System

Abstract

To solve existing problems by providing a learned artificial intelligence model in each industry and field.SOLUTION: A system includes: means for providing a learned artificial intelligence model for each industry and field; means for collecting, analyzing, and learning data in each industry and field; and means for performing application evaluation of the model in each industry and field.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] The present invention aims to solve existing problems by providing trained artificial intelligence models in various industries and fields. [Means for solving the problem]

[0005] A system including a means for providing trained artificial intelligence models for each industry or field, a means for collecting, analyzing, and learning data in each industry or field, and a means for evaluating the applicability of the model in each industry or field. [Brief description 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. [Diagram 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

[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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[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 wide area network (WAN) and / or a local area network (LAN).

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

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

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[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 processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing 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] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data collection, preprocessing, feature extraction, and model learning are automatically performed to make data learning in various industries and fields more efficient. In addition, the accuracy of the models in various industries and fields is improved by improving the models through feedback of the application results of the models. (Example 2) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data in various industries and fields is collected, the collected data is preprocessed, features are extracted, and model learning is performed to generate trained artificial intelligence models. In addition, the generated models are applied to various industries and fields, and the application results are evaluated to confirm the usefulness of the models. (Example 3) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes a means for collecting, analyzing, and learning data, and a means for applying and evaluating models. Specifically, a trained artificial intelligence model is constructed by collecting data in various industries and fields, analyzing the collected data, and conducting learning. In addition, the constructed model is applied to various industries and fields, and the application results are evaluated to confirm the performance of the model.

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

[0028] (Example 1) Step 1: Collect data in each industry or field as a means of collecting, analyzing, and learning data. For example, collect data from publicly available data on the Internet or industry-specific databases. Step 2: Preprocess the collected data and extract the necessary information. For example, in the case of text data, morphological and grammatical analysis is performed to extract the characteristics of words and phrases. Step 3: The extracted features are used to train a model, for example, using a machine learning algorithm to train the data and generate a trained artificial intelligence model. Step 4: Apply and evaluate the model. Apply the generated model to various industries and fields. For example, input new data and evaluate whether the model makes appropriate predictions or classifications. Step 5: Feedback the results of the application to improve the model. For example, improve the model's performance by analyzing errors in the application results and adjusting the model's parameters and architecture. (Example 2) Step 1: Collect data for each industry or field. For example, collect industry-specific data such as sensor data and customer data. Step 2: Preprocess the collected data and extract the necessary features, for example, handle missing values ​​in the data, scale and normalize the features. Step 3: Use the extracted features to train a model. For example, build a deep learning model and train it on the data to generate a trained artificial intelligence model. Step 4: The generated model is applied to various industries and fields. For example, new data is input and the model makes predictions and classifications, contributing to solving problems in the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as precision and recall. Step 6: Improve the model based on the results of application. For example, improve the model's performance by analyzing misclassified data and adjusting the model's parameters and architecture. (Example 3) Step 1: Collect data for your industry or field. For example, gather market research data, competitor data, and other data relevant to your industry. Step 2: Analyze the collected data and extract trends and characteristics, for example using statistical analysis or data mining to analyze the data for patterns and relationships. Step 3: Use the extracted features to build a trained artificial intelligence model, for example by applying a machine learning algorithm to generate a prediction or classification model from the data. Step 4: Apply the model to a particular industry or field. For example, input new data and have the model make predictions or classifications to meet the needs of the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as accuracy and error rate. Step 6: Improve the model based on the application results. For example, improve the model performance by analyzing the incorrect prediction results and adjusting the model parameters and feature selection.

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

[0030] (Example 1) In an embodiment of the present invention, a system for providing trained artificial intelligence models for each industry or field is combined with an emotion engine to provide a service more suitable for the user. Specifically, emotions are recognized from the user's voice and text data, and the emotions are utilized for model learning and application. For example, when a user asks a question by voice, the emotion engine determines the emotion from the user's tone of voice and choice of words, and based on that, selects an appropriate model and provides an answer. (Example 2) An embodiment of the present invention provides personalized services based on a user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's text message and the model output is adjusted accordingly. For example, when a user posts a product review, the emotion engine extracts the user's emotions from the text expression and weights the model when rating the product based on the emotions. (Example 3) An embodiment of the present invention provides a service that is sensitive to the user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's voice and text data, and the model output is adjusted based on the emotions. For example, when a user requests the playback of music by voice, the emotion engine determines the emotion from the intonation and rhythm of the user's voice, and the model selects and plays an appropriate song accordingly.

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

[0032] (Example 1) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data. For example, in the case of voice data, emotions are extracted from the tone and rhythm of the voice, and in the case of text data, emotions are extracted from word choice and expressions. Step 3: Use the recognized emotions to train and apply models. For example, select an appropriate model based on the emotion and provide optimal answers and services to the user's request. As a specific example, if a user asks by voice, "What's the weather like today?", the emotion engine will recognize the user's emotions of interest and anxiety, and adjust the output of the weather forecast model based on that to provide the user with appropriate information. (Example 2) Step 1: Receive user's text data as input. Step 2: Use the emotion engine to recognize emotions from the user's text data, for example by extracting emotions from text expressions and word choices. Step 3: Reflect the recognized emotion in the model output. For example, the model output is weighted according to the emotion to provide a personalized service that matches the user's emotion. As a specific example, when a user posts a product review, the emotion engine extracts emotion from the user's text and weights the output of the product evaluation model based on the emotion to recommend more appropriate products to the user. (Example 3) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data, for example by analyzing the intonation and rhythm of the voice in the case of voice data, and extracting emotions from expressions and word choice in the case of text data. Step 3: Adjust the model output based on the recognized emotion. For example, adjust the model parameters and feature selection according to the emotion to provide a service that is sensitive to the user's emotion. For example, when a user requests to play music by voice, the emotion engine extracts the emotion from the user's voice, and the model selects and plays an appropriate song according to the emotion.

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

[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 making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0035] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, 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 wide area network (WAN) and / or a local area network (LAN).

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

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

[0043] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

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

[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 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.

[0048] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0049] (Example 1) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data collection, preprocessing, feature extraction, and model learning are automatically performed to make data learning in various industries and fields more efficient. In addition, the accuracy of the models in various industries and fields is improved by improving the models through feedback of the application results of the models. (Example 2) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data in various industries and fields is collected, the collected data is preprocessed, features are extracted, and model learning is performed to generate trained artificial intelligence models. In addition, the generated models are applied to various industries and fields, and the application results are evaluated to confirm the usefulness of the models. (Example 3) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes a means for collecting, analyzing, and learning data, and a means for applying and evaluating models. Specifically, a trained artificial intelligence model is constructed by collecting data in various industries and fields, analyzing the collected data, and conducting learning. In addition, the constructed model is applied to various industries and fields, and the application results are evaluated to confirm the performance of the model.

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

[0051] (Example 1) Step 1: Collect data in each industry or field as a means of collecting, analyzing, and learning data. For example, collect data from publicly available data on the Internet or industry-specific databases. Step 2: Preprocess the collected data and extract the necessary information. For example, in the case of text data, morphological and grammatical analysis is performed to extract the characteristics of words and phrases. Step 3: The extracted features are used to train a model, for example, using a machine learning algorithm to train the data and generate a trained artificial intelligence model. Step 4: Apply and evaluate the model. Apply the generated model to various industries and fields. For example, input new data and evaluate whether the model makes appropriate predictions or classifications. Step 5: Feedback the results of the application to improve the model. For example, improve the model's performance by analyzing errors in the application results and adjusting the model's parameters and architecture. (Example 2) Step 1: Collect data for each industry or field. For example, collect industry-specific data such as sensor data and customer data. Step 2: Preprocess the collected data and extract the necessary features, for example, handle missing values ​​in the data, scale and normalize the features. Step 3: Use the extracted features to train a model. For example, build a deep learning model and train it on the data to generate a trained artificial intelligence model. Step 4: The generated model is applied to various industries and fields. For example, new data is input and the model makes predictions and classifications, contributing to solving problems in the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as precision and recall. Step 6: Improve the model based on the results of application. For example, improve the model's performance by analyzing misclassified data and adjusting the model's parameters and architecture. (Example 3) Step 1: Collect data for your industry or field. For example, gather market research data, competitor data, and other data relevant to your industry. Step 2: Analyze the collected data and extract trends and characteristics, for example using statistical analysis or data mining to analyze the data for patterns and relationships. Step 3: Use the extracted features to build a trained artificial intelligence model, for example by applying a machine learning algorithm to generate a prediction or classification model from the data. Step 4: Apply the model to a particular industry or field. For example, input new data and have the model make predictions or classifications to meet the needs of the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as accuracy and error rate. Step 6: Improve the model based on the application results. For example, improve the model performance by analyzing the incorrect prediction results and adjusting the model parameters and feature selection.

[0052] In addition, 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) In an embodiment of the present invention, a system for providing trained artificial intelligence models for each industry or field is combined with an emotion engine to provide a service more suitable for the user. Specifically, emotions are recognized from the user's voice and text data, and the emotions are utilized for model learning and application. For example, when a user asks a question by voice, the emotion engine determines the emotion from the user's tone of voice and choice of words, and based on that, selects an appropriate model and provides an answer. (Example 2) An embodiment of the present invention provides personalized services based on a user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's text message and the model output is adjusted accordingly. For example, when a user posts a product review, the emotion engine extracts the user's emotions from the text expression and weights the model when rating the product based on the emotions. (Example 3) An embodiment of the present invention provides a service that is sensitive to the user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's voice and text data, and the model output is adjusted based on the emotions. For example, when a user requests the playback of music by voice, the emotion engine determines the emotion from the intonation and rhythm of the user's voice, and the model selects and plays an appropriate song accordingly.

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

[0055] (Example 1) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data. For example, in the case of voice data, emotions are extracted from the tone and rhythm of the voice, and in the case of text data, emotions are extracted from word choice and expressions. Step 3: Use the recognized emotions to train and apply models. For example, select an appropriate model based on the emotion and provide optimal answers and services to the user's request. As a specific example, if a user asks by voice, "What's the weather like today?", the emotion engine will recognize the user's emotions of interest and anxiety, and adjust the output of the weather forecast model based on that to provide the user with appropriate information. (Example 2) Step 1: Receive user's text data as input. Step 2: Use the emotion engine to recognize emotions from the user's text data, for example by extracting emotions from text expressions and word choices. Step 3: Reflect the recognized emotion in the model output. For example, the model output is weighted according to the emotion to provide a personalized service that matches the user's emotion. As a specific example, when a user posts a product review, the emotion engine extracts emotion from the user's text and weights the output of the product evaluation model based on the emotion to recommend more appropriate products to the user. (Example 3) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data, for example by analyzing the intonation and rhythm of the voice in the case of voice data, and extracting emotions from expressions and word choice in the case of text data. Step 3: Adjust the model output based on the recognized emotion. For example, adjust the model parameters and feature selection according to the emotion to provide a service that is sensitive to the user's emotion. For example, when a user requests to play music by voice, the emotion engine extracts the emotion from the user's voice, and the model selects and plays an appropriate song according to the emotion.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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 making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0058] 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 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 type 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 wide area network (WAN) and / or a local area network (LAN).

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

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

[0066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0067] Fig. 6 shows an example of 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, the 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] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0072] (Example 1) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data collection, preprocessing, feature extraction, and model learning are automatically performed to make data learning in various industries and fields more efficient. In addition, the accuracy of the models in various industries and fields is improved by improving the models through feedback of the application results of the models. (Example 2) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data in various industries and fields is collected, the collected data is preprocessed, features are extracted, and model learning is performed to generate trained artificial intelligence models. In addition, the generated models are applied to various industries and fields, and the application results are evaluated to confirm the usefulness of the models. (Example 3) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes a means for collecting, analyzing, and learning data, and a means for applying and evaluating models. Specifically, a trained artificial intelligence model is constructed by collecting data in various industries and fields, analyzing the collected data, and conducting learning. In addition, the constructed model is applied to various industries and fields, and the application results are evaluated to confirm the performance of the model.

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

[0074] (Example 1) Step 1: Collect data in each industry or field as a means of collecting, analyzing, and learning data. For example, collect data from publicly available data on the Internet or industry-specific databases. Step 2: Preprocess the collected data and extract the necessary information. For example, in the case of text data, morphological and grammatical analysis is performed to extract the characteristics of words and phrases. Step 3: The extracted features are used to train a model, for example, using a machine learning algorithm to train the data and generate a trained artificial intelligence model. Step 4: Apply and evaluate the model. Apply the generated model to various industries and fields. For example, input new data and evaluate whether the model makes appropriate predictions or classifications. Step 5: Feedback the results of the application to improve the model. For example, improve the model's performance by analyzing errors in the application results and adjusting the model's parameters and architecture. (Example 2) Step 1: Collect data for each industry or field. For example, collect industry-specific data such as sensor data and customer data. Step 2: Preprocess the collected data and extract the necessary features, for example, handle missing values ​​in the data, scale and normalize the features. Step 3: Use the extracted features to train a model. For example, build a deep learning model and train it on the data to generate a trained artificial intelligence model. Step 4: The generated model is applied to various industries and fields. For example, new data is input and the model makes predictions and classifications, contributing to solving problems in the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as precision and recall. Step 6: Improve the model based on the results of application. For example, improve the model's performance by analyzing misclassified data and adjusting the model's parameters and architecture. (Example 3) Step 1: Collect data for your industry or field. For example, gather market research data, competitor data, and other data relevant to your industry. Step 2: Analyze the collected data and extract trends and characteristics, for example using statistical analysis or data mining to analyze the data for patterns and relationships. Step 3: Use the extracted features to build a trained artificial intelligence model, for example by applying a machine learning algorithm to generate a prediction or classification model from the data. Step 4: Apply the model to a particular industry or field. For example, input new data and have the model make predictions or classifications to meet the needs of the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as accuracy and error rate. Step 6: Improve the model based on the application results. For example, improve the model performance by analyzing the incorrect prediction results and adjusting the model parameters and feature selection.

[0075] In addition, 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) In an embodiment of the present invention, a system for providing trained artificial intelligence models for each industry or field is combined with an emotion engine to provide a service more suitable for the user. Specifically, emotions are recognized from the user's voice and text data, and the emotions are utilized for model learning and application. For example, when a user asks a question by voice, the emotion engine determines the emotion from the user's tone of voice and choice of words, and based on that, selects an appropriate model and provides an answer. (Example 2) An embodiment of the present invention provides personalized services based on a user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's text message and the model output is adjusted accordingly. For example, when a user posts a product review, the emotion engine extracts the user's emotions from the text expression and weights the model when rating the product based on the emotions. (Example 3) An embodiment of the present invention provides a service that is sensitive to the user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's voice and text data, and the model output is adjusted based on the emotions. For example, when a user requests the playback of music by voice, the emotion engine determines the emotion from the intonation and rhythm of the user's voice, and the model selects and plays an appropriate song accordingly.

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

[0078] (Example 1) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data. For example, in the case of voice data, emotions are extracted from the tone and rhythm of the voice, and in the case of text data, emotions are extracted from word choice and expressions. Step 3: Use the recognized emotions to train and apply models. For example, select an appropriate model based on the emotion and provide optimal answers and services to the user's request. As a specific example, if a user asks by voice, "What's the weather like today?", the emotion engine will recognize the user's emotions of interest and anxiety, and adjust the output of the weather forecast model based on that to provide the user with appropriate information. (Example 2) Step 1: Receive user's text data as input. Step 2: Use the emotion engine to recognize emotions from the user's text data, for example by extracting emotions from text expressions and word choices. Step 3: Reflect the recognized emotion in the model output. For example, the model output is weighted according to the emotion to provide a personalized service that matches the user's emotion. As a specific example, when a user posts a product review, the emotion engine extracts emotion from the user's text and weights the output of the product evaluation model based on the emotion to recommend more appropriate products to the user. (Example 3) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data, for example by analyzing the intonation and rhythm of the voice in the case of voice data, and extracting emotions from expressions and word choice in the case of text data. Step 3: Adjust the model output based on the recognized emotion. For example, adjust the model parameters and feature selection according to the emotion to provide a service that is sensitive to the user's emotion. For example, when a user requests to play music by voice, the emotion engine extracts the emotion from the user's voice, and the model selects and plays an appropriate song according to the emotion.

[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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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 making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[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 wide area network (WAN) and / or a local area network (LAN).

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

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

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0089] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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 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 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.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0095] (Example 1) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data collection, preprocessing, feature extraction, and model learning are automatically performed to make data learning in various industries and fields more efficient. In addition, the accuracy of the models in various industries and fields is improved by improving the models through feedback of the application results of the models. (Example 2) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes means for collecting, analyzing, and learning data, and means for applying and evaluating models. Specifically, data in various industries and fields is collected, the collected data is preprocessed, features are extracted, and model learning is performed to generate trained artificial intelligence models. In addition, the generated models are applied to various industries and fields, and the application results are evaluated to confirm the usefulness of the models. (Example 3) An embodiment of the present invention is a system for providing trained artificial intelligence models in various industries and fields. This system includes a means for collecting, analyzing, and learning data, and a means for applying and evaluating models. Specifically, a trained artificial intelligence model is constructed by collecting data in various industries and fields, analyzing the collected data, and conducting learning. In addition, the constructed model is applied to various industries and fields, and the application results are evaluated to confirm the performance of the model.

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

[0097] (Example 1) Step 1: Collect data in each industry or field as a means of collecting, analyzing, and learning data. For example, collect data from publicly available data on the Internet or industry-specific databases. Step 2: Preprocess the collected data and extract the necessary information. For example, in the case of text data, morphological and grammatical analysis is performed to extract the characteristics of words and phrases. Step 3: The extracted features are used to train a model, for example, using a machine learning algorithm to train the data and generate a trained artificial intelligence model. Step 4: Apply and evaluate the model. Apply the generated model to various industries and fields. For example, input new data and evaluate whether the model makes appropriate predictions or classifications. Step 5: Feedback the results of the application to improve the model. For example, improve the model's performance by analyzing errors in the application results and adjusting the model's parameters and architecture. (Example 2) Step 1: Collect data for each industry or field. For example, collect industry-specific data such as sensor data and customer data. Step 2: Preprocess the collected data and extract the necessary features, for example, handle missing values ​​in the data, scale and normalize the features. Step 3: Use the extracted features to train a model. For example, build a deep learning model and train it on the data to generate a trained artificial intelligence model. Step 4: The generated model is applied to various industries and fields. For example, new data is input and the model makes predictions and classifications, contributing to solving problems in the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as precision and recall. Step 6: Improve the model based on the results of application. For example, improve the model's performance by analyzing misclassified data and adjusting the model's parameters and architecture. (Example 3) Step 1: Collect data for your industry or field. For example, gather market research data, competitor data, and other data relevant to your industry. Step 2: Analyze the collected data and extract trends and characteristics, for example using statistical analysis or data mining to analyze the data for patterns and relationships. Step 3: Use the extracted features to build a trained artificial intelligence model, for example by applying a machine learning algorithm to generate a prediction or classification model from the data. Step 4: Apply the model to a particular industry or field. For example, input new data and have the model make predictions or classifications to meet the needs of the industry. Step 5: Evaluate the application results and confirm the performance of the model. For example, evaluate the model using indicators such as accuracy and error rate. Step 6: Improve the model based on the application results. For example, improve the model performance by analyzing the incorrect prediction results and adjusting the model parameters and feature selection.

[0098] In addition, 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) In an embodiment of the present invention, a system for providing trained artificial intelligence models for each industry or field is combined with an emotion engine to provide a service more suitable for the user. Specifically, emotions are recognized from the user's voice and text data, and the emotions are utilized for model learning and application. For example, when a user asks a question by voice, the emotion engine determines the emotion from the user's tone of voice and choice of words, and based on that, selects an appropriate model and provides an answer. (Example 2) An embodiment of the present invention provides personalized services based on a user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's text message and the model output is adjusted accordingly. For example, when a user posts a product review, the emotion engine extracts the user's emotions from the text expression and weights the model when rating the product based on the emotions. (Example 3) An embodiment of the present invention provides a service that is sensitive to the user's emotions by combining an emotion engine in a system for providing trained artificial intelligence models for each industry or field. Specifically, emotions are recognized from the user's voice and text data, and the model output is adjusted based on the emotions. For example, when a user requests the playback of music by voice, the emotion engine determines the emotion from the intonation and rhythm of the user's voice, and the model selects and plays an appropriate song accordingly.

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

[0101] (Example 1) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data. For example, in the case of voice data, emotions are extracted from the tone and rhythm of the voice, and in the case of text data, emotions are extracted from word choice and expressions. Step 3: Use the recognized emotions to train and apply models. For example, select an appropriate model based on the emotion and provide optimal answers and services to the user's request. As a specific example, if a user asks by voice, "What's the weather like today?", the emotion engine will recognize the user's emotions of interest and anxiety, and adjust the output of the weather forecast model based on that to provide the user with appropriate information. (Example 2) Step 1: Receive user's text data as input. Step 2: Use the emotion engine to recognize emotions from the user's text data, for example by extracting emotions from text expressions and word choices. Step 3: Reflect the recognized emotion in the model output. For example, the model output is weighted according to the emotion to provide a personalized service that matches the user's emotion. As a specific example, when a user posts a product review, the emotion engine extracts emotion from the user's text and weights the output of the product evaluation model based on the emotion to recommend more appropriate products to the user. (Example 3) Step 1: Receive user voice or text data as input. Step 2: Use the emotion engine to recognize emotions from the user's voice and text data, for example by analyzing the intonation and rhythm of the voice in the case of voice data, and extracting emotions from expressions and word choice in the case of text data. Step 3: Adjust the model output based on the recognized emotion. For example, adjust the model parameters and feature selection according to the emotion to provide a service that is sensitive to the user's emotion. For example, when a user requests to play music by voice, the emotion engine extracts the emotion from the user's voice, and the model selects and plays an appropriate song according to the emotion.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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 making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

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

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

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

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

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

[0109] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

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

[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 Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0114] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

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

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

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

[0119] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0120] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

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

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

[0123] (Appendix 1) A system including a means for providing trained artificial intelligence models for each industry or field, a means for collecting, analyzing, and learning data in each industry or field, and a means for evaluating the applicability of the model in each industry or field. (Appendix 2) In the system described in Appendix 1, the means for collecting, analyzing, and learning data in each industry or field is characterized by automatically collecting, preprocessing, extracting features, and learning models of data. (Appendix 3) In the system described in Appendix 1, the means for evaluating the applicability of the model in each industry or field is characterized in that the results of the application of the model are fed back to improve the model.

[0124] (Appendix 1) This system provides trained artificial intelligence models for each industry and field, and is characterized by its combination with an emotion engine. (Appendix 2) In the system described in Appendix 1, the emotion engine is characterized by recognizing emotions from the user's voice and text data and using them to learn and apply models. (Appendix 3) In the system described in Appendix 1 or 2, the emotion engine is characterized in that it provides a more personalized service by adjusting the output of the model based on the user's emotions. [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 a means for providing trained artificial intelligence models for each industry or field, a means for collecting, analyzing, and learning data in each industry or field, and a means for evaluating the applicability of the model in each industry or field.

2. In the system according to claim 1, the means for collecting, analyzing and learning data in each industry or field is characterized by automatically collecting data, pre-processing, extracting features and learning a model.

3. In the system according to claim 1, the means for evaluating the applicability of the model in each industry or field is characterized in that it improves the model by feeding back the results of the application of the model.

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