system

Abstract

The system according to this embodiment aims to provide support for users to efficiently achieve what they "want to do". [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a collection unit, a presentation unit, and an execution unit. The reception unit receives input from the user about what they want to do. The analysis unit analyzes the information received by the reception unit. The collection unit collects necessary information based on the information analyzed by the analysis unit. The presentation unit analyzes the information collected by the collection unit and presents options. The execution unit executes the options presented by the presentation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that information collection and arrangements for a user to efficiently realize "what they want to do" are complicated and may cause stress.

[0005] The system according to the embodiment aims to provide support for a user to efficiently realize "what they want to do".

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a collection unit, a presentation unit, and an execution unit. The reception unit receives input from the user regarding what they want to do. The analysis unit analyzes the information received by the reception unit. The collection unit collects necessary information based on the information analyzed by the analysis unit. The presentation unit analyzes the information collected by the collection unit and presents options. The execution unit executes the options presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide support to help users efficiently achieve what they "want to do." [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The service according to an embodiment of the present invention is a novel service for individuals to efficiently realize their "desires." This service is a mechanism in which an AI agent understands the user's "desires" and provides the necessary support. Users can concentrate on achieving their goals without stress by entrusting the AI ​​with gathering information and making arrangements necessary to solve problems, and by being quickly offered appropriate choices. For example, if a user has a need to hang a picture on a wall, the AI ​​agent understands that need and considers the necessity of drilling holes. Next, it presents problem-solving options such as drills and double-sided tape. The user can choose the best method from these options and execute it. This service consists of the following steps: First, the user inputs their "desire." Next, the AI ​​agent analyzes the content and collects the necessary information. For example, it gathers information on repairing holes in walls and presents options such as DIY kits and estimates from contractors. The user can choose the best method from these options and execute it. This mechanism frees users from the hassle of gathering information and making arrangements, allowing them to concentrate on what they truly want to do. For example, regarding repairing holes in walls, the user simply takes a picture and sends it, and the AI ​​agent suggests the best option and makes the necessary arrangements. This reduces decision-making and arrangements that previously took weeks to complete in just 15 minutes. The service acts as a partner that understands what the user wants to do and stays by their side until it is realized. By freeing users from the hassles of information gathering and arrangements, they can focus on what they truly want to do. This allows the service to efficiently gather, analyze, present, and execute information to realize the user's goals.

[0029] The service according to this embodiment comprises a reception unit, an analysis unit, a collection unit, a presentation unit, and an execution unit. The reception unit receives input from the user about what they want to do. The reception unit receives information from the user, for example, in natural language. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, performs natural language processing to understand what the user wants to do. The collection unit collects necessary information based on the information analyzed by the analysis unit. The collection unit collects necessary information, for example, using web scraping. The presentation unit analyzes the information collected by the collection unit and presents options. The presentation unit, for example, presents the optimal option to the user. The execution unit executes the options presented by the presentation unit. The execution unit, for example, manages arrangements and progress. As a result, the service according to this embodiment enables information collection, analysis, presentation, and execution to efficiently realize what the user wants to do.

[0030] The reception desk receives input from the user about what they want to do. The reception desk accepts information entered by the user in natural language, for example. Specifically, the user accesses a dedicated application or website using a device such as a smartphone or computer and enters what they want to do into a text box. The user can enter specific requests in natural language, such as "I want to plan a trip" or "I want to find a new recipe." The reception desk receives this input in real time and stores it in a database. Furthermore, the reception desk is equipped with input completion and typographical error correction functions to accurately receive the user's input. This allows the user to smoothly enter what they want to do, and the reception desk to obtain accurate information.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs natural language processing to understand what the user wants to do. Specifically, it uses natural language processing technology to perform morphological analysis on the user's input and analyze its grammatical structure. This allows it to accurately grasp the user's intentions and desires. For example, if a user inputs "I want to plan a trip," the analysis unit extracts specific elements such as the destination, dates, and budget to understand the user's desires in detail. The analysis unit can also refer to past user input data and related information to gain hints for finding the optimal solution to the user's desires. Furthermore, the analysis unit can use machine learning algorithms to classify the user's input and categorize it appropriately. This allows the analysis unit to accurately understand what the user wants to do and prepare to proceed to the next step.

[0032] The data collection unit collects necessary information based on the information analyzed by the analysis unit. For example, the data collection unit uses web scraping to collect necessary information. Specifically, the data collection unit automatically collects relevant information from websites and databases on the internet based on keywords and conditions provided by the analysis unit. The data collection unit uses web scraping technology to analyze the content of specified web pages and extract the necessary data. For example, when planning a trip, the data collection unit collects information such as tourist information, accommodation information, and transportation information for the destination. Furthermore, the data collection unit cross-checks multiple information sources to verify the reliability and timeliness of the collected information, selecting the most reliable information. In addition, the data collection unit stores the collected information in a database so that it can be used for subsequent processing. This allows the data collection unit to efficiently collect the information necessary to achieve the user's goals and support the overall information processing of the system.

[0033] The presentation unit analyzes the information collected by the collection unit and presents options. For example, the presentation unit presents the user with the most suitable options. Specifically, the presentation unit analyzes the information provided by the collection unit and selects the option that best suits the user's needs. For example, when planning a trip, the presentation unit presents the user with multiple options based on the collected information on accommodations and tourist attractions. The presentation unit displays the best options in a ranking format according to the user's preferences and conditions, allowing the user to easily compare and consider them. The presentation unit can also refer to the user's past selection history and ratings to prioritize and present options that match the user's preferences. Furthermore, the presentation unit displays detailed information, ratings, and reviews for each option to help the user make a choice. In this way, the presentation unit can support the user in making the best choice and present concrete means to realize the user's "goals."

[0034] The execution unit executes the options presented by the presentation unit. For example, the execution unit manages arrangements and progress. Specifically, the execution unit automatically makes the necessary arrangements and reservations based on the options selected by the user. For example, when planning a trip, the execution unit makes reservations for accommodations, arranges transportation, and purchases tickets for tourist attractions. After the arrangements are completed, the execution unit notifies the user of the progress and provides necessary information. Furthermore, the execution unit can handle changes and cancellations to arrangements, and can flexibly respond to user requests. The execution unit manages the progress of arrangements in real time, supporting users so that they can use the service with peace of mind. In this way, the execution unit can take concrete actions to realize what the user wants to do and increase user satisfaction.

[0035] The reception desk can accept information entered by the user in natural language. For example, the reception desk accepts information entered by the user in natural language. By accepting information entered by the user in natural language, the reception desk enables intuitive operation. Natural language includes, but is not limited to, Japanese, English, and other languages.

[0036] The analysis unit can perform natural language processing to understand what the user wants to do. For example, the analysis unit can perform natural language processing to understand what the user wants to do. By performing natural language processing, the analysis unit can accurately understand what the user wants to do. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis.

[0037] The data collection unit can collect necessary information using web scraping. For example, the data collection unit can collect necessary information using web scraping. By using web scraping, the data collection unit can efficiently collect the necessary information. Web scraping includes, but is not limited to, Python's BeautifulSoup and Scrapy.

[0038] The presentation unit can present the user with the optimal options. For example, the presentation unit presents the user with the optimal options. By presenting the user with the optimal options, the presentation unit enables the user to make choices efficiently. Optimal options include, but are not limited to, the user's past selection history and current situation.

[0039] The execution unit can manage arrangements and progress. For example, the execution unit manages arrangements and progress. By managing arrangements and progress, the execution unit enables users to efficiently achieve their goals. Arrangements include, but are not limited to, reservations, orders, and scheduling. Progress management includes, but is not limited to, task management tools and the frequency of progress reports.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions "things the user wants to do" that they have frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest "things the user wants to do" to be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested. Past input history includes, but is not limited to, examples such as how input content is saved and how historical data is analyzed.

[0041] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do. For example, when a user opens the app, the reception desk automatically acquires their current location and suggests relevant things they want to do. For example, when a user inputs what they want to do, the reception desk suggests the best options considering the distance from their current location. For example, if a user uses the app while on the move, the reception desk updates their current location in real time and suggests relevant things they want to do. This simplifies input by automatically acquiring the user's current location information. Location information includes, but is not limited to, GPS data and location estimation from IP addresses.

[0042] The reception desk can automatically suggest options when the user enters their "things they want to do," by referring to the user's past activity history. For example, the reception desk can automatically display as options the user has frequently done in the past. For example, the reception desk can predict what the user will do on specific days of the week or time slots and suggest them as options. For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable options. In this way, the system can automatically suggest the most suitable options by referring to the user's past activity history. Past activity history includes, but is not limited to, examples such as how activity logs are saved and how historical data is analyzed.

[0043] The reception desk can refer to the user's calendar information when they input their "things they want to do" and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the "things they want to do." For example, the reception desk can suggest "things they want to do" related to a specific event as candidates from the user's calendar information. For example, the reception desk can suggest the most suitable "things they want to do" based on the user's calendar information and their schedule. In this way, it can make suggestions based on the schedule by referring to the user's calendar information. Calendar information includes, but is not limited to, examples such as integration with calendar apps and methods of analyzing schedules.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past "things they want to do." For example, the analysis unit can suggest the optimal option based on the user's past behavior history. For example, the analysis unit can analyze the user's past behavior history and suggest the most efficient analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past behavior history. Improvements in analysis accuracy include, but are not limited to, improving data quality and improving algorithms.

[0045] The analysis unit can perform analysis while considering the user's current interests and trends. For example, the analysis unit performs analysis based on topics the user is currently interested in. For example, the analysis unit proposes the optimal option by considering the user's current trends. For example, the analysis unit analyzes the user's current interests and proposes the most appropriate analysis method. In this way, by considering the user's current interests and trends, it is possible to provide optimal analysis results. Interests and trends include, but are not limited to, social media analysis and news trend analysis.

[0046] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can suggest the optimal option based on the user's current location. For example, the analysis unit can suggest the optimal analysis method by considering the user's geographical location information. For example, the analysis unit can suggest the most efficient analysis method based on the user's current location. In this way, by considering the user's geographical location information, the optimal analysis results can be provided. Geographical location information includes, but is not limited to, GPS data and the use of location information services.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature and data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. For example, the analysis unit can suggest the optimal option by referring to the user's relevant data. For example, the analysis unit can analyze the user's relevant literature and suggest the most efficient analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature and data. Relevant literature and data include, but are not limited to, database searches and methods of citing literature.

[0048] The data collection unit can collect the most relevant information by referring to the user's past search history during data collection. For example, the data collection unit collects the most relevant information based on information the user has previously searched for. For example, the data collection unit collects relevant information from the user's past search history. For example, the data collection unit analyzes the user's past search history and proposes the most efficient method of data collection. This allows the system to collect the most relevant information by referring to the user's past search history. Search history includes, but is not limited to, methods for saving search queries and methods for analyzing historical data.

[0049] The data collection unit can collect information while considering the user's current interests and trends. For example, the data collection unit collects information based on topics that the user is currently interested in. For example, the data collection unit collects the most appropriate information by considering the user's current trends. For example, the data collection unit analyzes the user's current interests and proposes the most appropriate information collection method. This allows for the collection of optimal information by considering the user's current interests and trends. Interests and trends include, but are not limited to, social media analysis and news trend analysis.

[0050] The data collection unit can prioritize the collection of relevant information by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of relevant information based on the user's current location. For example, the data collection unit prioritizes the collection of optimal information by considering the user's geographical location information. For example, the data collection unit proposes the most efficient information collection method based on the user's current location. This allows for the priority collection of relevant information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location-based services.

[0051] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects relevant information based on information shared by the user on social media. For example, the data collection unit collects optimal information from the user's social media activity. For example, the data collection unit analyzes the user's social media activity and proposes the most efficient information collection method. In this way, relevant information can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis.

[0052] The presentation unit can present the optimal option by referring to the user's past selection history at the time of presentation. For example, the presentation unit presents the optimal option based on the options the user has previously selected. For example, the presentation unit presents relevant options from the user's past selection history. For example, the presentation unit analyzes the user's past selection history and presents the most efficient option. In this way, the optimal option can be presented by referring to the user's past selection history. The selection history includes, but is not limited to, examples such as how selections are saved and how historical data is analyzed.

[0053] The presentation unit can customize the options based on the user's current situation and environment at the time of presentation. For example, the presentation unit can present the optimal option based on the user's current location. For example, the presentation unit can present the optimal option considering the user's current situation and environment. For example, the presentation unit can analyze the user's current situation and present the most efficient option. In this way, the optimal option can be provided by customizing the options based on the user's current situation and environment. Current situation and environment include, but are not limited to, the acquisition of real-time data and the use of environmental sensors.

[0054] The presentation unit can prioritize the presentation of relevant options by considering the user's geographical location information. For example, the presentation unit can prioritize the presentation of relevant options based on the user's current location. For example, the presentation unit can prioritize the presentation of the optimal option by considering the user's geographical location information. For example, the presentation unit can prioritize the presentation of the most efficient option based on the user's current location. In this way, relevant options can be prioritized by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location-based services.

[0055] The presentation unit can analyze the user's social media activity and present relevant options at the time of presentation. For example, the presentation unit can present relevant options based on information shared by the user on social media. For example, the presentation unit can present the optimal option based on the user's social media activity. For example, the presentation unit can analyze the user's social media activity and present the most efficient option. In this way, relevant options can be presented by analyzing the user's social media activity. Social media activity includes, but is not limited to, analyzing post content and follower analysis.

[0056] The execution unit can select the optimal execution method by referring to the user's past behavior history during execution. For example, the execution unit selects the optimal execution method based on the execution methods the user has performed in the past. For example, the execution unit proposes the optimal execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior history and selects the most efficient execution method. In this way, the optimal execution method can be selected by referring to the user's past behavior history. Past behavior history includes, but is not limited to, examples such as how behavior logs are saved and how historical data is analyzed.

[0057] The execution unit can customize the means of execution at runtime based on the user's current situation and environment. For example, the execution unit may suggest the optimal means of execution based on the user's current location. For example, the execution unit may suggest the optimal means of execution considering the user's current situation and environment. For example, the execution unit may analyze the user's current situation and suggest the most efficient means of execution. In this way, the optimal means of execution can be provided by customizing the means of execution based on the user's current situation and environment. Current situation and environment include, but are not limited to, the acquisition of real-time data and the use of environmental sensors.

[0058] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit selects the optimal execution method based on the user's current location. For example, the execution unit selects the optimal execution method by taking into account the user's geographical location information. For example, the execution unit selects the most efficient execution method based on the user's current location. In this way, the optimal execution method can be selected by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location information services.

[0059] The execution unit can analyze the user's social media activity at runtime and propose a means of execution. For example, the execution unit proposes the optimal means of execution based on information shared by the user on social media. For example, the execution unit proposes the optimal means of execution based on the user's social media activity. For example, the execution unit analyzes the user's social media activity and proposes the most efficient means of execution. In this way, by analyzing the user's social media activity, the optimal means of execution can be proposed. Social media activity includes, but is not limited to, analysis of posted content and follower analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception unit can accept voice input from users. For example, users can intuitively operate the system without using their hands by inputting what they want to do by voice. Voice input can utilize, for example, speech recognition technology to convert the user's speech into text and send it to the analysis unit. This allows users to easily input what they want to do by voice, even when their hands are occupied.

[0062] The data collection unit can collect the most relevant information by referring to the user's past search history. For example, it can prioritize the collection of relevant information based on the information the user has previously searched for. This allows for the efficient collection of necessary information by utilizing the user's past search history.

[0063] The reception desk can automatically acquire the user's current location information to simplify input. For example, when a user opens the app, it can automatically acquire their current location and suggest related "things they want to do." This simplifies input by utilizing the user's current location information.

[0064] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral history. For example, it can improve the accuracy of its analysis based on the "things the user wants to do" that they have done in the past. In this way, the accuracy of the analysis can be improved by utilizing the user's past behavioral history.

[0065] The data collection unit can collect information while considering the user's current interests and trends. For example, it can collect information based on topics the user is currently interested in. This allows for the collection of optimal information by considering the user's current interests and trends.

[0066] The presentation unit can analyze the user's social media activity and present relevant options. For example, it can present relevant options based on information the user has shared on social media. This allows for the presentation of relevant options by analyzing the user's social media activity.

[0067] The execution unit can select the optimal execution method by considering the user's geographical location. For example, it can select the optimal execution method based on the user's current location. This allows for the selection of the optimal execution method by considering the user's geographical location.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk takes in the user's "desires." For example, it accepts information entered by the user in natural language. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it performs natural language processing to understand what the user wants to do. Step 3: The collection unit collects the necessary information based on the information analyzed by the analysis unit. For example, it may collect the necessary information using web scraping. Step 4: The presentation unit analyzes the information collected by the collection unit and presents options. For example, it presents the user with the most suitable option. Step 5: The execution unit carries out the options presented by the presentation unit. For example, it may make arrangements or manage progress.

[0070] (Example of form 2) The service according to an embodiment of the present invention is a novel service for individuals to efficiently realize their "desires." This service is a mechanism in which an AI agent understands the user's "desires" and provides the necessary support. Users can concentrate on achieving their goals without stress by entrusting the AI ​​with gathering information and making arrangements necessary to solve problems, and by being quickly offered appropriate choices. For example, if a user has a need to hang a picture on a wall, the AI ​​agent understands that need and considers the necessity of drilling holes. Next, it presents problem-solving options such as drills and double-sided tape. The user can choose the best method from these options and execute it. This service consists of the following steps: First, the user inputs their "desire." Next, the AI ​​agent analyzes the content and collects the necessary information. For example, it gathers information on repairing holes in walls and presents options such as DIY kits and estimates from contractors. The user can choose the best method from these options and execute it. This mechanism frees users from the hassle of gathering information and making arrangements, allowing them to concentrate on what they truly want to do. For example, regarding repairing holes in walls, the user simply takes a picture and sends it, and the AI ​​agent suggests the best option and makes the necessary arrangements. This reduces decision-making and arrangements that previously took weeks to complete in just 15 minutes. The service acts as a partner that understands what the user wants to do and stays by their side until it is realized. By freeing users from the hassles of information gathering and arrangements, they can focus on what they truly want to do. This allows the service to efficiently gather, analyze, present, and execute information to realize the user's goals.

[0071] The service according to this embodiment comprises a reception unit, an analysis unit, a collection unit, a presentation unit, and an execution unit. The reception unit receives input from the user about what they want to do. The reception unit receives information from the user, for example, in natural language. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, performs natural language processing to understand what the user wants to do. The collection unit collects necessary information based on the information analyzed by the analysis unit. The collection unit collects necessary information, for example, using web scraping. The presentation unit analyzes the information collected by the collection unit and presents options. The presentation unit, for example, presents the optimal option to the user. The execution unit executes the options presented by the presentation unit. The execution unit, for example, manages arrangements and progress. As a result, the service according to this embodiment enables information collection, analysis, presentation, and execution to efficiently realize what the user wants to do.

[0072] The reception desk receives input from the user about what they want to do. The reception desk accepts information entered by the user in natural language, for example. Specifically, the user accesses a dedicated application or website using a device such as a smartphone or computer and enters what they want to do into a text box. The user can enter specific requests in natural language, such as "I want to plan a trip" or "I want to find a new recipe." The reception desk receives this input in real time and stores it in a database. Furthermore, the reception desk is equipped with input completion and typographical error correction functions to accurately receive the user's input. This allows the user to smoothly enter what they want to do, and the reception desk to obtain accurate information.

[0073] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs natural language processing to understand what the user wants to do. Specifically, it uses natural language processing technology to perform morphological analysis on the user's input and analyze its grammatical structure. This allows it to accurately grasp the user's intentions and desires. For example, if a user inputs "I want to plan a trip," the analysis unit extracts specific elements such as the destination, dates, and budget to understand the user's desires in detail. The analysis unit can also refer to past user input data and related information to gain hints for finding the optimal solution to the user's desires. Furthermore, the analysis unit can use machine learning algorithms to classify the user's input and categorize it appropriately. This allows the analysis unit to accurately understand what the user wants to do and prepare to proceed to the next step.

[0074] The data collection unit collects necessary information based on the information analyzed by the analysis unit. For example, the data collection unit uses web scraping to collect necessary information. Specifically, the data collection unit automatically collects relevant information from websites and databases on the internet based on keywords and conditions provided by the analysis unit. The data collection unit uses web scraping technology to analyze the content of specified web pages and extract the necessary data. For example, when planning a trip, the data collection unit collects information such as tourist information, accommodation information, and transportation information for the destination. Furthermore, the data collection unit cross-checks multiple information sources to verify the reliability and timeliness of the collected information, selecting the most reliable information. In addition, the data collection unit stores the collected information in a database so that it can be used for subsequent processing. This allows the data collection unit to efficiently collect the information necessary to achieve the user's goals and support the overall information processing of the system.

[0075] The presentation unit analyzes the information collected by the collection unit and presents options. For example, the presentation unit presents the user with the most suitable options. Specifically, the presentation unit analyzes the information provided by the collection unit and selects the option that best suits the user's needs. For example, when planning a trip, the presentation unit presents the user with multiple options based on the collected information on accommodations and tourist attractions. The presentation unit displays the best options in a ranking format according to the user's preferences and conditions, allowing the user to easily compare and consider them. The presentation unit can also refer to the user's past selection history and ratings to prioritize and present options that match the user's preferences. Furthermore, the presentation unit displays detailed information, ratings, and reviews for each option to help the user make a choice. In this way, the presentation unit can support the user in making the best choice and present concrete means to realize the user's "goals."

[0076] The execution unit executes the options presented by the presentation unit. For example, the execution unit manages arrangements and progress. Specifically, the execution unit automatically makes the necessary arrangements and reservations based on the options selected by the user. For example, when planning a trip, the execution unit makes reservations for accommodations, arranges transportation, and purchases tickets for tourist attractions. After the arrangements are completed, the execution unit notifies the user of the progress and provides necessary information. Furthermore, the execution unit can handle changes and cancellations to arrangements, and can flexibly respond to user requests. The execution unit manages the progress of arrangements in real time, supporting users so that they can use the service with peace of mind. In this way, the execution unit can take concrete actions to realize what the user wants to do and increase user satisfaction.

[0077] The reception desk can accept information entered by the user in natural language. For example, the reception desk accepts information entered by the user in natural language. By accepting information entered by the user in natural language, the reception desk enables intuitive operation. Natural language includes, but is not limited to, Japanese, English, and other languages.

[0078] The analysis unit can perform natural language processing to understand what the user wants to do. For example, the analysis unit can perform natural language processing to understand what the user wants to do. By performing natural language processing, the analysis unit can accurately understand what the user wants to do. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis.

[0079] The data collection unit can collect necessary information using web scraping. For example, the data collection unit can collect necessary information using web scraping. By using web scraping, the data collection unit can efficiently collect the necessary information. Web scraping includes, but is not limited to, Python's BeautifulSoup and Scrapy.

[0080] The presentation unit can present the user with the optimal options. For example, the presentation unit presents the user with the optimal options. By presenting the user with the optimal options, the presentation unit enables the user to make choices efficiently. Optimal options include, but are not limited to, the user's past selection history and current situation.

[0081] The execution unit can manage arrangements and progress. For example, the execution unit manages arrangements and progress. By managing arrangements and progress, the execution unit enables users to efficiently achieve their goals. Arrangements include, but are not limited to, reservations, orders, and scheduling. Progress management includes, but is not limited to, task management tools and the frequency of progress reports.

[0082] The reception desk can estimate the user's emotions and adjust the input method for "what to do" based on the estimated emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of "what to do." This allows the user to input comfortably by adjusting the input method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions "things the user wants to do" that they have frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest "things the user wants to do" to be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested. Past input history includes, but is not limited to, examples such as how input content is saved and how historical data is analyzed.

[0084] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do. For example, when a user opens the app, the reception desk automatically acquires their current location and suggests relevant things they want to do. For example, when a user inputs what they want to do, the reception desk suggests the best options considering the distance from their current location. For example, if a user uses the app while on the move, the reception desk updates their current location in real time and suggests relevant things they want to do. This simplifies input by automatically acquiring the user's current location information. Location information includes, but is not limited to, GPS data and location estimation from IP addresses.

[0085] The reception system can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception system can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception system can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception system can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface according to the user's emotions, the user can input comfortably. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The reception desk can automatically suggest options when the user enters their "things they want to do," by referring to the user's past activity history. For example, the reception desk can automatically display as options the user has frequently done in the past. For example, the reception desk can predict what the user will do on specific days of the week or time slots and suggest them as options. For example, the reception desk can analyze the user's past behavior patterns and suggest the most suitable options. In this way, the system can automatically suggest the most suitable options by referring to the user's past activity history. Past activity history includes, but is not limited to, examples such as how activity logs are saved and how historical data is analyzed.

[0087] The reception desk can refer to the user's calendar information when they input their "things they want to do" and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the "things they want to do." For example, the reception desk can suggest "things they want to do" related to a specific event as candidates from the user's calendar information. For example, the reception desk can suggest the most suitable "things they want to do" based on the user's calendar information and their schedule. In this way, it can make suggestions based on the schedule by referring to the user's calendar information. Calendar information includes, but is not limited to, examples such as integration with calendar apps and methods of analyzing schedules.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and presents multiple options. For example, if the user is in a hurry, the analysis unit performs a rapid analysis and presents the most appropriate option. For example, if the user is excited, the analysis unit presents analysis results with visually stimulating effects. In this way, by adjusting the analysis algorithm according to the user's emotions, the optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past "things they want to do." For example, the analysis unit can suggest the optimal option based on the user's past behavior history. For example, the analysis unit can analyze the user's past behavior history and suggest the most efficient analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past behavior history. Improvements in analysis accuracy include, but are not limited to, improving data quality and improving algorithms.

[0090] The analysis unit can perform analysis while considering the user's current interests and trends. For example, the analysis unit performs analysis based on topics the user is currently interested in. For example, the analysis unit proposes the optimal option by considering the user's current trends. For example, the analysis unit analyzes the user's current interests and proposes the most appropriate analysis method. In this way, by considering the user's current interests and trends, it is possible to provide optimal analysis results. Interests and trends include, but are not limited to, social media analysis and news trend analysis.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can suggest the optimal option based on the user's current location. For example, the analysis unit can suggest the optimal analysis method by considering the user's geographical location information. For example, the analysis unit can suggest the most efficient analysis method based on the user's current location. In this way, by considering the user's geographical location information, the optimal analysis results can be provided. Geographical location information includes, but is not limited to, GPS data and the use of location information services.

[0093] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature and data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. For example, the analysis unit can suggest the optimal option by referring to the user's relevant data. For example, the analysis unit can analyze the user's relevant literature and suggest the most efficient analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature and data. Relevant literature and data include, but are not limited to, database searches and methods of citing literature.

[0094] The data collection unit can estimate the user's emotions and adjust its information collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect detailed information. If the user is in a hurry, the data collection unit will quickly collect necessary information. If the user is excited, the data collection unit will collect visually stimulating information. This allows for optimal information collection by adjusting the information collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The data collection unit can collect the most relevant information by referring to the user's past search history during data collection. For example, the data collection unit collects the most relevant information based on information the user has previously searched for. For example, the data collection unit collects relevant information from the user's past search history. For example, the data collection unit analyzes the user's past search history and proposes the most efficient method of data collection. This allows the system to collect the most relevant information by referring to the user's past search history. Search history includes, but is not limited to, methods for saving search queries and methods for analyzing historical data.

[0096] The data collection unit can collect information while considering the user's current interests and trends. For example, the data collection unit collects information based on topics that the user is currently interested in. For example, the data collection unit collects the most appropriate information by considering the user's current trends. For example, the data collection unit analyzes the user's current interests and proposes the most appropriate information collection method. This allows for the collection of optimal information by considering the user's current interests and trends. Interests and trends include, but are not limited to, social media analysis and news trend analysis.

[0097] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit will prioritize collecting information that is needed quickly. This allows for the collection of optimal information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Information prioritization includes, but is not limited to, evaluation of importance or user interest.

[0098] The data collection unit can prioritize the collection of relevant information by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of relevant information based on the user's current location. For example, the data collection unit prioritizes the collection of optimal information by considering the user's geographical location information. For example, the data collection unit proposes the most efficient information collection method based on the user's current location. This allows for the priority collection of relevant information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location-based services.

[0099] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects relevant information based on information shared by the user on social media. For example, the data collection unit collects optimal information from the user's social media activity. For example, the data collection unit analyzes the user's social media activity and proposes the most efficient information collection method. In this way, relevant information can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis.

[0100] The presentation unit can estimate the user's emotions and adjust the way choices are presented based on the estimated emotions. For example, if the user is nervous, the presentation unit will present simple and highly visible choices. For example, if the user is relaxed, the presentation unit will present choices that include detailed information. For example, if the user is in a hurry, the presentation unit will present choices that get straight to the point. In this way, by adjusting the way choices are presented according to the user's emotions, choices that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The method of presenting choices includes, but is not limited to, changing the display format or adjusting the timing of presentation.

[0101] The presentation unit can present the optimal option by referring to the user's past selection history at the time of presentation. For example, the presentation unit presents the optimal option based on the options the user has previously selected. For example, the presentation unit presents relevant options from the user's past selection history. For example, the presentation unit analyzes the user's past selection history and presents the most efficient option. In this way, the optimal option can be presented by referring to the user's past selection history. The selection history includes, but is not limited to, examples such as how selections are saved and how historical data is analyzed.

[0102] The presentation unit can customize the options based on the user's current situation and environment at the time of presentation. For example, the presentation unit can present the optimal option based on the user's current location. For example, the presentation unit can present the optimal option considering the user's current situation and environment. For example, the presentation unit can analyze the user's current situation and present the most efficient option. In this way, the optimal option can be provided by customizing the options based on the user's current situation and environment. Current situation and environment include, but are not limited to, the acquisition of real-time data and the use of environmental sensors.

[0103] The presentation unit can estimate the user's emotions and determine the priority of options based on the estimated emotions. For example, if the user is nervous, the presentation unit will prioritize presenting important options. For example, if the user is relaxed, the presentation unit will prioritize presenting detailed options. For example, if the user is in a hurry, the presentation unit will prioritize presenting options that are needed quickly. In this way, by determining the priority of options according to the user's emotions, the optimal options can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The priority of options includes, but is not limited to, evaluation of importance or the user's level of interest.

[0104] The presentation unit can prioritize the presentation of relevant options by considering the user's geographical location information. For example, the presentation unit can prioritize the presentation of relevant options based on the user's current location. For example, the presentation unit can prioritize the presentation of the optimal option by considering the user's geographical location information. For example, the presentation unit can prioritize the presentation of the most efficient option based on the user's current location. In this way, relevant options can be prioritized by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location-based services.

[0105] The presentation unit can analyze the user's social media activity and present relevant options at the time of presentation. For example, the presentation unit can present relevant options based on information shared by the user on social media. For example, the presentation unit can present the optimal option based on the user's social media activity. For example, the presentation unit can analyze the user's social media activity and present the most efficient option. In this way, relevant options can be presented by analyzing the user's social media activity. Social media activity includes, but is not limited to, analyzing post content and follower analysis.

[0106] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is relaxed, the execution unit will execute at a leisurely pace. If the user is in a hurry, the execution unit will execute quickly. If the user is excited, the execution unit will suggest an execution method that includes visually stimulating effects. In this way, the system can provide the optimal execution method by adjusting the execution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The execution method includes, but is not limited to, changing the execution procedure or adjusting the execution timing.

[0107] The execution unit can select the optimal execution method by referring to the user's past behavior history during execution. For example, the execution unit selects the optimal execution method based on the execution methods the user has performed in the past. For example, the execution unit proposes the optimal execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior history and selects the most efficient execution method. In this way, the optimal execution method can be selected by referring to the user's past behavior history. Past behavior history includes, but is not limited to, examples such as how behavior logs are saved and how historical data is analyzed.

[0108] The execution unit can customize the means of execution at runtime based on the user's current situation and environment. For example, the execution unit may suggest the optimal means of execution based on the user's current location. For example, the execution unit may suggest the optimal means of execution considering the user's current situation and environment. For example, the execution unit may analyze the user's current situation and suggest the most efficient means of execution. In this way, the optimal means of execution can be provided by customizing the means of execution based on the user's current situation and environment. Current situation and environment include, but are not limited to, the acquisition of real-time data and the use of environmental sensors.

[0109] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize important executions. For example, if the user is relaxed, the execution unit will prioritize detailed executions. For example, if the user is in a hurry, the execution unit will prioritize executions that are needed quickly. In this way, by determining the priority of executions according to the user's emotions, the optimal execution method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization of executions includes, but is not limited to, evaluation of importance or user interest.

[0110] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit selects the optimal execution method based on the user's current location. For example, the execution unit selects the optimal execution method by taking into account the user's geographical location information. For example, the execution unit selects the most efficient execution method based on the user's current location. In this way, the optimal execution method can be selected by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and the use of location information services.

[0111] The execution unit can analyze the user's social media activity at runtime and propose a means of execution. For example, the execution unit proposes the optimal means of execution based on information shared by the user on social media. For example, the execution unit proposes the optimal means of execution based on the user's social media activity. For example, the execution unit analyzes the user's social media activity and proposes the most efficient means of execution. In this way, by analyzing the user's social media activity, the optimal means of execution can be proposed. Social media activity includes, but is not limited to, analysis of posted content and follower analysis.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception unit can accept voice input from users. For example, users can intuitively operate the system without using their hands by inputting what they want to do by voice. Voice input can utilize, for example, speech recognition technology to convert the user's speech into text and send it to the analysis unit. This allows users to easily input what they want to do by voice, even when their hands are occupied.

[0114] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a quick analysis and present concise results. If the user is relaxed, it can perform a detailed analysis and present multiple options. In this way, by adjusting the accuracy of the analysis according to the user's emotions, the system can provide the most optimal analysis results.

[0115] The data collection unit can collect the most relevant information by referring to the user's past search history. For example, it can prioritize the collection of relevant information based on the information the user has previously searched for. This allows for the efficient collection of necessary information by utilizing the user's past search history.

[0116] The presentation unit can estimate the user's emotions and adjust the way choices are presented based on those emotions. For example, if the user is nervous, simple and easily visible choices can be presented. If the user is relaxed, choices containing more detailed information can be presented. By adjusting the way choices are presented according to the user's emotions, choices that are easy for the user to understand can be provided.

[0117] The execution unit can estimate the user's emotions and adjust the execution method based on those emotions. For example, if the user is relaxed, the execution can be performed at a leisurely pace. If the user is in a hurry, the execution can be performed quickly. In this way, by adjusting the execution method according to the user's emotions, the optimal execution method can be provided.

[0118] The reception desk can automatically acquire the user's current location information to simplify input. For example, when a user opens the app, it can automatically acquire their current location and suggest related "things they want to do." This simplifies input by utilizing the user's current location information.

[0119] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral history. For example, it can improve the accuracy of its analysis based on the "things the user wants to do" that they have done in the past. In this way, the accuracy of the analysis can be improved by utilizing the user's past behavioral history.

[0120] The data collection unit can collect information while considering the user's current interests and trends. For example, it can collect information based on topics the user is currently interested in. This allows for the collection of optimal information by considering the user's current interests and trends.

[0121] The presentation unit can analyze the user's social media activity and present relevant options. For example, it can present relevant options based on information the user has shared on social media. This allows for the presentation of relevant options by analyzing the user's social media activity.

[0122] The execution unit can select the optimal execution method by considering the user's geographical location. For example, it can select the optimal execution method based on the user's current location. This allows for the selection of the optimal execution method by considering the user's geographical location.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk takes in the user's "desires." For example, it accepts information entered by the user in natural language. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it performs natural language processing to understand what the user wants to do. Step 3: The collection unit collects the necessary information based on the information analyzed by the analysis unit. For example, it may collect the necessary information using web scraping. Step 4: The presentation unit analyzes the information collected by the collection unit and presents options. For example, it presents the user with the most suitable option. Step 5: The execution unit carries out the options presented by the presentation unit. For example, it may make arrangements or manage progress.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, presentation unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs the user's "desires" using the touch panel 38A and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's "desires" through natural language processing. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects necessary information using web scraping. The presentation unit presents the user with the best options using the display 40A of the smart device 14. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages arrangements and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, presentation unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the smart glasses 214 to input what the user wants to do. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing to understand what the user wants to do. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects necessary information using web scraping. The presentation unit uses the display of the smart glasses 214 to present the user with the best options. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages arrangements and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, presentation unit, and execution unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the headset terminal 314 to input what the user wants to do. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs natural language processing to understand what the user wants to do. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects necessary information using web scraping. The presentation unit uses the display 343 of the headset terminal 314 to present the user with the most suitable options. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages arrangements and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, presentation unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs the user's "desire" using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the user's "desire" through natural language processing. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects necessary information using web scraping. The presentation unit presents the user with the best options using the display of the robot 414. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages arrangements and progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A reception area where users input what they want to do, An analysis unit that analyzes the information received by the reception unit, A collection unit that collects necessary information based on the information analyzed by the aforementioned analysis unit, The information collected by the collection unit is analyzed and presented by the presentation unit, The system comprises an execution unit that executes the options presented by the presentation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts information entered by the user in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It uses natural language processing to understand what the user wants to do. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect necessary information using web scraping. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, Present the user with the best possible options. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Manage arrangements and progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for "what you want to do" based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter what they want to do, the system automatically retrieves their current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter what they want to do, the system automatically suggests options based on their past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input what they want to do, the system will refer to their calendar information and provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the analysis will take into account the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system improves the accuracy of the analysis by referencing the user's relevant literature and data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is It estimates the user's emotions and adjusts the information gathering method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When gathering information, we refer to the user's past search history to collect the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When gathering information, we take into account the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting information, the system prioritizes collecting relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts how choices are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting options, the system refers to the user's past selection history to suggest the most suitable choice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting options, customize the choices based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting options, prioritize relevant choices based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When presenting options, the system analyzes the user's social media activity and suggests relevant choices. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, During execution, the system selects the optimal execution method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, At runtime, the execution method is customized based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input what they want to do, An analysis unit that analyzes the information received by the reception unit, A collection unit that collects necessary information based on the information analyzed by the aforementioned analysis unit, The information collected by the collection unit is analyzed and presented by the presentation unit, The system comprises an execution unit that executes the options presented by the presentation unit. A system characterized by the following features.

2. The aforementioned reception unit is It accepts information entered by the user in natural language. The system according to feature 1.

3. The aforementioned analysis unit, It performs natural language processing to understand what the user wants to do. The system according to feature 1.

4. The aforementioned collection unit is We collect necessary information using web scraping. The system according to feature 1.

5. The aforementioned display unit is, Present the user with the best possible options. The system according to feature 1.

6. The execution unit is, Manage arrangements and progress. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for what the user wants to do based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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