System

Abstract

To provide a system which enables a user to refer to case examples of the past when planning new tasks.SOLUTION: A system includes: a reception part, a retrieval part, and a provision part. The reception part is used by a user for inputting tasks. The retrieval part retrieves cases of the past on the basis of the tasks inputted by the reception part. The provision part provides tasks and viewpoints that are worth considering, of similar cases retrieved by the retrieval part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques have the problem that it is difficult for a user to efficiently refer to past cases when planning a new task.

[0005] The system according to the embodiment aims to enable a user to efficiently refer to past cases when planning a new task. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit receives a task from a user. The search unit searches for past cases based on the task input by the reception unit. The provision unit provides tasks and perspectives to consider for similar cases searched for by the search unit. Effect of the Invention

[0007] The system according to the embodiment can enable a user to efficiently refer to past cases when planning a new task. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The task identification support system according to the embodiment of the present invention is a system that prepares a chat that has all past cases loaded, and when a user throws in what to do this time, the system provides tasks of cases similar to the to-do and perspectives to consider. This task identification support system includes a reception unit that inputs what the user needs to do, a search unit that searches past cases based on what to do input by the reception unit, and a provision unit that provides tasks of similar cases and perspectives to consider searched by the search unit. For example, when a user inputs what to do such as "plan a new project," the system searches a database related to past project plans and identifies similar cases. As search results, the system provides the user with tasks and perspectives such as "project goal setting," "resource allocation," and "risk management." This allows the user to efficiently identify tasks and respond without overlooking perspectives to consider. The system also includes an interface that provides the user with easy-to-understand results, such as responses in chat format and display on a dashboard. This allows the task identification support system to efficiently identify tasks while referring to past cases.

[0029] The task identification support system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit inputs what the user needs to do. The things the user needs to do include, but are not limited to, business tasks, personal tasks, and project tasks. The reception unit provides an interface for inputting what the user needs to do, for example, using a graphical user interface (GUI). The reception unit can also input what the user needs to do by voice, using a voice input interface. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The search unit reads past cases from a database and searches for cases similar to the inputted things to do. The database includes, for example, a relational database, a NoSQL database, and the like, but is not limited to such examples. The search unit efficiently searches past cases, for example, using a search algorithm. The search algorithm includes, for example, a keyword matching algorithm, a similarity calculation algorithm, and the like. The provision unit includes an interface for providing the search results to the user. The provision unit visually displays the search results, for example, using a graphical user interface (GUI). The providing unit can also provide the search results by voice using a voice output interface. For example, the providing unit uses a voice synthesis technology to output the search results by voice. This allows the task identification support system according to the embodiment to efficiently identify what the user needs to do and respond without overlooking any aspect that should be considered.

[0030] The reception unit inputs what the user needs to do. Examples of what the user needs to do include, but are not limited to, business tasks, personal tasks, and project tasks. The reception unit provides an interface for the user to input what he or she needs to do, for example, using a graphical user interface (GUI). The GUI is designed to be intuitive and to allow the user to easily input tasks. For example, it has input fields such as text boxes, drop-down menus, and check boxes, allowing the user to specifically input the details of the task. The reception unit can also input what the user needs to do by voice, using a voice input interface. The voice input interface receives the user's voice through a microphone and converts it into text data using voice recognition technology. The voice recognition technology incorporates natural language processing (NLP) technology, and can accurately analyze the contents of the user's speech and convert it into appropriate text data. For example, when a user says "prepare for tomorrow's meeting," the voice recognition technology analyzes this and converts it into text data saying "prepare for tomorrow's meeting." Furthermore, the reception unit also has a function of organizing the tasks entered by the user by category. For example, tasks are automatically classified into categories such as business tasks, personal tasks, and project tasks, making it easier for users to manage tasks. This allows the reception unit to efficiently input and organize tasks that the user needs to do.

[0031] The search unit reads past cases from a database and searches for cases similar to the input to-do. The database includes, but is not limited to, a relational database, a NoSQL database, and the like. The search unit uses, for example, a search algorithm to efficiently search for past cases. The search algorithm includes, for example, a keyword matching algorithm, a similarity calculation algorithm, and the like. The keyword matching algorithm searches for related cases in the database based on the keywords of the task input by the user. For example, if the user inputs "preparing for a meeting", the search unit searches for past cases related to "meeting" and "preparation". The similarity calculation algorithm calculates the similarity between the task input by the user and past cases and searches for the most similar case. For example, the content and context of the task are analyzed using a method such as cosine similarity or Jaccard coefficient to calculate the similarity. Furthermore, the search unit can also improve the search accuracy using machine learning technology. For example, past search results and user feedback are used as learning data to continuously improve the search algorithm. This allows the search unit to quickly and accurately search for and provide past cases that are optimal for the task input by the user.

[0032] The providing unit has an interface for providing the search results to the user. The providing unit visually displays the search results using, for example, a graphical user interface (GUI). The GUI is designed to organize the search results in an easy-to-view manner so that the user can easily understand them. For example, the search results are displayed in a list format or a card format, and an overview and detailed information of each case are provided. The providing unit also has a function for filtering the search results, allowing the user to narrow down the search results based on specific conditions. For example, the search results can be filtered by specifying conditions such as the category, importance, and deadline of the task. Furthermore, the providing unit can provide the search results by voice using a voice output interface. The voice output interface uses voice synthesis technology to output the search results in a natural voice. For example, when the user instructs "Tell me the search results by voice," the providing unit reads out the search results by voice. This allows the user to check the search results not only visually but also by voice. Furthermore, the providing unit also has a function for the user to provide feedback on the search results. For example, a button for rating whether the search results were useful or not and a comment field are provided to collect user feedback. This enables the providing unit to provide optimal search results to the user, thereby improving the accuracy of the entire system and user satisfaction.

[0033] The reception unit can provide an interface for the user to input what to do. The interface includes, but is not limited to, for example, a graphical user interface (GUI) and a voice input interface. The reception unit provides, for example, a graphical user interface (GUI) for the user to input what to do. For example, the reception unit provides an interface including a text box and a drop-down menu for the user to input what to do. The reception unit can also provide a voice input interface. For example, the reception unit converts the user's voice input into text data using voice recognition technology. This allows the user to easily input what to do.

[0034] The search unit can read past cases from a database and search for cases similar to the input to-do. The database includes, but is not limited to, a relational database, a NoSQL database, and the like. The search unit searches for past cases using, for example, a relational database. A relational database manages data in a table format and searches for data using SQL (Structured Query Language). The search unit searches for past cases using, for example, a NoSQL database. A NoSQL database is schema-less and has a flexible data model, and therefore can efficiently manage large amounts of data. This allows the search unit to efficiently search for past cases.

[0035] The providing unit may include an interface for providing the search results to the user. The interface may include, but is not limited to, a graphical user interface (GUI) and a voice output interface. The providing unit may provide, for example, a graphical user interface (GUI) for visually displaying the search results. For example, the providing unit may display the search results in a list format or a graph format. The providing unit may also provide a voice output interface. For example, the providing unit may output the search results by voice using a voice synthesis technology. This allows the user to receive the search results in an easy-to-understand manner.

[0036] The search unit may include an algorithm for efficiently searching past cases. Examples of the algorithm include, but are not limited to, a search algorithm and a matching algorithm. The search unit searches past cases using, for example, a keyword matching algorithm. The keyword matching algorithm compares an input keyword with a keyword of a past case, and searches for a case with a high similarity. The search unit searches past cases using, for example, a similarity calculation algorithm. The similarity calculation algorithm compares the input to-do with the contents of a past case, and calculates the similarity. This allows the search unit to efficiently search past cases.

[0037] The providing unit may include an interface for providing the search results to the user in an easy-to-understand manner. The interface may include, but is not limited to, a graphical user interface (GUI) and a voice output interface. The providing unit may provide, for example, a graphical user interface (GUI) for visually displaying the search results. For example, the providing unit may display the search results in a list format or a graph format. The providing unit may also provide a voice output interface. For example, the providing unit may output the search results by voice using a voice synthesis technology. This allows the user to receive the search results in an easy-to-understand manner.

[0038] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The input history includes, for example, past input data, input frequency, input pattern, etc., but is not limited to such examples. The reception unit, for example, automatically displays to-dos that the user frequently input in the past as candidates. In addition, the reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest to-dos to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method to the user.

[0039] The reception unit can provide an automatic completion function for the input contents when the user inputs. For example, when the user starts inputting, the reception unit automatically completes related tasks and viewpoints from past cases. The automatic completion function includes, for example, a prediction algorithm, completion accuracy, and the like, but is not limited to such examples. For example, when the user inputs a specific keyword, the reception unit displays past cases related to the keyword as candidates. In addition, when the user stops inputting midway, the reception unit can also suggest the most relevant content from past cases. This enables the reception unit to automatically complete the input contents.

[0040] The reception unit can present highly relevant input candidates in consideration of the geographical location information of the user. The reception unit presents highly relevant input candidates in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the reception unit displays tasks related to the location as candidates. In addition, when the user is moving, the reception unit can also suggest related tasks based on the current location. Furthermore, when the user is in a specific area, the reception unit can also display past cases related to the area as candidates. This allows the reception unit to present input candidates based on the geographical location information.

[0041] The reception unit can analyze the social media activity of the user and present related input candidates. The reception unit, for example, analyzes the social media activity of the user and presents related input candidates. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The reception unit, for example, automatically displays tasks mentioned by the user on social media as candidates. The reception unit can also suggest related tasks based on the activities of accounts the user follows on social media. Furthermore, the reception unit can display related tasks as candidates based on information shared by the user on social media. This allows the reception unit to present input candidates based on the social media activity.

[0042] The search unit can analyze past search history and suggest an appropriate search method. The search unit can, for example, analyze past search history and suggest an optimal search method. The search history includes, for example, past search keywords, search frequency, click rate of search results, etc., but is not limited to such examples. The search unit can, for example, automatically display keywords that the user has frequently searched for in the past as candidates. In addition, the search unit can also preferentially suggest search methods (filters, sorts, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest a search method to be used in a specific time period from the user's past search history. This allows the search unit to suggest an optimal search method.

[0043] The search unit may filter based on the user's current project or field of interest during the search. The search unit may filter based on the user's current project or field of interest, for example. Projects include, but are not limited to, business projects, research projects, and personal projects. Fields of interest include, but are not limited to, past search history, social media activities, and survey results. The search unit may preferentially display cases related to a project currently being carried out by the user. The search unit may also filter and display cases related to a particular field of interest of the user. Furthermore, the search unit may suggest optimal cases to the user depending on the progress of the project. This allows the search unit to provide search results based on projects and fields of interest.

[0044] The search unit can search for cases with high relevance in consideration of the geographical location information of the user at the time of search. The search unit, for example, searches for cases with high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the search unit can display cases related to the location in a preferential manner. In addition, when the user is moving, the search unit can also search for related cases based on the current location. Furthermore, when the user is in a specific area, the search unit can also display past cases related to the area in a preferential manner. This allows the search unit to provide search results based on the geographical location information.

[0045] The search unit can analyze the user's social media activity and search for related cases at the time of search. The search unit, for example, analyzes the user's social media activity and searches for related cases. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The search unit, for example, preferentially displays cases related to tasks mentioned by the user on social media. The search unit can also search for related cases based on the activities of accounts the user follows on social media. Furthermore, the search unit can also search for related cases based on information shared by the user on social media. This allows the search unit to provide search results based on social media activity.

[0046] The providing unit can adjust the level of detail of the provided content based on the importance of the information at the time of providing. The providing unit adjusts the level of detail of the provided content based on, for example, the importance of the information. The importance of the information includes, for example, the user's intention, the urgency of the information, the reliability of the information, and the like, but is not limited to such examples. For example, the providing unit provides important information in detail so that the user can easily understand it. In addition, the providing unit can also provide information of low importance in a concise manner to reduce the burden on the user. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the user's interest. Thereby, the providing unit can provide a level of detail according to the importance of the information.

[0047] The providing unit can apply different providing methods depending on the category of information when providing. The providing unit applies different providing methods depending on, for example, the category of information. The categories of information include, for example, technical information, business information, entertainment information, and the like, but are not limited to such examples. For example, the providing unit provides information about tasks in a list format so that the user can easily check it. The providing unit can also provide information about viewpoints in bullet points so that the user can easily understand it. Furthermore, the providing unit can provide information about projects in a dashboard format so that the user can easily grasp the overall picture. In this way, the providing unit can provide a providing method according to the category of information.

[0048] The providing unit can provide highly relevant information preferentially in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, provides highly relevant information preferentially in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the providing unit preferentially provides information related to the location. In addition, when the user is moving, the providing unit can also provide related information based on the current location. Furthermore, when the user is in a specific area, the providing unit can also provide information related to the area preferentially. In this way, the providing unit can provide information based on the geographical location information.

[0049] The providing unit can analyze the user's social media activity at the time of providing and provide related information. The providing unit, for example, analyzes the user's social media activity and provides related information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The providing unit, for example, preferentially provides information related to a task mentioned by the user on social media. In addition, the providing unit can also provide related information based on the activity of an account that the user follows on social media. Furthermore, the providing unit can also provide related information based on information shared by the user on social media. This allows the providing unit to provide information based on social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's input in real time and automatically suggest related tasks based on the input. For example, if the user inputs "preparing for a meeting," the reception unit searches a database for tasks related to past meeting preparations and automatically suggests tasks such as "creating an agenda," "confirming participants," and "preparing materials." In addition, even if the user stops inputting midway, the reception unit can suggest the most relevant task based on the input. Furthermore, the reception unit can analyze the user's input and set priorities for related tasks. This allows the user to efficiently identify tasks.

[0052] The search unit can analyze the user's past search history and provide optimal search results. For example, keywords that the user has frequently searched for in the past can be automatically displayed as candidates. The search unit can also preferentially suggest search methods (filters, sorts, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest search methods to be used at specific times of the day based on the user's past search history. This allows the search unit to provide the user with optimal search results.

[0053] The reception unit can present highly relevant input candidates in consideration of the geographical location information of the user. For example, when the user is in a specific location, tasks related to that location are displayed as candidates. Also, when the user is moving, related tasks can be suggested based on the current location. Furthermore, when the user is in a specific area, the reception unit can also display past cases related to that area as candidates. In this way, the reception unit can present input candidates based on the geographical location information.

[0054] The search unit can analyze the user's social media activity and search for related cases. For example, cases related to tasks mentioned by the user on social media can be preferentially displayed. Also, related cases can be searched for based on the activities of accounts the user follows on social media. Furthermore, related cases can be searched for based on information shared by the user on social media. This allows the search unit to provide search results based on social media activity.

[0055] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, to-do items that the user frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest to-do items to be used in a specific time period from the user's past input history. This allows the reception unit to suggest the most suitable input method for the user.

[0056] The providing unit can adjust the level of detail of the provided content based on the importance of the information when providing the information. For example, important information is provided in detail to make it easier for the user to understand. Also, information of low importance can be provided briefly to reduce the burden on the user. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the user's interest. This allows the providing unit to provide a level of detail according to the importance of the information.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The reception unit inputs what the user needs to do. The user's needs to do include, for example, business tasks, personal tasks, project tasks, and the like. The reception unit provides an interface for the user to input what they need to do using a graphical user interface (GUI) or a voice input interface. In the case of voice input, voice recognition technology is used to convert the user's voice input into text data. Step 2: The search unit reads past cases from the database and searches for cases similar to the input task. The database can be a relational database or a NoSQL database. The search unit uses a search algorithm to efficiently search past cases. The search algorithm can be a keyword matching algorithm or a similarity calculation algorithm. Step 3: The providing unit has an interface for providing the search results to the user. The providing unit visually displays the search results using a graphical user interface (GUI). The providing unit can also provide the search results by voice using a voice output interface. The search results are output by voice using voice synthesis technology.

[0059] (Example 2) The task identification support system according to the embodiment of the present invention is a system that prepares a chat that has all past cases loaded, and when a user throws in what to do this time, the system provides tasks of cases similar to the to-do and perspectives to consider. This task identification support system includes a reception unit that inputs what the user needs to do, a search unit that searches past cases based on what to do input by the reception unit, and a provision unit that provides tasks of similar cases and perspectives to consider searched by the search unit. For example, when a user inputs what to do such as "plan a new project," the system searches a database related to past project plans and identifies similar cases. As search results, the system provides the user with tasks and perspectives such as "project goal setting," "resource allocation," and "risk management." This allows the user to efficiently identify tasks and respond without overlooking perspectives to consider. The system also includes an interface that provides the user with easy-to-understand results, such as responses in chat format and display on a dashboard. This allows the task identification support system to efficiently identify tasks while referring to past cases.

[0060] The task identification support system according to the embodiment includes a reception unit, a search unit, and a provision unit. The reception unit inputs what the user needs to do. The things the user needs to do include, but are not limited to, business tasks, personal tasks, and project tasks. The reception unit provides an interface for inputting what the user needs to do, for example, using a graphical user interface (GUI). The reception unit can also input what the user needs to do by voice, using a voice input interface. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The search unit reads past cases from a database and searches for cases similar to the inputted things to do. The database includes, for example, a relational database, a NoSQL database, and the like, but is not limited to such examples. The search unit efficiently searches past cases, for example, using a search algorithm. The search algorithm includes, for example, a keyword matching algorithm, a similarity calculation algorithm, and the like. The provision unit includes an interface for providing the search results to the user. The provision unit visually displays the search results, for example, using a graphical user interface (GUI). The providing unit can also provide the search results by voice using a voice output interface. For example, the providing unit uses a voice synthesis technology to output the search results by voice. This allows the task identification support system according to the embodiment to efficiently identify what the user needs to do and respond without overlooking any aspect that should be considered.

[0061] The reception unit inputs what the user needs to do. Examples of what the user needs to do include, but are not limited to, business tasks, personal tasks, and project tasks. The reception unit provides an interface for the user to input what he or she needs to do, for example, using a graphical user interface (GUI). The GUI is designed to be intuitive and to allow the user to easily input tasks. For example, it has input fields such as text boxes, drop-down menus, and check boxes, allowing the user to specifically input the details of the task. The reception unit can also input what the user needs to do by voice, using a voice input interface. The voice input interface receives the user's voice through a microphone and converts it into text data using voice recognition technology. The voice recognition technology incorporates natural language processing (NLP) technology, and can accurately analyze the contents of the user's speech and convert it into appropriate text data. For example, when a user says "prepare for tomorrow's meeting," the voice recognition technology analyzes this and converts it into text data saying "prepare for tomorrow's meeting." Furthermore, the reception unit also has a function of organizing the tasks entered by the user by category. For example, tasks are automatically classified into categories such as business tasks, personal tasks, and project tasks, making it easier for users to manage tasks. This allows the reception unit to efficiently input and organize tasks that the user needs to do.

[0062] The search unit reads past cases from a database and searches for cases similar to the input to-do. The database includes, but is not limited to, a relational database, a NoSQL database, and the like. The search unit uses, for example, a search algorithm to efficiently search for past cases. The search algorithm includes, for example, a keyword matching algorithm, a similarity calculation algorithm, and the like. The keyword matching algorithm searches for related cases in the database based on the keywords of the task input by the user. For example, if the user inputs "preparing for a meeting", the search unit searches for past cases related to "meeting" and "preparation". The similarity calculation algorithm calculates the similarity between the task input by the user and past cases, and searches for the most similar case. For example, the content and context of the task are analyzed using a method such as cosine similarity or Jaccard coefficient, and the similarity is calculated. Furthermore, the search unit can improve the search accuracy using machine learning technology. For example, past search results and user feedback are used as learning data to continuously improve the search algorithm. This allows the search unit to quickly and accurately search for and provide past cases that are optimal for the task input by the user.

[0063] The providing unit has an interface for providing the search results to the user. The providing unit visually displays the search results using, for example, a graphical user interface (GUI). The GUI is designed to organize the search results in an easy-to-view manner so that the user can easily understand them. For example, the search results are displayed in a list format or a card format, and an overview and detailed information of each case are provided. The providing unit also has a function for filtering the search results, allowing the user to narrow down the search results based on specific conditions. For example, the search results can be filtered by specifying conditions such as the category, importance, and deadline of the task. Furthermore, the providing unit can provide the search results by voice using a voice output interface. The voice output interface uses voice synthesis technology to output the search results in a natural voice. For example, when the user instructs "Tell me the search results by voice," the providing unit reads out the search results by voice. This allows the user to check the search results not only visually but also by voice. Furthermore, the providing unit also has a function for the user to provide feedback on the search results. For example, a button for rating whether the search results were useful or not and a comment field are provided to collect user feedback. This enables the providing unit to provide optimal search results to the user, thereby improving the accuracy of the entire system and user satisfaction.

[0064] The reception unit can provide an interface for the user to input what to do. The interface includes, but is not limited to, for example, a graphical user interface (GUI) and a voice input interface. The reception unit provides, for example, a graphical user interface (GUI) for the user to input what to do. For example, the reception unit provides an interface including a text box and a drop-down menu for the user to input what to do. The reception unit can also provide a voice input interface. For example, the reception unit converts the user's voice input into text data using voice recognition technology. This allows the user to easily input what to do.

[0065] The search unit can read past cases from a database and search for cases similar to the input to-do. The database includes, but is not limited to, a relational database, a NoSQL database, and the like. The search unit searches for past cases using, for example, a relational database. A relational database manages data in a table format and searches for data using SQL (Structured Query Language). The search unit searches for past cases using, for example, a NoSQL database. A NoSQL database is schema-less and has a flexible data model, and therefore can efficiently manage large amounts of data. This allows the search unit to efficiently search for past cases.

[0066] The providing unit may include an interface for providing the search results to the user. The interface may include, but is not limited to, a graphical user interface (GUI) and a voice output interface. The providing unit may provide, for example, a graphical user interface (GUI) for visually displaying the search results. For example, the providing unit may display the search results in a list format or a graph format. The providing unit may also provide a voice output interface. For example, the providing unit may output the search results by voice using a voice synthesis technology. This allows the user to receive the search results in an easy-to-understand manner.

[0067] The search unit may include an algorithm for efficiently searching past cases. Examples of the algorithm include, but are not limited to, a search algorithm and a matching algorithm. The search unit searches past cases using, for example, a keyword matching algorithm. The keyword matching algorithm compares an input keyword with a keyword of a past case, and searches for a case with a high similarity. The search unit searches past cases using, for example, a similarity calculation algorithm. The similarity calculation algorithm compares the input to-do with the contents of a past case, and calculates the similarity. This allows the search unit to efficiently search past cases.

[0068] The providing unit may include an interface for providing the search results to the user in an easy-to-understand manner. The interface may include, but is not limited to, a graphical user interface (GUI) and a voice output interface. The providing unit may provide, for example, a graphical user interface (GUI) for visually displaying the search results. For example, the providing unit may display the search results in a list format or a graph format. The providing unit may also provide a voice output interface. For example, the providing unit may output the search results by voice using a voice synthesis technology. This allows the user to receive the search results in an easy-to-understand manner.

[0069] The reception unit can estimate the user's emotion and change the design of the input interface in real time based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This allows the reception unit to provide an interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit provides a simple and intuitive interface and minimizes input procedures. The reception unit can also provide detailed input options and suggest customizable input methods when the user is relaxed, and can prioritize voice input when the user is in a hurry to quickly input what needs to be done.

[0070] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The input history includes, for example, past input data, input frequency, input pattern, etc., but is not limited to such examples. The reception unit, for example, automatically displays to-dos that the user frequently input in the past as candidates. In addition, the reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest to-dos to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method to the user.

[0071] The reception unit can provide an automatic completion function for the input contents when the user inputs. For example, when the user starts inputting, the reception unit automatically completes related tasks and viewpoints from past cases. The automatic completion function includes, for example, a prediction algorithm, completion accuracy, and the like, but is not limited to such examples. For example, when the user inputs a specific keyword, the reception unit displays past cases related to the keyword as candidates. In addition, when the user stops inputting midway, the reception unit can also suggest the most relevant content from past cases. This enables the reception unit to automatically complete the input contents.

[0072] The reception unit can estimate the user's emotion and set the priority of the input contents based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This allows the reception unit to determine the priority of the input contents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit allows important tasks to be input preferentially. The reception unit can also allow the user to input detailed tasks when the user is relaxed, and can also allow the user to quickly input the most important tasks when the user is in a hurry.

[0073] The reception unit can present highly relevant input candidates in consideration of the geographical location information of the user. The reception unit presents highly relevant input candidates in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the reception unit displays tasks related to the location as candidates. In addition, when the user is moving, the reception unit can also suggest related tasks based on the current location. Furthermore, when the user is in a specific area, the reception unit can also display past cases related to the area as candidates. This allows the reception unit to present input candidates based on the geographical location information.

[0074] The reception unit can analyze the social media activity of the user and present related input candidates. The reception unit, for example, analyzes the social media activity of the user and presents related input candidates. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The reception unit, for example, automatically displays tasks mentioned by the user on social media as candidates. The reception unit can also suggest related tasks based on the activities of accounts the user follows on social media. Furthermore, the reception unit can display related tasks as candidates based on information shared by the user on social media. This allows the reception unit to present input candidates based on the social media activity.

[0075] The search unit can estimate the user's emotion and adjust the parameters of the search algorithm in real time based on the estimated user's emotion. For example, the search unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on the change in facial expression. The search unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the search unit analyzes the tone and speed of the voice and calculates an emotion score. The search unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on the fluctuation of the heart rate. This allows the search unit to provide search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the search unit prioritizes simple and intuitive search results when the user is feeling stressed. The search unit can also provide detailed search results when the user is relaxed, and can also quickly display the most relevant search results when the user is in a hurry.

[0076] The search unit can analyze past search history and suggest an appropriate search method. The search unit can, for example, analyze past search history and suggest an optimal search method. The search history includes, for example, past search keywords, search frequency, click rate of search results, etc., but is not limited to such examples. The search unit can, for example, automatically display keywords that the user has frequently searched for in the past as candidates. In addition, the search unit can also preferentially suggest search methods (filters, sorts, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest a search method to be used in a specific time period from the user's past search history. This allows the search unit to suggest an optimal search method.

[0077] The search unit may filter based on the user's current project or field of interest during the search. The search unit may filter based on the user's current project or field of interest, for example. Projects include, but are not limited to, business projects, research projects, and personal projects. Fields of interest include, but are not limited to, past search history, social media activities, and survey results. The search unit may preferentially display cases related to a project currently being carried out by the user. The search unit may also filter and display cases related to a particular field of interest of the user. Furthermore, the search unit may suggest optimal cases to the user depending on the progress of the project. This allows the search unit to provide search results based on projects and fields of interest.

[0078] The search unit can estimate the user's emotion and set the display order of search results based on the estimated user's emotion. For example, the search unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the search unit calculates an emotion score based on the change in facial expression. The search unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the search unit analyzes the tone and speed of the voice and calculates the emotion score. The search unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the search unit calculates the emotion score based on the fluctuation of the heart rate. This allows the search unit to provide a display order of search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the search unit preferentially displays the most relevant search results. The search unit may also display detailed search results when the user is relaxed, or may preferentially display search results that can be viewed quickly when the user is in a hurry.

[0079] The search unit can search for cases with high relevance in consideration of the geographical location information of the user at the time of search. The search unit, for example, searches for cases with high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the search unit can display cases related to the location in a preferential manner. In addition, when the user is moving, the search unit can also search for related cases based on the current location. Furthermore, when the user is in a specific area, the search unit can also display past cases related to the area in a preferential manner. This allows the search unit to provide search results based on the geographical location information.

[0080] The search unit can analyze the user's social media activity and search for related cases at the time of search. The search unit, for example, analyzes the user's social media activity and searches for related cases. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The search unit, for example, preferentially displays cases related to tasks mentioned by the user on social media. The search unit can also search for related cases based on the activities of accounts the user follows on social media. Furthermore, the search unit can also search for related cases based on information shared by the user on social media. This allows the search unit to provide search results based on social media activity.

[0081] The providing unit can estimate the user's emotion and set a method of expressing information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in the facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. This allows the providing unit to provide a method of expressing information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the providing unit provides simple and intuitive information when the user is feeling stressed. The providing unit can also provide detailed information when the user is relaxed, and can also provide quick, to-the-point information when the user is in a hurry.

[0082] The providing unit can adjust the level of detail of the provided content based on the importance of the information at the time of providing. The providing unit adjusts the level of detail of the provided content based on, for example, the importance of the information. The importance of the information includes, for example, the user's intention, the urgency of the information, the reliability of the information, and the like, but is not limited to such examples. For example, the providing unit provides important information in detail so that the user can easily understand it. In addition, the providing unit can also provide information of low importance in a concise manner to reduce the burden on the user. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the user's interest. Thereby, the providing unit can provide a level of detail according to the importance of the information.

[0083] The providing unit can apply different providing methods depending on the category of information when providing. The providing unit applies different providing methods depending on, for example, the category of information. The categories of information include, for example, technical information, business information, entertainment information, and the like, but are not limited to such examples. For example, the providing unit provides information about tasks in a list format so that the user can easily check it. The providing unit can also provide information about viewpoints in bullet points so that the user can easily understand it. Furthermore, the providing unit can provide information about projects in a dashboard format so that the user can easily grasp the overall picture. In this way, the providing unit can provide a providing method according to the category of information.

[0084] The providing unit can estimate the user's emotion and set a priority order of information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. This allows the providing unit to provide a priority order of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the providing unit provides the most important information preferentially. The providing unit can also provide detailed information when the user is relaxed, and can also provide quick, to-the-point information when the user is in a hurry.

[0085] The providing unit can provide highly relevant information preferentially in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, provides highly relevant information preferentially in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, location information service, etc., but is not limited to such examples. For example, when the user is in a specific location, the providing unit preferentially provides information related to the location. In addition, when the user is moving, the providing unit can also provide related information based on the current location. Furthermore, when the user is in a specific area, the providing unit can also provide information related to the area preferentially. In this way, the providing unit can provide information based on the geographical location information.

[0086] The providing unit can analyze the user's social media activity at the time of providing and provide related information. The providing unit, for example, analyzes the user's social media activity and provides related information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The providing unit, for example, preferentially provides information related to a task mentioned by the user on social media. In addition, the providing unit can also provide related information based on the activity of an account that the user follows on social media. Furthermore, the providing unit can also provide related information based on information shared by the user on social media. This allows the providing unit to provide information based on social media activity.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can analyze the user's input in real time and automatically suggest related tasks based on the input. For example, if the user inputs "preparing for a meeting," the reception unit searches a database for tasks related to past meeting preparations and automatically suggests tasks such as "creating an agenda," "confirming participants," and "preparing materials." In addition, even if the user stops inputting midway, the reception unit can suggest the most relevant task based on the input. Furthermore, the reception unit can analyze the user's input and set priorities for related tasks. This allows the user to efficiently identify tasks.

[0089] The search unit can analyze the user's past search history and provide optimal search results. For example, keywords that the user has frequently searched for in the past can be automatically displayed as candidates. The search unit can also preferentially suggest search methods (filters, sorts, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest search methods to be used at specific times of the day based on the user's past search history. This allows the search unit to provide the user with optimal search results.

[0090] The providing unit can estimate the user's emotion and set a method of expressing information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, simple and intuitive information can be provided. When the user is relaxed, detailed information can be provided. Furthermore, when the user is in a hurry, information that focuses on the main points can be provided quickly. In this way, the providing unit can provide a method of expressing information according to the user's emotion.

[0091] The reception unit can present highly relevant input candidates in consideration of the geographical location information of the user. For example, when the user is in a specific location, tasks related to that location are displayed as candidates. Also, when the user is moving, related tasks can be suggested based on the current location. Furthermore, when the user is in a specific area, the reception unit can also display past cases related to that area as candidates. In this way, the reception unit can present input candidates based on the geographical location information.

[0092] The search unit can analyze the user's social media activity and search for related cases. For example, cases related to tasks mentioned by the user on social media can be preferentially displayed. Also, related cases can be searched for based on the activities of accounts the user follows on social media. Furthermore, related cases can be searched for based on information shared by the user on social media. This allows the search unit to provide search results based on social media activity.

[0093] The providing unit can estimate the user's emotions and set a priority order for information to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the most important information can be provided preferentially. When the user is relaxed, detailed information can be provided. Furthermore, when the user is in a hurry, information that covers the main points can be provided quickly. In this way, the providing unit can provide a priority order for information according to the user's emotions.

[0094] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, to-do items that the user frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest to-do items to be used in a specific time period from the user's past input history. This allows the reception unit to suggest the most suitable input method for the user.

[0095] The search unit can estimate the user's emotions and adjust the parameters of the search algorithm in real time based on the estimated user's emotions. For example, if the user is stressed, simple and intuitive search results can be prioritized. Also, if the user is relaxed, detailed search results can be provided. Furthermore, if the user is in a hurry, the most relevant search results can be quickly displayed. In this way, the search unit can provide search results according to the user's emotions.

[0096] The providing unit can adjust the level of detail of the provided content based on the importance of the information when providing the information. For example, important information is provided in detail to make it easier for the user to understand. Also, information of low importance can be provided briefly to reduce the burden on the user. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the user's interest. This allows the providing unit to provide a level of detail according to the importance of the information.

[0097] The providing unit can estimate the user's emotion and set a method of expressing information to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, simple and intuitive information can be provided. When the user is relaxed, detailed information can be provided. Furthermore, when the user is in a hurry, information that focuses on the main points can be provided quickly. In this way, the providing unit can provide a method of expressing information according to the user's emotion.

[0098] The process flow of the second embodiment will be briefly described below.

[0099] Step 1: The reception unit inputs what the user needs to do. The user's needs to do include, for example, business tasks, personal tasks, project tasks, and the like. The reception unit provides an interface for the user to input what they need to do using a graphical user interface (GUI) or a voice input interface. In the case of voice input, voice recognition technology is used to convert the user's voice input into text data. Step 2: The search unit reads past cases from the database and searches for cases similar to the input task. The database can be a relational database or a NoSQL database. The search unit uses a search algorithm to efficiently search past cases. The search algorithm can be a keyword matching algorithm or a similarity calculation algorithm. Step 3: The providing unit has an interface for providing the search results to the user. The providing unit visually displays the search results using a graphical user interface (GUI). The providing unit can also provide the search results by voice using a voice output interface. The search results are output by voice using voice synthesis technology.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0102] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements including the above-mentioned reception unit, search unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and provides an interface for the user to input the things to do. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reads past cases from the database 24 and searches for cases similar to the input things to do. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and has an interface for providing the search results to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0111] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements including the above-mentioned reception unit, search unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for the user to input the things to do. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reads past cases from the database 24 and searches for cases similar to the input things to do. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and has an interface for providing the search results to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements including the above-mentioned reception unit, search unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the user to input the things to do. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reads past cases from the database 24 and searches for cases similar to the input things to do. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and has an interface for providing the search results to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] Each of the multiple elements including the above-mentioned reception unit, search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and provides an interface for the user to input what to do. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reads past cases from the database 24 and searches for cases similar to the inputted what to do. The provision unit is realized, for example, by the control unit 46A of the robot 414, and has an interface for providing the search results to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0154] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0158] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0165] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0169] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0171] (Appendix 1) a reception unit where a user inputs a task; a search unit that searches for past cases based on the task input by the reception unit; a providing unit that provides tasks and points of view to be considered for the similar cases searched for by the search unit; Equipped A system characterized by: (Appendix 2) The reception unit is Provide an interface for users to input what needs to be done 2. The system of claim 1, (Appendix 3) The search unit is Load past cases from a database and search for cases similar to the input task 2. The system of claim 1, (Appendix 4) The providing unit is Provide an interface for presenting search results to users 2. The system of claim 1, (Appendix 5) The search unit is Includes algorithms for efficient search of past cases 2. The system of claim 1, (Appendix 6) The providing unit is Includes an interface to provide search results to users in an easy-to-understand manner 2. The system of claim 1, (Appendix 7) The reception unit is Estimate user emotions and change the input interface design in real time based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 9) The reception unit is Provides auto-completion of input as the user types 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize inputs based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is Provides relevant suggestions based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is Analyze your social media activity and provide relevant suggestions 2. The system of claim 1, (Appendix 13) The search unit is Estimate user sentiment and adjust search algorithm parameters in real time based on the estimated user sentiment 2. The system of claim 1, (Appendix 14) The search unit is Analyze your past search history and suggest the right search methods 2. The system of claim 1, (Appendix 15) The search unit is When searching, filter based on the user's current projects or interests 2. The system of claim 1, (Appendix 16) The search unit is Infer user sentiment and set the display order of search results based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The search unit is When searching, the system takes into account the user's geographic location to prioritize relevant cases. 2. The system of claim 1, (Appendix 18) The search unit is When you search, we analyze your social media activity to find relevant cases. 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and set the presentation method of the information to be provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The providing unit is At the time of delivery, adjust the level of detail of the delivery based on the importance of the information 2. The system of claim 1, (Appendix 21) The providing unit is When providing information, apply different methods of providing information depending on the category of information 2. The system of claim 1, (Appendix 22) The providing unit is Estimate user emotions and prioritize information to be provided based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The providing unit is When providing information, the system takes into account the user's geographic location information and prioritizes providing information that is more relevant. 2. The system of claim 1, (Appendix 24) The providing unit is When provided, to analyze your social media activity and provide you with relevant information 2. The system of claim 1, [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit where a user inputs a task; a search unit that searches for past cases based on the task input by the reception unit; a providing unit that provides tasks and points of view to be considered for the similar cases searched for by the search unit; Equipped A system characterized by:

2. The reception unit is Provide an interface for users to input what needs to be done 2. The system of claim 1 .

3. The search unit is Load past cases from a database and search for cases similar to the input task 2. The system of claim 1 .

4. The providing unit is Provide an interface for presenting search results to users 2. The system of claim 1 .

5. The search unit is Includes algorithms for efficient search of past cases 2. The system of claim 1 .

6. The providing unit is Includes an interface to provide search results to users in an easy-to-understand manner 2. The system of claim 1 .

7. The reception unit is Estimate user emotions and change the input interface design in real time based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1 .

Images