system

Abstract

The system according to this embodiment aims to improve productivity by enabling information input and retrieval via a SaaS API. [Solution] The system according to the embodiment comprises a reception unit, a reference unit, an input unit, and a provision unit. The reception unit receives information from the user. The reference unit refers to the information received by the reception unit. The input unit inputs information based on the information referred to by the reference unit. The provision unit provides the information input by the input unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since it depends on the UI design and operability of SaaS, there is a limit to the improvement of productivity, and there is room for improvement.

[0005] The system according to the embodiment aims to improve productivity by performing information input and reference via the API of SaaS.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a reference unit, an input unit, and a provision unit. The reception unit receives information from a user. The reference unit refers to the information received by the reception unit. The input unit inputs information based on the information referred to by the reference unit. The provision unit provides the information input by the input unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve productivity by allowing information input and retrieval via a SaaS API. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a mechanism in which a DAAA (Data Acquisition Engineer) inputs and retrieves information via the SaaS's API to a DAP (Data Acquisition Program) that interacts with the SaaS's UI. This system is expected to have effects such as improved productivity that is not dependent on the UI design or usability of the SaaS, avoidance of the need to use multiple SaaS applications for different purposes, and reduction of DAP license costs. For example, the DAAA inputs and retrieves information via the SaaS's API to SaaS applications for core business systems such as SFA, CRM, and ERP. This improves productivity in using core business systems, allowing users to concentrate on their core tasks. As a result, the system can improve productivity regardless of the UI design or usability of the SaaS. Furthermore, it eliminates the need to use multiple SaaS applications for different purposes, allowing users to concentrate on their core tasks. In addition, a reduction in DAP license costs can be expected.

[0029] The system according to this embodiment comprises a reception unit, a reference unit, an input unit, and a provision unit. The reception unit receives information from the user. For example, the reception unit can receive information from the user through form input. The reception unit can also receive information from the user through voice input. Furthermore, the reception unit can also receive information from the user through sensor data. The reference unit references the information received by the reception unit. For example, the reference unit can reference information through database search. The reference unit can also reference information through cache lookup. Furthermore, the reference unit can also reference information via API. The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information through keyboard input. The input unit can also input information through voice input. Furthermore, the input unit can also input information through touch input. The provision unit provides the information entered by the input unit. For example, the provision unit can provide information through screen display. Furthermore, the provision unit can provide information through voice output. Furthermore, the provision unit can provide information through file transmission. This enables the system to efficiently receive, reference, input, and provide information from users.

[0030] The reception desk receives information from users. For example, the reception desk can receive information from users through form input. Specifically, it provides a form that operates on a web browser, allowing users to input the necessary information. The form includes input fields such as text boxes, checkboxes, radio buttons, and dropdown lists, and is designed for intuitive user operation. The reception desk can also receive information from users through voice input. In the case of voice input, speech recognition technology is used to convert the user's speech into text data and input it into the system. Speech recognition technology uses advanced algorithms for noise reduction and accurate speech analysis, enabling it to accurately understand the user's intent. Furthermore, the reception desk can also receive information from users through sensor data. Sensor data includes temperature, humidity, location information, and acceleration, and this data is collected in real time and input into the system. Sensors are installed on the user's device and in the surrounding environment, and by continuously collecting data, the system can understand the user's situation and changes in the environment. This allows the reception desk to receive information from users and input it into the system in a variety of ways.

[0031] The reference unit accesses information received by the reception unit. For example, the reference unit can access information through database searches. Specifically, it accesses the database using SQL queries to retrieve the necessary information. The database stores user profile information, past transaction history, sensor data, etc., and the reference unit quickly searches this data and extracts the necessary information. The reference unit can also access information through cache access. The cache is a memory area that temporarily stores frequently accessed data, reducing database access and improving the speed of information retrieval. The reference unit prioritizes accessing data stored in the cache and retrieves the latest information from the database as needed. Furthermore, the reference unit can access information via APIs. APIs are interfaces for interacting with other systems and services, and the reference unit can retrieve information from external data sources through APIs. For example, external data such as weather information and traffic information can be retrieved in real time and used within the system. This allows the reference unit to access information received by the reception unit in various ways and retrieve the necessary information quickly and accurately.

[0032] The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information through keyboard input. Specifically, the user uses a keyboard to input text data, which is then incorporated into the system. Keyboard input allows for accurate and rapid information input and is particularly suitable for detailed information and long texts. The input unit can also input information through voice input. In the case of voice input, speech recognition technology is used to convert the user's speech into text data, which is then incorporated into the system. Voice input is suitable for information input when hands are occupied or when keyboard input is difficult. Furthermore, the input unit can also input information through touch input. Touch input is a method of inputting information using touchscreen devices such as tablets and smartphones, and allows for intuitive operation. Users can input information using an on-screen keyboard or handwriting input and incorporate it into the system. As a result, the input unit can input information in a variety of ways based on the information referenced by the reference unit and incorporate it into the system.

[0033] The information provider provides the information entered by the input unit. For example, the information provider can provide information through screen display. Specifically, it visually displays the entered information through a user interface, making it easy for the user to confirm. The screen display includes text, graphs, charts, images, etc., to present the information in an easy-to-understand manner. The information provider can also provide information through voice output. In the case of voice output, text data is converted into voice using speech synthesis technology and provided to the user. Voice output is suitable for providing information to users with visual impairments or in situations where it is difficult to operate while looking at a screen. Furthermore, the information provider can also provide information through file transmission. File transmission is a method of saving the entered information in file format and sending it to the user, which is done via email or cloud storage. This allows the user to save the necessary information as a file and refer to it later. In this way, the information provider can provide the information entered by the input unit in a variety of ways, enabling users to efficiently obtain the information they need.

[0034] The reception unit can obtain information via APIs. For example, the reception unit can obtain information via a REST API. It can also obtain information via a SOAP API. Furthermore, it can obtain information via a GraphQL API. This makes information retrieval more efficient by obtaining information via APIs. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input information obtained via APIs into a generating AI and have the generating AI perform information analysis.

[0035] The reference unit can access information via APIs. For example, the reference unit can access information via a REST API. It can also access information via a SOAP API. Furthermore, it can access information via a GraphQL API. This streamlines information retrieval by accessing information via APIs. Some or all of the above-described processes in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information accessed via API into a generating AI and have the generating AI perform the analysis of the information.

[0036] The input unit can receive information via APIs. For example, the input unit can receive information via a REST API. It can also receive information via a SOAP API. Furthermore, it can receive information via a GraphQL API. This streamlines information input by allowing it to be received via APIs. Some or all of the above-described processes in the input unit may be performed using AI, or without AI. For example, the input unit can input the information received via API into a generating AI and have the generating AI perform the analysis of the information.

[0037] The service provider can provide information via APIs. For example, the service provider can provide information via a REST API. It can also provide information via a SOAP API. Furthermore, it can provide information via a GraphQL API. This streamlines information provision by providing information via APIs. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the information provided via API into a generating AI and have the generating AI perform the analysis of the information.

[0038] The reception desk can analyze the user's past information input history and select the optimal data retrieval method. For example, the reception desk can prioritize data retrieval methods that the user has frequently used in the past. Furthermore, the reception desk can suggest the optimal data retrieval method for a specific time period based on the user's past input history. In addition, the reception desk can analyze the user's past input history and select the most efficient data retrieval method. Thus, by analyzing the user's past information input history, the optimal data retrieval method can be selected. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal data retrieval method.

[0039] The reception unit can filter information based on the user's current projects and areas of interest when acquiring it. For example, the reception unit can acquire only information related to the project the user is currently working on. The reception unit can also prioritize acquiring highly relevant information based on the user's areas of interest. Furthermore, the reception unit can filter and provide necessary information according to the progress of the user's project. This allows for the acquisition of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the information filtering.

[0040] The reception unit can prioritize retrieving highly relevant information based on the user's geographical location when acquiring information. For example, the reception unit prioritizes retrieving information related to the user's current location. The reception unit can also provide optimal information based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also acquire information based on their current location. This allows for the provision of more appropriate information by prioritizing the acquisition of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant information.

[0041] The reception unit can analyze the user's social media activity and obtain relevant information when acquiring data. For example, the reception unit can acquire relevant information based on information shared by the user on social media. The reception unit can also provide information related to topics of interest based on the user's social media activity. Furthermore, the reception unit can acquire relevant information based on information shared by the user's social media followers and friends. This allows for the acquisition of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI acquire relevant information.

[0042] The reference section can adjust the level of detail of a reference based on the importance of the information being referenced. For example, the reference section can display highly important information in detail and less important information concisely. Furthermore, the reference section can dynamically adjust the level of detail of a reference according to the importance of the information. In addition, the reference section can highlight highly important information to attract the user's attention. This allows important information to be referenced in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the reference section may be performed using AI, for example, or without AI. For example, the reference section can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reference.

[0043] The reference unit can apply different reference algorithms depending on the category of information during the reference process. For example, the reference unit can select the optimal reference algorithm for each category and display the information. The reference unit can also apply different filtering algorithms depending on the category of information. Furthermore, the reference unit can provide information using different display formats for each category. This allows for the provision of more appropriate information by applying different reference algorithms depending on the category of information. Some or all of the above-described processes in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information category data into a generating AI and have the generating AI perform the application of the reference algorithm.

[0044] The reference unit can determine the priority of references based on when the information was acquired. For example, the reference unit prioritizes the most recent information. It can also postpone the retrieval of older information based on when it was acquired. Furthermore, the reference unit can dynamically adjust the priority of references according to when the information was acquired. This allows for priority retrieval of the most recent information by determining the priority of references based on when the information was acquired. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information acquisition time data into a generating AI and have the generating AI perform the determination of the reference priority.

[0045] The reference unit can adjust the order of references based on the relevance of the information during the reference process. For example, the reference unit prioritizes referencing highly relevant information. Furthermore, the reference unit can dynamically adjust the order of references based on the relevance of the information. In addition, the reference unit can highlight highly relevant information to attract the user's attention. This allows for priority referencing of highly relevant information by adjusting the order of references based on relevance. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the reference order.

[0046] The input unit can adjust the level of detail of the input based on the importance of the information during input. For example, the input unit can input highly important information in detail and less important information concisely. Furthermore, the input unit can dynamically adjust the level of detail according to the importance of the information. In addition, the input unit can highlight highly important information to attract the user's attention. This allows for detailed input of important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0047] The input unit can apply different input algorithms depending on the information category during input. For example, the input unit can select the optimal input algorithm for each category and input the information. The input unit can also apply different filtering algorithms depending on the information category. Furthermore, the input unit can provide information using different input formats for each category. This allows for more appropriate input by applying different input algorithms depending on the information category. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input information category data into a generating AI and have the generating AI execute the application of the input algorithm.

[0048] The input unit can adjust the order of input based on when the information was acquired. For example, the input unit prioritizes inputting the latest information. The input unit can also postpone inputting older information based on when it was acquired. Furthermore, the input unit can dynamically adjust the order of input according to when the information was acquired. This allows for priority input of the latest information by adjusting the order of input based on when the information was acquired. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data on when the information was acquired into a generating AI and have the generating AI perform the adjustment of the input order.

[0049] The input unit can adjust the input method based on the relevance of the information during input. For example, the input unit prioritizes inputting highly relevant information. Furthermore, the input unit can dynamically adjust the input method based on the relevance of the information. In addition, the input unit can highlight highly relevant information to attract the user's attention. This allows for the priority input of highly relevant information by adjusting the input method based on the relevance of the information. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the input method.

[0050] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide detailed information for high importance and concise information for low importance. The delivery unit can also dynamically adjust the level of detail provided according to the importance of the information. Furthermore, the delivery unit can highlight high-importance information to attract the user's attention. In this way, by adjusting the level of detail provided based on the importance of the information, important information can be provided in detail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0051] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can select the optimal information provision algorithm for each category and provide the information. The information provider can also apply different filtering algorithms depending on the information category. Furthermore, the information provider can use different display formats for each category and provide the information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0052] The information provider can adjust the order of information provision based on when the information was acquired. For example, the provider can prioritize providing the latest information. The provider can also postpone providing older information based on when it was acquired. Furthermore, the provider can dynamically adjust the order of information provision according to when the information was acquired. This allows the provider to prioritize providing the latest information by adjusting the order of provision based on when the information was acquired. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input information acquisition time data into a generating AI and have the generating AI perform the adjustment of the order of provision.

[0053] The information delivery unit can adjust the method of delivery based on the relevance of the information at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant information. The delivery unit can also dynamically adjust the method of delivery based on the relevance of the information. Furthermore, the delivery unit can highlight highly relevant information to attract the user's attention. This allows the delivery unit to prioritize the delivery of highly relevant information by adjusting the method of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can learn the user's past behavior patterns and suggest the optimal method of information retrieval. For example, the reception desk will prioritize suggesting methods that the user has frequently used in the past. Furthermore, based on the user's past behavior patterns, the reception desk can suggest the optimal method of information retrieval for a specific time period. In addition, the reception desk can analyze the user's past behavior patterns and suggest the most efficient method of information retrieval. Thus, by learning the user's past behavior patterns, it can suggest the optimal method of information retrieval.

[0056] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location. For example, the reception desk prioritizes retrieving information related to the user's current location. Furthermore, the reception desk can provide optimal information based on the user's geographical location. Additionally, if the user is on the move, the reception desk can retrieve information based on their current location. This allows for the provision of more appropriate information by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0057] The reference section can adjust the level of detail of references based on the importance of the information. For example, it can display highly important information in detail and less important information concisely. Furthermore, the reference section can dynamically adjust the level of detail of references according to the importance of the information. It can also highlight highly important information to attract the user's attention. This allows users to access important information in detail by adjusting the level of detail of references based on the importance of the information.

[0058] The input unit can apply different input algorithms depending on the information category. For example, the input unit can select the optimal input algorithm for each category and input the information. Furthermore, the input unit can apply different filtering algorithms depending on the information category. In addition, the input unit can provide information using different input formats for each category. This allows for more appropriate input by applying different input algorithms depending on the information category.

[0059] The information provider can adjust the order of information delivery based on when the information was acquired. For example, the provider can prioritize the delivery of the latest information. It can also postpone the delivery of older information based on when it was acquired. Furthermore, the provider can dynamically adjust the order of information delivery according to when the information was acquired. This allows for the prioritization of the latest information by adjusting the delivery order based on when the information was acquired.

[0060] The information provider can adjust its delivery method based on the relevance of the information. For example, it can prioritize providing highly relevant information. Furthermore, it can dynamically adjust its delivery method based on the relevance of the information. In addition, it can highlight highly relevant information to attract the user's attention. This allows for the prioritization of highly relevant information by adjusting the delivery method based on its relevance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information from the user. For example, the reception desk can receive information from the user through form input, voice input, or sensor data. Step 2: The reference unit references the information received by the reception unit. For example, the reference unit can access information via database search, cache lookup, or API. Step 3: The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information via keyboard input, voice input, or touch input. Step 4: The providing unit provides the information entered by the input unit. For example, the providing unit can provide information through screen display, audio output, or file transmission.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a mechanism in which a DAAA (Data Acquisition Engineer) inputs and retrieves information via the SaaS's API to a DAP (Data Acquisition Program) that interacts with the SaaS's UI. This system is expected to have effects such as improved productivity that is not dependent on the UI design or usability of the SaaS, avoidance of the need to use multiple SaaS applications for different purposes, and reduction of DAP license costs. For example, the DAAA inputs and retrieves information via the SaaS's API to SaaS applications for core business systems such as SFA, CRM, and ERP. This improves productivity in using core business systems, allowing users to concentrate on their core tasks. As a result, the system can improve productivity regardless of the UI design or usability of the SaaS. Furthermore, it eliminates the need to use multiple SaaS applications for different purposes, allowing users to concentrate on their core tasks. In addition, a reduction in DAP license costs can be expected.

[0064] The system according to this embodiment comprises a reception unit, a reference unit, an input unit, and a provision unit. The reception unit receives information from the user. For example, the reception unit can receive information from the user through form input. The reception unit can also receive information from the user through voice input. Furthermore, the reception unit can also receive information from the user through sensor data. The reference unit references the information received by the reception unit. For example, the reference unit can reference information through database search. The reference unit can also reference information through cache lookup. Furthermore, the reference unit can also reference information via API. The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information through keyboard input. The input unit can also input information through voice input. Furthermore, the input unit can also input information through touch input. The provision unit provides the information entered by the input unit. For example, the provision unit can provide information through screen display. Furthermore, the provision unit can provide information through voice output. Furthermore, the provision unit can provide information through file transmission. This enables the system to efficiently receive, reference, input, and provide information from users.

[0065] The reception desk receives information from users. For example, the reception desk can receive information from users through form input. Specifically, it provides a form that operates on a web browser, allowing users to input the necessary information. The form includes input fields such as text boxes, checkboxes, radio buttons, and dropdown lists, and is designed for intuitive user operation. The reception desk can also receive information from users through voice input. In the case of voice input, speech recognition technology is used to convert the user's speech into text data and input it into the system. Speech recognition technology uses advanced algorithms for noise reduction and accurate speech analysis, enabling it to accurately understand the user's intent. Furthermore, the reception desk can also receive information from users through sensor data. Sensor data includes temperature, humidity, location information, and acceleration, and this data is collected in real time and input into the system. Sensors are installed on the user's device and in the surrounding environment, and by continuously collecting data, the system can understand the user's situation and changes in the environment. This allows the reception desk to receive information from users and input it into the system in a variety of ways.

[0066] The reference unit accesses information received by the reception unit. For example, the reference unit can access information through database searches. Specifically, it accesses the database using SQL queries to retrieve the necessary information. The database stores user profile information, past transaction history, sensor data, etc., and the reference unit quickly searches this data and extracts the necessary information. The reference unit can also access information through cache access. The cache is a memory area that temporarily stores frequently accessed data, reducing database access and improving the speed of information retrieval. The reference unit prioritizes accessing data stored in the cache and retrieves the latest information from the database as needed. Furthermore, the reference unit can access information via APIs. APIs are interfaces for interacting with other systems and services, and the reference unit can retrieve information from external data sources through APIs. For example, external data such as weather information and traffic information can be retrieved in real time and used within the system. This allows the reference unit to access information received by the reception unit in various ways and retrieve the necessary information quickly and accurately.

[0067] The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information through keyboard input. Specifically, the user uses a keyboard to input text data, which is then incorporated into the system. Keyboard input allows for accurate and rapid information input and is particularly suitable for detailed information and long texts. The input unit can also input information through voice input. In the case of voice input, speech recognition technology is used to convert the user's speech into text data, which is then incorporated into the system. Voice input is suitable for information input when hands are occupied or when keyboard input is difficult. Furthermore, the input unit can also input information through touch input. Touch input is a method of inputting information using touchscreen devices such as tablets and smartphones, and allows for intuitive operation. Users can input information using an on-screen keyboard or handwriting input and incorporate it into the system. As a result, the input unit can input information in a variety of ways based on the information referenced by the reference unit and incorporate it into the system.

[0068] The information provider provides the information entered by the input unit. For example, the information provider can provide information through screen display. Specifically, it visually displays the entered information through a user interface, making it easy for the user to confirm. The screen display includes text, graphs, charts, images, etc., to present the information in an easy-to-understand manner. The information provider can also provide information through voice output. In the case of voice output, text data is converted into voice using speech synthesis technology and provided to the user. Voice output is suitable for providing information to users with visual impairments or in situations where it is difficult to operate while looking at a screen. Furthermore, the information provider can also provide information through file transmission. File transmission is a method of saving the entered information in file format and sending it to the user, which is done via email or cloud storage. This allows the user to save the necessary information as a file and refer to it later. In this way, the information provider can provide the information entered by the input unit in a variety of ways, enabling users to efficiently obtain the information they need.

[0069] The reception unit can obtain information via APIs. For example, the reception unit can obtain information via a REST API. It can also obtain information via a SOAP API. Furthermore, it can obtain information via a GraphQL API. This makes information retrieval more efficient by obtaining information via APIs. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input information obtained via APIs into a generating AI and have the generating AI perform information analysis.

[0070] The reference unit can access information via APIs. For example, the reference unit can access information via a REST API. It can also access information via a SOAP API. Furthermore, it can access information via a GraphQL API. This streamlines information retrieval by accessing information via APIs. Some or all of the above-described processes in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information accessed via API into a generating AI and have the generating AI perform the analysis of the information.

[0071] The input unit can receive information via APIs. For example, the input unit can receive information via a REST API. It can also receive information via a SOAP API. Furthermore, it can receive information via a GraphQL API. This streamlines information input by allowing it to be received via APIs. Some or all of the above-described processes in the input unit may be performed using AI, or without AI. For example, the input unit can input the information received via API into a generating AI and have the generating AI perform the analysis of the information.

[0072] The service provider can provide information via APIs. For example, the service provider can provide information via a REST API. It can also provide information via a SOAP API. Furthermore, it can provide information via a GraphQL API. This streamlines information provision by providing information via APIs. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the information provided via API into a generating AI and have the generating AI perform the analysis of the information.

[0073] The reception desk can estimate the user's emotions and adjust the timing of information retrieval based on the estimated emotions. For example, if the user is stressed, the reception desk can delay information retrieval and wait until the user is relaxed. Conversely, if the user is in a hurry, the reception desk can speed up information retrieval and provide information quickly. Furthermore, if the user is concentrating, the reception desk can adjust the timing of information retrieval to avoid interrupting their work. This allows for information to be retrieved at a more appropriate time by adjusting the timing of information retrieval according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can analyze the user's past information input history and select the optimal data retrieval method. For example, the reception desk can prioritize data retrieval methods that the user has frequently used in the past. Furthermore, the reception desk can suggest the optimal data retrieval method for a specific time period based on the user's past input history. In addition, the reception desk can analyze the user's past input history and select the most efficient data retrieval method. Thus, by analyzing the user's past information input history, the optimal data retrieval method can be selected. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal data retrieval method.

[0075] The reception unit can filter information based on the user's current projects and areas of interest when acquiring it. For example, the reception unit can acquire only information related to the project the user is currently working on. The reception unit can also prioritize acquiring highly relevant information based on the user's areas of interest. Furthermore, the reception unit can filter and provide necessary information according to the progress of the user's project. This allows for the acquisition of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the information filtering.

[0076] The reception desk can estimate the user's emotions and determine the priority of information to retrieve based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone retrieving less important information. Conversely, if the user is relaxed, the reception desk can prioritize retrieving more important information. Furthermore, if the user is in a hurry, the reception desk can quickly retrieve the most important information. This allows for the acquisition of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize retrieving highly relevant information based on the user's geographical location when acquiring information. For example, the reception unit prioritizes retrieving information related to the user's current location. The reception unit can also provide optimal information based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also acquire information based on their current location. This allows for the provision of more appropriate information by prioritizing the acquisition of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant information.

[0078] The reception unit can analyze the user's social media activity and obtain relevant information when acquiring data. For example, the reception unit can acquire relevant information based on information shared by the user on social media. The reception unit can also provide information related to topics of interest based on the user's social media activity. Furthermore, the reception unit can acquire relevant information based on information shared by the user's social media followers and friends. This allows for the acquisition of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI acquire relevant information.

[0079] The reference unit can estimate the user's emotions and adjust the way the reference is presented based on the estimated emotions. For example, if the user is tense, the reference unit can provide a simple and easily visible presentation. If the user is relaxed, it can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise presentation. By adjusting the presentation of the reference according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI, for example, or not using AI. For example, the reference unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reference section can adjust the level of detail of a reference based on the importance of the information being referenced. For example, the reference section can display highly important information in detail and less important information concisely. Furthermore, the reference section can dynamically adjust the level of detail of a reference according to the importance of the information. In addition, the reference section can highlight highly important information to attract the user's attention. This allows important information to be referenced in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the reference section may be performed using AI, for example, or without AI. For example, the reference section can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reference.

[0081] The reference unit can apply different reference algorithms depending on the category of information during the reference process. For example, the reference unit can select the optimal reference algorithm for each category and display the information. The reference unit can also apply different filtering algorithms depending on the category of information. Furthermore, the reference unit can provide information using different display formats for each category. This allows for the provision of more appropriate information by applying different reference algorithms depending on the category of information. Some or all of the above-described processes in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information category data into a generating AI and have the generating AI perform the application of the reference algorithm.

[0082] The reference section can estimate the user's emotions and adjust the length of the reference based on the estimated emotions. For example, if the user is tense, the reference section can provide a short, concise reference. If the user is relaxed, it can provide a longer reference with more detailed explanations. Furthermore, if the user is in a hurry, the reference section can summarize the information concisely for quick reference. By adjusting the length of the reference according to the user's emotions, more relevant information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference section may be performed using AI or not. For example, the reference section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reference unit can determine the priority of references based on when the information was acquired. For example, the reference unit prioritizes the most recent information. It can also postpone the retrieval of older information based on when it was acquired. Furthermore, the reference unit can dynamically adjust the priority of references according to when the information was acquired. This allows for priority retrieval of the most recent information by determining the priority of references based on when the information was acquired. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information acquisition time data into a generating AI and have the generating AI perform the determination of the reference priority.

[0084] The reference unit can adjust the order of references based on the relevance of the information during the reference process. For example, the reference unit prioritizes referencing highly relevant information. Furthermore, the reference unit can dynamically adjust the order of references based on the relevance of the information. In addition, the reference unit can highlight highly relevant information to attract the user's attention. This allows for priority referencing of highly relevant information by adjusting the order of references based on relevance. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the reference order.

[0085] The input unit can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is nervous, the input unit can provide a simple and highly visible input method. If the user is relaxed, the input unit can also provide detailed input options. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick information entry. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not using AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The input unit can adjust the level of detail of the input based on the importance of the information during input. For example, the input unit can input highly important information in detail and less important information concisely. Furthermore, the input unit can dynamically adjust the level of detail according to the importance of the information. In addition, the input unit can highlight highly important information to attract the user's attention. This allows for detailed input of important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0087] The input unit can apply different input algorithms depending on the information category during input. For example, the input unit can select the optimal input algorithm for each category and input the information. The input unit can also apply different filtering algorithms depending on the information category. Furthermore, the input unit can provide information using different input formats for each category. This allows for more appropriate input by applying different input algorithms depending on the information category. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input information category data into a generating AI and have the generating AI execute the application of the input algorithm.

[0088] The input unit can estimate the user's emotions and determine the priority of input based on the estimated emotions. For example, if the user is stressed, the input unit will postpone less important information. Conversely, if the user is relaxed, the input unit can prioritize inputting more important information. Furthermore, if the user is in a hurry, the input unit can quickly input the most important information. This allows for the input of more appropriate information by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The input unit can adjust the order of input based on when the information was acquired. For example, the input unit prioritizes inputting the latest information. The input unit can also postpone inputting older information based on when it was acquired. Furthermore, the input unit can dynamically adjust the order of input according to when the information was acquired. This allows for priority input of the latest information by adjusting the order of input based on when the information was acquired. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data on when the information was acquired into a generating AI and have the generating AI perform the adjustment of the input order.

[0090] The input unit can adjust the input method based on the relevance of the information during input. For example, the input unit prioritizes inputting highly relevant information. Furthermore, the input unit can dynamically adjust the input method based on the relevance of the information. In addition, the input unit can highlight highly relevant information to attract the user's attention. This allows for the priority input of highly relevant information by adjusting the input method based on the relevance of the information. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the input method.

[0091] The service provider can estimate the user's emotions and adjust the presentation of the information provided based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and easily understandable presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise presentation. By adjusting the presentation of information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide detailed information for high importance and concise information for low importance. The delivery unit can also dynamically adjust the level of detail provided according to the importance of the information. Furthermore, the delivery unit can highlight high-importance information to attract the user's attention. In this way, by adjusting the level of detail provided based on the importance of the information, important information can be provided in detail. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0093] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can select the optimal information provision algorithm for each category and provide the information. The information provider can also apply different filtering algorithms depending on the information category. Furthermore, the information provider can use different display formats for each category and provide the information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0094] The service provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, the service provider may postpone less important information. Conversely, if the user is relaxed, the service provider may prioritize providing more important information. Furthermore, if the user is in a hurry, the service provider may quickly provide the most important information. This allows for the provision of more appropriate information by prioritizing the information provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The information provider can adjust the order of information provision based on when the information was acquired. For example, the provider can prioritize providing the latest information. The provider can also postpone providing older information based on when it was acquired. Furthermore, the provider can dynamically adjust the order of information provision according to when the information was acquired. This allows the provider to prioritize providing the latest information by adjusting the order of provision based on when the information was acquired. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input information acquisition time data into a generating AI and have the generating AI perform the adjustment of the order of provision.

[0096] The information delivery unit can adjust the method of delivery based on the relevance of the information at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant information. The delivery unit can also dynamically adjust the method of delivery based on the relevance of the information. Furthermore, the delivery unit can highlight highly relevant information to attract the user's attention. This allows the delivery unit to prioritize the delivery of highly relevant information by adjusting the method of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can learn the user's past behavior patterns and suggest the optimal method of information retrieval. For example, the reception desk will prioritize suggesting methods that the user has frequently used in the past. Furthermore, based on the user's past behavior patterns, the reception desk can suggest the optimal method of information retrieval for a specific time period. In addition, the reception desk can analyze the user's past behavior patterns and suggest the most efficient method of information retrieval. Thus, by learning the user's past behavior patterns, it can suggest the optimal method of information retrieval.

[0099] The reference section can estimate the user's emotions and adjust the way the reference is presented based on those emotions. For example, if the user is stressed, the reference section can provide a simple and easily visible presentation. If the user is relaxed, it can also provide a more detailed presentation. Furthermore, if the user is in a hurry, it can provide a concise presentation. By adjusting the presentation of the reference according to the user's emotions, more relevant information can be provided.

[0100] The input unit can estimate the user's emotions and adjust the input method based on those emotions. For example, if the user is nervous, the input unit can provide a simple and highly visible input method. If the user is relaxed, it can also provide more detailed input options. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick information entry. This allows for more appropriate input by adjusting the input method according to the user's emotions.

[0101] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is stressed, the provider can provide a simple and easily understandable presentation. If the user is relaxed, the provider can also provide a more detailed presentation. Furthermore, if the user is in a hurry, the provider can provide a concise presentation. By adjusting the presentation of information according to the user's emotions, the provider can deliver more relevant information.

[0102] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location. For example, the reception desk prioritizes retrieving information related to the user's current location. Furthermore, the reception desk can provide optimal information based on the user's geographical location. Additionally, if the user is on the move, the reception desk can retrieve information based on their current location. This allows for the provision of more appropriate information by prioritizing the retrieval of highly relevant information based on the user's geographical location.

[0103] The reference section can adjust the level of detail of references based on the importance of the information. For example, it can display highly important information in detail and less important information concisely. Furthermore, the reference section can dynamically adjust the level of detail of references according to the importance of the information. It can also highlight highly important information to attract the user's attention. This allows users to access important information in detail by adjusting the level of detail of references based on the importance of the information.

[0104] The input unit can apply different input algorithms depending on the information category. For example, the input unit can select the optimal input algorithm for each category and input the information. Furthermore, the input unit can apply different filtering algorithms depending on the information category. In addition, the input unit can provide information using different input formats for each category. This allows for more appropriate input by applying different input algorithms depending on the information category.

[0105] The information provider can adjust the order of information delivery based on when the information was acquired. For example, the provider can prioritize the delivery of the latest information. It can also postpone the delivery of older information based on when it was acquired. Furthermore, the provider can dynamically adjust the order of information delivery according to when the information was acquired. This allows for the prioritization of the latest information by adjusting the delivery order based on when the information was acquired.

[0106] The reception desk can estimate the user's emotions and prioritize the information to retrieve based on those emotions. For example, if the user is stressed, the reception desk will postpone retrieving less important information. Conversely, if the user is relaxed, the reception desk can prioritize retrieving more important information. Furthermore, if the user is in a hurry, the reception desk can quickly retrieve the most important information. By prioritizing information according to the user's emotions, more relevant information can be obtained.

[0107] The information provider can adjust its delivery method based on the relevance of the information. For example, it can prioritize providing highly relevant information. Furthermore, it can dynamically adjust its delivery method based on the relevance of the information. In addition, it can highlight highly relevant information to attract the user's attention. This allows for the prioritization of highly relevant information by adjusting the delivery method based on its relevance.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives information from the user. For example, the reception desk can receive information from the user through form input, voice input, or sensor data. Step 2: The reference unit references the information received by the reception unit. For example, the reference unit can access information via database search, cache lookup, or API. Step 3: The input unit inputs information based on the information referenced by the reference unit. For example, the input unit can input information via keyboard input, voice input, or touch input. Step 4: The providing unit provides the information entered by the input unit. For example, the providing unit can provide information through screen display, audio output, or file transmission.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the receiving unit, reference unit, input unit, and providing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the receiving unit is implemented by the control unit 46A of the smart device 14 and receives information from the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to the information received by the receiving unit. The input unit is implemented by the control unit 46A of the smart device 14 and inputs information based on the information referred to by the reference unit. The providing unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the information input by the input unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, reference unit, input unit, and providing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information from the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to the information received by the reception unit. The input unit is implemented by the control unit 46A of the smart glasses 214 and inputs information based on the information referred to by the reference unit. The providing unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the information input by the input unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, reference unit, input unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information from the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to the information received by the reception unit. The input unit is implemented by the control unit 46A of the headset terminal 314 and inputs information based on the information referred by the reference unit. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the information input by the input unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, reference unit, input unit, and providing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information from the user. The reference unit is implemented by the specific processing unit 290 of the data processing unit 12 and refers to the information received by the reception unit. The input unit is implemented by the control unit 46A of the robot 414 and inputs information based on the information referred to by the reference unit. The providing unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the information input by the input unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception desk that receives information from users, A reference unit that refers to the information received by the aforementioned reception unit, An input unit that inputs information based on the information referenced by the aforementioned reference unit, The system comprises a providing unit that provides the information input by the input unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Information is retrieved via API. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reference section is, Access information via API The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is Enter information via API The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Information is provided via API. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past information input history and select the optimal method for data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When retrieving information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When acquiring information, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reference section is, It estimates the user's emotions and adjusts how references are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reference section is, When referencing, adjust the level of detail of the reference based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reference section is, When referencing information, different referencing algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reference section is, It estimates the user's sentiment and adjusts the length of the reference based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reference section is, When referencing information, prioritize references based on when the information was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reference section is, When referencing, adjust the order of references based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is It estimates the user's emotions and adjusts the input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned input unit is When entering information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned input unit is When inputting information, different input algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned input unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned input unit is When inputting information, adjust the input order based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned input unit is When inputting information, the input method is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we adjust the method of provision based on its relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, A reference unit that refers to the information received by the aforementioned reception unit, An input unit that inputs information based on the information referenced by the aforementioned reference unit, The system comprises a providing unit that provides the information input by the input unit. A system characterized by the following features.

2. The aforementioned reception unit is Information is retrieved via API. The system according to feature 1.

3. The aforementioned reference section is, Access information via API The system according to feature 1.

4. The aforementioned input unit is Enter information via API The system according to feature 1.

5. The aforementioned supply unit is, Information is provided via API. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past information input history and select the optimal method for data acquisition. The system according to feature 1.

8. The aforementioned reception unit is When retrieving information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When acquiring information, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system according to feature 1.

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