System

Abstract

To provide a system according to an embodiment that extracts request items from a corporate customer efficiently, and makes proper arrangements.SOLUTION: A system according to an embodiment comprises: a reception unit; an extraction unit; a determination unit; a first suggestion unit; and a second suggestion unit. The reception unit is configured to receive mail texts from a corporate customer. The extraction unit is configured to analyze the mail text received by the reception unit, and extract a request item. The determination unit is configured to determine a required arrangement on the basis of the request item extracted by the extraction unit. The first suggestion unit is configured to suggest information indicative of the arrangement determined by the determination unit to a person in charge. The second suggestion unit is configured to suggest information required for the arrangement determined by the determination unit to the person in charge in coordination with an internal system.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently extract requests from corporate customers from email text and make appropriate arrangements.

[0005] The system according to the embodiment aims to efficiently extract requests from corporate customers and make appropriate arrangements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an extraction unit, a determination unit, a first presentation unit, and a second presentation unit. The reception unit receives email text from corporate clients. The extraction unit analyzes the email text received by the reception unit and extracts requested items. The determination unit determines necessary arrangements based on the requested items extracted by the extraction unit. The first presentation unit presents information indicating the arrangements determined by the determination unit to the person in charge. The second presentation unit cooperates with an internal system to obtain information necessary for the arrangements determined by the determination unit and presents it to the person in charge. Effect of the Invention

[0007] The system according to the embodiment can efficiently extract requests from corporate customers and make appropriate arrangements. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that analyzes requests from corporate customers from email text and presents arrangements that are deemed necessary to a person in charge. This system accepts email text from a corporate customer, analyzes the received email text, and extracts the requested items. Based on the extracted requested items, the system determines the necessary arrangements. Information indicating the determined arrangements is presented to the person in charge, and further information required for the arrangements is obtained in cooperation with an internal system and presented to the person in charge. This mechanism makes it possible to respond quickly and accurately to requests from corporate customers. For example, a corporate customer sends a request such as "I would like to reserve a conference room" by email. This email text is accepted by the acceptance unit. Next, the accepted email text is analyzed and the requested items are extracted. The extraction unit uses AI processing or generation AI processing to extract the requested items such as "reserving a conference room" from the email text. For example, the request item "reserving a conference room" is extracted from the text "I would like to reserve a conference room". Based on the extracted requested items, the system determines the necessary arrangements. The determination unit uses AI processing or generation AI processing to determine that "reservation of a conference room is required" based on the extracted request item. For example, based on the request item "reservation of a conference room", it determines that "reservation of a conference room is required". It presents information indicating the determined arrangement to the person in charge. The first presentation unit uses processing that may include AI processing to present information indicating the determined arrangement to the person in charge. For example, it presents information that "reservation of a conference room is required" to the person in charge. Furthermore, it cooperates with an internal system to obtain information necessary for the determined arrangement and presents it to the person in charge. The second presentation unit cooperates with an internal system using processing that may include AI processing to obtain information necessary for reserving a conference room. For example, it obtains a list of available conference rooms and reservation status and presents it to the person in charge. This mechanism makes it possible to respond quickly and accurately to requests from corporate customers. For example, if a customer requests "I want to reserve a conference room", the system can respond quickly by automatically obtaining information necessary for reserving a conference room and presenting it to the person in charge. This allows the system to respond quickly and accurately to requests from corporate customers.

[0029] The system according to the embodiment includes a reception unit, an extraction unit, a determination unit, a first presentation unit, and a second presentation unit. The reception unit receives email text from a corporate customer. The email text from the corporate customer includes, for example, text format, HTML format, and the presence or absence of an attachment, but is not limited to such examples. The reception unit, for example, receives an email from a mail server and imports the email text as data for analyzing the email text. The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts request items. The request items include, for example, service requests, product orders, inquiries, etc., but are not limited to such examples. The extraction unit extracts keywords from the email text using, for example, natural language processing technology, and identifies the request items. The determination unit uses AI processing or generation AI processing to determine necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, for example, meeting arrangements, product delivery, and service provision, but are not limited to such examples. The determination unit determines appropriate arrangements based on, for example, the contents of the request items. The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge, using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge, for example, using a display or a mobile device. The second presentation unit obtains information necessary for the arrangement determined by the determination unit in cooperation with an in-house system, using a process that may include AI processing, and presents the information to the person in charge. The second presentation unit obtains the necessary information, for example, by cooperation with an in-house database or reservation system. This enables the system to quickly and accurately respond to requests from corporate customers.

[0030] The reception unit receives email text from corporate customers. Email text from corporate customers includes, but is not limited to, for example, text format, HTML format, and the presence or absence of an attachment. The reception unit, for example, receives email from a mail server and imports the email text as data for analyzing the email text. Specifically, the reception unit communicates with the mail server using a protocol such as IMAP or POP3, and stores the received email in a database for analysis. The analysis of the email text includes preprocessing such as character code conversion, removal of HTML tags, and decompression of attachments. As a result, the email text is converted into a format suitable for processing by the analysis unit. Furthermore, the reception unit simultaneously acquires metadata such as sender information, reception date and time, and subject of the email, and provides this information to the analysis unit. As a result, the reception unit can comprehensively collect data including not only the content of the email text but also related metadata, and provide information necessary for subsequent processing.

[0031] The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts the request items. The request items include, but are not limited to, service requests, product orders, inquiries, etc. The extraction unit extracts keywords from the email text using, for example, natural language processing technology to identify the request items. Specifically, the extraction unit analyzes the email text in detail using natural language processing technologies such as tokenization, part-of-speech tagging, and dependency analysis. When using generation AI, for example, a model trained in advance can be used to understand the content of the email text and automatically generate the request items. The generation AI uses, for example, a large-scale language model such as Gemini or GPT to understand the context and extract appropriate request items. This allows the extraction unit to analyze the content of the email text with high accuracy and quickly and accurately identify the request items. Furthermore, the extraction unit can classify the extracted request items by category and organize the data to facilitate subsequent processing.

[0032] The determination unit uses AI processing or generation AI processing to determine necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, but are not limited to, for example, arranging a meeting, shipping a product, and providing a service. The determination unit determines appropriate arrangements, for example, based on the contents of the request items. Specifically, the determination unit analyzes the contents of the request items and determines optimal arrangements based on past data and pre-set rules. When the generation AI is used, for example, appropriate arrangements can be automatically generated using the contents of the request items as input. The generation AI can, for example, learn past arrangement data and predict optimal arrangements for similar request items. This allows the determination unit to quickly and accurately determine appropriate arrangements for the request items and smoothly proceed with subsequent processing. Furthermore, the determination unit can set the priority order of arrangements and quickly respond to requests with high urgency.

[0033] The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge using, for example, a display or a mobile device. Specifically, the first presentation unit displays the arrangement information received from the determination unit in a visually easy-to-understand format. For example, information such as the contents of the arrangement, the deadline, and the person in charge is displayed in a list so that the person in charge can respond quickly. Furthermore, the first presentation unit updates the progress of the arrangement in real time so that the person in charge can understand the current situation. When AI is used, for example, notifications and alerts for the person in charge can be automatically generated based on the priority and urgency of the arrangement. As a result, the first presentation unit can provide information for the person in charge to efficiently proceed with the arrangement and support the smooth execution of the business.

[0034] The second presentation unit obtains the information necessary for arrangements determined by the determination unit in cooperation with an in-house system using a process that may include AI processing, and presents it to the person in charge. The second presentation unit obtains the necessary information by, for example, cooperating with an in-house database or reservation system. Specifically, the second presentation unit automatically obtains related data from the in-house system and provides the person in charge with the information necessary for arrangements. For example, the availability of a conference room necessary for arranging a meeting and inventory information necessary for shipping a product are obtained in real time, and presented to the person in charge. When AI is used, for example, related data can be automatically analyzed and optimal information for arrangements can be extracted. This allows the second presentation unit to enable the person in charge to quickly and accurately obtain the information necessary for arrangements, and improve the efficiency of work. Furthermore, the second presentation unit displays the obtained information in a visually easy-to-understand format, so that the person in charge can easily understand it. This allows the second presentation unit to provide information for the person in charge to quickly and accurately proceed with arrangements, and to support the smooth execution of work.

[0035] When the arrangement is for a conference, the second presentation unit can obtain a list of available conference rooms or a reservation status as information required for reserving a conference room. Information required for reserving a conference room includes, for example, the location of the conference room, the available time, and the presence or absence of equipment, but is not limited to such examples. For example, the second presentation unit cooperates with an in-house conference room reservation system to obtain a list of available conference rooms and a reservation status. For example, the second presentation unit obtains a list of currently available conference rooms from the conference room reservation system and presents it to the person in charge. In addition, the second presentation unit can also obtain the reservation status of the conference room in real time and present it to the person in charge. This makes it possible to quickly obtain information required for reserving a conference room. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit inputs data obtained from the conference room reservation system to the generation AI, and causes the generation AI to execute information required for reserving a conference room.

[0036] The reception unit can analyze the past email reception history and select an efficient reception method. For example, the reception unit analyzes the patterns of emails that have been frequently received in the past, and preferentially receives emails with similar patterns. In addition, if the reception unit finds that many emails arrive during a specific time period based on the past reception history, it can strengthen reception during that time period. Furthermore, the reception unit can preferentially receive emails from specific customers based on the past reception history. This allows efficient email reception by selecting the optimal reception method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past email reception history data to the generation AI and cause the generation AI to select an efficient reception method.

[0037] The reception unit can filter the email text based on the user's current work situation and field of interest when receiving the email text. For example, the reception unit preferentially receives emails related to a project currently in progress by the user. The reception unit can also filter emails related to the user's field of interest and preferentially receive important emails. Furthermore, the reception unit can also preferentially receive emails with a high level of urgency according to the user's work situation. This makes it possible to filter emails according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation and field of interest data to the generation AI and cause the generation AI to execute email filtering.

[0038] The reception unit can preferentially receive highly relevant emails in consideration of the geographical location information of the user when receiving the text of the email. For example, when the user is in a specific area, the reception unit preferentially receives emails related to the area. In addition, when the user is on a business trip, the reception unit can also preferentially receive emails related to the business trip destination. Furthermore, when the user is at home, the reception unit can also preferentially receive emails related to the home. This allows highly relevant emails to be preferentially received based on the geographical location information of the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant emails.

[0039] The reception unit can analyze the user's social media activity and receive related emails when receiving the text of the email. For example, the reception unit preferentially receives emails related to topics mentioned by the user on social media. The reception unit can also preferentially receive emails related to areas of high interest from the user's social media activity. Furthermore, the reception unit can also preferentially receive emails from companies and organizations that the user follows on social media. This makes it possible to preferentially receive related emails based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the prioritization of related emails.

[0040] The extraction unit can adjust the level of detail of the extraction based on the importance of the email text during extraction. For example, the extraction unit extracts detailed request items from email text with high importance. The extraction unit can also extract concise request items from email text with low importance. Furthermore, the extraction unit can dynamically adjust the level of detail of the extraction according to the importance. This allows efficient extraction of request items by adjusting the level of detail of the extraction according to the importance of the email text. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input importance data of the email text to the generation AI and cause the generation AI to adjust the level of detail of the extraction.

[0041] The extraction unit can apply different extraction algorithms depending on the category of the email at the time of extraction. For example, the extraction unit applies an extraction algorithm related to conference room reservations to a meeting-related email. The extraction unit can also apply an extraction algorithm related to purchasing procedures to a purchase-related email. Furthermore, the extraction unit can apply an extraction algorithm related to support requests to a technical support-related email. This makes it possible to extract more accurate request items by applying an extraction algorithm according to the category of the email. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email category data to the generation AI and cause the generation AI to apply different extraction algorithms.

[0042] The extraction unit can determine the extraction priority based on the time when the email was sent during extraction. For example, the extraction unit preferentially extracts emails sent recently. The extraction unit can also adjust the extraction priority of emails sent old, depending on their importance. Furthermore, the extraction unit can also preferentially extract emails with high urgency based on the time when they were sent. This allows the extraction priority to be determined based on the time when the email was sent, so that requests with high urgency can be preferentially extracted. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email sending time data to the generation AI and have the generation AI execute the determination of the extraction priority.

[0043] The extraction unit can adjust the extraction order based on the relevance of the emails at the time of extraction. For example, the extraction unit preferentially extracts emails with high relevance. The extraction unit can also adjust the extraction order of emails with low relevance according to their importance. Furthermore, the extraction unit can dynamically adjust the extraction order based on the relevance. As a result, by adjusting the extraction order based on the relevance of the emails, highly relevant requests can be preferentially extracted. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email relevance data to the generation AI and cause the generation AI to adjust the extraction order.

[0044] The determination unit can improve the accuracy of the determination by considering the interrelationships of the requested items when making a determination. For example, when there are multiple requested items, the determination unit determines the optimal arrangement by considering the interrelationships between them. In addition, when the requested items are related, the determination unit can also determine the arrangement by considering the relevance. Furthermore, the determination unit can adjust the priority of the arrangement based on the interrelationships between the requested items. This makes it possible to make a more accurate determination of the arrangement by considering the interrelationships between the requested items. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input interrelationship data between the requested items to the generation AI and cause the generation AI to improve the accuracy of the determination.

[0045] The determination unit can make a determination while taking into consideration attribute information of the person who submitted the request item. For example, if the person who submitted the request item is an important customer, the determination unit determines the request item as a priority. The determination unit can also determine the arrangement by taking into consideration the past request history of the person who submitted the request item. Furthermore, the determination unit can adjust the priority of the arrangement based on the attribute information of the person who submitted the request item. In this way, by taking into consideration the attribute information of the person who submitted the request item, it is possible to determine the request items of important customers as a priority. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute information data of the person who submitted the request item to the generation AI and have the generation AI execute the determination.

[0046] The determination unit can make a determination taking into consideration the geographical distribution of the requested items when making a determination. For example, when the requested items are concentrated in a specific region, the determination unit determines arrangements for that region as a priority. The determination unit can also adjust the priority of arrangements based on the geographical distribution of the requested items. Furthermore, the determination unit can also determine optimal arrangements taking into consideration the geographical distribution. In this way, arrangements related to a region can be determined as a priority by taking into consideration the geographical distribution of the requested items. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data of the requested items to the generation AI and cause the generation AI to execute the determination.

[0047] The judgment unit can improve the accuracy of the judgment by referring to literature related to the requested item at the time of judgment. The judgment unit, for example, refers to literature related to the requested item and judges the arrangement. The judgment unit can also adjust the priority of the arrangement based on the related literature. Furthermore, the judgment unit can also improve the accuracy of the judgment by referring to literature related to the requested item. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data of the requested item to the generation AI and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0048] The first presentation unit can adjust the level of detail of the presentation based on the importance of the arrangement at the time of presentation. For example, the first presentation unit presents detailed information for arrangements with high importance. In addition, the first presentation unit can also present concise information for arrangements with low importance. Furthermore, the first presentation unit can dynamically adjust the level of detail of the presentation according to the importance of the arrangement. This allows efficient information presentation by adjusting the level of detail of the presentation according to the importance of the arrangement. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input the importance data of the arrangement to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0049] The first presentation unit can apply different presentation algorithms depending on the category of arrangements at the time of presentation. For example, the first presentation unit applies a presentation algorithm related to conference room reservations to meeting-related arrangements. The first presentation unit can also apply a presentation algorithm related to purchasing procedures to purchasing-related arrangements. Furthermore, the first presentation unit can also apply a presentation algorithm related to support requests to technical support-related arrangements. This makes it possible to present information more accurately by applying a presentation algorithm according to the category of arrangements. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input category data of arrangements to the generation AI and cause the generation AI to apply different presentation algorithms.

[0050] The first presentation unit can determine the priority of presentation based on the submission time of the arrangement at the time of presentation. For example, the first presentation unit preferentially presents arrangements submitted recently. In addition, the first presentation unit can also adjust the priority of presentation of arrangements submitted older according to their importance. Furthermore, the first presentation unit can also preferentially present arrangements with high urgency based on the submission time. In this way, by determining the priority of presentation based on the submission time of the arrangement, arrangements with high urgency can be presented preferentially. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input the submission time data of the arrangement to the generation AI and cause the generation AI to execute the determination of the priority of presentation.

[0051] The first presentation unit can adjust the order of presentation based on the relevance of the arrangements at the time of presentation. The first presentation unit, for example, preferentially presents arrangements with high relevance. The first presentation unit can also adjust the order of presentation of arrangements with low relevance according to the importance. Furthermore, the first presentation unit can dynamically adjust the order of presentation based on the relevance. As a result, by adjusting the order of presentation based on the relevance of the arrangements, arrangements with high relevance can be presented preferentially. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the first presentation unit can input the relevance data of the arrangements to the generation AI and cause the generation AI to adjust the order of presentation.

[0052] The second presentation unit can improve the accuracy of presentation by considering the interrelationships of arrangements when presenting. For example, when there are multiple arrangements, the second presentation unit presents optimal information by considering the interrelationships between them. In addition, when arrangements are related, the second presentation unit can also present information by considering the relevance. Furthermore, the second presentation unit can adjust the priority of information based on the interrelationships between arrangements. This makes it possible to present information with higher accuracy by considering the interrelationships between arrangements. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input interrelationship data between arrangements to the generation AI and cause the generation AI to execute improvement of the accuracy of presentation.

[0053] The second presentation unit can present information taking into consideration attribute information of the person who submitted the arrangement when presenting the information. For example, if the submitter is an important customer, the second presentation unit preferentially presents information related to the arrangement. The second presentation unit can also present information taking into consideration the submitter's past request history. Furthermore, the second presentation unit can adjust the priority of information based on the submitter's attribute information. In this way, by taking into consideration the attribute information of the submitter of the arrangement, information related to the arrangement of an important customer can be preferentially presented. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input attribute information data of the submitter to the generation AI and cause the generation AI to execute the presentation.

[0054] The second presentation unit can present the information while taking into consideration the geographic distribution of the arrangements. For example, when arrangements are concentrated in a specific area, the second presentation unit preferentially presents information about that area. The second presentation unit can also adjust the priority of information based on the geographic distribution of the arrangements. Furthermore, the second presentation unit can present optimal information while taking into consideration the geographic distribution. This makes it possible to preferentially present information related to the area by taking into consideration the geographic distribution of the arrangements. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input the geographic distribution data of the arrangements to the generation AI and cause the generation AI to execute the presentation.

[0055] The second presentation unit can improve the accuracy of presentation by referring to related literature of the arrangement at the time of presentation. The second presentation unit, for example, refers to literature related to the arrangement and presents information. The second presentation unit can also adjust the priority of information based on the related literature. Furthermore, the second presentation unit can also improve the accuracy of presentation by referring to related literature of the arrangement. As a result, the accuracy of presentation is improved by referring to related literature. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input related literature data of the arrangement to the generation AI and cause the generation AI to execute improvement of the accuracy of presentation.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past behavior history and select the optimal response when receiving the email text. For example, for a user who has sent many emails in a specific time period in the past, reception during that time period can be strengthened. In addition, emails containing specific keywords can be preferentially received from the past behavior history. Furthermore, emails from specific users can be preferentially received based on the past behavior history. This allows efficient email reception by selecting the optimal reception method based on the past behavior history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past behavior history data to the generation AI and cause the generation AI to select an efficient reception method.

[0058] The extraction unit can estimate the urgency of the request based on the contents of the email text, and determine the priority of extraction according to the urgency. For example, the request is extracted with priority from an email text including a request with a high urgency. Also, the request can be extracted with normal priority from an email text including a request with a low urgency. Furthermore, the level of detail of extraction can be dynamically adjusted according to the urgency. This allows efficient extraction of the request by determining the priority of extraction according to the urgency of the email text. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input the urgency data of the email text to the generation AI, and cause the generation AI to determine the priority of extraction.

[0059] The determination unit can estimate the cost of arrangements based on the contents of the request items and determine the priority of arrangements according to the cost. For example, arrangements with high costs can be determined as a priority. Also, arrangements with low costs can be determined with normal priority. Furthermore, the level of detail of arrangements can be dynamically adjusted according to the cost. This enables efficient determination of arrangements by determining the priority of arrangements according to the cost of arrangements. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input cost data of arrangements to the generation AI and cause the generation AI to determine the priority of arrangements.

[0060] The second presentation unit can monitor the progress of the arrangement in real time and update the presented information according to the progress. For example, when the arrangement is completed, the information is immediately presented to the person in charge. Also, when the arrangement is delayed, the delay information can be presented to the person in charge. Furthermore, necessary additional arrangements can be presented according to the progress of the arrangement. This allows the progress of the arrangement to be monitored in real time and appropriate information to be presented, thereby enabling efficient arrangement management. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input the progress data of the arrangement to the generation AI and cause the generation AI to update the information.

[0061] The extraction unit can automatically classify the category of the request based on the content of the email text, and apply different extraction algorithms to each category. For example, an extraction algorithm related to conference room reservations can be applied to meeting-related emails. Also, an extraction algorithm related to purchasing procedures can be applied to purchasing-related emails. Furthermore, an extraction algorithm related to support requests can be applied to technical support-related emails. This makes it possible to extract more accurate request items by applying an extraction algorithm according to the category of the email. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email category data to the generation AI and cause the generation AI to apply different extraction algorithms.

[0062] The determination unit can evaluate the risk of arrangements based on the contents of the request items and determine the priority of arrangements according to the risk. For example, arrangements with high risk are determined as a priority. Also, arrangements with low risk can be determined with normal priority. Furthermore, the level of detail of arrangements can be dynamically adjusted according to the risk. This enables efficient determination of arrangements by determining the priority of arrangements according to the risk of the arrangements. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input risk data of arrangements to the generation AI and cause the generation AI to determine the priority of arrangements.

[0063] The second presentation unit can analyze the arrangement history data and present optimal information based on past arrangement patterns. For example, if a similar arrangement has been made in the past, information related to that arrangement is presented preferentially. In addition, a specific pattern can be extracted from the past arrangement history and information can be presented based on the pattern. Furthermore, information related to a specific arrangement can be presented preferentially based on the past arrangement history. This allows efficient arrangement management by presenting optimal information based on the past arrangement history. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the second presentation unit can input the arrangement history data to the generation AI and cause the generation AI to present optimal information.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit receives the text of an email from a corporate client. The text of an email from a corporate client may be in text format, HTML format, or may have an attachment, but is not limited to these examples. The reception unit receives the email from a mail server, for example, and imports the email text as data for analysis. Step 2: The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts the request items. The request items include, but are not limited to, requests for services, product orders, inquiries, etc. The extraction unit extracts keywords from the email text using, for example, natural language processing technology, and identifies the request items. Step 3: The determination unit uses AI processing or generation AI processing to determine the necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, but are not limited to, arrangements for meetings, delivery of goods, provision of services, etc. The determination unit determines appropriate arrangements based on, for example, the contents of the request items. Step 4: The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge using, for example, a display or a mobile device. Step 5: The second presentation unit, using a process that may include AI processing, cooperates with an in-house system to obtain the information necessary for arrangements determined by the determination unit, and presents it to the person in charge. The second presentation unit cooperates with, for example, an in-house database or reservation system to obtain the necessary information.

[0066] (Example 2) The system according to the embodiment of the present invention is a system that analyzes requests from corporate customers from email text and presents arrangements that are deemed necessary to a person in charge. This system accepts email text from a corporate customer, analyzes the received email text, and extracts the requested items. Based on the extracted requested items, the system determines the necessary arrangements. Information indicating the determined arrangements is presented to the person in charge, and further information required for the arrangements is obtained in cooperation with an internal system and presented to the person in charge. This mechanism makes it possible to respond quickly and accurately to requests from corporate customers. For example, a corporate customer sends a request such as "I would like to reserve a conference room" by email. This email text is accepted by the acceptance unit. Next, the accepted email text is analyzed and the requested items are extracted. The extraction unit uses AI processing or generation AI processing to extract the requested items such as "reserving a conference room" from the email text. For example, the request item "reserving a conference room" is extracted from the text "I would like to reserve a conference room". Based on the extracted requested items, the system determines the necessary arrangements. The determination unit uses AI processing or generation AI processing to determine that "reservation of a conference room is required" based on the extracted request item. For example, based on the request item "reservation of a conference room", it determines that "reservation of a conference room is required". It presents information indicating the determined arrangement to the person in charge. The first presentation unit uses processing that may include AI processing to present information indicating the determined arrangement to the person in charge. For example, it presents information that "reservation of a conference room is required" to the person in charge. Furthermore, it cooperates with an internal system to obtain information necessary for the determined arrangement and presents it to the person in charge. The second presentation unit cooperates with an internal system using processing that may include AI processing to obtain information necessary for reserving a conference room. For example, it obtains a list of available conference rooms and reservation status and presents it to the person in charge. This mechanism makes it possible to respond quickly and accurately to requests from corporate customers. For example, if a customer requests "I want to reserve a conference room", the system can respond quickly by automatically obtaining information necessary for reserving a conference room and presenting it to the person in charge. This allows the system to respond quickly and accurately to requests from corporate customers.

[0067] The system according to the embodiment includes a reception unit, an extraction unit, a determination unit, a first presentation unit, and a second presentation unit. The reception unit receives email text from a corporate customer. The email text from the corporate customer includes, for example, text format, HTML format, and the presence or absence of an attachment, but is not limited to such examples. The reception unit, for example, receives an email from a mail server and imports the email text as data for analyzing the email text. The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts request items. The request items include, for example, service requests, product orders, inquiries, etc., but are not limited to such examples. The extraction unit extracts keywords from the email text using, for example, natural language processing technology, and identifies the request items. The determination unit uses AI processing or generation AI processing to determine necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, for example, meeting arrangements, product delivery, and service provision, but are not limited to such examples. The determination unit determines appropriate arrangements based on, for example, the contents of the request items. The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge, using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge, for example, using a display or a mobile device. The second presentation unit obtains information necessary for the arrangement determined by the determination unit in cooperation with an in-house system, using a process that may include AI processing, and presents the information to the person in charge. The second presentation unit obtains the necessary information, for example, by cooperation with an in-house database or reservation system. This enables the system to quickly and accurately respond to requests from corporate customers.

[0068] The reception unit receives email text from corporate customers. Email text from corporate customers includes, but is not limited to, for example, text format, HTML format, and the presence or absence of an attachment. The reception unit, for example, receives email from a mail server and imports the email text as data for analyzing the email text. Specifically, the reception unit communicates with the mail server using a protocol such as IMAP or POP3, and stores the received email in a database for analysis. The analysis of the email text includes preprocessing such as character code conversion, removal of HTML tags, and decompression of attachments. As a result, the email text is converted into a format suitable for processing by the analysis unit. Furthermore, the reception unit simultaneously acquires metadata such as sender information, reception date and time, and subject of the email, and provides this information to the analysis unit. As a result, the reception unit can comprehensively collect data including not only the content of the email text but also related metadata, and provide information necessary for subsequent processing.

[0069] The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts the request items. The request items include, but are not limited to, service requests, product orders, inquiries, etc. The extraction unit extracts keywords from the email text using, for example, natural language processing technology to identify the request items. Specifically, the extraction unit analyzes the email text in detail using natural language processing technologies such as tokenization, part-of-speech tagging, and dependency analysis. When using generation AI, for example, a model trained in advance can be used to understand the content of the email text and automatically generate the request items. The generation AI uses, for example, a large-scale language model such as Gemini or GPT to understand the context and extract appropriate request items. This allows the extraction unit to analyze the content of the email text with high accuracy and quickly and accurately identify the request items. Furthermore, the extraction unit can classify the extracted request items by category and organize the data to facilitate subsequent processing.

[0070] The determination unit uses AI processing or generation AI processing to determine necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, but are not limited to, for example, arranging a meeting, shipping a product, and providing a service. The determination unit determines appropriate arrangements, for example, based on the contents of the request items. Specifically, the determination unit analyzes the contents of the request items and determines optimal arrangements based on past data and pre-set rules. When the generation AI is used, for example, appropriate arrangements can be automatically generated using the contents of the request items as input. The generation AI can, for example, learn past arrangement data and predict optimal arrangements for similar request items. This allows the determination unit to quickly and accurately determine appropriate arrangements for the request items and smoothly proceed with subsequent processing. Furthermore, the determination unit can set the priority order of arrangements and quickly respond to requests with high urgency.

[0071] The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge using, for example, a display or a mobile device. Specifically, the first presentation unit displays the arrangement information received from the determination unit in a visually easy-to-understand format. For example, information such as the contents of the arrangement, the deadline, and the person in charge is displayed in a list so that the person in charge can respond quickly. Furthermore, the first presentation unit updates the progress of the arrangement in real time so that the person in charge can understand the current situation. When AI is used, for example, notifications and alerts for the person in charge can be automatically generated based on the priority and urgency of the arrangement. As a result, the first presentation unit can provide information for the person in charge to efficiently proceed with the arrangement and support the smooth execution of the business.

[0072] The second presentation unit obtains the information necessary for arrangements determined by the determination unit in cooperation with an in-house system using a process that may include AI processing, and presents it to the person in charge. The second presentation unit obtains the necessary information by, for example, cooperating with an in-house database or reservation system. Specifically, the second presentation unit automatically obtains related data from the in-house system and provides the person in charge with the information necessary for arrangements. For example, the availability of a conference room necessary for arranging a meeting and inventory information necessary for shipping a product are obtained in real time, and presented to the person in charge. When AI is used, for example, related data can be automatically analyzed and optimal information for arrangements can be extracted. This allows the second presentation unit to enable the person in charge to quickly and accurately obtain the information necessary for arrangements, and improve the efficiency of work. Furthermore, the second presentation unit displays the obtained information in a visually easy-to-understand format, so that the person in charge can easily understand it. This allows the second presentation unit to provide information for the person in charge to quickly and accurately proceed with arrangements, and to support the smooth execution of work.

[0073] When the arrangement is for a conference, the second presentation unit can obtain a list of available conference rooms or a reservation status as information required for reserving a conference room. Information required for reserving a conference room includes, for example, the location of the conference room, the available time, and the presence or absence of equipment, but is not limited to such examples. For example, the second presentation unit cooperates with an in-house conference room reservation system to obtain a list of available conference rooms and a reservation status. For example, the second presentation unit obtains a list of currently available conference rooms from the conference room reservation system and presents it to the person in charge. In addition, the second presentation unit can also obtain the reservation status of the conference room in real time and present it to the person in charge. This makes it possible to quickly obtain information required for reserving a conference room. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit inputs data obtained from the conference room reservation system to the generation AI, and causes the generation AI to execute information required for reserving a conference room.

[0074] The reception unit can estimate the user's emotion and adjust the timing of receiving the email text based on the estimated user's emotion. For example, when the user is stressed, the reception unit quickly receives the email and responds immediately. Also, when the user is relaxed, the reception unit can receive the email at the normal reception timing. Furthermore, when the user is in a hurry, the reception unit can preferentially receive the email and quickly start processing. This allows for more appropriate response by adjusting the reception timing of the email text according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the past email reception history and select an efficient reception method. For example, the reception unit analyzes the patterns of emails that have been frequently received in the past, and preferentially receives emails with similar patterns. In addition, if the reception unit finds that many emails arrive during a specific time period based on the past reception history, it can strengthen reception during that time period. Furthermore, the reception unit can preferentially receive emails from specific customers based on the past reception history. This allows efficient email reception by selecting the optimal reception method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past email reception history data to the generation AI and cause the generation AI to select an efficient reception method.

[0076] The reception unit can filter the email text based on the user's current work situation and field of interest when receiving the email text. For example, the reception unit preferentially receives emails related to a project currently in progress by the user. The reception unit can also filter emails related to the user's field of interest and preferentially receive important emails. Furthermore, the reception unit can also preferentially receive emails with a high level of urgency according to the user's work situation. This makes it possible to filter emails according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation and field of interest data to the generation AI and cause the generation AI to execute email filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the received emails based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives important emails. Also, when the user is relaxed, the reception unit can also preferentially receive emails with a normal priority. Furthermore, when the user is in a hurry, the reception unit can preferentially receive emails with a high urgency. In this way, by determining the priority of emails according to the user's emotion, important emails can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The reception unit can preferentially receive highly relevant emails in consideration of the geographical location information of the user when receiving the text of the email. For example, when the user is in a specific area, the reception unit preferentially receives emails related to the area. In addition, when the user is on a business trip, the reception unit can also preferentially receive emails related to the business trip destination. Furthermore, when the user is at home, the reception unit can also preferentially receive emails related to the home. This allows highly relevant emails to be preferentially received based on the geographical location information of the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant emails.

[0079] The reception unit can analyze the user's social media activity and receive related emails when receiving the text of the email. For example, the reception unit preferentially receives emails related to topics mentioned by the user on social media. The reception unit can also preferentially receive emails related to areas of high interest from the user's social media activity. Furthermore, the reception unit can also preferentially receive emails from companies and organizations that the user follows on social media. This makes it possible to preferentially receive related emails based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the prioritization of related emails.

[0080] The extraction unit can estimate the user's emotion and adjust the extraction method of the request items based on the estimated user's emotion. For example, when the user is feeling stressed, the extraction unit preferentially extracts concise request items. In addition, when the user is relaxed, the extraction unit can also extract detailed request items. Furthermore, when the user is in a hurry, the extraction unit can preferentially extract request items with high urgency. This allows more appropriate request items to be extracted by adjusting the extraction method of the request items according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the extraction method of the request items.

[0081] The extraction unit can adjust the level of detail of the extraction based on the importance of the email text during extraction. For example, the extraction unit extracts detailed request items from email text with high importance. The extraction unit can also extract concise request items from email text with low importance. Furthermore, the extraction unit can dynamically adjust the level of detail of the extraction according to the importance. This allows efficient extraction of request items by adjusting the level of detail of the extraction according to the importance of the email text. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input importance data of the email text to the generation AI and cause the generation AI to adjust the level of detail of the extraction.

[0082] The extraction unit can apply different extraction algorithms depending on the category of the email at the time of extraction. For example, the extraction unit applies an extraction algorithm related to conference room reservations to a meeting-related email. The extraction unit can also apply an extraction algorithm related to purchasing procedures to a purchase-related email. Furthermore, the extraction unit can apply an extraction algorithm related to support requests to a technical support-related email. This makes it possible to extract more accurate request items by applying an extraction algorithm according to the category of the email. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email category data to the generation AI and cause the generation AI to apply different extraction algorithms.

[0083] The extraction unit can estimate the user's emotion and determine the priority of the request items based on the estimated user's emotion. For example, when the user is feeling stressed, the extraction unit preferentially extracts important request items. In addition, when the user is relaxed, the extraction unit can also preferentially extract request items with a normal priority. Furthermore, when the user is in a hurry, the extraction unit can preferentially extract request items with a high urgency. In this way, by determining the priority of the request items according to the user's emotion, it is possible to preferentially extract important request items. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the request items.

[0084] The extraction unit can determine the extraction priority based on the time when the email was sent during extraction. For example, the extraction unit preferentially extracts emails sent recently. The extraction unit can also adjust the extraction priority of emails sent old, depending on their importance. Furthermore, the extraction unit can also preferentially extract emails with high urgency based on the time when they were sent. This allows the extraction priority to be determined based on the time when the email was sent, so that requests with high urgency can be preferentially extracted. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email sending time data to the generation AI and have the generation AI execute the determination of the extraction priority.

[0085] The extraction unit can adjust the extraction order based on the relevance of the emails at the time of extraction. For example, the extraction unit preferentially extracts emails with high relevance. The extraction unit can also adjust the extraction order of emails with low relevance according to their importance. Furthermore, the extraction unit can dynamically adjust the extraction order based on the relevance. As a result, by adjusting the extraction order based on the relevance of the emails, highly relevant requests can be preferentially extracted. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email relevance data to the generation AI and cause the generation AI to adjust the extraction order.

[0086] The determination unit can estimate the user's emotion and adjust the criteria for arrangement based on the estimated user's emotion. For example, when the user is stressed, the determination unit determines a request item that needs to be arranged quickly with priority. In addition, when the user is relaxed, the determination unit can also make arrangements with normal criteria. Furthermore, when the user is in a hurry, the determination unit can also determine arrangements with high urgency with priority. This allows for more appropriate arrangement determination by adjusting the arrangement criteria according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the arrangement criteria.

[0087] The determination unit can improve the accuracy of the determination by considering the interrelationships of the requested items when making a determination. For example, when there are multiple requested items, the determination unit determines the optimal arrangement by considering the interrelationships between them. In addition, when the requested items are related, the determination unit can also determine the arrangement by considering the relevance. Furthermore, the determination unit can adjust the priority of the arrangement based on the interrelationships between the requested items. This makes it possible to make a more accurate determination of the arrangement by considering the interrelationships between the requested items. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input interrelationship data between the requested items to the generation AI and cause the generation AI to improve the accuracy of the determination.

[0088] The determination unit can make a determination while taking into consideration attribute information of the person who submitted the request item. For example, if the person who submitted the request item is an important customer, the determination unit determines the request item as a priority. The determination unit can also determine the arrangement by taking into consideration the past request history of the person who submitted the request item. Furthermore, the determination unit can adjust the priority of the arrangement based on the attribute information of the person who submitted the request item. In this way, by taking into consideration the attribute information of the person who submitted the request item, it is possible to determine the request items of important customers as a priority. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute information data of the person who submitted the request item to the generation AI and have the generation AI execute the determination.

[0089] The determination unit can estimate the user's emotion and adjust the display order of the determination results based on the estimated user's emotion. For example, when the user is stressed, the determination unit preferentially displays important determination results. Furthermore, when the user is relaxed, the determination unit can also display the determination results in a normal order. Furthermore, when the user is in a hurry, the determination unit can also preferentially display judgment results with a high degree of urgency. In this way, by adjusting the display order of the judgment results according to the user's emotion, important judgment results can be preferentially displayed. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the judgment results.

[0090] The determination unit can make a determination taking into consideration the geographical distribution of the requested items when making a determination. For example, when the requested items are concentrated in a specific region, the determination unit determines arrangements for that region as a priority. The determination unit can also adjust the priority of arrangements based on the geographical distribution of the requested items. Furthermore, the determination unit can also determine optimal arrangements taking into consideration the geographical distribution. In this way, arrangements related to a region can be determined as a priority by taking into consideration the geographical distribution of the requested items. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data of the requested items to the generation AI and cause the generation AI to execute the determination.

[0091] The judgment unit can improve the accuracy of the judgment by referring to literature related to the requested item at the time of judgment. The judgment unit, for example, refers to literature related to the requested item and judges the arrangement. The judgment unit can also adjust the priority of the arrangement based on the related literature. Furthermore, the judgment unit can also improve the accuracy of the judgment by referring to literature related to the requested item. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data of the requested item to the generation AI and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0092] The first presentation unit can estimate the user's emotion and adjust the presentation expression method based on the estimated user's emotion. For example, when the user is stressed, the first presentation unit provides a concise and highly visible expression method. In addition, when the user is relaxed, the first presentation unit can also provide a detailed information-containing expression method. Furthermore, when the user is in a hurry, the first presentation unit can also provide a concise expression method that focuses on the main points. This allows more appropriate information presentation by adjusting the presentation expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the presentation expression method.

[0093] The first presentation unit can adjust the level of detail of the presentation based on the importance of the arrangement at the time of presentation. For example, the first presentation unit presents detailed information for arrangements with high importance. In addition, the first presentation unit can also present concise information for arrangements with low importance. Furthermore, the first presentation unit can dynamically adjust the level of detail of the presentation according to the importance of the arrangement. This allows efficient information presentation by adjusting the level of detail of the presentation according to the importance of the arrangement. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input the importance data of the arrangement to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0094] The first presentation unit can apply different presentation algorithms depending on the category of arrangements at the time of presentation. For example, the first presentation unit applies a presentation algorithm related to conference room reservations to meeting-related arrangements. The first presentation unit can also apply a presentation algorithm related to purchasing procedures to purchasing-related arrangements. Furthermore, the first presentation unit can also apply a presentation algorithm related to support requests to technical support-related arrangements. This makes it possible to present information more accurately by applying a presentation algorithm according to the category of arrangements. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input category data of arrangements to the generation AI and cause the generation AI to apply different presentation algorithms.

[0095] The first presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. For example, when the user is stressed, the first presentation unit presents a short, to-the-point presentation. In addition, when the user is relaxed, the first presentation unit can also present a longer presentation including detailed information. Furthermore, when the user is in a hurry, the first presentation unit can present a short, quickly understandable presentation. This allows for more appropriate information presentation by adjusting the length of the presentation according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the presentation.

[0096] The first presentation unit can determine the priority of presentation based on the submission time of the arrangement at the time of presentation. For example, the first presentation unit preferentially presents arrangements submitted recently. In addition, the first presentation unit can also adjust the priority of presentation of arrangements submitted older according to their importance. Furthermore, the first presentation unit can also preferentially present arrangements with high urgency based on the submission time. In this way, by determining the priority of presentation based on the submission time of the arrangement, arrangements with high urgency can be presented preferentially. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the first presentation unit can input the submission time data of the arrangement to the generation AI and cause the generation AI to execute the determination of the priority of presentation.

[0097] The first presentation unit can adjust the order of presentation based on the relevance of the arrangements at the time of presentation. The first presentation unit, for example, preferentially presents arrangements with high relevance. The first presentation unit can also adjust the order of presentation of arrangements with low relevance according to the importance. Furthermore, the first presentation unit can dynamically adjust the order of presentation based on the relevance. As a result, by adjusting the order of presentation based on the relevance of the arrangements, arrangements with high relevance can be presented preferentially. A part or all of the above-mentioned processing in the first presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the first presentation unit can input the relevance data of the arrangements to the generation AI and cause the generation AI to adjust the order of presentation.

[0098] The second presentation unit can estimate the user's emotion and determine the priority of information to be presented based on the estimated user's emotion. For example, when the user is feeling stressed, the second presentation unit preferentially presents important information. In addition, when the user is relaxed, the second presentation unit can also present information with normal priority. Furthermore, when the user is in a hurry, the second presentation unit can also present information with high urgency preferentially. In this way, important information can be presented preferentially by determining the priority of information to be presented according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the second presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the second presentation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0099] The second presentation unit can improve the accuracy of presentation by considering the interrelationships of arrangements when presenting. For example, when there are multiple arrangements, the second presentation unit presents optimal information by considering the interrelationships between them. In addition, when arrangements are related, the second presentation unit can also present information by considering the relevance. Furthermore, the second presentation unit can adjust the priority of information based on the interrelationships between arrangements. This makes it possible to present information with higher accuracy by considering the interrelationships between arrangements. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input interrelationship data between arrangements to the generation AI and cause the generation AI to execute improvement of the accuracy of presentation.

[0100] The second presentation unit can present information taking into consideration attribute information of the person who submitted the arrangement when presenting the information. For example, if the submitter is an important customer, the second presentation unit preferentially presents information related to the arrangement. The second presentation unit can also present information taking into consideration the submitter's past request history. Furthermore, the second presentation unit can adjust the priority of information based on the submitter's attribute information. In this way, by taking into consideration the attribute information of the submitter of the arrangement, information related to the arrangement of an important customer can be preferentially presented. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input attribute information data of the submitter to the generation AI and cause the generation AI to execute the presentation.

[0101] The second presentation unit can estimate the user's emotion and adjust the display method of the information to be presented based on the estimated user's emotion. For example, when the user is stressed, the second presentation unit provides a simple and highly visible display method. In addition, when the user is relaxed, the second presentation unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the second presentation unit can also provide a simple display method that focuses on the main points. This allows for more appropriate information presentation by adjusting the display method of the information to be presented according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the second presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the second presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0102] The second presentation unit can present the information while taking into consideration the geographic distribution of the arrangements. For example, when arrangements are concentrated in a specific area, the second presentation unit preferentially presents information about that area. The second presentation unit can also adjust the priority of information based on the geographic distribution of the arrangements. Furthermore, the second presentation unit can present optimal information while taking into consideration the geographic distribution. This makes it possible to preferentially present information related to the area by taking into consideration the geographic distribution of the arrangements. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input the geographic distribution data of the arrangements to the generation AI and cause the generation AI to execute the presentation.

[0103] The second presentation unit can improve the accuracy of presentation by referring to related literature of the arrangement at the time of presentation. The second presentation unit, for example, refers to literature related to the arrangement and presents information. The second presentation unit can also adjust the priority of information based on the related literature. Furthermore, the second presentation unit can also improve the accuracy of presentation by referring to related literature of the arrangement. As a result, the accuracy of presentation is improved by referring to related literature. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input related literature data of the arrangement to the generation AI and cause the generation AI to execute improvement of the accuracy of presentation.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the user's past behavior history and select the optimal response when receiving the email text. For example, for a user who has sent many emails in a specific time period in the past, reception during that time period can be strengthened. In addition, emails containing specific keywords can be preferentially received from the past behavior history. Furthermore, emails from specific users can be preferentially received based on the past behavior history. This allows efficient email reception by selecting the optimal reception method based on the past behavior history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past behavior history data to the generation AI and cause the generation AI to select an efficient reception method.

[0106] The extraction unit can estimate the urgency of the request based on the contents of the email text, and determine the priority of extraction according to the urgency. For example, the request is extracted with priority from an email text including a request with a high urgency. Also, the request can be extracted with normal priority from an email text including a request with a low urgency. Furthermore, the level of detail of extraction can be dynamically adjusted according to the urgency. This allows efficient extraction of the request by determining the priority of extraction according to the urgency of the email text. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input the urgency data of the email text to the generation AI, and cause the generation AI to determine the priority of extraction.

[0107] The determination unit can estimate the cost of arrangements based on the contents of the request items and determine the priority of arrangements according to the cost. For example, arrangements with high costs can be determined as a priority. Also, arrangements with low costs can be determined with normal priority. Furthermore, the level of detail of arrangements can be dynamically adjusted according to the cost. This enables efficient determination of arrangements by determining the priority of arrangements according to the cost of arrangements. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input cost data of arrangements to the generation AI and cause the generation AI to determine the priority of arrangements.

[0108] The first presentation unit can estimate the user's emotion and adjust the format of the information to be presented based on the estimated user's emotion. For example, when the user is stressed, a concise and highly visible format can be provided. When the user is relaxed, a format including detailed information can be provided. When the user is in a hurry, a concise format with the main points can be provided. This allows more appropriate information presentation by adjusting the format of the information to be presented according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the format.

[0109] The second presentation unit can monitor the progress of the arrangement in real time and update the presented information according to the progress. For example, when the arrangement is completed, the information is immediately presented to the person in charge. Also, when the arrangement is delayed, the delay information can be presented to the person in charge. Furthermore, necessary additional arrangements can be presented according to the progress of the arrangement. This allows the progress of the arrangement to be monitored in real time and appropriate information to be presented, thereby enabling efficient arrangement management. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the second presentation unit can input the progress data of the arrangement to the generation AI and cause the generation AI to update the information.

[0110] The reception unit can estimate the user's emotion and adjust the filtering criteria of the received email based on the estimated user's emotion. For example, when the user is stressed, important emails are preferentially received. Also, when the user is relaxed, emails can be received with normal filtering criteria. Furthermore, when the user is in a hurry, emails with high urgency can be preferentially received. In this way, important emails can be preferentially received by adjusting the filtering criteria of emails according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the filtering criteria.

[0111] The extraction unit can automatically classify the category of the request based on the content of the email text, and apply different extraction algorithms to each category. For example, an extraction algorithm related to conference room reservations can be applied to meeting-related emails. Also, an extraction algorithm related to purchasing procedures can be applied to purchasing-related emails. Furthermore, an extraction algorithm related to support requests can be applied to technical support-related emails. This makes it possible to extract more accurate request items by applying an extraction algorithm according to the category of the email. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input email category data to the generation AI and cause the generation AI to apply different extraction algorithms.

[0112] The determination unit can evaluate the risk of arrangements based on the contents of the request items and determine the priority of arrangements according to the risk. For example, arrangements with high risk are determined as a priority. Also, arrangements with low risk can be determined with normal priority. Furthermore, the level of detail of arrangements can be dynamically adjusted according to the risk. This enables efficient determination of arrangements by determining the priority of arrangements according to the risk of the arrangements. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input risk data of arrangements to the generation AI and cause the generation AI to determine the priority of arrangements.

[0113] The first presentation unit can estimate the user's emotion and adjust the order of information to be presented based on the estimated user's emotion. For example, when the user is feeling stressed, important information is presented preferentially. Also, when the user is relaxed, information can be presented in a normal order. Furthermore, when the user is in a hurry, information with a high degree of urgency can be presented preferentially. In this way, important information can be presented preferentially by adjusting the order of information to be presented according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the order of information.

[0114] The second presentation unit can analyze the arrangement history data and present optimal information based on past arrangement patterns. For example, if a similar arrangement has been made in the past, information related to that arrangement is presented preferentially. In addition, a specific pattern can be extracted from the past arrangement history and information can be presented based on the pattern. Furthermore, information related to a specific arrangement can be presented preferentially based on the past arrangement history. This allows efficient arrangement management by presenting optimal information based on the past arrangement history. A part or all of the above-mentioned processing in the second presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the second presentation unit can input the arrangement history data to the generation AI and cause the generation AI to present optimal information.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The reception unit receives the text of an email from a corporate client. The text of an email from a corporate client may be in text format, HTML format, or may have an attachment, but is not limited to these examples. The reception unit receives the email from a mail server, for example, and imports the email text as data for analysis. Step 2: The extraction unit uses AI processing or generation AI processing to analyze the email text received by the reception unit and extracts the request items. The request items include, but are not limited to, requests for services, product orders, inquiries, etc. The extraction unit extracts keywords from the email text using, for example, natural language processing technology, and identifies the request items. Step 3: The determination unit uses AI processing or generation AI processing to determine the necessary arrangements based on the request items extracted by the extraction unit. The necessary arrangements include, but are not limited to, arrangements for meetings, delivery of goods, provision of services, etc. The determination unit determines appropriate arrangements based on, for example, the contents of the request items. Step 4: The first presentation unit presents information indicating the arrangement determined by the determination unit to the person in charge using a process that may include AI processing. The first presentation unit displays the arrangement information to the person in charge using, for example, a display or a mobile device. Step 5: The second presentation unit, using a process that may include AI processing, cooperates with an in-house system to obtain the information necessary for arrangements determined by the determination unit, and presents it to the person in charge. The second presentation unit cooperates with, for example, an in-house database or reservation system to obtain the necessary information.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the first presentation unit is realized by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the second presentation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0128] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the first presentation unit is realized by the display of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the second presentation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the first presentation unit is realized by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the second presentation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the first presentation unit is realized by the display of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the second presentation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) a reception section for receiving email messages from corporate customers; an extraction unit that analyzes the text of the email accepted by the acceptance unit and extracts a request item; a determination unit for determining necessary arrangements based on the request items extracted by the extraction unit; a first presentation unit that presents information indicating the arrangement determined by the determination unit to a person in charge; a second presentation unit that cooperates with an in-house system to acquire the information necessary for the arrangement determined by the determination unit and presents the information to the person in charge. A system characterized by: (Appendix 2) The second presentation unit is When the arrangement is for a conference, obtain a list of available conference rooms or their reservation status as information necessary for reserving a conference room. 2. The system of claim 1, (Appendix 3) The reception unit is Estimate the user's emotions and adjust the timing of email acceptance based on the estimated user emotions 2. The system of claim 1, (Appendix 4) The reception unit is Analyze past email reception history and select the most efficient reception method 2. The system of claim 1, (Appendix 5) The reception unit is When receiving emails, filters are performed based on the user's current work situation and areas of interest. 2. The system of claim 1, (Appendix 6) The reception unit is Infer user emotions and prioritize emails based on the inferred emotions 2. The system of claim 1, (Appendix 7) The reception unit is When accepting emails, the system takes into account the user's geographic location information and prioritizes emails that are highly relevant. 2. The system of claim 1, (Appendix 8) The reception unit is When accepting emails, analyze the user's social media activity and send relevant emails to them. 2. The system of claim 1, (Appendix 9) The extraction unit is Estimate the user's emotions and adjust the method of extracting requests based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The extraction unit is During extraction, adjust extraction granularity based on importance of email content 2. The system of claim 1, (Appendix 11) The extraction unit is During extraction, apply different extraction algorithms depending on the category of the email 2. The system of claim 1, (Appendix 12) The extraction unit is Estimate the user's emotions and prioritize requests based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The extraction unit is During extraction, prioritize extractions based on when the email was sent 2. The system of claim 1, (Appendix 14) The extraction unit is During extraction, adjust extraction order based on email relevance 2. The system of claim 1, (Appendix 15) The determination unit is To estimate a user's emotion and adjust a criteria for dispatching based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The determination unit is When making judgments, improve the accuracy of judgments by considering the interrelationships between requests. 2. The system of claim 1, (Appendix 17) The determination unit is When making a judgment, the attribute information of the person submitting the request is taken into consideration. 2. The system of claim 1, (Appendix 18) The determination unit is A user's emotion is estimated, and a display order of the judgment results is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The determination unit is When making a judgment, the geographical distribution of the requested items will be taken into consideration. 2. The system of claim 1, (Appendix 20) The determination unit is When making a judgment, refer to related literature on the requested item to improve the accuracy of the judgment. 2. The system of claim 1, (Appendix 21) The first presentation unit is Estimate the user's emotions and adjust the presentation based on the estimated user's emotions 2. The system of claim 1, (Appendix 22) The first presentation unit is When making a submission, adjust the level of detail in the submission based on the importance of the arrangement 2. The system of claim 1, (Appendix 23) The first presentation unit is When making offers, apply different offer algorithms depending on the category of the offer 2. The system of claim 1, (Appendix 24) The first presentation unit is Estimate the user's emotion and adjust the presentation length based on the estimated user emotion. 2. The system of claim 1, (Appendix 25) The first presentation unit is At the time of submission, prioritize submissions based on when the arrangements were submitted 2. The system of claim 1, (Appendix 26) The first presentation unit is During presentation, adjust the order of presentation based on the relevance of arrangements 2. The system of claim 1, (Appendix 27) The second presentation unit is Estimate the user's emotions and prioritize the information to be presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The second presentation unit is When presenting, improve the accuracy of the presentation by taking into account the interrelationships of arrangements 2. The system of claim 1, (Appendix 29) The second presentation unit is When presenting, the attribute information of the person submitting the request is taken into consideration. 2. The system of claim 1, (Appendix 30) The second presentation unit is Estimate the user's emotions and adjust the way information is displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 31) The second presentation unit is When making a presentation, the presentation should take into account the geographical distribution of arrangements. 2. The system of claim 1, (Appendix 32) The second presentation unit is When presenting, refer to related literature on the arrangement to improve the accuracy of the presentation 2. The system of claim 1, [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving email messages from corporate customers; an extraction unit that analyzes the text of the email accepted by the acceptance unit and extracts a request item; a determination unit for determining necessary arrangements based on the request items extracted by the extraction unit; a first presentation unit that presents information indicating the arrangement determined by the determination unit to a person in charge; a second presentation unit that cooperates with an in-house system to acquire the information necessary for the arrangement determined by the determination unit and presents the information to the person in charge. A system characterized by:

2. The second presentation unit is When the arrangement is for a conference, obtain a list of available conference rooms or their reservation status as information necessary for reserving a conference room.

2. The system of claim 1 .

3. The reception unit is Estimate the user's emotions and adjust the timing of email acceptance based on the estimated user emotions 2. The system of claim 1 .

4. The reception unit is Analyze past email reception history and select the most efficient reception method 2. The system of claim 1 .

5. The reception unit is When receiving emails, filters are performed based on the user's current work situation and areas of interest.

2. The system of claim 1 .

6. The reception unit is Infer user emotions and prioritize emails based on the inferred emotions 2. The system of claim 1 .

7. The reception unit is When accepting emails, the system takes into account the user's geographic location information and prioritizes emails that are highly relevant.

2. The system of claim 1 .

8. The reception unit is When accepting emails, analyze the user's social media activity and send relevant emails to them.

2. The system of claim 1 .

9. The extraction unit is Estimate the user's emotions and adjust the method of extracting requests based on the estimated user emotions.

2. The system of claim 1 .

10. The extraction unit is During extraction, adjust extraction granularity based on importance of email content 2. The system of claim 1 .

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