system

Abstract

The system according to this embodiment aims to easily extract data using voice input. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an extraction unit, and an output unit. The reception unit receives voice input. The analysis unit analyzes the voice input received by the reception unit. The extraction unit extracts data based on the request recognized by the analysis unit. The output unit outputs the data extracted by the extraction unit as an image.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, knowledge of SQL is required to extract necessary data from a data warehouse (DWH), which takes a long time to learn for beginners and may rely on professionals.

[0005] The system according to the embodiment aims to easily extract data using voice input.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, and an output unit. The reception unit receives voice input. The analysis unit analyzes the voice input received by the reception unit. The extraction unit extracts data based on the request recognized by the analysis unit. The output unit outputs the data extracted by the extraction unit as an image. [Effects of the Invention]

[0007] The system according to this embodiment can easily extract data using voice input. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The data extraction system according to an embodiment of the present invention is a system that extracts necessary data from a data warehouse using voice input. In this data extraction system, the user inputs a data extraction request to an agent by voice, the agent extracts the data based on the request, and outputs it as an image. This eliminates the need for learning time even for beginners in data warehouses (SQL), and avoids the reliance on specific individuals that is common in specialized fields. Furthermore, even for experts in data warehouses (SQL), it is expected to reduce work time as they can extract the necessary data verbally on a spot basis. For example, the user voice inputs, "I want the monthly sales data for smartphones." This voice input is analyzed by the agent and recognized as a data extraction request. Next, the agent extracts data from the data warehouse based on the request and presents the output data as an image, asking, "Is this correct?" The user then voices an additional request, "Please narrow the period to November." The agent extracts data again and presents the output data as an image, asking, "Is this correct?" Furthermore, if the user requests "I'd also like to see the buyer's age group," the agent extracts the data again and presents the output data as an image. In this way, the agent extracts data based on the user's voice input and presents the output data sequentially, allowing the user to easily obtain the necessary data. This enables the data extraction system to extract the necessary data from the data warehouse based on the user's voice input and output it as an image.

[0029] The data extraction system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, and an output unit. The reception unit receives voice input from a user. The reception unit can, for example, receive voice input through a microphone. The reception unit can also receive audio file input. The analysis unit analyzes the voice input received by the reception unit. The analysis unit can, for example, convert the voice input into text data using speech recognition technology. The analysis unit can also analyze the content of the voice input using natural language processing technology. The analysis unit analyzes the content of the voice input and recognizes a data extraction request. The extraction unit extracts data based on the request recognized by the analysis unit. The extraction unit can, for example, extract data from a data warehouse using a database query. The extraction unit can also extract data by setting filtering conditions. The output unit outputs the data extracted by the extraction unit as an image. The output unit can, for example, generate graphs or charts to visually display the data. The output unit can also generate infographics to visually display the data. As a result, the data extraction system according to the embodiment can extract necessary data from a data warehouse using voice input and output it as an image.

[0030] The reception unit receives voice input from the user. For example, the reception unit can receive voice input via a microphone. Specifically, voice data is collected when the user speaks into the microphone. Using a high-sensitivity microphone allows for the elimination of ambient noise and the acquisition of clear audio. The reception unit can also accept audio files. For example, users can provide audio data to the reception unit by uploading pre-recorded audio files to the system. The system supports common audio formats such as MP3, WAV, and AAC. Furthermore, the reception unit can apply audio processing technologies such as noise reduction and echo cancellation to improve the quality of the voice input. This ensures that the reception unit accurately receives voice input from the user, allowing for smooth processing in the subsequent analysis unit.

[0031] The analysis unit analyzes the voice input received by the reception unit. The analysis unit can convert voice input into text data using, for example, speech recognition technology. Specifically, the speech recognition engine analyzes the voice data, identifies phonemes and words, and converts them into text. The speech recognition engine achieves high-precision speech recognition by using a model based on deep learning. The analysis unit can also analyze the content of the voice input using natural language processing technology. For example, it performs morphological analysis on the text data to understand the structure and meaning of sentences. Furthermore, the analysis unit analyzes the content of the voice input and recognizes data extraction requests. Specifically, it extracts keywords and intentions from the user's utterances and determines what data is needed. By performing these processes in real time, the analysis unit can quickly recognize data extraction requests and issue instructions to the next extraction unit. In this way, the analysis unit accurately analyzes voice input and supports data extraction according to the user's requests.

[0032] The extraction unit extracts data based on the requests recognized by the analysis unit. For example, the extraction unit can extract data from a data warehouse using database queries. Specifically, it generates SQL queries and queries the database to retrieve the necessary data. Since data warehouses store large amounts of data, the extraction unit efficiently searches the data and extracts the required information. The extraction unit can also extract data by setting filtering conditions. For example, it can add filtering conditions to the query to extract data limited to a specific period or region. Furthermore, the extraction unit can integrate and extract data from multiple data sources. This allows the extraction unit to quickly and accurately extract data according to user requests and provide it to the next output unit. The extraction unit can also perform data preprocessing and cleaning to maintain data integrity and consistency. This allows the extraction unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0033] The output unit outputs the data extracted by the extraction unit as an image. The output unit can, for example, generate graphs and charts to visually display the data. Specifically, it creates visual representations such as bar graphs, line graphs, and pie charts based on the extracted data. This allows users to intuitively understand data trends and patterns. The output unit can also generate infographics to visually display the data. Infographics are a method for visually representing data in an attractive and easy-to-understand way, conveying complex information concisely. Furthermore, the output unit can improve visibility and comprehensibility by carefully considering design elements such as color, font, and layout in data visualization. The output unit not only displays the generated images on the user's device but also provides the functionality to save them as PDFs or image files. This allows users to refer to the necessary data at any time. Additionally, the output unit can regenerate images in real time in response to data updates, providing the latest information. This ensures that the output unit always provides users with the most up-to-date data visually, supporting data understanding and decision-making.

[0034] The analysis unit can analyze additional user requests and extract data again. For example, if a user voice-inputs "Please narrow the period to November," the analysis unit can analyze this additional request and change the data extraction conditions. Similarly, if a user voice-inputs "Please also include the age of the purchasers," the analysis unit can analyze this additional request and change the data extraction conditions. This allows the data to be extracted again in response to additional user requests.

[0035] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice input history and receive them accordingly. In addition, the reception unit can analyze the user's past voice input history and propose the most efficient reception method. This allows for the selection of the optimal reception method by analyzing the user's past voice input history. Some or all of the above processing in the reception unit may be performed using AI, or it may not.

[0036] The reception unit can filter voice input based on the user's current situation and areas of interest. For example, the reception unit can prioritize voice input related to the user's current work. It can also filter relevant voice input based on the user's areas of interest. Furthermore, the reception unit can accept appropriate voice input based on the user's current situation (e.g., in a meeting). By filtering voice input based on the user's current situation and areas of interest, it is possible to receive more appropriate voice input. Some or all of the above processing in the reception unit may be performed using AI or not.

[0037] The reception unit can prioritize receiving voice inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice inputs related to that location. The reception unit can also filter relevant voice inputs based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can receive appropriate voice inputs based on their current location. This allows for the priority of receiving highly relevant voice inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0038] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize receiving voice input related to the user's current interests based on their social media activity. The reception unit can also analyze the user's social media activity and filter relevant voice input. Furthermore, the reception unit can accept appropriate voice input based on the user's social media activity. In this way, relevant voice input can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing voice input. For example, the analysis unit can perform a detailed analysis for high-importance requests. It can also perform a simplified analysis for low-importance requests. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the request. This allows for more efficient analysis by adjusting the level of detail of the analysis based on the importance of the request. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the request when analyzing voice input. For example, the analysis unit can apply a database query analysis algorithm to a data extraction request. It can also apply an information retrieval algorithm to an information retrieval request. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the category of the request. This allows for more accurate analysis by applying the most appropriate analysis algorithm according to the category of the request. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI.

[0041] The analysis unit can determine the priority of analysis based on the submission date of requests when analyzing voice input. For example, the analysis unit can prioritize the analysis of requests submitted earlier. It can also postpone the analysis of requests submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for more efficient analysis by determining the analysis priority based on the submission date of requests. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0042] The analysis unit can adjust the order of analysis based on the relevance of requests when analyzing voice input. For example, the analysis unit can prioritize the analysis of highly relevant requests. It can also postpone the analysis of less relevant requests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of requests. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of requests. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0043] The extraction unit can adjust the level of detail of the extraction based on the importance of the request during data extraction. For example, the extraction unit can extract detailed data for high-importance requests. Conversely, the extraction unit can extract simplified data for low-importance requests. Furthermore, the extraction unit can dynamically adjust the level of detail of the extraction according to the importance of the request. This allows for more efficient data extraction by adjusting the level of detail of the extraction based on the importance of the request. Some or all of the above processing in the extraction unit may be performed using AI, or it may be performed without using AI.

[0044] The extraction unit can apply different extraction algorithms depending on the category of the request when extracting data. For example, the extraction unit can apply a database query extraction algorithm to a database query request. It can also apply an information retrieval extraction algorithm to an information retrieval request. Furthermore, the extraction unit can select and apply the most suitable extraction algorithm depending on the category of the request. This allows for more accurate data extraction by applying the most suitable extraction algorithm according to the category of the request. Some or all of the above-described processes in the extraction unit may be performed using AI, or they may not.

[0045] The extraction unit can determine the extraction priority based on the submission date of the requests during data extraction. For example, the extraction unit can prioritize the extraction of requests submitted earlier. It can also postpone the extraction of requests submitted later. Furthermore, the extraction unit can dynamically adjust the extraction priority based on the submission date. This enables more efficient data extraction by determining the extraction priority based on the submission date of the requests. Some or all of the above processing in the extraction unit may be performed using AI or not.

[0046] The extraction unit can adjust the extraction order based on the relevance of the requests during data extraction. For example, the extraction unit can prioritize the extraction of highly relevant requests. It can also postpone the extraction of less relevant requests. Furthermore, the extraction unit can dynamically adjust the extraction order based on the relevance of the requests. This allows for more efficient data extraction by adjusting the extraction order based on the relevance of the requests. Some or all of the above processing in the extraction unit may be performed using AI or not.

[0047] The output unit can select the optimal display method when displaying output data by referring to the user's past operation history. For example, the output unit can prioritize providing display methods that the user has previously preferred. Furthermore, the output unit can predict and provide the optimal display method based on the user's past operation history. In addition, the output unit can analyze the user's past operation history and propose the most efficient display method. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or without AI.

[0048] The output unit can customize the display method based on the user's current situation when displaying output data. For example, if the user is in a meeting, the output unit can provide a concise and highly visible display method. It can also provide a display method optimized for smartphones if the user is on the go. Furthermore, if the user is relaxed, the output unit can provide a display method that includes detailed information. This allows for more appropriate display by customizing the display method based on the user's current situation. Some or all of the processing described above in the output unit may be performed using AI or not.

[0049] The output unit can select the optimal display method when displaying output data, taking into account the user's geographical location information. For example, if the user is in a specific location, the output unit can prioritize displaying data related to that location. The output unit can also filter and display relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the output unit can display appropriate data based on their current location. This allows for the selection of the optimal display method by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, or it may be performed without using AI.

[0050] The output unit can analyze the user's social media activity and suggest a display method when displaying output data. For example, the output unit can prioritize displaying data related to the user's current interests based on their social media activity. The output unit can also analyze the user's social media activity and filter and display relevant data. Furthermore, the output unit can display appropriate data based on the user's social media activity. This allows the system to suggest the optimal display method by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, or it may not.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analysis unit can analyze the background noise of the user's voice input and correct the content of the voice input based on the background noise. For example, if the user makes a voice input in a noisy environment, the analysis unit can remove the background noise and clarify the content of the voice input. Furthermore, if the user makes a voice input in a quiet environment, the analysis unit can perform analysis that minimizes the influence of background noise. In addition, if the user makes a voice input in a specific environment, such as inside a car, the analysis unit can perform voice correction appropriate for that environment. This makes it possible to perform more accurate voice analysis by taking into account the background noise of the user's voice input.

[0053] The reception unit can analyze the user's voice input speed and adjust the voice input reception method based on that speed. For example, if the user speaks quickly, the reception unit can adjust the voice input speed to accurately recognize the content. Conversely, if the user speaks slowly, the reception unit can also accept the voice input at that speed. Furthermore, the reception unit can provide appropriate feedback according to the user's voice input speed. This allows for more accurate voice input reception by taking the user's voice input speed into consideration.

[0054] The analysis unit can analyze the content of the user's voice input and provide appropriate feedback based on that content. For example, if the user provides voice input in the form of a question, the analysis unit can provide an answer to that question. Also, if the user provides voice input in the form of a command, the analysis unit can perform an appropriate action based on that command. Furthermore, if the user provides voice input expressing their thoughts or opinions, the analysis unit can provide appropriate feedback based on that content. In this way, by considering the content of the user's voice input, more appropriate feedback becomes possible.

[0055] The extraction unit can analyze the content of the user's voice input and adjust the data extraction method based on that content. For example, if the user makes a voice input requesting specific data, the extraction unit can extract detailed data based on that request. If the user makes a voice input requesting general data, the extraction unit can also extract summary data based on that request. Furthermore, if the user makes a voice input requesting multiple data items, the extraction unit can extract multiple data items simultaneously based on that request. This makes it possible to extract more appropriate data by taking into account the content of the user's voice input.

[0056] The output unit can analyze the content of the user's voice input and adjust the display method of the output data based on that content. For example, if the user makes a voice input requesting detailed data, the output unit can provide a detailed display method based on that request. Also, if the user makes a voice input requesting summary data, the output unit can provide a concise display method based on that request. Furthermore, if the user makes a voice input requesting data to be displayed in a specific format, the output unit can display the data in an appropriate format based on that request. In this way, by taking into account the content of the user's voice input, more appropriate data display becomes possible.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives voice input from the user. The reception desk can, for example, receive voice input via a microphone or audio file input. Step 2: The analysis unit analyzes the voice input received by the reception unit. The analysis unit converts the voice input into text data using speech recognition technology and analyzes the content of the voice input using natural language processing technology. This allows it to analyze the content of the voice input and recognize the request for data extraction. Step 3: The extraction unit extracts data based on the requests recognized by the analysis unit. The extraction unit can also extract data from the data warehouse using database queries and set filtering conditions. Step 4: The output unit outputs the data extracted by the extraction unit as an image. The output unit can also generate graphs, charts, and infographics to visually display the data.

[0059] (Example of form 2) The data extraction system according to an embodiment of the present invention is a system that extracts necessary data from a data warehouse using voice input. In this data extraction system, the user inputs a data extraction request to an agent by voice, the agent extracts the data based on the request, and outputs it as an image. This eliminates the need for learning time even for beginners in data warehouses (SQL), and avoids the reliance on specific individuals that is common in specialized fields. Furthermore, even for experts in data warehouses (SQL), it is expected to reduce work time as they can extract the necessary data verbally on a spot basis. For example, the user voice inputs, "I want the monthly sales data for smartphones." This voice input is analyzed by the agent and recognized as a data extraction request. Next, the agent extracts data from the data warehouse based on the request and presents the output data as an image, asking, "Is this correct?" The user then voices an additional request, "Please narrow the period to November." The agent extracts data again and presents the output data as an image, asking, "Is this correct?" Furthermore, if the user requests "I'd also like to see the buyer's age group," the agent extracts the data again and presents the output data as an image. In this way, the agent extracts data based on the user's voice input and presents the output data sequentially, allowing the user to easily obtain the necessary data. This enables the data extraction system to extract the necessary data from the data warehouse based on the user's voice input and output it as an image.

[0060] The data extraction system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, and an output unit. The reception unit receives voice input from a user. The reception unit can, for example, receive voice input through a microphone. The reception unit can also receive audio file input. The analysis unit analyzes the voice input received by the reception unit. The analysis unit can, for example, convert the voice input into text data using speech recognition technology. The analysis unit can also analyze the content of the voice input using natural language processing technology. The analysis unit analyzes the content of the voice input and recognizes a data extraction request. The extraction unit extracts data based on the request recognized by the analysis unit. The extraction unit can, for example, extract data from a data warehouse using a database query. The extraction unit can also extract data by setting filtering conditions. The output unit outputs the data extracted by the extraction unit as an image. The output unit can, for example, generate graphs or charts to visually display the data. The output unit can also generate infographics to visually display the data. As a result, the data extraction system according to the embodiment can extract necessary data from a data warehouse using voice input and output it as an image.

[0061] The reception unit receives voice input from the user. For example, the reception unit can receive voice input via a microphone. Specifically, voice data is collected when the user speaks into the microphone. Using a high-sensitivity microphone allows for the elimination of ambient noise and the acquisition of clear audio. The reception unit can also accept audio files. For example, users can provide audio data to the reception unit by uploading pre-recorded audio files to the system. The system supports common audio formats such as MP3, WAV, and AAC. Furthermore, the reception unit can apply audio processing technologies such as noise reduction and echo cancellation to improve the quality of the voice input. This ensures that the reception unit accurately receives voice input from the user, allowing for smooth processing in the subsequent analysis unit.

[0062] The analysis unit analyzes the voice input received by the reception unit. The analysis unit can convert voice input into text data using, for example, speech recognition technology. Specifically, the speech recognition engine analyzes the voice data, identifies phonemes and words, and converts them into text. The speech recognition engine achieves high-precision speech recognition by using a model based on deep learning. The analysis unit can also analyze the content of the voice input using natural language processing technology. For example, it performs morphological analysis on the text data to understand the structure and meaning of sentences. Furthermore, the analysis unit analyzes the content of the voice input and recognizes data extraction requests. Specifically, it extracts keywords and intentions from the user's utterances and determines what data is needed. By performing these processes in real time, the analysis unit can quickly recognize data extraction requests and issue instructions to the next extraction unit. In this way, the analysis unit accurately analyzes voice input and supports data extraction according to the user's requests.

[0063] The extraction unit extracts data based on the requests recognized by the analysis unit. For example, the extraction unit can extract data from a data warehouse using database queries. Specifically, it generates SQL queries and queries the database to retrieve the necessary data. Since data warehouses store large amounts of data, the extraction unit efficiently searches the data and extracts the required information. The extraction unit can also extract data by setting filtering conditions. For example, it can add filtering conditions to the query to extract data limited to a specific period or region. Furthermore, the extraction unit can integrate and extract data from multiple data sources. This allows the extraction unit to quickly and accurately extract data according to user requests and provide it to the next output unit. The extraction unit can also perform data preprocessing and cleaning to maintain data integrity and consistency. This allows the extraction unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0064] The output unit outputs the data extracted by the extraction unit as an image. The output unit can, for example, generate graphs and charts to visually display the data. Specifically, it creates visual representations such as bar graphs, line graphs, and pie charts based on the extracted data. This allows users to intuitively understand data trends and patterns. The output unit can also generate infographics to visually display the data. Infographics are a method for visually representing data in an attractive and easy-to-understand way, conveying complex information concisely. Furthermore, the output unit can improve visibility and comprehensibility by carefully considering design elements such as color, font, and layout in data visualization. The output unit not only displays the generated images on the user's device but also provides the functionality to save them as PDFs or image files. This allows users to refer to the necessary data at any time. Additionally, the output unit can regenerate images in real time in response to data updates, providing the latest information. This ensures that the output unit always provides users with the most up-to-date data visually, supporting data understanding and decision-making.

[0065] The analysis unit can analyze additional user requests and extract data again. For example, if a user voice-inputs "Please narrow the period to November," the analysis unit can analyze this additional request and change the data extraction conditions. Similarly, if a user voice-inputs "Please also include the age of the purchasers," the analysis unit can analyze this additional request and change the data extraction conditions. This allows the data to be extracted again in response to additional user requests.

[0066] The reception desk can estimate the user's emotions and adjust the timing of voice input reception based on the estimated emotions. For example, if the user is anxious, the reception desk can shorten the voice input reception timing to respond quickly. Conversely, if the user is relaxed, the reception desk can extend the voice input reception timing to provide a slower response. Furthermore, if the user is feeling anxious, the reception desk can adjust the voice input reception timing to provide a sense of security. By adjusting the voice input reception timing according to the user's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice input history and receive them accordingly. In addition, the reception unit can analyze the user's past voice input history and propose the most efficient reception method. This allows for the selection of the optimal reception method by analyzing the user's past voice input history. Some or all of the above processing in the reception unit may be performed using AI, or it may not.

[0068] The reception unit can filter voice input based on the user's current situation and areas of interest. For example, the reception unit can prioritize voice input related to the user's current work. It can also filter relevant voice input based on the user's areas of interest. Furthermore, the reception unit can accept appropriate voice input based on the user's current situation (e.g., in a meeting). By filtering voice input based on the user's current situation and areas of interest, it is possible to receive more appropriate voice input. Some or all of the above processing in the reception unit may be performed using AI or not.

[0069] The reception system can estimate the user's emotions and determine the priority of voice input to be received based on the estimated emotions. For example, if the user is anxious, the reception system can prioritize receiving voice input of high urgency. Conversely, if the user is relaxed, the reception system can prioritize receiving normal voice input. Furthermore, if the user is feeling uneasy, the reception system can prioritize receiving voice input that provides reassurance. This allows for more appropriate responses by prioritizing voice input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception unit can prioritize receiving voice inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice inputs related to that location. The reception unit can also filter relevant voice inputs based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can receive appropriate voice inputs based on their current location. This allows for the priority of receiving highly relevant voice inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0071] The reception unit can analyze the user's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize receiving voice input related to the user's current interests based on their social media activity. The reception unit can also analyze the user's social media activity and filter relevant voice input. Furthermore, the reception unit can accept appropriate voice input based on the user's social media activity. In this way, relevant voice input can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not.

[0072] The analysis unit can estimate the user's emotions and adjust the voice input analysis method based on the estimated emotions. For example, if the user is anxious, the analysis unit can apply a simplified analysis method for quick analysis. If the user is relaxed, the analysis unit can apply a more time-consuming analysis method for detailed analysis. Furthermore, if the user is feeling anxious, the analysis unit can apply a more careful analysis method to provide reassurance. By adjusting the voice input analysis method according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the request when analyzing voice input. For example, the analysis unit can perform a detailed analysis for high-importance requests. It can also perform a simplified analysis for low-importance requests. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the request. This allows for more efficient analysis by adjusting the level of detail of the analysis based on the importance of the request. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the request when analyzing voice input. For example, the analysis unit can apply a database query analysis algorithm to a data extraction request. It can also apply an information retrieval algorithm to an information retrieval request. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the category of the request. This allows for more accurate analysis by applying the most appropriate analysis algorithm according to the category of the request. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI.

[0075] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is anxious, the analysis unit can prioritize high-urgency analyses. If the user is relaxed, the analysis unit can prioritize normal analyses. Furthermore, if the user is feeling uneasy, the analysis unit can prioritize analyses that provide reassurance. This allows for more appropriate responses by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can determine the priority of analysis based on the submission date of requests when analyzing voice input. For example, the analysis unit can prioritize the analysis of requests submitted earlier. It can also postpone the analysis of requests submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for more efficient analysis by determining the analysis priority based on the submission date of requests. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0077] The analysis unit can adjust the order of analysis based on the relevance of requests when analyzing voice input. For example, the analysis unit can prioritize the analysis of highly relevant requests. It can also postpone the analysis of less relevant requests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of requests. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of requests. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0078] The extraction unit can estimate the user's emotions and adjust the data extraction method based on the estimated emotions. For example, if the user is anxious, the extraction unit can apply a simplified method to quickly extract data. If the user is relaxed, the extraction unit can apply a more time-consuming method to extract detailed data. Furthermore, if the user is feeling anxious, the extraction unit can apply a more careful method to provide reassurance. By adjusting the data extraction method according to the user's emotions, more appropriate data extraction becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The extraction unit can adjust the level of detail of the extraction based on the importance of the request during data extraction. For example, the extraction unit can extract detailed data for high-importance requests. Conversely, the extraction unit can extract simplified data for low-importance requests. Furthermore, the extraction unit can dynamically adjust the level of detail of the extraction according to the importance of the request. This allows for more efficient data extraction by adjusting the level of detail of the extraction based on the importance of the request. Some or all of the above processing in the extraction unit may be performed using AI, or it may be performed without using AI.

[0080] The extraction unit can apply different extraction algorithms depending on the category of the request when extracting data. For example, the extraction unit can apply a database query extraction algorithm to a database query request. It can also apply an information retrieval extraction algorithm to an information retrieval request. Furthermore, the extraction unit can select and apply the most suitable extraction algorithm depending on the category of the request. This allows for more accurate data extraction by applying the most suitable extraction algorithm according to the category of the request. Some or all of the above-described processes in the extraction unit may be performed using AI, or they may not.

[0081] The extraction unit can estimate the user's emotions and determine the priority of data to extract based on the estimated emotions. For example, if the user is anxious, the extraction unit can prioritize extracting data of high urgency. If the user is relaxed, the extraction unit can prioritize extracting normal data. Furthermore, if the user is feeling uneasy, the extraction unit can prioritize extracting data that provides a sense of security. This allows for a more appropriate response by determining the priority of data to extract according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The extraction unit can determine the extraction priority based on the submission date of the requests during data extraction. For example, the extraction unit can prioritize the extraction of requests submitted earlier. It can also postpone the extraction of requests submitted later. Furthermore, the extraction unit can dynamically adjust the extraction priority based on the submission date. This enables more efficient data extraction by determining the extraction priority based on the submission date of the requests. Some or all of the above processing in the extraction unit may be performed using AI or not.

[0083] The extraction unit can adjust the extraction order based on the relevance of the requests during data extraction. For example, the extraction unit can prioritize the extraction of highly relevant requests. It can also postpone the extraction of less relevant requests. Furthermore, the extraction unit can dynamically adjust the extraction order based on the relevance of the requests. This allows for more efficient data extraction by adjusting the extraction order based on the relevance of the requests. Some or all of the above processing in the extraction unit may be performed using AI or not.

[0084] The output unit can estimate the user's emotions and adjust the display method of the output data based on the estimated emotions. For example, if the user is anxious, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can also provide a display method that includes detailed information. Furthermore, if the user is feeling uneasy, the output unit can provide a display method that provides a sense of security. By adjusting the display method of the output data according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The output unit can select the optimal display method when displaying output data by referring to the user's past operation history. For example, the output unit can prioritize providing display methods that the user has previously preferred. Furthermore, the output unit can predict and provide the optimal display method based on the user's past operation history. In addition, the output unit can analyze the user's past operation history and propose the most efficient display method. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or without AI.

[0086] The output unit can customize the display method based on the user's current situation when displaying output data. For example, if the user is in a meeting, the output unit can provide a concise and highly visible display method. It can also provide a display method optimized for smartphones if the user is on the go. Furthermore, if the user is relaxed, the output unit can provide a display method that includes detailed information. This allows for more appropriate display by customizing the display method based on the user's current situation. Some or all of the processing described above in the output unit may be performed using AI or not.

[0087] The output unit can estimate the user's emotions and prioritize output data based on the estimated emotions. For example, if the user is anxious, the output unit can prioritize displaying data of high urgency. If the user is relaxed, the output unit can prioritize displaying normal data. Furthermore, if the user is feeling uneasy, the output unit can prioritize displaying data that provides a sense of security. This allows for a more appropriate response by prioritizing output data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The output unit can select the optimal display method when displaying output data, taking into account the user's geographical location information. For example, if the user is in a specific location, the output unit can prioritize displaying data related to that location. The output unit can also filter and display relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the output unit can display appropriate data based on their current location. This allows for the selection of the optimal display method by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, or it may be performed without using AI.

[0089] The output unit can analyze the user's social media activity and suggest a display method when displaying output data. For example, the output unit can prioritize displaying data related to the user's current interests based on their social media activity. The output unit can also analyze the user's social media activity and filter and display relevant data. Furthermore, the output unit can display appropriate data based on the user's social media activity. This allows the system to suggest the optimal display method by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, or it may not.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can analyze the background noise of the user's voice input and correct the content of the voice input based on the background noise. For example, if the user makes a voice input in a noisy environment, the analysis unit can remove the background noise and clarify the content of the voice input. Furthermore, if the user makes a voice input in a quiet environment, the analysis unit can perform analysis that minimizes the influence of background noise. In addition, if the user makes a voice input in a specific environment, such as inside a car, the analysis unit can perform voice correction appropriate for that environment. This makes it possible to perform more accurate voice analysis by taking into account the background noise of the user's voice input.

[0092] The reception unit can analyze the user's voice input speed and adjust the voice input reception method based on that speed. For example, if the user speaks quickly, the reception unit can adjust the voice input speed to accurately recognize the content. Conversely, if the user speaks slowly, the reception unit can also accept the voice input at that speed. Furthermore, the reception unit can provide appropriate feedback according to the user's voice input speed. This allows for more accurate voice input reception by taking the user's voice input speed into consideration.

[0093] The analysis unit can analyze the content of the user's voice input and provide appropriate feedback based on that content. For example, if the user provides voice input in the form of a question, the analysis unit can provide an answer to that question. Also, if the user provides voice input in the form of a command, the analysis unit can perform an appropriate action based on that command. Furthermore, if the user provides voice input expressing their thoughts or opinions, the analysis unit can provide appropriate feedback based on that content. In this way, by considering the content of the user's voice input, more appropriate feedback becomes possible.

[0094] The extraction unit can analyze the content of the user's voice input and adjust the data extraction method based on that content. For example, if the user makes a voice input requesting specific data, the extraction unit can extract detailed data based on that request. If the user makes a voice input requesting general data, the extraction unit can also extract summary data based on that request. Furthermore, if the user makes a voice input requesting multiple data items, the extraction unit can extract multiple data items simultaneously based on that request. This makes it possible to extract more appropriate data by taking into account the content of the user's voice input.

[0095] The output unit can analyze the content of the user's voice input and adjust the display method of the output data based on that content. For example, if the user makes a voice input requesting detailed data, the output unit can provide a detailed display method based on that request. Also, if the user makes a voice input requesting summary data, the output unit can provide a concise display method based on that request. Furthermore, if the user makes a voice input requesting data to be displayed in a specific format, the output unit can display the data in an appropriate format based on that request. In this way, by taking into account the content of the user's voice input, more appropriate data display becomes possible.

[0096] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice input analysis based on the estimated emotions. For example, if the user is anxious, the analysis unit can slightly reduce accuracy to perform a quick analysis. Conversely, if the user is relaxed, the analysis unit can increase accuracy to perform a more detailed analysis. Furthermore, if the user is feeling uneasy, the analysis unit can perform a thorough analysis to provide reassurance. In this way, by adjusting the accuracy of the voice input analysis according to the user's emotions, more appropriate analysis becomes possible.

[0097] The reception system can estimate the user's emotions and adjust the voice input reception method based on those estimates. For example, if the user is anxious, the reception system can apply a simplified method to quickly receive the voice input. Conversely, if the user is relaxed, the reception system can apply a more time-consuming method to receive detailed voice input. Furthermore, if the user is feeling uneasy, the reception system can apply a more polite reception method to provide reassurance. By adjusting the voice input reception method according to the user's emotions, more appropriate reception becomes possible.

[0098] The extraction unit can estimate the user's emotions and determine the priority of data extraction based on those emotions. For example, if the user is anxious, it can prioritize extracting data of high urgency. If the user is relaxed, it can prioritize extracting normal data. Furthermore, if the user is feeling uneasy, it can prioritize extracting data that provides a sense of security. By determining the priority of data extraction according to the user's emotions, a more appropriate response becomes possible.

[0099] The output unit can estimate the user's emotions and adjust the display order of the output data based on those emotions. For example, if the user is anxious, important data can be displayed preferentially. If the user is relaxed, normal data can be displayed preferentially. Furthermore, if the user is feeling uneasy, data that provides a sense of security can be displayed preferentially. By adjusting the display order of the output data according to the user's emotions, a more appropriate display becomes possible.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is anxious, it can prioritize analyses that are of high urgency. If the user is relaxed, it can prioritize normal analyses. Furthermore, if the user is feeling uneasy, it can prioritize analyses that provide reassurance. By prioritizing analyses according to the user's emotions, a more appropriate response becomes possible.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives voice input from the user. The reception desk can, for example, receive voice input via a microphone or audio file input. Step 2: The analysis unit analyzes the voice input received by the reception unit. The analysis unit converts the voice input into text data using speech recognition technology and analyzes the content of the voice input using natural language processing technology. This allows it to analyze the content of the voice input and recognize the request for data extraction. Step 3: The extraction unit extracts data based on the requests recognized by the analysis unit. The extraction unit can also extract data from the data warehouse using database queries and set filtering conditions. Step 4: The output unit outputs the data extracted by the extraction unit as an image. The output unit can also generate graphs, charts, and infographics to visually display the data.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 38B of the smart device 14. The analysis unit converts the voice input into text data and analyzes its contents using the specific processing unit 290 of the data processing unit 12. The extraction unit extracts data from the data warehouse using the specific processing unit 290 of the data processing unit 12. The output unit can display the extracted data as an image using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, and output unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the smart glasses 214. The analysis unit converts the voice input into text data and analyzes its contents, for example, using the specific processing unit 290 of the data processing unit 12. The extraction unit extracts data from the data warehouse, for example, using the specific processing unit 290 of the data processing unit 12. The output unit can display the extracted data as an image using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, and output unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the headset terminal 314. The analysis unit converts the voice input into text data using, for example, the specific processing unit 290 of the data processing unit 12 and analyzes its contents. The extraction unit extracts data from the data warehouse using, for example, the specific processing unit 290 of the data processing unit 12. The output unit can display the extracted data as an image using, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, and output unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive voice input using the microphone 238 of the robot 414. The analysis unit converts the voice input into text data and analyzes its contents, for example, using the specific processing unit 290 of the data processing unit 12. The extraction unit extracts data from the data warehouse, for example, using the specific processing unit 290 of the data processing unit 12. The output unit can display the extracted data as an image, for example, using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A reception desk that accepts voice input, An analysis unit analyzes the voice input received by the reception unit, An extraction unit extracts data based on the requests recognized by the analysis unit, The system includes an output unit that outputs the data extracted by the extraction unit as an image. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the user's additional requests and extract the data again. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving voice input, the system prioritizes accepting voice input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving voice input, the system analyzes the user's social media activity and accepts relevant voice input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the voice input analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing voice input, adjust the level of detail of the analysis based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing voice input, different analysis algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing voice input, the analysis priority is determined based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing voice input, the order of analysis is adjusted based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is We estimate the user's emotions and adjust the data extraction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is When extracting data, adjust the level of detail of the extraction based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is When extracting data, different extraction algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is It estimates the user's emotions and determines the priority of data to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is When extracting data, prioritize extractions based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is When extracting data, adjust the extraction order based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, It estimates the user's emotions and adjusts how the output data is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, When displaying output data, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When displaying output data, customize the display method based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, It estimates the user's emotions and determines the priority of output data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, When displaying output data, the system selects the optimal display method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, When displaying output data, the system analyzes the user's social media activity and suggests display methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts voice input, An analysis unit analyzes the voice input received by the reception unit, An extraction unit extracts data based on the requests recognized by the analysis unit, The system includes an output unit that outputs the data extracted by the extraction unit as an image. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the user's additional requests and extract the data again. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal reception method. The system according to feature 1.

5. The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice input to accept based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving voice input, the system prioritizes accepting voice input that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When receiving voice input, the system analyzes the user's social media activity and accepts relevant voice input. The system according to feature 1.

Images