System

Abstract

An object of a system according to an embodiment is to utilize behavioral big data to formulate an optimal answer to a customer's question or a data strategy.SOLUTION: A system according to an embodiment includes a data-acquiring unit, an analysis unit, an answer generation unit, and an strategy planning unit. The data acquisition unit acquires behavioral big data. The analysis unit analyzes the behavior big data acquired by the data acquisition unit. The answer generation unit generates an answer to the question of the customer based on the data analyzed by the analysis unit. The strategy planning unit plans an optimal data strategy based on the answer generated by the answer generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to fully utilize behavioral big data to provide optimal answers to customer questions or develop data strategies.

[0005] The system according to the embodiment aims to utilize behavioral big data to develop optimal answers to customer questions and data strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a data acquisition unit, an analysis unit, an answer generation unit, and a strategy planning unit. The data acquisition unit acquires behavioral big data. The analysis unit analyzes the behavioral big data acquired by the data acquisition unit. The answer generation unit generates answers to customer questions based on the data analyzed by the analysis unit. The strategy planning unit plans an optimal data strategy based on the answers generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize behavioral big data to develop optimal answers to customer questions and data strategies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A consulting system according to an embodiment of the present invention combines generative AI and behavioral big data to answer various questions from customers and solve their problems. As a result, the consulting system can utilize generative AI and behavioral big data to provide accurate answers and optimal data strategies that meet the needs of customers.

[0029] A consulting system according to an embodiment includes a data acquisition unit, an analysis unit, an answer generation unit, and a strategy planning unit. The data acquisition unit acquires behavioral big data. For example, the data acquisition unit collects website browsing history. The data acquisition unit can also collect purchase history. The data acquisition unit can also collect location information. For example, the data acquisition unit collects GPS data. The analysis unit analyzes the behavioral big data acquired by the data acquisition unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze data using a machine learning algorithm. The analysis unit can also analyze data trends. For example, the analysis unit predicts trends based on past data. The answer generation unit generates answers to customer questions based on the data analyzed by the analysis unit. For example, the answer generation unit generates answers using natural language generation technology. The answer generation unit can also evaluate the accuracy and consistency of the answers. The answer generation unit can also generate specific answers to customer questions. For example, the answer generation unit provides detailed answers to customer questions. The strategy planning unit plans an optimal data strategy based on the answers generated by the answer generation unit. For example, the strategy planning unit proposes a data collection method. The strategy planning unit can also propose a data utilization method. The strategy planning unit can also propose evaluation criteria for the strategy. For example, the strategy planning unit proposes an effective data utilization method. As a result, the consulting system according to the embodiment can utilize the generation AI and behavioral big data to provide correct answers to various customer questions and optimal data strategies, thereby solving problems. For example, when a customer plans a new marketing strategy, the generation AI can propose an optimal strategy based on the behavioral big data, allowing for effective marketing activities. Furthermore, when a customer wants to know consumer behavior in a specific area, the generation AI can analyze location information and provide specific data, thereby meeting the customer's needs.

[0030] The data acquisition unit analyzes the location information included in the behavioral big data in real time, and can instantly grasp the customer's current behavioral patterns. The data acquisition unit, for example, analyzes the location information in real time, and can instantly grasp the customer's current behavioral patterns. For example, when a customer visits a specific store, the information is instantly analyzed, and the customer's behavioral patterns are grasped. This makes it possible to grasp the customer's current behavioral patterns in real time.

[0031] The analysis unit combines search information and location information to cross-reference customer interests and actual behavior, enabling more accurate needs analysis. The analysis unit, for example, combines search information and location information to cross-reference customer interests and actual behavior. For example, it analyzes the keywords searched by customers and the places they visited to understand their needs. This cross-references customer interests and actual behavior, enabling more accurate needs analysis.

[0032] The data acquisition unit adds voice data to the behavioral big data, and can also include the customer's voice instructions and conversation content in the analysis targets. The data acquisition unit, for example, adds voice data to the behavioral big data and analyzes the customer's voice instructions and conversation content. For example, it analyzes the content of the voice instructions given by the customer and understands behavioral patterns. This makes it possible to include the customer's voice instructions and conversation content in the analysis targets.

[0033] The data acquisition unit can promote the sharing of behavioral big data between different companies and enable analysis using a wider range of data sets. The data acquisition unit, for example, shares behavioral big data between different companies and performs analysis using a wider range of data sets. For example, data from multiple companies is integrated and customer behavior patterns are analyzed. This promotes the sharing of behavioral big data between different companies and enables analysis using a wider range of data sets.

[0034] The answer generation unit can refer to the past answer history when generating an answer and improve the consistency and accuracy of the answer. For example, when the generation AI generates an answer, the answer generation unit refers to the past answer history to maintain the consistency of the answer. For example, it provides a consistent answer to the same question. This makes it possible to refer to the past answer history and improve the consistency and accuracy of the answer.

[0035] The answer generation unit can integrate multiple data sources to generate a more comprehensive answer to the customer's question. The answer generation unit, for example, integrates multiple data sources to generate a comprehensive answer to the customer's question. For example, the answer generation unit integrates search information and location information to generate an answer. This allows the multiple data sources to be integrated to generate a more comprehensive answer.

[0036] The answer generation unit automatically generates visual data when generating an answer, and can provide an answer that is visually easy to understand. For example, when the generation AI generates an answer, the answer generation unit automatically generates visual data and provides an answer that is visually easy to understand. For example, consumer behavior data is displayed in a graph. This makes it possible to automatically generate visual data and provide an answer that is visually easy to understand.

[0037] The answer generation unit can add a multilingual support function to the generation AI so that it can respond to questions in different languages. For example, the answer generation unit can add a multilingual support function to the generation AI so that it can respond to questions in different languages. For example, it can generate accurate answers to questions in English and Chinese. This makes it possible to respond to questions in different languages.

[0038] When formulating a data strategy, the strategy planning department can refer to past success and failure cases to assess the risks and probability of success. For example, when a generative AI plans a data strategy, the strategy planning department can refer to past success and failure cases to assess the risks and probability of success. For example, it can analyze the factors that led to the success and failure of past marketing strategies and propose the optimal strategy. This allows it to refer to past success and failure cases to assess the risks and probability of success.

[0039] The strategy planning department can propose a customized data strategy that takes into account trends and regulations specific to the client's industry. For example, generative AI can propose a customized data strategy that takes into account trends and regulations specific to the client's industry. For example, generative AI can propose a strategy based on the latest trends and regulations in a specific industry. This makes it possible to propose a customized data strategy that takes into account trends and regulations specific to the client's industry.

[0040] The strategy planning department can verify the proposed data strategy in advance using a simulation tool and select the optimal strategy. The strategy planning department, for example, can verify the data strategy proposed by the generation AI in advance using a simulation tool and select the optimal strategy. For example, the effectiveness of the strategy can be evaluated based on the simulation results. This makes it possible to verify the proposed data strategy in advance using a simulation tool and select the optimal strategy.

[0041] The strategy planning department can integrate data from different industries and markets and propose new data strategies from a cross-industry perspective. The strategy planning department can, for example, integrate data from different industries and markets and propose new data strategies from a cross-industry perspective. For example, it can formulate strategies based on success stories from different industries. This makes it possible to integrate data from different industries and markets and propose new data strategies from a cross-industry perspective.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data acquisition unit can also analyze customers' social media activities to understand their interests. For example, the data acquisition unit collects and analyzes content posted by customers and posts they have "liked." The data acquisition unit can also collect information on accounts that customers follow and groups that they participate in. This allows for a more detailed understanding of customers' interests through their social media activities.

[0044] The analysis unit can combine customer purchasing history with weather data to analyze the impact of weather on purchasing behavior. For example, the analysis unit can analyze whether a particular product sells well on rainy days. The analysis unit can also analyze seasonal purchasing patterns and predict seasonal demand. This makes it possible to use weather data to analyze customer purchasing behavior with greater accuracy.

[0045] The data acquisition unit can acquire biometric data from the customer's wearable device and analyze their health condition and stress level. For example, the data acquisition unit can collect and analyze their heart rate and sleep patterns. The data acquisition unit can also collect the amount of exercise and calorie consumption. This allows the customer's health condition and stress level to be understood and data useful for health management to be provided.

[0046] The answer generation unit can automatically generate video content related to customer questions and provide answers that are visually easy to understand. For example, the answer generation unit generates explanatory videos or tutorial videos in response to customer questions. The answer generation unit can also automatically generate graphics and animations to be used in the videos. This makes it possible to provide answers that are visually easy to understand through video content.

[0047] The strategy planning department can compare data from different regions and propose customized strategies that are tailored to the characteristics of each region. For example, the strategy planning department can compare data from urban and rural areas and propose the optimal marketing strategy for each region. The strategy planning department can also compare data from different countries and propose strategies that are tailored to the culture and consumption behavior of each country. This makes it possible to propose customized strategies that are tailored to the characteristics of each region.

[0048] The Strategy Planning Department can combine data from different industries to propose new business models from a cross-industry perspective. For example, the Strategy Planning Department can combine data from the manufacturing and service industries to propose new product services. The Strategy Planning Department can also combine data from the IT and healthcare industries to propose new digital healthcare solutions. This allows the department to combine data from different industries to propose new business models from a cross-industry perspective.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data acquisition unit acquires behavioral big data. For example, the data acquisition unit collects website browsing history, purchase history, and location information (such as GPS data). Step 2: The analysis unit analyzes the behavioral big data acquired by the data acquisition unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms and predicts trends in the data. Step 3: The answer generation unit generates an answer to the customer's question based on the data analyzed by the analysis unit. For example, the answer generation unit may generate an answer using natural language generation technology and evaluate the accuracy and consistency of the answer. Step 4: The strategy planning department plans an optimal data strategy based on the answers generated by the answer generation department. For example, the strategy planning department proposes methods for collecting and utilizing data, as well as criteria for evaluating the strategy.

[0051] (Example 2) A consulting system according to an embodiment of the present invention combines generative AI and behavioral big data to answer various questions from customers and solve their problems. As a result, the consulting system can utilize generative AI and behavioral big data to provide accurate answers and optimal data strategies that meet the needs of customers.

[0052] A consulting system according to an embodiment includes a data acquisition unit, an analysis unit, an answer generation unit, and a strategy planning unit. The data acquisition unit acquires behavioral big data. For example, the data acquisition unit collects website browsing history. The data acquisition unit can also collect purchase history. The data acquisition unit can also collect location information. For example, the data acquisition unit collects GPS data. The analysis unit analyzes the behavioral big data acquired by the data acquisition unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze data using a machine learning algorithm. The analysis unit can also analyze data trends. For example, the analysis unit predicts trends based on past data. The answer generation unit generates answers to customer questions based on the data analyzed by the analysis unit. For example, the answer generation unit generates answers using natural language generation technology. The answer generation unit can also evaluate the accuracy and consistency of the answers. The answer generation unit can also generate specific answers to customer questions. For example, the answer generation unit provides detailed answers to customer questions. The strategy planning unit plans an optimal data strategy based on the answers generated by the answer generation unit. For example, the strategy planning unit proposes a data collection method. The strategy planning unit can also propose a data utilization method. The strategy planning unit can also propose evaluation criteria for the strategy. For example, the strategy planning unit proposes an effective data utilization method. As a result, the consulting system according to the embodiment can utilize the generation AI and behavioral big data to provide correct answers to various customer questions and optimal data strategies, thereby solving problems. For example, when a customer plans a new marketing strategy, the generation AI can propose an optimal strategy based on the behavioral big data, allowing for effective marketing activities. Furthermore, when a customer wants to know consumer behavior in a specific area, the generation AI can analyze location information and provide specific data, thereby meeting the customer's needs.

[0053] The data acquisition unit analyzes the location information included in the behavioral big data in real time, and can instantly grasp the customer's current behavioral patterns. The data acquisition unit, for example, analyzes the location information in real time, and can instantly grasp the customer's current behavioral patterns. For example, when a customer visits a specific store, the information is instantly analyzed, and the customer's behavioral patterns are grasped. This makes it possible to grasp the customer's current behavioral patterns in real time.

[0054] The analysis unit combines search information and location information to cross-reference customer interests and actual behavior, enabling more accurate needs analysis. The analysis unit, for example, combines search information and location information to cross-reference customer interests and actual behavior. For example, it analyzes the keywords searched by customers and the places they visited to understand their needs. This cross-references customer interests and actual behavior, enabling more accurate needs analysis.

[0055] The analysis unit can use the emotion estimation function to estimate changes in emotions from a customer's search history and perform data analysis based on those emotions. The analysis unit, for example, estimates emotions based on the search history and analyzes changes in customer emotions. For example, it estimates fluctuations in customer emotions from changes in search keywords. This makes it possible to estimate changes in customer emotions and analyze data based on those emotions.

[0056] The data acquisition unit adds voice data to the behavioral big data, and can also include the customer's voice instructions and conversation content in the analysis targets. The data acquisition unit, for example, adds voice data to the behavioral big data and analyzes the customer's voice instructions and conversation content. For example, it analyzes the content of the voice instructions given by the customer and understands behavioral patterns. This makes it possible to include the customer's voice instructions and conversation content in the analysis targets.

[0057] The data acquisition unit can promote the sharing of behavioral big data between different companies and enable analysis using a wider range of data sets. The data acquisition unit, for example, shares behavioral big data between different companies and performs analysis using a wider range of data sets. For example, data from multiple companies is integrated and customer behavior patterns are analyzed. This promotes the sharing of behavioral big data between different companies and enables analysis using a wider range of data sets.

[0058] The analysis unit uses the emotion estimation function to analyze the emotions of customers when they input data in real time, and can provide an interface for eliciting positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of customers when they input data in real time. For example, it analyzes facial expressions and voices at the time of input and calculates an emotion score. This makes it possible to analyze the emotions of customers when they input data in real time, and provide an interface for eliciting positive emotions.

[0059] The answer generation unit can refer to the past answer history when generating an answer and improve the consistency and accuracy of the answer. For example, when the generation AI generates an answer, the answer generation unit refers to the past answer history to maintain the consistency of the answer. For example, it provides a consistent answer to the same question. This makes it possible to refer to the past answer history and improve the consistency and accuracy of the answer.

[0060] The answer generation unit can integrate multiple data sources to generate a more comprehensive answer to the customer's question. The answer generation unit, for example, integrates multiple data sources to generate a comprehensive answer to the customer's question. For example, the answer generation unit integrates search information and location information to generate an answer. This allows the multiple data sources to be integrated to generate a more comprehensive answer.

[0061] The answer generation unit can use the emotion estimation function to analyze the customer's emotional reaction to the question and adjust the tone and content of the answer based on that reaction. The answer generation unit can, for example, use the emotion estimation function to analyze the customer's emotional reaction to the question and adjust the tone of the answer based on that reaction. For example, if the customer is feeling anxious, the answer can be given in a tone that gives a sense of security. This makes it possible to analyze the customer's emotional reaction to the question and adjust the tone and content of the answer based on that reaction.

[0062] The answer generation unit automatically generates visual data when generating an answer, and can provide an answer that is visually easy to understand. For example, when the generation AI generates an answer, the answer generation unit automatically generates visual data and provides an answer that is visually easy to understand. For example, consumer behavior data is displayed in a graph. This makes it possible to automatically generate visual data and provide an answer that is visually easy to understand.

[0063] The answer generation unit can add a multilingual support function to the generation AI so that it can respond to questions in different languages. For example, the answer generation unit can add a multilingual support function to the generation AI so that it can respond to questions in different languages. For example, it can generate accurate answers to questions in English and Chinese. This makes it possible to respond to questions in different languages.

[0064] The answer generation unit can use the emotion estimation function to analyze in real time the emotions of the customer when inputting a question and make suggestions to optimize the content of the question. For example, the answer generation unit can use the emotion estimation function to analyze in real time the emotions of the customer when inputting a question and make suggestions to optimize the content of the question. For example, if the customer is feeling anxious, a suggestion is made to simplify the content of the question. This makes it possible to analyze in real time the emotions of the customer when inputting a question and make suggestions to optimize the content of the question.

[0065] When formulating a data strategy, the strategy planning department can refer to past success and failure cases to assess the risks and probability of success. For example, when a generative AI plans a data strategy, the strategy planning department can refer to past success and failure cases to assess the risks and probability of success. For example, it can analyze the factors that led to the success and failure of past marketing strategies and propose the optimal strategy. This allows it to refer to past success and failure cases to assess the risks and probability of success.

[0066] The strategy planning department can propose a customized data strategy that takes into account trends and regulations specific to the client's industry. For example, generative AI can propose a customized data strategy that takes into account trends and regulations specific to the client's industry. For example, generative AI can propose a strategy based on the latest trends and regulations in a specific industry. This makes it possible to propose a customized data strategy that takes into account trends and regulations specific to the client's industry.

[0067] The strategy planning unit can use the emotion estimation function to analyze the emotional needs of customers and develop a data strategy based on those emotions. The strategy planning unit, for example, uses the emotion estimation function to analyze the emotional needs of customers and develop a data strategy based on those emotions. For example, if a customer is feeling anxious, the strategy planning unit proposes a strategy to alleviate that anxiety. This makes it possible to analyze the emotional needs of customers and develop a data strategy based on those emotions.

[0068] The strategy planning department can verify the proposed data strategy in advance using a simulation tool and select the optimal strategy. The strategy planning department, for example, can verify the data strategy proposed by the generation AI in advance using a simulation tool and select the optimal strategy. For example, the effectiveness of the strategy can be evaluated based on the simulation results. This makes it possible to verify the proposed data strategy in advance using a simulation tool and select the optimal strategy.

[0069] The strategy planning department can integrate data from different industries and markets and propose new data strategies from a cross-industry perspective. The strategy planning department can, for example, integrate data from different industries and markets and propose new data strategies from a cross-industry perspective. For example, it can formulate strategies based on success stories from different industries. This makes it possible to integrate data from different industries and markets and propose new data strategies from a cross-industry perspective.

[0070] The strategy planning unit can use the emotion estimation function to monitor the customer's emotional reaction to the proposed data strategy in real time and adjust the strategy. The strategy planning unit can, for example, use the emotion estimation function to monitor the customer's emotional reaction to the proposed data strategy in real time and adjust the strategy. For example, if the customer is feeling anxious, the strategy can be adjusted to alleviate the anxiety. In this way, the customer's emotional reaction to the proposed data strategy can be monitored in real time and the strategy can be adjusted.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data acquisition unit can also analyze customers' social media activities to understand their interests. For example, the data acquisition unit collects and analyzes content posted by customers and posts they have "liked." The data acquisition unit can also collect information on accounts that customers follow and groups that they participate in. This allows for a more detailed understanding of customers' interests through their social media activities.

[0073] The analysis unit can combine customer purchasing history with weather data to analyze the impact of weather on purchasing behavior. For example, the analysis unit can analyze whether a particular product sells well on rainy days. The analysis unit can also analyze seasonal purchasing patterns and predict seasonal demand. This makes it possible to use weather data to analyze customer purchasing behavior with greater accuracy.

[0074] The analysis unit uses the emotion estimation function to analyze the emotions expressed by customers on social media and can propose marketing strategies based on those emotions. For example, if a customer expresses positive emotions, it can propose a campaign to reinforce those emotions. Also, if a customer expresses negative emotions, it can propose countermeasures to alleviate those emotions. In this way, it is possible to propose marketing strategies based on customer emotions.

[0075] The data acquisition unit can acquire biometric data from the customer's wearable device and analyze their health condition and stress level. For example, the data acquisition unit can collect and analyze their heart rate and sleep patterns. The data acquisition unit can also collect the amount of exercise and calorie consumption. This allows the customer's health condition and stress level to be understood and data useful for health management to be provided.

[0076] The analysis unit can use the emotion estimation function to analyze the emotions of the customer when using the wearable device and provide feedback based on those emotions. For example, if the customer feels positive emotions while exercising, the analysis unit can provide feedback to reinforce those emotions. Also, if the customer feels stressed, the analysis unit can provide advice to reduce that stress. In this way, it is possible to provide feedback based on the customer's emotions.

[0077] The answer generation unit can automatically generate video content related to customer questions and provide answers that are visually easy to understand. For example, the answer generation unit generates explanatory videos or tutorial videos in response to customer questions. The answer generation unit can also automatically generate graphics and animations to be used in the videos. This makes it possible to provide answers that are visually easy to understand through video content.

[0078] The answer generation unit can use the emotion estimation function to analyze the emotions of customers when they watch video content and adjust the content of the video based on those emotions. For example, if a customer feels positive emotions while watching a video, the answer generation unit can provide additional content to reinforce those emotions. Also, if a customer feels negative emotions while watching a video, the answer generation unit can provide content to alleviate those emotions. In this way, the content of the video can be adjusted based on the customer's emotions.

[0079] The strategy planning department can compare data from different regions and propose customized strategies that are tailored to the characteristics of each region. For example, the strategy planning department can compare data from urban and rural areas and propose the optimal marketing strategy for each region. The strategy planning department can also compare data from different countries and propose strategies that are tailored to the culture and consumption behavior of each country. This makes it possible to propose customized strategies that are tailored to the characteristics of each region.

[0080] The strategy planning unit can use the emotion estimation function to analyze the emotional reactions of customers and evaluate the effectiveness of a strategy based on those reactions. For example, if a customer expresses positive emotions toward a new strategy, the strategy planning unit can make a proposal to strengthen the strategy. Also, if a customer expresses negative emotions, the strategy planning unit can make a proposal to review the strategy. This makes it possible to evaluate the effectiveness of a strategy based on the emotional reactions of customers.

[0081] The Strategy Planning Department can combine data from different industries to propose new business models from a cross-industry perspective. For example, the Strategy Planning Department can combine data from the manufacturing and service industries to propose new product services. The Strategy Planning Department can also combine data from the IT and healthcare industries to propose new digital healthcare solutions. This allows the department to combine data from different industries to propose new business models from a cross-industry perspective.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data acquisition unit acquires behavioral big data. For example, the data acquisition unit collects website browsing history, purchase history, and location information (such as GPS data). Step 2: The analysis unit analyzes the behavioral big data acquired by the data acquisition unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms and predicts trends in the data. Step 3: The answer generation unit generates an answer to the customer's question based on the data analyzed by the analysis unit. For example, the answer generation unit may generate an answer using natural language generation technology and evaluate the accuracy and consistency of the answer. Step 4: The strategy planning department plans an optimal data strategy based on the answers generated by the answer generation department. For example, the strategy planning department proposes methods for collecting and utilizing data, as well as criteria for evaluating the strategy.

[0084] 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.

[0085] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] 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.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] 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.

[0147] 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.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] 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. [Explanation of symbols]

[0151] 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 data acquisition unit that acquires behavioral big data; an analysis unit that analyzes the behavioral big data acquired by the data acquisition unit; an answer generation unit that generates an answer to the customer's question based on the data analyzed by the analysis unit; a strategy planning unit that plans an optimal data strategy based on the answer generated by the answer generating unit. A system characterized by:

2. The data acquisition unit The location information contained in the behavioral big data is analyzed in real time to immediately grasp the current behavioral patterns of the customer.

2. The system of claim 1.

3. The data acquisition unit Add voice data to the behavioral big data, and analyze the customer's voice instructions and conversation content.

2. The system of claim 1.

4. The answer generation unit When generating the answer, reference is made to past answer history to improve the consistency and accuracy of the answer.

2. The system of claim 1.

5. The strategic planning unit When formulating the data strategy, look at past successes and failures to assess the risks and probability of success.

2. The system of claim 1.

6. The analysis unit Estimate changes in emotions from the customer's search history and perform data analysis based on those emotions.

2. The system of claim 1.

7. The answer generation unit Analyzing the customer's emotional response to the question and adjusting the tone and content of the response based on the response.

2. The system of claim 1.

8. The strategic planning unit Analyze the emotional needs of the customer and develop the data strategy based on those emotions.

2. The system of claim 1.

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