System

Abstract

An object of a system according to an embodiment is to automate the creation of a daily work report, improve efficiency, and prevent omission of report items.SOLUTION: A system includes a reception unit, a transcription unit, a question unit, and a creation unit. The reception unit receives a voice input from a user. The transcription unit analyzes the voice information received by the reception unit and converts the voice information into text data. The questioning unit asks the user a question based on the text data generated by the transcription unit. The creation part creates a business daily report based on the text data including the additional information obtained by the question part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques require manual input of text when creating a daily business report, which is inefficient and can lead to omission of report items.

[0005] The system according to the embodiment aims to automate the creation of daily business reports, improve efficiency, and prevent omission of report items. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a transcription unit, a question unit, and a creation unit. The reception unit receives voice input from a user. The transcription unit analyzes the voice information received by the reception unit and converts it into text data. The question unit asks the user questions based on the text data generated by the transcription unit. The creation unit creates a daily business report based on the text data including additional information obtained by the question unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the creation of daily business reports, thereby improving efficiency and preventing omission of report items. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A daily work report creation system according to an embodiment of the present invention automatically creates daily work reports by speaking into a wirelessly connected smartphone. The daily work report creation system automatically transcribes what a user says into the smartphone in an interactive manner. This eliminates the hassle of text input and streamlines daily work reports. Furthermore, the interactive nature of the system prevents missing report items. For example, in the daily work report creation system, a user speaks into the smartphone. The user verbally reports the work content and progress status. For example, the user might say, "Today, I will report on the progress of Project A. I had a meeting in the morning, and prepared materials in the afternoon." This information is input into the smartphone. The smartphone then analyzes the input voice information and automatically transcribes it. The smartphone uses voice recognition technology to convert the user's speech into text data. For example, the voice information, "Today, I will report on the progress of Project A. I had a meeting in the morning, and prepared materials in the afternoon," is converted into text data. Furthermore, the daily work report creation system interactively asks the user questions via the smartphone, preventing missing report items. For example, a smartphone can ask a question such as, "Is there anything else you need to report?" and the user can provide additional information to enrich the report. This allows the daily work report creation system to streamline daily work reports and reduce work time. Users are freed from the hassle of typing text and can create daily work reports smoothly and interactively. It also prevents omissions of report items, enabling accurate work reports. This allows the daily work report creation system to efficiently transcribe the user's voice input, supplement information through questions, and automatically create daily work reports. For example, if a user says, "Today, I'll report on the progress of Project A. I had a meeting in the morning and prepared materials in the afternoon," the smartphone converts this content into text data and asks, "Is there anything else you need to report?" If the user answers, "Nothing in particular," the daily work report is automatically created. This allows the daily work report creation system to contribute to streamlining daily work reports and reducing work time.

[0029] A daily work report creation system according to an embodiment includes a reception unit, a transcription unit, a questioning unit, and a creation unit. The reception unit receives voice input from a user. Examples of voice input from the user include, but are not limited to, voice input using a microphone or a smartphone. The reception unit receives voice input from, for example, a wirelessly connected smartphone. Examples of wireless connections include, but are not limited to, Bluetooth (registered trademark) connections and Wi-Fi connections. The transcription unit analyzes the voice information received by the reception unit and converts it into text data. The transcription unit converts the voice information into text data using, for example, a voice recognition technology. Examples of voice recognition technology include, but are not limited to, voice recognition using deep learning and HMM (hidden Markov model). The questioning unit asks the user questions based on the text data generated by the transcription unit. The questioning unit asks the user questions to prevent the user from missing report items, for example. Examples of the question formats include, but are not limited to, checklist-style questions and open-ended questions. The creation unit creates a daily work report based on text data including additional information obtained by the questioning unit. The creation unit creates a daily work report based on text data including additional information from a user, for example. The format of the daily work report includes, for example, daily report items and formats, but is not limited to these examples. As a result, the daily work report creation system according to the embodiment can efficiently transcribe a user's voice input, supplement information through questions, and automatically create a daily work report.

[0030] The transcription unit can convert speech information into text data using speech recognition technology. Examples of speech recognition technology include, but are not limited to, speech recognition using deep learning and HMM (Hidden Markov Model). The transcription unit can convert speech information into text data using, for example, speech recognition technology using deep learning. The transcription unit can also convert speech information into text data using HMM (Hidden Markov Model). Furthermore, the transcription unit can convert what the user says into text data with high accuracy using speech recognition technology. As a result, speech information can be accurately converted into text data using speech recognition technology.

[0031] The questioning unit can ask the user questions to prevent omission of report items. The questioning unit, for example, asks the user questions in the form of a checklist. For example, the questioning unit asks a question such as, "Is there anything else you need to report?" The questioning unit can also ask open-ended questions. For example, the questioning unit asks a question such as, "Please tell me in detail about today's work." Furthermore, the questioning unit can ask additional questions based on the user's answers. For example, if the user answers, "I had a meeting in the morning," the questioning unit asks an additional question such as, "Please tell me about the content of the meeting." This prevents omission of report items and enriches the content of the daily work report.

[0032] The creation unit can create a daily business report based on text data including additional information from the user. The creation unit creates the daily business report based on, for example, text data including additional information from the user. The format of the daily business report includes, for example, daily report items, formats, etc., but is not limited to these examples. The creation unit creates a detailed daily business report based on, for example, the additional information from the user. The creation unit can also create a concise daily business report based on the user's answers. Furthermore, the creation unit can customize the content of the daily business report according to the user's business content. This makes it possible to create an accurate daily business report based on the text data including the additional information.

[0033] The reception unit can receive voice input from a wirelessly connected smartphone. Examples of wireless connections include, but are not limited to, Bluetooth and Wi-Fi connections. For example, the reception unit receives voice input from the smartphone using a Bluetooth connection. The reception unit can also receive voice input from the smartphone using a Wi-Fi connection. Furthermore, the reception unit can efficiently receive voice input from a user using a wirelessly connected smartphone. This facilitates voice input using a wirelessly connected smartphone.

[0034] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit preferentially suggests a voice input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the most efficient reception method. In this way, the optimal reception method can be provided to the user by analyzing the past voice input history.

[0035] The reception unit can filter the voice input based on the user's current work situation and areas of interest when receiving the voice input. For example, the reception unit preferentially receives voice input related to a project currently being carried out by the user. The reception unit can also filter related voice input based on the user's areas of interest. Furthermore, the reception unit can also preferentially receive voice input of high importance according to the user's work situation. In this way, by filtering the voice input based on the user's work situation and areas of interest, highly relevant information can be preferentially received.

[0036] When receiving voice input, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit converts the voice into text using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze information using image recognition technology. This allows the optimal reception means to be selected depending on the user's input method, thereby enabling smooth reception of voice input.

[0037] When receiving a voice input, the reception unit can prioritize receiving a highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving a voice input related to that location. The reception unit can also filter highly relevant voice inputs based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can also receive the optimal voice input based on the user's current location. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving a highly relevant voice input.

[0038] The reception unit can analyze the user's social media activity and receive related inputs when receiving a voice input. For example, the reception unit can preferentially receive related voice inputs based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and filter out highly relevant voice inputs. Furthermore, the reception unit can also receive related voice inputs with reference to the social media activity of the user's friends. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant voice inputs.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving voice input. The reception unit can, for example, propose an optimal voice input reception method based on feedback provided by the user in the past. The reception unit can also improve the voice input reception procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the voice input reception interface based on the user's feedback. In this way, the optimal voice input reception method can be provided by reflecting the user's past feedback.

[0040] The transcription unit can adjust the level of detail of the transcription based on the importance of the audio when transcribing. For example, the transcription unit can transcribe the audio of an important meeting in detail. The transcription unit can also transcribe the audio of a daily business report in a concise manner. Furthermore, the transcription unit can transcribe audio with a high level of urgency by focusing on the main points. In this way, by adjusting the level of detail of the transcription based on the importance of the audio, efficient transcription can be performed.

[0041] The transcription unit can apply different transcription algorithms depending on the category of audio when transcribing. For example, the transcription unit applies a transcription algorithm specifically for meetings to audio from meetings. The transcription unit can also apply a transcription algorithm specifically for presentations to audio from presentations. Furthermore, the transcription unit can apply a general transcription algorithm to audio from daily business reports. In this way, by applying different transcription algorithms depending on the category of audio, highly accurate transcription can be achieved.

[0042] When transcribing, the transcription unit can improve the accuracy of the transcription by referring to the user's past transcription results. For example, the transcription unit analyzes the user's past transcription results and corrects errors. The transcription unit can also learn specific terms and expressions based on the user's past transcription results. Furthermore, the transcription unit can also improve the accuracy of the transcription by referring to the user's past transcription results. In this way, the accuracy of the transcription is improved by referring to the past transcription results.

[0043] When transcribing, the transcription unit can determine the priority of transcription based on the time of submission of the audio. For example, the transcription unit prioritizes transcription of audio with a high level of urgency. The transcription unit can also prioritize transcription of audio with an approaching submission deadline. Furthermore, the transcription unit can postpone transcription of audio with more time to submit. In this way, by determining the priority of transcription based on the time of submission of the audio, audio with a high level of urgency can be prioritized.

[0044] The transcription unit can adjust the transcription order based on the relevance of the audio during transcription. For example, the transcription unit can prioritize the transcription of audio from important meetings. The transcription unit can also postpone the transcription of audio from daily business reports. Furthermore, the transcription unit can prioritize the transcription of audio with high relevance. In this way, by adjusting the transcription order based on the relevance of the audio, important audio can be prioritized.

[0045] During transcription, the transcription unit can adjust the use of technical terms in the transcription according to the user's level of expertise. For example, if the user has technical knowledge, the transcription unit can transcribe using a lot of technical terms. Also, if the user has general knowledge, the transcription unit can transcribe while avoiding technical terms. Furthermore, the transcription unit can transcribe using appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, the transcription can be performed using appropriate expressions.

[0046] When asking a question, the questioning unit can optimize the current question by referring to past question data. For example, the questioning unit asks a related question based on questions that the user has answered in the past. The questioning unit can also analyze past question data and suggest the most appropriate question. Furthermore, the questioning unit can also optimize the current question by referring to the user's past answer history. In this way, the current question can be optimized by referring to past question data.

[0047] When asking a question, the questioning unit can ask the question taking into consideration the user's attribute information. The questioning unit asks relevant questions depending on the user's job duties, for example. The questioning unit can also ask appropriate questions depending on the user's age and gender. Furthermore, the questioning unit can ask optimal questions based on the user's work experience. In this way, by taking into consideration the user's attribute information, highly relevant questions can be asked.

[0048] When asking a question, the questioning unit can weight the question based on the frequency of the user's answers. For example, the questioning unit prioritizes questions that the user frequently answers. The questioning unit can also weight questions with high importance based on the frequency of the user's answers. Furthermore, the questioning unit can weight the current question with reference to questions that the user has answered in the past. In this way, by weighting questions based on the frequency of the user's answers, important questions can be prioritized.

[0049] When asking a question, the questioning unit can ask the question taking into consideration the geographical distribution of the user. For example, if the user is in a specific area, the questioning unit asks a question related to that area. The questioning unit can also ask a highly relevant question based on the geographical distribution of the user. Furthermore, if the user is moving, the questioning unit can also ask an optimal question based on the user's current location. In this way, by taking into consideration the geographical distribution of the user, it is possible to ask a highly relevant question.

[0050] When asking a question, the questioning unit can improve the accuracy of the question by referring to related literature. The questioning unit, for example, asks a detailed question based on related literature. The questioning unit can also improve the accuracy of the question by referring to related literature. Furthermore, the questioning unit can ask an optimal question based on literature related to the user's business content. In this way, by referring to related literature, the accuracy of the question can be improved.

[0051] The questioning unit can ask a question taking into consideration the market value of the user. For example, the questioning unit asks a question of high importance based on the market value of the user. The questioning unit can also ask a question of high relevance taking into consideration the market value of the user. Furthermore, the questioning unit can ask an optimal question based on the market value of the user. In this way, by taking into consideration the market value of the user, a question of high relevance can be asked.

[0052] The creation unit can analyze the user's past business reports and select the optimal creation method when creating the report. For example, the creation unit can suggest the optimal creation method based on the user's past business reports. The creation unit can also analyze the user's past business reports and select an efficient creation method. Furthermore, the creation unit can improve the creation method by referring to the user's past business reports. In this way, the optimal creation method can be selected by analyzing the past business reports.

[0053] The creation unit can customize the content of the daily work report based on the user's current work status when creating the report. For example, the creation unit includes relevant content in the daily work report based on the user's current work status. The creation unit can also adjust the content of the daily work report according to the user's work progress. Furthermore, the creation unit can create an optimal daily work report taking into account the user's current work status. In this way, by customizing the content of the daily work report based on the current work status, a more accurate daily work report can be created.

[0054] The creation unit can improve the creation method of the daily work report by reflecting user feedback when creating the report. The creation unit can improve the creation method of the daily work report based on, for example, user feedback. The creation unit can also customize the content of the daily work report by reflecting past user feedback. Furthermore, the creation unit can also optimize the creation procedure for the daily work report by referring to user feedback. In this way, the creation method of the daily work report can be improved by reflecting user feedback.

[0055] The creation unit can create an optimal daily work report by taking into account the user's geographical location information. For example, when the user is in a specific location, the creation unit includes work content related to that location in the daily work report. The creation unit can also reflect related work content in the daily work report based on the user's geographical location information. Furthermore, when the user is traveling, the creation unit can also create an optimal daily work report based on the user's current location. In this way, by taking into account the user's geographical location information, a highly relevant daily work report can be created.

[0056] The creation unit can analyze the user's social media activity and suggest content for the daily work report when creating the report. For example, the creation unit includes related work content in the daily work report based on content posted by the user on social media. The creation unit can also analyze the user's social media activity and suggest related work content. Furthermore, the creation unit can also reflect related work content in the daily work report by referring to the social media activity of the user's friends. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant content for the daily work report.

[0057] The creation unit can customize the content of the daily work report by reflecting the user's past feedback when creating the report. The creation unit customizes the content of the daily work report based on, for example, the user's past feedback. The creation unit can also improve the content of the daily work report by reflecting the user's past feedback. Furthermore, the creation unit can also optimize the content of the daily work report by referring to the user's past feedback. In this way, the content of the daily work report can be customized by reflecting the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When accepting a user's voice input, the acceptance unit can learn the user's past input patterns and select the optimal voice recognition model. For example, if the user has a particular accent or dialect, a voice recognition model that reflects those characteristics can be used. Also, if the user frequently uses specific technical terms, a custom dictionary containing those terms can be applied. Furthermore, the acceptance unit can analyze the user's past input patterns and automatically adjust the most efficient voice recognition settings. This enables voice recognition that meets the user's individual needs and improves transcription accuracy.

[0060] When the questioning unit asks a user questions to prevent overlooking of report items, it can customize the content of the questions by referring to the user's past answer history. For example, if a user has frequently reported a particular item in the past, it can prioritize questions related to that item. It can also ask questions using a different approach for items that the user has avoided answering in the past. Furthermore, it can analyze the user's answer history and select the most effective question format. This allows it to ask effective questions based on the user's past answer history.

[0061] When receiving voice input from a wirelessly connected smartphone, the reception unit can analyze the user's current environmental sounds and automatically adjust the optimal voice input settings. For example, if the user is in a noisy environment, the reception unit can strengthen the noise canceling function. Also, if the user is in a quiet environment, the reception unit can increase the sensitivity of the voice input. Furthermore, if the user is moving, the reception unit can apply voice input settings according to the user's movement. This enables optimal voice input according to the user's environment.

[0062] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, it can preferentially suggest a voice input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the most efficient reception method. In this way, it is possible to provide the optimal reception method to the user by analyzing the past voice input history.

[0063] The transcription unit can apply different transcription algorithms depending on the category of audio when transcribing. For example, a transcription algorithm specifically for meetings can be applied to audio from meetings. The transcription unit can also apply a transcription algorithm specifically for presentations to audio from presentations. Furthermore, the transcription unit can apply a general transcription algorithm to audio from daily business reports. In this way, by applying different transcription algorithms depending on the category of audio, highly accurate transcription can be achieved.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives voice input from the user. Voice input from the user includes, for example, voice input using a microphone or voice input using a smartphone. The reception unit can receive voice input from a wirelessly connected smartphone, and wireless connections include Bluetooth connections and Wi-Fi connections. Step 2: The transcription unit analyzes the audio information received by the reception unit and converts it into text data. The transcription unit converts the audio information into text data using speech recognition technology, which includes deep learning and HMM (hidden Markov model). Step 3: The questioning section asks the user questions based on the text data generated by the transcription section. The questioning section asks the user questions to prevent omission of report items, and the question formats include checklist-style questions and open-ended questions. Step 4: The creation unit creates a daily business report based on the text data including the additional information obtained by the query unit. The creation unit creates a daily business report based on the text data including the additional information from the user, and the format of the daily business report includes daily report items and formats.

[0066] (Example 2) A daily work report creation system according to an embodiment of the present invention automatically creates daily work reports by speaking into a wirelessly connected smartphone. The daily work report creation system automatically transcribes what a user says into the smartphone in an interactive manner. This eliminates the hassle of text input and streamlines daily work reports. Furthermore, the interactive nature of the system prevents missing report items. For example, in the daily work report creation system, a user speaks into the smartphone. The user verbally reports the work content and progress status. For example, the user might say, "Today, I will report on the progress of Project A. I had a meeting in the morning, and prepared materials in the afternoon." This information is input into the smartphone. The smartphone then analyzes the input voice information and automatically transcribes it. The smartphone uses voice recognition technology to convert the user's speech into text data. For example, the voice information, "Today, I will report on the progress of Project A. I had a meeting in the morning, and prepared materials in the afternoon," is converted into text data. Furthermore, the daily work report creation system interactively asks the user questions via the smartphone, preventing missing report items. For example, a smartphone can ask a question such as, "Is there anything else you need to report?" and the user can provide additional information to enrich the report. This allows the daily work report creation system to streamline daily work reports and reduce work time. Users are freed from the hassle of typing text and can create daily work reports smoothly and interactively. It also prevents omissions of report items, enabling accurate work reports. This allows the daily work report creation system to efficiently transcribe the user's voice input, supplement information through questions, and automatically create daily work reports. For example, if a user says, "Today, I'll report on the progress of Project A. I had a meeting in the morning and prepared materials in the afternoon," the smartphone converts this content into text data and asks, "Is there anything else you need to report?" If the user answers, "Nothing in particular," the daily work report is automatically created. This allows the daily work report creation system to contribute to streamlining daily work reports and reducing work time.

[0067] A daily work report creation system according to an embodiment includes a reception unit, a transcription unit, a questioning unit, and a creation unit. The reception unit receives voice input from a user. Examples of voice input from the user include, but are not limited to, voice input using a microphone or a smartphone. The reception unit receives voice input from, for example, a wirelessly connected smartphone. Examples of wireless connections include, but are not limited to, Bluetooth and Wi-Fi connections. The transcription unit analyzes the voice information received by the reception unit and converts it into text data. The transcription unit converts the voice information into text data using, for example, a voice recognition technology. Examples of voice recognition technology include, but are not limited to, voice recognition using deep learning and HMM (hidden Markov model). The questioning unit asks the user questions based on the text data generated by the transcription unit. The questioning unit asks the user questions to prevent the user from missing report items, for example. Examples of the question formats include, but are not limited to, checklist-style questions and open-ended questions. The creation unit creates a daily work report based on text data including additional information obtained by the questioning unit. The creation unit creates a daily work report based on text data including additional information from a user, for example. The format of the daily work report includes, for example, daily report items and formats, but is not limited to these examples. As a result, the daily work report creation system according to the embodiment can efficiently transcribe a user's voice input, supplement information through questions, and automatically create a daily work report.

[0068] The transcription unit can convert speech information into text data using speech recognition technology. Examples of speech recognition technology include, but are not limited to, speech recognition using deep learning and HMM (Hidden Markov Model). The transcription unit can convert speech information into text data using, for example, speech recognition technology using deep learning. The transcription unit can also convert speech information into text data using HMM (Hidden Markov Model). Furthermore, the transcription unit can convert what the user says into text data with high accuracy using speech recognition technology. As a result, speech information can be accurately converted into text data using speech recognition technology.

[0069] The questioning unit can ask the user questions to prevent omission of report items. The questioning unit, for example, asks the user questions in the form of a checklist. For example, the questioning unit asks a question such as, "Is there anything else you need to report?" The questioning unit can also ask open-ended questions. For example, the questioning unit asks a question such as, "Please tell me in detail about today's work." Furthermore, the questioning unit can ask additional questions based on the user's answers. For example, if the user answers, "I had a meeting in the morning," the questioning unit asks an additional question such as, "Please tell me about the content of the meeting." This prevents omission of report items and enriches the content of the daily work report.

[0070] The creation unit can create a daily business report based on text data including additional information from the user. The creation unit creates the daily business report based on, for example, text data including additional information from the user. The format of the daily business report includes, for example, daily report items, formats, etc., but is not limited to these examples. The creation unit creates a detailed daily business report based on, for example, the additional information from the user. The creation unit can also create a concise daily business report based on the user's answers. Furthermore, the creation unit can customize the content of the daily business report according to the user's business content. This makes it possible to create an accurate daily business report based on the text data including the additional information.

[0071] The reception unit can receive voice input from a wirelessly connected smartphone. Examples of wireless connections include, but are not limited to, Bluetooth and Wi-Fi connections. For example, the reception unit receives voice input from the smartphone using a Bluetooth connection. The reception unit can also receive voice input from the smartphone using a Wi-Fi connection. Furthermore, the reception unit can efficiently receive voice input from a user using a wirelessly connected smartphone. This facilitates voice input using a wirelessly connected smartphone.

[0072] The reception unit can estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving the voice input to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of receiving the voice input to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can immediately accept the voice input to provide a prompt response. In this way, by adjusting the timing of receiving the voice input according to the user's emotion, the voice input can be accepted at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit preferentially suggests a voice input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the most efficient reception method. In this way, the optimal reception method can be provided to the user by analyzing the past voice input history.

[0074] The reception unit can filter the voice input based on the user's current work situation and areas of interest when receiving the voice input. For example, the reception unit preferentially receives voice input related to a project currently being carried out by the user. The reception unit can also filter related voice input based on the user's areas of interest. Furthermore, the reception unit can also preferentially receive voice input of high importance according to the user's work situation. In this way, by filtering the voice input based on the user's work situation and areas of interest, highly relevant information can be preferentially received.

[0075] When receiving voice input, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit converts the voice into text using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also analyze information using image recognition technology. This allows the optimal reception means to be selected depending on the user's input method, thereby enabling smooth reception of voice input.

[0076] The reception unit can estimate the user's emotion and determine the priority of the voice input to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit can prioritize receiving voice input with high importance. Furthermore, when the user is relaxed, the reception unit can also receive voice input with normal priority. Furthermore, when the user is in a hurry, the reception unit can also receive voice input with high urgency as the highest priority. In this way, by determining the priority of voice input according to the user's emotion, important information can be received preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When receiving a voice input, the reception unit can prioritize receiving a highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving a voice input related to that location. The reception unit can also filter highly relevant voice inputs based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can also receive the optimal voice input based on the user's current location. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving a highly relevant voice input.

[0078] The reception unit can analyze the user's social media activity and receive related inputs when receiving a voice input. For example, the reception unit can preferentially receive related voice inputs based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and filter out highly relevant voice inputs. Furthermore, the reception unit can also receive related voice inputs with reference to the social media activity of the user's friends. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant voice inputs.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving voice input. The reception unit can, for example, propose an optimal voice input reception method based on feedback provided by the user in the past. The reception unit can also improve the voice input reception procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the voice input reception interface based on the user's feedback. In this way, the optimal voice input reception method can be provided by reflecting the user's past feedback.

[0080] The transcription unit can estimate the user's emotions and adjust the transcription expression method based on the estimated user emotions. For example, if the user is relaxed, the transcription unit can transcribe using natural expressions. If the user is nervous, the transcription unit can also transcribe using concise and clear expressions. If the user is in a hurry, the transcription unit can also transcribe using expressions that focus on the main points. This allows for more appropriate transcription by adjusting the transcription expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The transcription unit can adjust the level of detail of the transcription based on the importance of the audio when transcribing. For example, the transcription unit can transcribe the audio of an important meeting in detail. The transcription unit can also transcribe the audio of a daily business report in a concise manner. Furthermore, the transcription unit can transcribe audio with a high level of urgency by focusing on the main points. In this way, by adjusting the level of detail of the transcription based on the importance of the audio, efficient transcription can be performed.

[0082] The transcription unit can apply different transcription algorithms depending on the category of audio when transcribing. For example, the transcription unit applies a transcription algorithm specifically for meetings to audio from meetings. The transcription unit can also apply a transcription algorithm specifically for presentations to audio from presentations. Furthermore, the transcription unit can apply a general transcription algorithm to audio from daily business reports. In this way, by applying different transcription algorithms depending on the category of audio, highly accurate transcription can be achieved.

[0083] When transcribing, the transcription unit can improve the accuracy of the transcription by referring to the user's past transcription results. For example, the transcription unit analyzes the user's past transcription results and corrects errors. The transcription unit can also learn specific terms and expressions based on the user's past transcription results. Furthermore, the transcription unit can also improve the accuracy of the transcription by referring to the user's past transcription results. In this way, the accuracy of the transcription is improved by referring to the past transcription results.

[0084] The transcription unit can estimate the user's emotions and adjust the length of the transcription based on the estimated user emotions. For example, if the user is relaxed, the transcription unit can provide a detailed transcription. If the user is nervous, the transcription unit can also provide a concise transcription. If the user is in a hurry, the transcription unit can also provide a short transcription that focuses on the main points. This allows the transcription length to be adjusted according to the user's emotions, resulting in a transcription of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] When transcribing, the transcription unit can determine the priority of transcription based on the time of submission of the audio. For example, the transcription unit prioritizes transcription of audio with a high level of urgency. The transcription unit can also prioritize transcription of audio with an approaching submission deadline. Furthermore, the transcription unit can postpone transcription of audio with more time to submit. In this way, by determining the priority of transcription based on the time of submission of the audio, audio with a high level of urgency can be prioritized.

[0086] The transcription unit can adjust the transcription order based on the relevance of the audio during transcription. For example, the transcription unit can prioritize the transcription of audio from important meetings. The transcription unit can also postpone the transcription of audio from daily business reports. Furthermore, the transcription unit can prioritize the transcription of audio with high relevance. In this way, by adjusting the transcription order based on the relevance of the audio, important audio can be prioritized.

[0087] During transcription, the transcription unit can adjust the use of technical terms in the transcription according to the user's level of expertise. For example, if the user has technical knowledge, the transcription unit can transcribe using a lot of technical terms. Also, if the user has general knowledge, the transcription unit can transcribe while avoiding technical terms. Furthermore, the transcription unit can transcribe using appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, the transcription can be performed using appropriate expressions.

[0088] The questioning unit can estimate the user's emotions and adjust the content of the questions based on the estimated user emotions. For example, if the user is relaxed, the questioning unit can ask detailed questions. If the user is nervous, the questioning unit can also ask concise questions. Furthermore, if the user is in a hurry, the questioning unit can also ask questions that focus on the main points. This allows the content of the questions to be adjusted according to the user's emotions, resulting in more appropriate questions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] When asking a question, the questioning unit can optimize the current question by referring to past question data. For example, the questioning unit asks a related question based on questions that the user has answered in the past. The questioning unit can also analyze past question data and suggest the most appropriate question. Furthermore, the questioning unit can also optimize the current question by referring to the user's past answer history. In this way, the current question can be optimized by referring to past question data.

[0090] When asking a question, the questioning unit can ask the question taking into consideration the user's attribute information. The questioning unit asks relevant questions depending on the user's job duties, for example. The questioning unit can also ask appropriate questions depending on the user's age and gender. Furthermore, the questioning unit can ask optimal questions based on the user's work experience. In this way, by taking into consideration the user's attribute information, highly relevant questions can be asked.

[0091] When asking a question, the questioning unit can weight the question based on the frequency of the user's answers. For example, the questioning unit prioritizes questions that the user frequently answers. The questioning unit can also weight questions with high importance based on the frequency of the user's answers. Furthermore, the questioning unit can weight the current question with reference to questions that the user has answered in the past. In this way, by weighting questions based on the frequency of the user's answers, important questions can be prioritized.

[0092] The questioning unit can estimate the user's emotions and adjust the order of questions based on the estimated user emotions. For example, if the user is relaxed, the questioning unit can ask detailed questions first. Also, if the user is nervous, the questioning unit can ask concise questions first. Furthermore, if the user is in a hurry, the questioning unit can ask questions that get to the point first. In this way, by adjusting the order of questions according to the user's emotions, the questions can be asked in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] When asking a question, the questioning unit can ask the question taking into consideration the geographical distribution of the user. For example, if the user is in a specific area, the questioning unit asks a question related to that area. The questioning unit can also ask a highly relevant question based on the geographical distribution of the user. Furthermore, if the user is moving, the questioning unit can also ask an optimal question based on the user's current location. In this way, by taking into consideration the geographical distribution of the user, it is possible to ask a highly relevant question.

[0094] When asking a question, the questioning unit can improve the accuracy of the question by referring to related literature. The questioning unit, for example, asks a detailed question based on related literature. The questioning unit can also improve the accuracy of the question by referring to related literature. Furthermore, the questioning unit can ask an optimal question based on literature related to the user's business content. In this way, by referring to related literature, the accuracy of the question can be improved.

[0095] The questioning unit can ask a question taking into consideration the market value of the user. For example, the questioning unit asks a question of high importance based on the market value of the user. The questioning unit can also ask a question of high relevance taking into consideration the market value of the user. Furthermore, the questioning unit can ask an optimal question based on the market value of the user. In this way, by taking into consideration the market value of the user, a question of high relevance can be asked.

[0096] The creation unit can estimate the user's emotions and adjust the creation method of the daily work report based on the estimated user emotions. For example, if the user is relaxed, the creation unit can create a detailed daily work report. Furthermore, if the user is nervous, the creation unit can also create a concise daily work report. Furthermore, if the user is in a hurry, the creation unit can also create a daily work report that focuses on the main points. In this way, by adjusting the creation method of the daily work report according to the user's emotions, a more appropriate daily work report is created. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The creation unit can analyze the user's past business reports and select the optimal creation method when creating the report. For example, the creation unit can suggest the optimal creation method based on the user's past business reports. The creation unit can also analyze the user's past business reports and select an efficient creation method. Furthermore, the creation unit can improve the creation method by referring to the user's past business reports. In this way, the optimal creation method can be selected by analyzing the past business reports.

[0098] The creation unit can customize the content of the daily work report based on the user's current work status when creating the report. For example, the creation unit includes relevant content in the daily work report based on the user's current work status. The creation unit can also adjust the content of the daily work report according to the user's work progress. Furthermore, the creation unit can create an optimal daily work report taking into account the user's current work status. In this way, by customizing the content of the daily work report based on the current work status, a more accurate daily work report can be created.

[0099] The creation unit can improve the creation method of the daily work report by reflecting user feedback when creating the report. The creation unit can improve the creation method of the daily work report based on, for example, user feedback. The creation unit can also customize the content of the daily work report by reflecting past user feedback. Furthermore, the creation unit can also optimize the creation procedure for the daily work report by referring to user feedback. In this way, the creation method of the daily work report can be improved by reflecting user feedback.

[0100] The creation unit can estimate the user's emotions and determine the priority of the daily work reports based on the estimated user emotions. For example, when the user is relaxed, the creation unit creates the daily work reports with normal priority. Furthermore, when the user is nervous, the creation unit can also create the daily work reports with high importance as a priority. Furthermore, when the user is in a hurry, the creation unit can also create the daily work reports with high urgency as a top priority. In this way, by determining the priority of the daily work reports according to the user's emotions, important daily work reports can be created with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The creation unit can create an optimal daily work report by taking into account the user's geographical location information. For example, when the user is in a specific location, the creation unit includes work content related to that location in the daily work report. The creation unit can also reflect related work content in the daily work report based on the user's geographical location information. Furthermore, when the user is traveling, the creation unit can also create an optimal daily work report based on the user's current location. In this way, by taking into account the user's geographical location information, a highly relevant daily work report can be created.

[0102] The creation unit can analyze the user's social media activity and suggest content for the daily work report when creating the report. For example, the creation unit includes related work content in the daily work report based on content posted by the user on social media. The creation unit can also analyze the user's social media activity and suggest related work content. Furthermore, the creation unit can also reflect related work content in the daily work report by referring to the social media activity of the user's friends. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant content for the daily work report.

[0103] The creation unit can customize the content of the daily work report by reflecting the user's past feedback when creating the report. The creation unit customizes the content of the daily work report based on, for example, the user's past feedback. The creation unit can also improve the content of the daily work report by reflecting the user's past feedback. Furthermore, the creation unit can also optimize the content of the daily work report by referring to the user's past feedback. In this way, the content of the daily work report can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, transcription unit, question unit, and creation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive voice input from the user using the microphone 38B of the smart device 14. For example, the transcription unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice information into text data. For example, the question unit is realized by the control unit 46A of the smart device 14 and asks the user questions to prevent omission of report items. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a daily business report based on the text data including additional information. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, transcription unit, question unit, and creation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a user's voice input using the microphone 238 of the smart glasses 214. For example, the transcription unit is realized by the specific processing unit 290 of the data processing device 12 and converts the voice information into text data. For example, the question unit is realized by the control unit 46A of the smart glasses 214 and asks the user questions to prevent omission of report items. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a daily business report based on the text data including additional information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, transcription unit, question unit, and creation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive voice input from the user using the microphone 238 of the headset-type terminal 314. For example, the transcription unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice information into text data. For example, the question unit is realized by the control unit 46A of the headset-type terminal 314 and asks the user questions to prevent omission of report items. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a daily business report based on text data including additional information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, transcription unit, question unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive voice input from the user using the microphone 238 of the robot 414. For example, the transcription unit is realized by the specific processing unit 290 of the data processing device 12 and converts voice information into text data. For example, the question unit is realized by the control unit 46A of the robot 414 and asks the user questions to prevent omission of report items. For example, the creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a daily business report based on the text data including additional information.

[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] When accepting a user's voice input, the acceptance unit can learn the user's past input patterns and select the optimal voice recognition model. For example, if the user has a particular accent or dialect, a voice recognition model that reflects those characteristics can be used. Also, if the user frequently uses specific technical terms, a custom dictionary containing those terms can be applied. Furthermore, the acceptance unit can analyze the user's past input patterns and automatically adjust the most efficient voice recognition settings. This enables voice recognition that meets the user's individual needs and improves transcription accuracy.

[0106] When converting audio information into text data using speech recognition technology, the transcription unit can estimate the user's emotions and adjust the transcription style based on the estimated emotions. For example, if the user is excited, the transcription unit can use more exclamation marks and emphasis. If the user is calm, the transcription unit can use simple, calm expressions. Furthermore, if the user is tired, the transcription unit can use concise, to-the-point expressions. This allows for appropriate transcription based on the user's emotions.

[0107] When the questioning unit asks a user questions to prevent overlooking of report items, it can customize the content of the questions by referring to the user's past answer history. For example, if a user has frequently reported a particular item in the past, it can prioritize questions related to that item. It can also ask questions using a different approach for items that the user has avoided answering in the past. Furthermore, it can analyze the user's answer history and select the most effective question format. This allows it to ask effective questions based on the user's past answer history.

[0108] When creating a daily work report based on text data including additional information from the user, the creation unit can estimate the user's emotions and adjust the tone and style of the daily report based on the estimated emotions. For example, if the user has positive emotions, the tone of the daily report can be brightened and success stories can be emphasized. If the user has negative emotions, the report can focus on problems and areas for improvement. Furthermore, if the user has neutral emotions, the report can be written in an objective, fact-based manner. This allows an appropriate daily work report to be created according to the user's emotions.

[0109] When receiving voice input from a wirelessly connected smartphone, the reception unit can analyze the user's current environmental sounds and automatically adjust the optimal voice input settings. For example, if the user is in a noisy environment, the reception unit can strengthen the noise canceling function. Also, if the user is in a quiet environment, the reception unit can increase the sensitivity of the voice input. Furthermore, if the user is moving, the reception unit can apply voice input settings according to the user's movement. This enables optimal voice input according to the user's environment.

[0110] The reception unit can estimate the user's emotions and adjust the timing of receiving the voice input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving the voice input to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of receiving the voice input to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can immediately accept the voice input to provide a prompt response. In this way, by adjusting the timing of receiving the voice input according to the user's emotions, the voice input can be accepted at a more appropriate timing.

[0111] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, it can preferentially suggest a voice input method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and select the most efficient reception method. In this way, it is possible to provide the optimal reception method to the user by analyzing the past voice input history.

[0112] The transcription unit can apply different transcription algorithms depending on the category of audio when transcribing. For example, a transcription algorithm specifically for meetings can be applied to audio from meetings. The transcription unit can also apply a transcription algorithm specifically for presentations to audio from presentations. Furthermore, the transcription unit can apply a general transcription algorithm to audio from daily business reports. In this way, by applying different transcription algorithms depending on the category of audio, highly accurate transcription can be achieved.

[0113] The questioning unit can estimate the user's emotions and adjust the content of the questions based on the estimated user's emotions. For example, if the user is relaxed, the questioning unit can ask detailed questions. If the user is nervous, the questioning unit can also ask concise questions. Furthermore, if the user is in a hurry, the questioning unit can also ask questions that focus on the main points. In this way, by adjusting the content of the questions according to the user's emotions, more appropriate questions can be asked.

[0114] The creation unit can estimate the user's emotions and adjust the creation method of the daily work report based on the estimated user's emotions. For example, if the user is relaxed, a detailed daily work report is created. The creation unit can also create a concise daily work report if the user is nervous. Furthermore, the creation unit can also create a daily work report that focuses on the main points if the user is in a hurry. In this way, by adjusting the creation method of the daily work report according to the user's emotions, a more appropriate daily work report is created.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives voice input from the user. Voice input from the user includes, for example, voice input using a microphone or voice input using a smartphone. The reception unit can receive voice input from a wirelessly connected smartphone, and wireless connections include Bluetooth connections and Wi-Fi connections. Step 2: The transcription unit analyzes the audio information received by the reception unit and converts it into text data. The transcription unit converts the audio information into text data using speech recognition technology, which includes deep learning and HMM (hidden Markov model). Step 3: The questioning section asks the user questions based on the text data generated by the transcription section. The questioning section asks the user questions to prevent omission of report items, and the question formats include checklist-style questions and open-ended questions. Step 4: The creation unit creates a daily business report based on the text data including the additional information obtained by the query unit. The creation unit creates a daily business report based on the text data including the additional information from the user, and the format of the daily business report includes daily report items and formats.

[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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications 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 a 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, RAM 30, and 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 audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical 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 carried out in a secure state.

[0128] Fig. 4 shows an example of the 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications 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 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, RAM 30, and 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 audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical 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 carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications 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, RAM 30, and 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 audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to 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 carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) 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 voice indicating a user input regarding 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0171] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by 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 on emotion map 400, and typically 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 in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," 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 USB (Universal Serial Bus) 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] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[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] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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 system configured with a single 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 system that uses a processor that realizes the functions of an 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, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0186] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0188] [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 unit that receives voice input from a user; a transcription unit that analyzes the voice information received by the reception unit and converts it into text data; a questioning unit that asks a user a question based on the text data generated by the transcription unit; a creation unit that creates a daily business report based on text data including the additional information obtained by the query unit. A system characterized by:

2. The transcription unit Converting voice information into text data using voice recognition technology 2. The system of claim 1.

3. The interrogation unit Ask users questions to ensure they do not miss any reporting items 2. The system of claim 1.

4. The creation unit Create a daily work report based on text data with additional information from the user 2. The system of claim 1.

5. The reception unit Accepts voice input via a wirelessly connected smartphone 2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When accepting voice input, it filters it based on the user's current work situation and areas of interest.

2. The system of claim 1.

Images