unit is realized by the control unit 46A of the headset type terminal 314, and adjusts the schedule based on the user's life pattern. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice to increase motivation based on the user's input. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, adjustment unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs a goal that he or she wants to achieve. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the goal using a generation AI and sets steps. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a schedule based on the set steps. The adjustment unit is realized by the control unit 46A of the robot 414, and adjusts the schedule based on the user's life pattern. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice to increase motivation based on the user's input. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0160] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0164] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0171] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0175] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0177] (Appendix 1) A reception unit for inputting a user's goal; an analysis unit that analyzes the goal input by the reception unit and sets steps; A generation unit that creates a schedule based on the steps set by the analysis unit. A system characterized by: (Appendix 2) The generation unit is Equipped with an adjustment unit that adjusts the schedule based on the user's lifestyle pattern 2. The system of claim 1, (Appendix 3) The analysis unit is A providing unit provides advice for increasing motivation based on user input. 2. The system of claim 1, (Appendix 4) The reception unit is Estimating a user's emotion and adjusting the timing of goal input based on the estimated user's emotion 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past goal setting history and suggest appropriate input methods 2. The system of claim 1, (Appendix 6) The reception unit is When entering goals, filters are performed based on the user's current life situation and interests 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and prioritize the goals to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is When entering goals, the system takes into account the user's geographic location information to prioritize goals that are more relevant to them. 2. The system of claim 1, (Appendix 9) The reception unit is When you enter a goal, it analyzes your social media activity and suggests related goals. 2. The system of claim 1, (Appendix 10) The analysis unit is A method of estimating a user's emotion and adjusting a step setting method based on the estimated user's emotion 2. The system of claim 1, (Appendix 11) The analysis unit is During goal analysis, refer to past goal achievement data to set appropriate steps 2. The system of claim 1, (Appendix 12) The analysis unit is Customize steps based on the user's current life situation during goal analysis 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and prioritize steps based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During goal analysis, consider the user's geographic location to set the appropriate steps 2. The system of claim 1, (Appendix 15) The analysis unit is During goal analysis, analyze users' social media activity to suggest steps 2. The system of claim 1, (Appendix 16) The generation unit is To estimate a user's emotion and adjust a schedule generation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The generation unit is When generating a schedule, create an appropriate schedule by referencing past schedule data 2. The system of claim 1, (Appendix 18) The generation unit is Customize the schedule based on the user's current life situation when the schedule is generated 2. The system of claim 1, (Appendix 19) The generation unit is Estimate user emotions and prioritize schedules based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The generation unit is When generating a schedule, consider the geographic location information of the user to create an appropriate schedule. 2. The system of claim 1, (Appendix 21) The generation unit is When generating a schedule, the system analyzes the user's social media activity to suggest a schedule. 2. The system of claim 1, (Appendix 22) The adjustment unit is Infer user emotions and change schedule adjustment methods based on the estimated user emotions 3. The system of claim 2, (Appendix 23) The adjustment unit is When adjusting schedules, make appropriate adjustments by referring to the user's past schedule history 3. The system of claim 2, (Appendix 24) The adjustment unit is To estimate a user's emotion and determine a schedule adjustment priority based on the estimated user's emotion. 3. The system of claim 2, (Appendix 25) The adjustment unit is When adjusting schedules, make appropriate adjustments taking into account user device information 3. The system of claim 2, (Appendix 26) The providing unit is Inferring a user's emotions and adjusting the way advice is provided based on the estimated user's emotions 4. The system of claim 3. (Appendix 27) The providing unit is When providing advice, the system provides appropriate advice by referring to the user's past behavior history. 4. The system of claim 3. (Appendix 28) The providing unit is Estimate the user's emotions and prioritize advice based on the estimated user emotions. 4. The system of claim 3. (Appendix 29) The providing unit is When providing advice, the system takes into account the user's device information to provide appropriate advice. 4. The system of claim 3. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting a user's goal; an analysis unit that analyzes the goal input by the reception unit and sets steps; A generation unit that creates a schedule based on the steps set by the analysis unit. A system characterized by:

2. The generation unit is An adjustment unit that adjusts a schedule based on the user's life pattern 2. The system of claim 1 .

3. The analysis unit is a providing unit that provides advice to enhance motivation based on the input of the user; 2. The system of claim 1 .

4. The reception unit is An emotion of the user is estimated, and a timing of a target input is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

5. The reception unit is Analyze the user's past goal setting history and suggest an appropriate input method 2. The system of claim 1 .

6. The reception unit is When entering goals, filtering is performed based on the user's current life situation and areas of interest.

2. The system of claim 1 .

7. The reception unit is Estimating the user's emotion and determining a priority of the input goals based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is When inputting a goal, the goal having high relevance is input preferentially in consideration of the geographical location information of the user.

2. The system of claim 1 .

9. The reception unit is Upon goal entry, the user's social media activity is analyzed to suggest related goals.

2. The system of claim 1 .

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