system

Abstract

The system according to the embodiment aims to identify customer needs and issues and, based on those needs and issues, propose appropriate methods for utilizing data. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a verification unit. The collection unit collects input information from customers. The analysis unit analyzes the information collected by the collection unit and identifies customer needs and issues. The proposal unit analyzes behavioral big data based on the needs and issues identified by the analysis unit and proposes appropriate data utilization methods. The verification unit verifies the effectiveness of the data utilization methods proposed by the proposal unit and improves them as necessary.

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 the drawback of making it difficult to accurately identify customer needs and issues and then propose appropriate ways to utilize data based on those needs.

[0005] The system according to the embodiment aims to identify customer needs and issues and, based on those needs and issues, propose appropriate methods for utilizing data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a verification unit. The collection unit collects input information from customers. The analysis unit analyzes the information collected by the collection unit and identifies customer needs and issues. The proposal unit analyzes behavioral big data based on the needs and issues identified by the analysis unit and proposes an appropriate data utilization method. The verification unit verifies the effectiveness of the data utilization method proposed by the proposal unit and improves it as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can identify customer needs and issues and propose appropriate methods of utilizing data based on those needs and issues. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A consulting system according to an embodiment of the present invention is a novel consulting system that combines generative AI and behavioral big data. This consulting system understands customer needs and challenges and proposes optimal data utilization methods using behavioral big data. For example, if a company wants to improve its marketing strategy, the consulting system uses generative AI to analyze search data and location data and propose optimal marketing measures. Similarly, if a university wants to efficiently utilize research data, the generative AI analyzes behavioral big data and proposes optimal utilization methods for the research data. This achieves problem-solving capabilities that only generative AI and big data can provide. The consulting system proposes optimal data utilization methods based on customer needs and challenges, and then verifies and improves their effectiveness, thereby achieving problem-solving capabilities that only generative AI and big data can provide. For example, companies can improve their marketing strategies and increase sales. Universities can efficiently utilize research data and improve research results.

[0029] A consulting system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a verification unit. The collection unit collects input information from customers. The input information from customers includes, but is not limited to, text, audio, and images. The collection unit collects customer information using, for example, a questionnaire. The collection unit can also collect customer behavioral data using a sensor. The collection unit can also collect log data. For example, the collection unit collects website click data to understand customer behavior patterns. The analysis unit analyzes the information collected by the collection unit to identify customer needs and issues. The analysis is performed using, for example, statistical analysis, machine learning, natural language processing, or other methods, but is not limited to these examples. For example, the analysis unit analyzes customer data using statistical analysis to identify customer needs. The analysis unit can also analyze customer behavioral patterns using machine learning to identify issues. The analysis unit can also analyze text data using natural language processing to identify customer requests. The proposal unit analyzes the behavioral big data based on the needs and issues identified by the analysis unit and proposes an optimal data utilization method. The proposals are made, for example, through methods such as marketing strategies, product development, and customer support, but are not limited to these examples. For example, the proposal unit analyzes search data and proposes optimal keywords. The proposal unit can also analyze location information data and propose optimal marketing measures. The proposal unit can also analyze purchase history and propose optimal products for customers. The verification unit verifies the effectiveness of the data utilization method proposed by the proposal unit and improves it as necessary. The verification is made, for example, through methods such as experiments, simulations, and feedback collection, but is not limited to these examples. For example, the verification unit conducts experiments to verify the effectiveness of the proposed measures. The verification unit can also conduct simulations to predict the effectiveness of the proposed measures. The verification unit can also collect feedback from customers and evaluate the effectiveness of the proposed measures.As a result, the consulting system according to the embodiment can propose optimal data utilization methods based on the customer's needs and issues, and by verifying and improving the effectiveness, it can realize problem-solving that can only be achieved with generative AI and big data.

[0030] The collection unit can analyze the customer's past input information and select an appropriate collection method. For example, if the customer has preferred text input in the past, the collection unit can preferentially suggest text input. Furthermore, if the customer has frequently used voice input in the past, the collection unit can also preferentially select voice input as the collection method. Furthermore, if the customer has provided a lot of image data in the past, the collection unit can also prioritize image data collection. This enables the selection of an optimal collection method by analyzing the customer's past input information, enabling efficient information collection. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the customer's past input data into a generation AI and have the generation AI select an optimal collection method.

[0031] When collecting input information, the collection unit can filter the input information based on the customer's current business situation and areas of interest. For example, the collection unit prioritizes collecting information related to projects currently underway by the customer. The collection unit can also filter and collect highly relevant information based on the customer's areas of interest. The collection unit can also collect only necessary information and eliminate unnecessary information depending on the customer's business situation. This allows highly relevant information to be efficiently collected by filtering information based on the customer's business situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer business situation data into a generation AI and have the generation AI filter the information.

[0032] When collecting input information, the collection unit can select an appropriate collection means depending on the customer's input method. For example, if the customer uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the customer provides image data, the collection unit can also collect information using image analysis technology. This enables efficient information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting input information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, if the customer is on the move, the collection unit can also collect highly relevant information based on the customer's current location. Furthermore, if the customer is staying in a specific place, the collection unit can also prioritize collecting information related to that place. This allows highly relevant information to be collected efficiently by taking the customer's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting input information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit collects related data based on information shared by the customer on social media. The collection unit can also analyze the customer's social media activities and collect related information. The collection unit can also refer to the activities of the customer's friends on social media to collect related information. In this way, by analyzing the customer's social media activities, highly relevant information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's social media data into the generation AI and cause the generation AI to collect related information.

[0035] When collecting input information, the collection unit can customize the collection method by reflecting past customer feedback. For example, the collection unit adjusts the collection method based on feedback provided by the customer in the past. The collection unit can also customize the type of information to be collected by reflecting past customer feedback. The collection unit can also optimize the collection timing and means based on customer feedback. This allows the collection method to be optimized by reflecting past customer feedback, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past customer feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the customer's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit can also improve the efficiency of the analysis by utilizing the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are expressed according to the customer's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0042] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the data utilization method. For example, the proposal unit makes a detailed proposal for a data utilization method with high importance. The proposal unit can also make a simplified proposal for a data utilization method with low importance. The proposal unit can also adjust the depth and scope of the proposal depending on the importance of the data utilization method. This enables efficient proposals by adjusting the level of detail of the proposal depending on the importance of the data utilization method. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input importance data of the data utilization method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the data category. For example, the suggestion unit can make a proposal by applying a natural language processing algorithm to text data. The suggestion unit can also make a proposal by applying an image analysis algorithm to image data. The suggestion unit can also make a proposal by applying a voice recognition algorithm to voice data. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the data category to the generation AI and cause the generation AI to apply an appropriate proposal algorithm.

[0044] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the customer's past proposal results. The proposal unit can also improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit can also improve the efficiency of the proposal by utilizing the customer's past proposal results. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of data submission. For example, the proposal unit prioritizes proposals based on the latest data. The proposal unit can also lower the priority of data that was submitted earlier when making a proposal. The proposal unit can also adjust the order of proposals based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of data submission. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of the proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of data when making suggestions. For example, the suggestion unit prioritizes suggestions based on highly relevant data. The suggestion unit can also postpone suggestions for less relevant data. The suggestion unit can also adjust the order of suggestions based on the relevance of data. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the customer's level of expertise. For example, if the customer has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the customer does not have technical expertise, the suggestion unit can make a proposal that avoids technical terminology. The suggestion unit can also adjust the way the proposal is expressed depending on the customer's level of expertise. This makes it possible to provide a more appropriate proposal by adjusting the use of technical terminology in the proposal depending on the customer's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input customer's expertise level data into a generation AI and cause the generation AI to use technical terminology in the proposal.

[0048] The verification unit can perform a detailed analysis of the effects of the proposed data utilization method during verification. The verification unit, for example, quantitatively analyzes the effects of the proposed data utilization method. The verification unit can also qualitatively analyze the effects of the proposed data utilization method. The verification unit can also analyze the effects of the proposed data utilization method from multiple angles. This enables effective verification by analyzing the effects of the proposed data utilization method in detail. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or may be performed without using AI. For example, the verification unit can input effect data of the proposed data utilization method into the generation AI and cause the generation AI to perform a detailed analysis of the effects.

[0049] During verification, the verification unit can improve the accuracy of the verification by referring to the customer's past data utilization results. The verification unit, for example, adjusts the verification algorithm based on the customer's past data utilization results. The verification unit can also improve the accuracy of the verification by referring to the customer's past data utilization results. The verification unit can also improve the efficiency of the verification by utilizing the customer's past data utilization results. In this way, the accuracy of the verification is improved by referring to the customer's past data utilization results. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's past data utilization results into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0050] The verification unit can improve the verification method by reflecting customer feedback during verification. The verification unit can adjust the verification method based on customer feedback, for example. The verification unit can also improve the accuracy of verification by reflecting customer feedback. The verification unit can also improve the efficiency of verification by utilizing customer feedback. In this way, by reflecting customer feedback, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit can input customer feedback data into the generation AI and cause the generation AI to improve the verification method.

[0051] During verification, the verification unit can select an appropriate verification method by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the verification unit performs verification based on data related to that area. Furthermore, if the customer is traveling, the verification unit can select the optimal verification method based on the customer's current location. Furthermore, if the customer is staying in a specific location, the verification unit can perform verification based on data related to that location. This allows the optimal verification method to be selected by taking into account the customer's geographical location information, enabling efficient verification. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's geographical location data into the generation AI and cause the generation AI to select an appropriate verification method.

[0052] During verification, the verification unit can analyze the customer's social media activity and suggest verification methods. The verification unit can suggest verification methods based on, for example, information shared by the customer on social media. The verification unit can also analyze the customer's social media activity and suggest optimal verification methods. The verification unit can also suggest verification methods based on the activity of the customer's friends on social media. This allows the analysis of the customer's social media activity to suggest optimal verification methods, enabling efficient verification. Some or all of the above-mentioned processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit can input the customer's social media data into a generation AI and have the generation AI execute the proposed verification methods.

[0053] During verification, the verification unit can customize the verification method by reflecting past customer feedback. The verification unit, for example, adjusts the verification method based on feedback provided by the customer in the past. The verification unit can also improve the accuracy of verification by reflecting past customer feedback. The verification unit can also optimize the timing and means of verification based on customer feedback. In this way, by reflecting past customer feedback, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input past customer feedback data into the generation AI and have the generation AI customize the verification method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can analyze the customer's past behavioral data and customize the analysis method based on the customer's behavioral patterns. For example, if the customer has shown a specific behavioral pattern in the past, the analysis method can be adjusted based on that pattern. Also, if the customer has used specific data frequently in the past, that data can be prioritized for analysis. Furthermore, if the customer has preferred a specific analysis method in the past, that method can be prioritized. This allows the analysis method to be customized by analyzing the customer's past behavioral data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past behavioral data into the generation AI and have the generation AI customize the analysis method.

[0056] The proposal unit can analyze the customer's past proposal history and customize the content of the proposal based on the customer's preferences. For example, if the customer has previously preferred and accepted a particular proposal, the proposal unit can make a new proposal based on that proposal. Also, if the customer has previously rejected a particular proposal, the proposal can be avoided. Furthermore, if the customer has previously shown interest in a particular field, the proposal unit can prioritize proposals related to that field. In this way, by analyzing the customer's past proposal history, the content of the proposal can be customized, enabling more appropriate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal history data into a generation AI and have the generation AI customize the content of the proposal.

[0057] The verification unit can analyze the customer's past verification results and optimize the verification method. For example, if the customer has preferred a particular verification method in the past, that method can be used preferentially. Also, if the customer has placed importance on a particular verification result in the past, new verification can be performed based on that result. Furthermore, if the customer has used particular data frequently in the past, that data can be used preferentially. In this way, by analyzing the customer's past verification results, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's past verification result data into the generation AI and have the generation AI optimize the verification method.

[0058] The collection unit can determine the type of information to collect by taking into account the customer's geographical location information. For example, if the customer is in a specific area, information related to that area can be collected preferentially. Also, if the customer is on the move, highly relevant information can be collected based on the customer's current location. Furthermore, if the customer is staying in a specific place, information related to that place can be collected preferentially. In this way, by taking the customer's geographical location information into account, highly relevant information can be collected efficiently. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and have the generation AI execute the type of information to be collected.

[0059] The suggestion unit can analyze a customer's social media activity and customize the content of suggestions based on the customer's interests. For example, if a customer frequently posts about a particular topic on social media, suggestions related to that topic can be made. Also, if a customer mentions a particular brand or product on social media, suggestions related to that brand or product can be made. Furthermore, if a customer is participating in a particular event on social media, suggestions related to that event can be made. In this way, by analyzing a customer's social media activity, the content of suggestions can be customized and more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's social media data into a generation AI and have the generation AI customize the content of suggestions.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects input information from customers. Input information from customers includes text, voice, images, etc. The collection unit can collect customer information using a questionnaire. It can also collect customer behavior data using sensors, and it is also possible to collect log data. For example, the collection unit collects website click data to understand customer behavior patterns. Step 2: The analysis unit analyzes the information collected by the collection unit to identify customer needs and issues. The analysis is performed using methods such as statistical analysis, machine learning, and natural language processing. For example, the analysis unit uses statistical analysis to analyze customer data and identify customer needs. It can also use machine learning to analyze customer behavior patterns and identify issues. It can also use natural language processing to analyze text data and identify customer requests. Step 3: The proposal department analyzes the behavioral big data based on the needs and issues identified by the analysis department and proposes the optimal way to utilize the data. Proposals are made in the form of marketing strategies, product development, customer support, etc. For example, the proposal department analyzes search data and proposes optimal keywords. It can also analyze location data and propose optimal marketing measures. It can also analyze purchase history and propose the most suitable products for customers. Step 4: The Verification Department verifies the effectiveness of the data utilization methods proposed by the Proposal Department and makes improvements as necessary. Verification is carried out using methods such as experiments, simulations, and feedback collection. For example, the Verification Department conducts experiments to verify the effectiveness of the proposed measures. It can also conduct simulations to predict the effectiveness of the proposed measures. It can also collect feedback from customers to evaluate the effectiveness of the proposed measures.

[0062] (Example 2) A consulting system according to an embodiment of the present invention is a novel consulting system that combines generative AI and behavioral big data. This consulting system understands customer needs and challenges and proposes optimal data utilization methods using behavioral big data. For example, if a company wants to improve its marketing strategy, the consulting system uses generative AI to analyze search data and location data and propose optimal marketing measures. Similarly, if a university wants to efficiently utilize research data, the generative AI analyzes behavioral big data and proposes optimal utilization methods for the research data. This achieves problem-solving capabilities that only generative AI and big data can provide. The consulting system proposes optimal data utilization methods based on customer needs and challenges, and then verifies and improves their effectiveness, thereby achieving problem-solving capabilities that only generative AI and big data can provide. For example, companies can improve their marketing strategies and increase sales. Universities can efficiently utilize research data and improve research results.

[0063] A consulting system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a verification unit. The collection unit collects input information from customers. The input information from customers includes, but is not limited to, text, audio, and images. The collection unit collects customer information using, for example, a questionnaire. The collection unit can also collect customer behavioral data using a sensor. The collection unit can also collect log data. For example, the collection unit collects website click data to understand customer behavior patterns. The analysis unit analyzes the information collected by the collection unit to identify customer needs and issues. The analysis is performed using, for example, statistical analysis, machine learning, natural language processing, or other methods, but is not limited to these examples. For example, the analysis unit analyzes customer data using statistical analysis to identify customer needs. The analysis unit can also analyze customer behavioral patterns using machine learning to identify issues. The analysis unit can also analyze text data using natural language processing to identify customer requests. The proposal unit analyzes the behavioral big data based on the needs and issues identified by the analysis unit and proposes an optimal data utilization method. The proposals are made, for example, through methods such as marketing strategies, product development, and customer support, but are not limited to these examples. For example, the proposal unit analyzes search data and proposes optimal keywords. The proposal unit can also analyze location information data and propose optimal marketing measures. The proposal unit can also analyze purchase history and propose optimal products for customers. The verification unit verifies the effectiveness of the data utilization method proposed by the proposal unit and improves it as necessary. The verification is made, for example, through methods such as experiments, simulations, and feedback collection, but is not limited to these examples. For example, the verification unit conducts experiments to verify the effectiveness of the proposed measures. The verification unit can also conduct simulations to predict the effectiveness of the proposed measures. The verification unit can also collect feedback from customers and evaluate the effectiveness of the proposed measures.As a result, the consulting system according to the embodiment can propose optimal data utilization methods based on the customer's needs and issues, and by verifying and improving the effectiveness, it can realize problem-solving that can only be achieved with generative AI and big data.

[0064] The collection unit estimates the customer's emotions and adjusts the timing of collecting input information based on the estimated customer emotions. For example, if the customer is feeling stressed, the collection unit delays the collection timing so that information can be provided in a relaxed state. Furthermore, if the customer is relaxed, the collection unit can collect information immediately and provide a prompt response. Furthermore, if the customer is in a hurry, the collection unit can advance the collection timing so that necessary information can be collected quickly. This enables more appropriate information collection by adjusting the collection timing according to the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0065] The collection unit can analyze the customer's past input information and select an appropriate collection method. For example, if the customer has preferred text input in the past, the collection unit can preferentially suggest text input. Furthermore, if the customer has frequently used voice input in the past, the collection unit can also preferentially select voice input as the collection method. Furthermore, if the customer has provided a lot of image data in the past, the collection unit can also prioritize image data collection. This enables the selection of an optimal collection method by analyzing the customer's past input information, enabling efficient information collection. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the customer's past input data into a generation AI and have the generation AI select an optimal collection method.

[0066] When collecting input information, the collection unit can filter the input information based on the customer's current business situation and areas of interest. For example, the collection unit prioritizes collecting information related to projects currently underway by the customer. The collection unit can also filter and collect highly relevant information based on the customer's areas of interest. The collection unit can also collect only necessary information and eliminate unnecessary information depending on the customer's business situation. This allows highly relevant information to be efficiently collected by filtering information based on the customer's business situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input customer business situation data into a generation AI and have the generation AI filter the information.

[0067] When collecting input information, the collection unit can select an appropriate collection means depending on the customer's input method. For example, if the customer uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the customer provides image data, the collection unit can also collect information using image analysis technology. This enables efficient information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0068] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer emotions. For example, if the customer is stressed, the collection unit can prioritize collecting information of high importance. Furthermore, if the customer is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the customer is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. Thus, by determining the priority of information to be collected according to the customer's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0069] When collecting input information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, if the customer is on the move, the collection unit can also collect highly relevant information based on the customer's current location. Furthermore, if the customer is staying in a specific place, the collection unit can also prioritize collecting information related to that place. This allows highly relevant information to be collected efficiently by taking the customer's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0070] When collecting input information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit collects related data based on information shared by the customer on social media. The collection unit can also analyze the customer's social media activities and collect related information. The collection unit can also refer to the activities of the customer's friends on social media to collect related information. In this way, by analyzing the customer's social media activities, highly relevant information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's social media data into the generation AI and cause the generation AI to collect related information.

[0071] When collecting input information, the collection unit can customize the collection method by reflecting past customer feedback. For example, the collection unit adjusts the collection method based on feedback provided by the customer in the past. The collection unit can also customize the type of information to be collected by reflecting past customer feedback. The collection unit can also optimize the collection timing and means based on customer feedback. This allows the collection method to be optimized by reflecting past customer feedback, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past customer feedback data into the generation AI and have the generation AI customize the collection method.

[0072] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. For example, if the customer is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the customer is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the customer is in a hurry, the analysis unit can provide a more concise analysis result. By adjusting the way the analysis is presented based on the customer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the customer's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit can also improve the efficiency of the analysis by utilizing the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit can provide a short and concise analysis result. If the customer is relaxed, the analysis unit can provide a detailed analysis result. If the customer is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the customer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are expressed according to the customer's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0080] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, if the customer is nervous, the suggestion unit can provide a simple, highly visible suggestion. Furthermore, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the customer is in a hurry, the suggestion unit can provide a suggestion that focuses on the main points. By adjusting the way the suggestion is presented based on the customer's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0081] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the data utilization method. For example, the proposal unit makes a detailed proposal for a data utilization method with high importance. The proposal unit can also make a simplified proposal for a data utilization method with low importance. The proposal unit can also adjust the depth and scope of the proposal depending on the importance of the data utilization method. This enables efficient proposals by adjusting the level of detail of the proposal depending on the importance of the data utilization method. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input importance data of the data utilization method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the data category. For example, the suggestion unit can make a proposal by applying a natural language processing algorithm to text data. The suggestion unit can also make a proposal by applying an image analysis algorithm to image data. The suggestion unit can also make a proposal by applying a voice recognition algorithm to voice data. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the data category to the generation AI and cause the generation AI to apply an appropriate proposal algorithm.

[0083] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the customer's past proposal results. The proposal unit can also improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit can also improve the efficiency of the proposal by utilizing the customer's past proposal results. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0084] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. For example, if the customer is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the customer is relaxed, the suggestion unit can provide a detailed suggestion. If the customer is excited, the suggestion unit can provide a suggestion with a visually stimulating effect. By adjusting the length of the suggestion according to the customer's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0085] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of data submission. For example, the proposal unit prioritizes proposals based on the latest data. The proposal unit can also lower the priority of data that was submitted earlier when making a proposal. The proposal unit can also adjust the order of proposals based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of data submission. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of the proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of data when making suggestions. For example, the suggestion unit prioritizes suggestions based on highly relevant data. The suggestion unit can also postpone suggestions for less relevant data. The suggestion unit can also adjust the order of suggestions based on the relevance of data. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0087] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the customer's level of expertise. For example, if the customer has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the customer does not have technical expertise, the suggestion unit can make a proposal that avoids technical terminology. The suggestion unit can also adjust the way the proposal is expressed depending on the customer's level of expertise. This makes it possible to provide a more appropriate proposal by adjusting the use of technical terminology in the proposal depending on the customer's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input customer's expertise level data into a generation AI and cause the generation AI to use technical terminology in the proposal.

[0088] The verification unit can estimate the customer's emotions and adjust the verification method based on the estimated customer emotions. For example, if the customer is nervous, the verification unit can provide a simple, highly visible verification method. Furthermore, if the customer is relaxed, the verification unit can provide a detailed verification method. Furthermore, if the customer is in a hurry, the verification unit can provide a verification method that focuses on the key points. This allows for more appropriate verification by adjusting the verification method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the verification unit can be performed using AI, or without AI. For example, the verification unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0089] The verification unit can perform a detailed analysis of the effects of the proposed data utilization method during verification. The verification unit, for example, quantitatively analyzes the effects of the proposed data utilization method. The verification unit can also qualitatively analyze the effects of the proposed data utilization method. The verification unit can also analyze the effects of the proposed data utilization method from multiple angles. This enables effective verification by analyzing the effects of the proposed data utilization method in detail. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or may be performed without using AI. For example, the verification unit can input effect data of the proposed data utilization method into the generation AI and cause the generation AI to perform a detailed analysis of the effects.

[0090] During verification, the verification unit can improve the accuracy of the verification by referring to the customer's past data utilization results. The verification unit, for example, adjusts the verification algorithm based on the customer's past data utilization results. The verification unit can also improve the accuracy of the verification by referring to the customer's past data utilization results. The verification unit can also improve the efficiency of the verification by utilizing the customer's past data utilization results. In this way, the accuracy of the verification is improved by referring to the customer's past data utilization results. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's past data utilization results into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0091] The verification unit can improve the verification method by reflecting customer feedback during verification. The verification unit can adjust the verification method based on customer feedback, for example. The verification unit can also improve the accuracy of verification by reflecting customer feedback. The verification unit can also improve the efficiency of verification by utilizing customer feedback. In this way, by reflecting customer feedback, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit can input customer feedback data into the generation AI and cause the generation AI to improve the verification method.

[0092] The verification unit can estimate the customer's emotions and determine the priority of verification based on the estimated customer emotions. For example, if the customer is stressed, the verification unit can prioritize verification of high importance. Furthermore, if the customer is relaxed, the verification unit can prioritize detailed verification. Furthermore, if the customer is in a hurry, the verification unit can prioritize items that can be verified quickly. Thus, by determining the priority of verification according to the customer's emotions, important verification can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or without AI. For example, the verification unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0093] During verification, the verification unit can select an appropriate verification method by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the verification unit performs verification based on data related to that area. Furthermore, if the customer is traveling, the verification unit can select the optimal verification method based on the customer's current location. Furthermore, if the customer is staying in a specific location, the verification unit can perform verification based on data related to that location. This allows the optimal verification method to be selected by taking into account the customer's geographical location information, enabling efficient verification. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's geographical location data into the generation AI and cause the generation AI to select an appropriate verification method.

[0094] During verification, the verification unit can analyze the customer's social media activity and suggest verification methods. The verification unit can suggest verification methods based on, for example, information shared by the customer on social media. The verification unit can also analyze the customer's social media activity and suggest optimal verification methods. The verification unit can also suggest verification methods based on the activity of the customer's friends on social media. This allows the analysis of the customer's social media activity to suggest optimal verification methods, enabling efficient verification. Some or all of the above-mentioned processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit can input the customer's social media data into a generation AI and have the generation AI execute the proposed verification methods.

[0095] During verification, the verification unit can customize the verification method by reflecting past customer feedback. The verification unit, for example, adjusts the verification method based on feedback provided by the customer in the past. The verification unit can also improve the accuracy of verification by reflecting past customer feedback. The verification unit can also optimize the timing and means of verification based on customer feedback. In this way, by reflecting past customer feedback, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input past customer feedback data into the generation AI and have the generation AI customize the verification method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and verification unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects customer input information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify customer needs and issues. The proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes an optimal data utilization method based on the analysis results. The verification unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, verifies the effectiveness of the proposed method and improves it as necessary. The collection unit, for example, can estimate customer emotions via the control unit 46A of the smart device 14 and adjust the collection timing. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer input information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify customer needs and issues. The proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes an optimal data utilization method based on the analysis results. The verification unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, verifies the effectiveness of the proposed method and improves it as necessary. The collection unit, for example, can estimate the customer's emotions via the control unit 46A of the smart glasses 214 and adjust the collection timing. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and verification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects customer input information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify customer needs and issues. The proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes an optimal data utilization method based on the analysis results. The verification unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, verifies the effectiveness of the proposed method and improves it as necessary. The collection unit, for example, can estimate customer emotions via the control unit 46A of the headset terminal 314 and adjust the collection timing. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and verification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects customer input information using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify customer needs and issues. The proposal unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal data utilization method based on the analysis results. The verification unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and verifies the effectiveness of the proposed method and improves it as necessary. The collection unit, for example, can estimate customer emotions via the control unit 46A of the robot 414 and adjust the collection timing.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the customer's emotions and determine the analysis priority based on the estimated customer emotions. For example, if the customer is stressed, it can prioritize analyzing information of high importance. Also, if the customer is relaxed, it can prioritize analyzing detailed information. Furthermore, if the customer is in a hurry, it can prioritize analyzing information that can be analyzed quickly. Thus, by determining the analysis priority according to the customer's emotions, it is possible to prioritize analyzing important information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0098] The suggestion unit can estimate the customer's emotions and adjust the timing of the suggestion based on the estimated customer emotions. For example, if the customer is stressed, the suggestion unit can delay the timing of the suggestion and make the suggestion in a relaxed state. Also, if the customer is relaxed, the suggestion unit can make the suggestion immediately and provide a quick response. Furthermore, if the customer is in a hurry, the suggestion unit can advance the timing of the suggestion and provide the necessary suggestion quickly. This allows for more appropriate suggestions by adjusting the timing of the suggestion according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0099] The verification unit can estimate the customer's emotions and adjust the verification feedback method based on the estimated customer emotions. For example, if the customer is nervous, simple, highly visible feedback can be provided. If the customer is relaxed, detailed feedback can be provided. Furthermore, if the customer is in a hurry, feedback that focuses on the main points can be provided. This allows for more appropriate feedback by adjusting the feedback method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the verification unit can be performed using AI, for example, or without AI. For example, the verification unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0100] The collection unit can estimate the customer's emotions and determine the type of information to collect based on the estimated customer emotions. For example, if the customer is feeling stressed, simple questions or a short questionnaire can be collected first. Also, if the customer is relaxed, detailed information can be collected first. Furthermore, if the customer is in a hurry, information that can be collected quickly can be collected first. This enables efficient information collection by determining the type of information to collect based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0101] The suggestion unit can estimate the customer's emotions and adjust the content of the suggestion based on the estimated customer emotions. For example, if the customer is stressed, the suggestion unit can provide simple and easy-to-implement suggestions. If the customer is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the customer is in a hurry, the suggestion unit can provide suggestions that can be implemented quickly. This allows the suggestion unit to adjust the content of the suggestion based on the customer's emotions, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0102] The analysis unit can analyze the customer's past behavioral data and customize the analysis method based on the customer's behavioral patterns. For example, if the customer has shown a specific behavioral pattern in the past, the analysis method can be adjusted based on that pattern. Also, if the customer has used specific data frequently in the past, that data can be prioritized for analysis. Furthermore, if the customer has preferred a specific analysis method in the past, that method can be prioritized. This allows the analysis method to be customized by analyzing the customer's past behavioral data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past behavioral data into the generation AI and have the generation AI customize the analysis method.

[0103] The proposal unit can analyze the customer's past proposal history and customize the content of the proposal based on the customer's preferences. For example, if the customer has previously preferred and accepted a particular proposal, the proposal unit can make a new proposal based on that proposal. Also, if the customer has previously rejected a particular proposal, the proposal can be avoided. Furthermore, if the customer has previously shown interest in a particular field, the proposal unit can prioritize proposals related to that field. In this way, by analyzing the customer's past proposal history, the content of the proposal can be customized, enabling more appropriate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal history data into a generation AI and have the generation AI customize the content of the proposal.

[0104] The verification unit can analyze the customer's past verification results and optimize the verification method. For example, if the customer has preferred a particular verification method in the past, that method can be used preferentially. Also, if the customer has placed importance on a particular verification result in the past, new verification can be performed based on that result. Furthermore, if the customer has used particular data frequently in the past, that data can be used preferentially. In this way, by analyzing the customer's past verification results, the verification method can be optimized and efficient verification can be achieved. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the customer's past verification result data into the generation AI and have the generation AI optimize the verification method.

[0105] The collection unit can determine the type of information to collect by taking into account the customer's geographical location information. For example, if the customer is in a specific area, information related to that area can be collected preferentially. Also, if the customer is on the move, highly relevant information can be collected based on the customer's current location. Furthermore, if the customer is staying in a specific place, information related to that place can be collected preferentially. In this way, by taking the customer's geographical location information into account, highly relevant information can be collected efficiently. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location data into the generation AI and have the generation AI execute the type of information to be collected.

[0106] The suggestion unit can analyze a customer's social media activity and customize the content of suggestions based on the customer's interests. For example, if a customer frequently posts about a particular topic on social media, suggestions related to that topic can be made. Also, if a customer mentions a particular brand or product on social media, suggestions related to that brand or product can be made. Furthermore, if a customer is participating in a particular event on social media, suggestions related to that event can be made. In this way, by analyzing a customer's social media activity, the content of suggestions can be customized and more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's social media data into a generation AI and have the generation AI customize the content of suggestions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects input information from customers. Input information from customers includes text, voice, images, etc. The collection unit can collect customer information using a questionnaire. It can also collect customer behavior data using sensors, and it is also possible to collect log data. For example, the collection unit collects website click data to understand customer behavior patterns. Step 2: The analysis unit analyzes the information collected by the collection unit to identify customer needs and issues. The analysis is performed using methods such as statistical analysis, machine learning, and natural language processing. For example, the analysis unit uses statistical analysis to analyze customer data and identify customer needs. It can also use machine learning to analyze customer behavior patterns and identify issues. It can also use natural language processing to analyze text data and identify customer requests. Step 3: The proposal department analyzes the behavioral big data based on the needs and issues identified by the analysis department and proposes the optimal way to utilize the data. Proposals are made in the form of marketing strategies, product development, customer support, etc. For example, the proposal department analyzes search data and proposes optimal keywords. It can also analyze location data and propose optimal marketing measures. It can also analyze purchase history and propose the most suitable products for customers. Step 4: The Verification Department verifies the effectiveness of the data utilization methods proposed by the Proposal Department and makes improvements as necessary. Verification is carried out using methods such as experiments, simulations, and feedback collection. For example, the Verification Department conducts experiments to verify the effectiveness of the proposed measures. It can also conduct simulations to predict the effectiveness of the proposed measures. It can also collect feedback from customers to evaluate the effectiveness of the proposed measures.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 collection unit that collects input information from customers; an analysis unit that analyzes the information collected by the collection unit and identifies customer needs and issues; a proposal unit that analyzes the behavioral big data based on the needs and issues identified by the analysis unit and proposes an appropriate data utilization method; a verification unit that verifies the effectiveness of the data utilization method proposed by the proposal unit and improves it as necessary. A system characterized by:

2. The collecting unit Estimate customer sentiment and adjust the timing of collecting input information based on the estimated sentiment 2. The system of claim 1.

3. The collecting unit Analyze the customer's past input information and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit As input information is collected, it is filtered based on the customer's current business situation and areas of interest.

2. The system of claim 1.

5. The collecting unit When collecting input information, select the appropriate collection method depending on the customer's input method.

2. The system of claim 1.

6. The collecting unit Estimate customer sentiment and prioritize the information to be collected based on the estimated sentiment 2. The system of claim 1.

7. The collecting unit When collecting input information, consider the customer's geographic location information to prioritize collecting the most relevant information 2. The system of claim 1.

8. The collecting unit When collecting input information, analyze your social media activity and collect relevant information 2. The system of claim 1.

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