System

Abstract

To provide a system.SOLUTION: A system comprises: means for receiving questions in natural language, aggregating relevant data, and outputting results; means for reading data from multiple reports and estimating factors of variation; and means for generating answers to the questions on the basis of the estimated factors of variation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previously, data analysis and report creation required knowledge of SQL and browsing multiple reports, which was time-consuming and labor-intensive. Additionally, understanding the causes of data fluctuations required specialized knowledge, making it difficult for average users. [Means for solving the problem]

[0005] This invention provides a means for accepting questions in natural language, aggregating the relevant data, and outputting the results; a means for reading data from multiple reports and estimating the factors of variation; and a means for generating answers to questions based on the estimated factors of variation. This allows users to obtain the necessary information simply by asking questions in natural language, without having to write SQL or browse multiple reports. In addition, because the estimation of factors of variation is also automated, it becomes possible to understand the factors of variation in data even without specialized knowledge. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0017] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0018] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0019] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0022] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0023] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0024] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0026] "Example 1" The system of the present invention has an interface that accepts questions from users in natural language. This interface can take the form of a chatbot, for example. The user poses a question in the form of, "What were the sales figures for XX last week?" The system analyzes the question and compiles the necessary data. The data is compiled through an internal database or an external API. The compiled data is used to generate the information the user is looking for. "Example 2" The system of the present invention has the ability to read data from multiple reports, making it possible to centrally grasp information scattered across different reports. Reports are read by analyzing reports provided in formats such as PDF, Excel, and CSV, and extracting the necessary data. "Example 3" The system of the present invention has a function to estimate the factors of variation from the loaded data. This estimation is performed using statistical methods and machine learning methods. The estimated factors of variation are output as part of the answer to the question posed by the user. For example, in response to the question "What is the reason for the increase in sales of XX last week?", an answer such as "Sales of XX last week increased due to the influence of XX" is generated based on the estimated factors of variation.

[0027] The processing flow of each embodiment will be described below.

[0028] "Example 1" Step 1: The user types a question into the chatbot in natural language, for example, "What was the sales situation for XX last week?" Step 2: The system analyzes the user's question and identifies the required data. This analysis is done using natural language processing techniques. Step 3: The system aggregates the required data through an internal database or external API. Step 4: The system generates the information the user wants based on the aggregated data and outputs it to the user. "Example 2" Step 1: The system loads multiple reports, which are available in various formats such as PDF, Excel, and CSV. Step 2: The system extracts the necessary data from the report using text analysis techniques. Step 3: The system aggregates the extracted data and generates the information the user is looking for. "Example 3" Step 1: The system analyzes the user's question and identifies data to estimate the factors of variation. Step 2: The system estimates the factors that cause variation from the identified data. This estimation is done using statistical and machine learning techniques. Step 3: Based on the variation factors estimated by the system, an answer to the user's question is generated and output to the user.

[0029] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0030] "Example 1" One embodiment of the present invention provides a system including: means for accepting questions in natural language from a user, aggregating relevant data, and outputting the results; means for reading data from multiple reports and estimating factors of fluctuation; means for generating an answer to the question based on the estimated factors of fluctuation; and an emotion engine for recognizing the user's emotions. Specifically, when a user asks, "What were sales last week?", the system aggregates last week's sales data and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors of fluctuation in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into consideration. For example, if the user looks anxious, the system can recognize that emotion and respond in accordance with the user's emotions, such as by providing more detailed information. "Example 2" Another embodiment of the present invention provides a system that, when a natural language question is about a specific period, aggregates data for the period, estimates factors that may cause fluctuations during that period, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How were sales in the first quarter of this year?", the system aggregates sales data for the first quarter of this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors that may cause fluctuations in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a happy expression on their face, the system can recognize that emotion and generate an answer that emphasizes strong sales, thereby responding in accordance with the user's emotions. "Example 3" In yet another embodiment of the present invention, a system is provided that, when a natural language question is about a specific indicator, aggregates data on the relevant indicator, estimates factors behind fluctuations in the relevant indicator, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How many new customers were acquired this year?", the system aggregates data on the number of new customers acquired this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors behind fluctuations in the number of new customers acquired. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a surprised expression, the system can recognize that emotion and respond according to the user's emotions, such as generating an answer that emphasizes the increase in the number of new customers acquired.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1" Step 1: Accept a natural language question from the user, such as "How were sales last week?" Step 2: Aggregate the relevant data. In this case, aggregate last week's sales data. Step 3: Output the aggregated results. Output the aggregated results of last week's sales data to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 2" Step 1: Accept a natural language question from the user, such as "How were sales in the first quarter of this year?" Step 2: Aggregate the relevant data. In this case, aggregate sales data for the first quarter of the year. Step 3: Output the aggregated results. Output the aggregated sales data for the first quarter of this year to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 3" Step 1: Accept a natural language question from the user, such as "How many new customers did we acquire this year?" Step 2: Aggregate the relevant data. In this case, aggregate data on the number of new customers acquired this year. Step 3: Output the aggregated results. The aggregated data on the number of new customers acquired this year is output to the user. Step 4: Load data from multiple reports. Load data from multiple reports related to new customer acquisition. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in the number of new customers acquired from the data that was loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions.

[0033] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0034] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0035] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0036] [Second embodiment]

[0037] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0038] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0039] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0040] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0041] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0042] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0043] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0044] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0045] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0046] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0047] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0049] "Example 1" The system of the present invention has an interface that accepts questions from users in natural language. This interface can take the form of a chatbot, for example. The user poses a question in the form of, "What were the sales figures for XX last week?" The system analyzes the question and compiles the necessary data. The data is compiled through an internal database or an external API. The compiled data is used to generate the information the user is looking for. "Example 2" The system of the present invention has the ability to read data from multiple reports, making it possible to centrally grasp information scattered across different reports. Reports are read by analyzing reports provided in formats such as PDF, Excel, and CSV, and extracting the necessary data. "Example 3" The system of the present invention has a function to estimate the factors of variation from the loaded data. This estimation is performed using statistical methods and machine learning methods. The estimated factors of variation are output as part of the answer to the question posed by the user. For example, in response to the question "What is the reason for the increase in sales of XX last week?", an answer such as "Sales of XX last week increased due to the influence of XX" is generated based on the estimated factors of variation.

[0050] The processing flow of each embodiment will be described below.

[0051] "Example 1" Step 1: The user types a question into the chatbot in natural language, for example, "What was the sales situation for XX last week?" Step 2: The system analyzes the user's question and identifies the required data. This analysis is done using natural language processing techniques. Step 3: The system aggregates the required data through an internal database or external API. Step 4: The system generates the information the user wants based on the aggregated data and outputs it to the user. "Example 2" Step 1: The system loads multiple reports, which are available in various formats such as PDF, Excel, and CSV. Step 2: The system extracts the necessary data from the report using text analysis techniques. Step 3: The system aggregates the extracted data and generates the information the user is looking for. "Example 3" Step 1: The system analyzes the user's question and identifies data to estimate the factors of variation. Step 2: The system estimates the factors that cause variation from the identified data. This estimation is done using statistical and machine learning techniques. Step 3: Based on the variation factors estimated by the system, an answer to the user's question is generated and output to the user.

[0052] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0053] "Example 1" One embodiment of the present invention provides a system including: means for accepting questions in natural language from a user, aggregating relevant data, and outputting the results; means for reading data from multiple reports and estimating factors of fluctuation; means for generating an answer to the question based on the estimated factors of fluctuation; and an emotion engine for recognizing the user's emotions. Specifically, when a user asks, "What were sales last week?", the system aggregates last week's sales data and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors of fluctuation in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into consideration. For example, if the user looks anxious, the system can recognize that emotion and respond in accordance with the user's emotions, such as by providing more detailed information. "Example 2" Another embodiment of the present invention provides a system that, when a natural language question is about a specific period, aggregates data for the period, estimates factors that may cause fluctuations during that period, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How were sales in the first quarter of this year?", the system aggregates sales data for the first quarter of this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors that may cause fluctuations in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a happy expression on their face, the system can recognize that emotion and generate an answer that emphasizes strong sales, thereby responding in accordance with the user's emotions. "Example 3" In yet another embodiment of the present invention, a system is provided that, when a natural language question is about a specific indicator, aggregates data on the relevant indicator, estimates factors behind fluctuations in the relevant indicator, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How many new customers were acquired this year?", the system aggregates data on the number of new customers acquired this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors behind fluctuations in the number of new customers acquired. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a surprised expression, the system can recognize that emotion and respond according to the user's emotions, such as generating an answer that emphasizes the increase in the number of new customers acquired.

[0054] The processing flow of each embodiment will be described below.

[0055] "Example 1" Step 1: Accept a natural language question from the user, such as "How were sales last week?" Step 2: Aggregate the relevant data. In this case, aggregate last week's sales data. Step 3: Output the aggregated results. Output the aggregated results of last week's sales data to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 2" Step 1: Accept a natural language question from the user, such as "How were sales in the first quarter of this year?" Step 2: Aggregate the relevant data. In this case, aggregate sales data for the first quarter of the year. Step 3: Output the aggregated results. Output the aggregated sales data for the first quarter of this year to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 3" Step 1: Accept a natural language question from the user, such as "How many new customers did we acquire this year?" Step 2: Aggregate the relevant data. In this case, aggregate data on the number of new customers acquired this year. Step 3: Output the aggregated results. The aggregated data on the number of new customers acquired this year is output to the user. Step 4: Load data from multiple reports. Load data from multiple reports related to new customer acquisition. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in the number of new customers acquired from the data that was loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions.

[0056] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0058] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0059] [Third embodiment]

[0060] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0061] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0063] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0064] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0065] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0067] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0068] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0069] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0070] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0071] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0072] "Example 1" The system of the present invention has an interface that accepts questions from users in natural language. This interface can take the form of a chatbot, for example. The user poses a question in the form of, "What were the sales figures for XX last week?" The system analyzes the question and compiles the necessary data. The data is compiled through an internal database or an external API. The compiled data is used to generate the information the user is looking for. "Example 2" The system of the present invention has the ability to read data from multiple reports, making it possible to centrally grasp information scattered across different reports. Reports are read by analyzing reports provided in formats such as PDF, Excel, and CSV, and extracting the necessary data. "Example 3" The system of the present invention has a function to estimate the factors of variation from the loaded data. This estimation is performed using statistical methods and machine learning methods. The estimated factors of variation are output as part of the answer to the question posed by the user. For example, in response to the question "What is the reason for the increase in sales of XX last week?", an answer such as "Sales of XX last week increased due to the influence of XX" is generated based on the estimated factors of variation.

[0073] The processing flow of each embodiment will be described below.

[0074] "Example 1" Step 1: The user types a question into the chatbot in natural language, for example, "What was the sales situation for XX last week?" Step 2: The system analyzes the user's question and identifies the required data. This analysis is done using natural language processing techniques. Step 3: The system aggregates the required data through an internal database or external API. Step 4: The system generates the information the user wants based on the aggregated data and outputs it to the user. "Example 2" Step 1: The system loads multiple reports, which are available in various formats such as PDF, Excel, and CSV. Step 2: The system extracts the necessary data from the report using text analysis techniques. Step 3: The system aggregates the extracted data and generates the information the user is looking for. "Example 3" Step 1: The system analyzes the user's question and identifies data to estimate the factors of variation. Step 2: The system estimates the factors that cause variation from the identified data. This estimation is done using statistical and machine learning techniques. Step 3: Based on the variation factors estimated by the system, an answer to the user's question is generated and output to the user.

[0075] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0076] "Example 1" One embodiment of the present invention provides a system including: means for accepting questions in natural language from a user, aggregating relevant data, and outputting the results; means for reading data from multiple reports and estimating factors of fluctuation; means for generating an answer to the question based on the estimated factors of fluctuation; and an emotion engine for recognizing the user's emotions. Specifically, when a user asks, "What were sales last week?", the system aggregates last week's sales data and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors of fluctuation in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into consideration. For example, if the user looks anxious, the system can recognize that emotion and respond in accordance with the user's emotions, such as by providing more detailed information. "Example 2" Another embodiment of the present invention provides a system that, when a natural language question is about a specific period, aggregates data for the period, estimates factors that may cause fluctuations during that period, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How were sales in the first quarter of this year?", the system aggregates sales data for the first quarter of this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors that may cause fluctuations in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a happy expression on their face, the system can recognize that emotion and generate an answer that emphasizes strong sales, thereby responding in accordance with the user's emotions. "Example 3" In yet another embodiment of the present invention, a system is provided that, when a natural language question is about a specific indicator, aggregates data on the relevant indicator, estimates factors behind fluctuations in the relevant indicator, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How many new customers were acquired this year?", the system aggregates data on the number of new customers acquired this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors behind fluctuations in the number of new customers acquired. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a surprised expression, the system can recognize that emotion and respond according to the user's emotions, such as generating an answer that emphasizes the increase in the number of new customers acquired.

[0077] The processing flow of each embodiment will be described below.

[0078] "Example 1" Step 1: Accept a natural language question from the user, such as "How were sales last week?" Step 2: Aggregate the relevant data. In this case, aggregate last week's sales data. Step 3: Output the aggregated results. Output the aggregated results of last week's sales data to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 2" Step 1: Accept a natural language question from the user, such as "How were sales in the first quarter of this year?" Step 2: Aggregate the relevant data. In this case, aggregate sales data for the first quarter of the year. Step 3: Output the aggregated results. Output the aggregated sales data for the first quarter of this year to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 3" Step 1: Accept a natural language question from the user, such as "How many new customers did we acquire this year?" Step 2: Aggregate the relevant data. In this case, aggregate data on the number of new customers acquired this year. Step 3: Output the aggregated results. The aggregated data on the number of new customers acquired this year is output to the user. Step 4: Load data from multiple reports. Load data from multiple reports related to new customer acquisition. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in the number of new customers acquired from the data that was loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions.

[0079] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0081] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314. [Fourth embodiment]

[0082] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0083] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0085] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0090] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0093] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0094] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0095] "Example 1" The system of the present invention has an interface that accepts questions from users in natural language. This interface can take the form of a chatbot, for example. The user poses a question in the form of, "What were the sales figures for XX last week?" The system analyzes the question and compiles the necessary data. The data is compiled through an internal database or an external API. The compiled data is used to generate the information the user is looking for. "Example 2" The system of the present invention has the ability to read data from multiple reports, making it possible to centrally grasp information scattered across different reports. Reports are read by analyzing reports provided in formats such as PDF, Excel, and CSV, and extracting the necessary data. "Example 3" The system of the present invention has a function to estimate the factors of variation from the loaded data. This estimation is performed using statistical methods and machine learning methods. The estimated factors of variation are output as part of the answer to the question posed by the user. For example, in response to the question "What is the reason for the increase in sales of XX last week?", an answer such as "Sales of XX last week increased due to the influence of XX" is generated based on the estimated factors of variation.

[0096] The processing flow of each embodiment will be described below.

[0097] "Example 1" Step 1: The user types a question into the chatbot in natural language, for example, "What was the sales situation for XX last week?" Step 2: The system analyzes the user's question and identifies the required data. This analysis is done using natural language processing techniques. Step 3: The system aggregates the required data through an internal database or external API. Step 4: The system generates the information the user wants based on the aggregated data and outputs it to the user. "Example 2" Step 1: The system loads multiple reports, which are available in various formats such as PDF, Excel, and CSV. Step 2: The system extracts the necessary data from the report using text analysis techniques. Step 3: The system aggregates the extracted data and generates the information the user is looking for. "Example 3" Step 1: The system analyzes the user's question and identifies data to estimate the factors of variation. Step 2: The system estimates the factors that cause variation from the identified data. This estimation is done using statistical and machine learning techniques. Step 3: Based on the variation factors estimated by the system, an answer to the user's question is generated and output to the user.

[0098] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0099] "Example 1" One embodiment of the present invention provides a system including: means for accepting questions in natural language from a user, aggregating relevant data, and outputting the results; means for reading data from multiple reports and estimating factors of fluctuation; means for generating an answer to the question based on the estimated factors of fluctuation; and an emotion engine for recognizing the user's emotions. Specifically, when a user asks, "What were sales last week?", the system aggregates last week's sales data and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors of fluctuation in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into consideration. For example, if the user looks anxious, the system can recognize that emotion and respond in accordance with the user's emotions, such as by providing more detailed information. "Example 2" Another embodiment of the present invention provides a system that, when a natural language question is about a specific period, aggregates data for the period, estimates factors that may cause fluctuations during that period, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How were sales in the first quarter of this year?", the system aggregates sales data for the first quarter of this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors that may cause fluctuations in sales. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a happy expression on their face, the system can recognize that emotion and generate an answer that emphasizes strong sales, thereby responding in accordance with the user's emotions. "Example 3" In yet another embodiment of the present invention, a system is provided that, when a natural language question is about a specific indicator, aggregates data on the relevant indicator, estimates factors behind fluctuations in the relevant indicator, and generates an answer that takes the user's emotions into account using an emotion engine that recognizes the user's emotions. Specifically, when a user asks, "How many new customers were acquired this year?", the system aggregates data on the number of new customers acquired this year and outputs the results. At the same time, the system also reads data from multiple reports and estimates factors behind fluctuations in the number of new customers acquired. Furthermore, the emotion engine recognizes the user's emotions and generates an answer that takes those emotions into account. For example, if the user has a surprised expression, the system can recognize that emotion and respond according to the user's emotions, such as generating an answer that emphasizes the increase in the number of new customers acquired.

[0100] The processing flow of each embodiment will be described below.

[0101] "Example 1" Step 1: Accept a natural language question from the user, such as "How were sales last week?" Step 2: Aggregate the relevant data. In this case, aggregate last week's sales data. Step 3: Output the aggregated results. Output the aggregated results of last week's sales data to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 2" Step 1: Accept a natural language question from the user, such as "How were sales in the first quarter of this year?" Step 2: Aggregate the relevant data. In this case, aggregate sales data for the first quarter of the year. Step 3: Output the aggregated results. Output the aggregated sales data for the first quarter of this year to the user. Step 4: Load data from multiple reports. Load data from multiple sales-related reports. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in sales from the data that you loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions. "Example 3" Step 1: Accept a natural language question from the user, such as "How many new customers did we acquire this year?" Step 2: Aggregate the relevant data. In this case, aggregate data on the number of new customers acquired this year. Step 3: Output the aggregated results. The aggregated data on the number of new customers acquired this year is output to the user. Step 4: Load data from multiple reports. Load data from multiple reports related to new customer acquisition. Step 5: Estimate the factors that cause fluctuations. Estimate the factors that cause fluctuations in the number of new customers acquired from the data that was loaded. Step 6: Recognize user emotions. Recognize user emotions using the emotion engine. Step 7: Generate an answer that takes emotions into account. Generate an answer to the question based on the estimated variation factors, taking into account the recognized emotions.

[0102] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0104] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[0105] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0106] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0107] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0108] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0109] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0110] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0111] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0112] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0113] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0114] 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.

[0115] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0116] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0117] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0118] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0119] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0120] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0121] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0122] The following is further disclosed regarding the above embodiment.

[0123] (Claim 1) A system including a means for accepting questions in natural language, aggregating relevant data, and outputting the results, a means for reading data from multiple reports and estimating factors of variation, and a means for generating answers to the questions based on the estimated factors of variation. (Claim 2) 2. The system according to claim 1, wherein, if the question in natural language is a question about a specific period, data for the period is compiled and fluctuation factors for the period are estimated. (Claim 3) 2. The system according to claim 1, wherein, when the question in natural language is a question about a specific index, data on the corresponding index is compiled and factors that cause fluctuations in the corresponding index are estimated.

[0124] (Claim 4) The system includes: a means for accepting questions in natural language, aggregating relevant data, and outputting the results; a means for reading data from multiple reports and estimating factors of variation; a means for generating answers to questions based on the estimated factors of variation; and an emotion engine for recognizing user emotions. (Claim 5) The system according to claim 4, wherein if the natural language question is about a specific period, data for the period is compiled, factors causing fluctuations in the period are estimated, and an emotion engine that recognizes the user's emotions is used to generate an answer that takes the user's emotions into consideration. (Claim 6) The system according to claim 4, wherein, if the question in natural language is a question about a specific index, data on the relevant index is compiled, factors that cause fluctuations in the relevant index are estimated, and an emotion engine that recognizes the user's emotions is used to generate an answer that takes the user's emotions into consideration. [Explanation of symbols]

[0125] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system including: means for accepting questions in natural language, aggregating corresponding data, and outputting the results; means for reading data from multiple reports and estimating factors of variation; and means for generating answers to the questions based on the estimated factors of variation.

2. The system according to claim 1 , wherein, if the question in natural language is a question about a specific period, data for the corresponding period is compiled and fluctuation factors for the corresponding period are estimated.

3. The system according to claim 1 , wherein, when the question in natural language is a question about a specific index, data on the corresponding index is compiled and a factor of fluctuation in the corresponding index is estimated.

Images