system

Abstract

The system according to this embodiment aims to analyze corporate data and provide accurate and reliable responses. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, a generation unit, and a response unit. The reading unit reads company data. The analysis unit analyzes the data read by the reading unit. The generation unit generates a Yes-No map based on the data analyzed by the analysis unit. The response unit provides a response based on the Yes-No map generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in a customer support system, AI often provides incorrect information, which may cause confusion and dissatisfaction among customers.

[0005] The system according to the embodiment aims to analyze enterprise data and provide accurate and reliable responses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a response unit. The reading unit reads company data. The analysis unit analyzes the data read by the reading unit. The generation unit generates a Yes-No map based on the data analyzed by the analysis unit. The response unit provides a response based on the Yes-No map generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze corporate data and provide accurate and reliable responses. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The customer support system according to an embodiment of the present invention is a system that utilizes AI to read corporate data, automatically generates Yes-No maps, and provides an accurate and reliable response system. This customer support system provides a rule-based, accurate response system by having the AI ​​read and understand specifications and manuals provided by the company and automatically generate Yes-No maps. This prevents the AI ​​from providing misinformation and improves the accuracy and reliability of customer support. For example, the customer support system has the AI ​​read and understand specifications and manuals provided by the company. For example, the AI ​​analyzes manuals that include product installation procedures, frequently asked questions, and troubleshooting information. Next, based on the read data, the AI ​​automatically generates Yes-No maps and creates response rules. For example, it generates troubleshooting procedures for when a product does not start as a Yes-No map. This allows the AI ​​to provide appropriate responses to specific questions. Furthermore, the rule-based responses using Yes-No maps provide accurate customer support AI. For example, if a customer inquires about a product installation error, the AI ​​provides appropriate troubleshooting procedures based on the Yes-No map. This allows the customer to receive quick and accurate support. This mechanism prevents the AI ​​from providing misinformation and improves the accuracy and reliability of customer support. Furthermore, it can quickly respond to frequently changing information, improving the quality and efficiency of customer support. For example, when a new product version is released, the AI ​​can read the new specifications and manuals and update the Yes-No map, providing support based on the latest information. Thus, this invention provides an accurate and reliable customer support system by using AI to read corporate data and automatically generate Yes-No maps. This improves the accuracy and reliability of customer responses and increases customer satisfaction. As a result, the customer support system can efficiently read and analyze corporate data, generate Yes-No maps, and provide accurate responses.

[0029] The customer support system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a response unit. The reading unit reads corporate data. Corporate data includes, but is not limited to, financial data, customer data, and product data. The reading unit reads, for example, specifications and manuals provided by the company. For example, the reading unit can read technical specifications, operation manuals, user guides, etc. The analysis unit analyzes the read data. The analysis unit analyzes the specifications and manuals using, for example, natural language processing technology. For example, the analysis unit can analyze the data using a text analysis algorithm. The generation unit generates a Yes-No map based on the analyzed data. For example, the generation unit generates a Yes-No map by mapping binary choices. For example, the generation unit can generate a Yes-No map in a visual display format. The response unit provides a response based on the generated Yes-No map. The response unit provides, for example, a text response. For example, the response unit can also provide a voice response. The response unit can also provide graphical feedback. As a result, the customer support system according to the embodiment can efficiently read and analyze corporate data, generate a Yes-No map, and provide accurate responses. Some or all of the above-described processes in the reading unit, analysis unit, generation unit, and response unit may be performed using AI, for example, or without AI. For example, the reading unit can input corporate data into the AI ​​and have the AI ​​perform data reading. The analysis unit can input the read data into the AI ​​and have the AI ​​perform data analysis. The generation unit can input the analyzed data into the AI ​​and have the AI ​​perform the generation of a Yes-No map. The response unit can input the generated Yes-No map into the AI ​​and have the AI ​​perform the provision of a response.

[0030] The data entry unit reads corporate data. Corporate data includes, but is not limited to, financial data, customer data, and product data. The data entry unit can read specifications and manuals provided by companies. Specifically, it can read technical specifications, operation manuals, user guides, etc. This data is often obtained from the company's internal systems or cloud storage. The data entry unit automatically recognizes the format and structure of the data and imports it in an appropriate manner. For example, it can analyze manuals in PDF format or financial data in Excel format and extract the necessary information. Furthermore, the data entry unit has a function to check the integrity of the data and issue warnings if there are inconsistencies or missing data. This allows the data entry unit to read corporate data efficiently and accurately. The data entry unit can also preprocess the data using AI. For example, it can analyze text data using natural language processing technology and extract important keywords and phrases. This allows subsequent analysis and generation units to process the data efficiently. Furthermore, the data entry unit is designed to respond quickly to data updates and additions and has a function to update data in real time. This enables analysis and responses based on the latest data at all times.

[0031] The analysis unit analyzes the loaded data. For example, it can analyze specifications and manuals using natural language processing technology. Specifically, it can analyze data using text analysis algorithms. For instance, the analysis unit can extract important keywords and phrases from a document and evaluate their relationships. Furthermore, it can analyze the structure of the document and classify its content by chapter and section. This makes it easier to understand the entire document. The analysis unit can use AI to understand the meaning of data and perform contextual analysis. For example, it can use machine learning algorithms to learn patterns from past data and perform highly accurate analysis on new data. Furthermore, the analysis unit can analyze data correlations and discover hidden patterns and trends. This can provide insights useful for corporate decision-making. The analysis unit also has a function to visually display the data analysis results, presenting the results clearly using graphs and charts. This allows users to intuitively understand the analysis results. Furthermore, the analysis unit can analyze data in real time and provide results immediately. This enables rapid decision-making.

[0032] The generation unit generates Yes / No maps based on the analyzed data. For example, it can map binary choices and generate Yes / No maps. Specifically, based on the analysis results, the generation unit provides Yes or No options for problems and questions that users may face. For example, in product troubleshooting, it can present the next action to take in a Yes / No format when a specific symptom occurs. The generation unit can generate Yes / No maps in a visual display format, making them easy for users to understand. Furthermore, the generation unit can automate the generation of Yes / No maps using AI. For example, it can use machine learning algorithms to learn the optimal Yes / No map from past data and apply it to new data. This allows the generation unit to always provide Yes / No maps based on the latest information. The generation unit also has a function to continuously improve Yes / No maps based on user feedback. This enables flexible responses to meet user needs. Additionally, the generation unit can simulate multiple scenarios and select the most appropriate Yes / No map. This allows it to provide users with the best possible solutions.

[0033] The response unit provides responses based on the generated Yes-No map. For example, it provides text responses. Specifically, when a user selects an option according to the Yes-No map, it generates an appropriate text response based on that selection. For example, in product troubleshooting, if a user selects a specific problem, it provides a text solution to that problem. The response unit can also provide voice responses. For example, it can use speech synthesis technology to provide text responses in voice. This allows users to obtain information without using their hands. The response unit can also provide graphical feedback. For example, it can make solutions easier for users to understand by showing them with diagrams or videos. Furthermore, the response unit can improve the accuracy of its responses using AI. For example, it can use natural language generation technology to generate more natural responses to user questions. This allows users to receive more satisfying support. The response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. For example, it can evaluate the solutions provided and revise the responses based on that evaluation. The response unit can also reliably transmit information using multiple communication methods. For example, information can be provided to users quickly and reliably by using a combination of text messages, emails, and chatbots. This allows the response unit to provide users with fast and accurate responses, improving the quality of customer support.

[0034] The reading unit can read specifications and manuals provided by companies. For example, the reading unit can read technical specifications. For example, the reading unit can read operation manuals. The reading unit can also read user guides. This allows for accurate reading of specifications and manuals provided by companies. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input specifications and manuals provided by companies into AI and have the AI ​​perform the data reading.

[0035] The analysis unit can analyze the loaded specifications and manuals. For example, the analysis unit can analyze the specifications and manuals using natural language processing technology. For example, the analysis unit can analyze data using text analysis algorithms. The analysis unit can also analyze data using data analysis algorithms. This allows for accurate analysis of the loaded specifications and manuals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the loaded specifications and manuals into an AI and have the AI ​​perform the data analysis.

[0036] The generation unit can generate a Yes / No map based on the analyzed data. For example, the generation unit can perform a binary choice mapping and generate a Yes / No map. For example, the generation unit can generate a Yes / No map in a visual display format. The generation unit can also generate a Yes / No map based on the data structure. This allows for the generation of an accurate Yes / No map based on the analyzed data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed data into AI and have the AI ​​perform the generation of the Yes / No map.

[0037] The response unit can provide a response based on the generated Yes-No map. For example, the response unit can provide a text response. For example, the response unit can also provide a voice response. The response unit can also provide graphical feedback. This allows for the provision of an accurate response based on the generated Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the generated Yes-No map into the AI ​​and have the AI ​​perform the task of providing the response.

[0038] The generation unit can update the Yes-No map. For example, the generation unit can update the Yes-No map by adding or deleting data. For example, the generation unit can update the Yes-No map based on the update frequency. The generation unit can also update the Yes-No map based on changes in the data. This allows the Yes-No map to be updated with the latest information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data changes to the AI ​​and have the AI ​​perform the Yes-No map update.

[0039] The data loading unit can prioritize data based on its importance when loading corporate data. For example, the loading unit can prioritize loading high-priority data (e.g., product troubleshooting information) and postpone loading low-priority data (e.g., general FAQs). The loading unit can also dynamically change the loading order according to the importance of the data. This allows important data to be loaded preferentially by prioritizing based on data importance. Some or all of the above processing in the loading unit may be performed using AI, or not. For example, the loading unit can input the data importance into the AI ​​and have the AI ​​perform the prioritization.

[0040] The data reading unit can apply different reading algorithms depending on the data format when reading corporate data. For example, in the case of text data, the reading unit can apply a natural language processing algorithm. For example, in the case of image data, the reading unit can apply an image recognition algorithm. Furthermore, in the case of audio data, the reading unit can apply a speech recognition algorithm. This allows for efficient data reading by applying the appropriate reading algorithm according to the data format. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the data format into the AI ​​and have the AI ​​execute the application of the appropriate reading algorithm.

[0041] The data loading unit can adjust the loading frequency when loading corporate data, taking into account the data update frequency. For example, the loading unit can periodically load data that is frequently updated (e.g., product version information). For example, the loading unit can load data that is not updated frequently (e.g., basic product specifications) as needed. The loading unit can also dynamically adjust the loading schedule based on the data update frequency. This ensures that the latest information is always maintained by adjusting the loading frequency based on the data update frequency. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can input the data update frequency into the AI ​​and have the AI ​​adjust the loading frequency.

[0042] The data loading unit can prioritize loading reliable data by considering the data source when loading corporate data. For example, the loading unit can prioritize loading official corporate documents. For example, the loading unit can postpone loading data from less reliable sources. The loading unit can also dynamically change the loading priority based on the data source. This allows for the provision of reliable information by prioritizing the loading of reliable data based on the data source. Some or all of the above processing in the loading unit may be performed using AI, for example, or not using AI. For example, the loading unit can input the data source into AI and have AI perform a reliability assessment.

[0043] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between product troubleshooting information and installation procedures. For example, the analysis unit can analyze the interrelationships between frequently asked questions and product specifications. Furthermore, the analysis unit can analyze the interrelationships between troubleshooting procedures and user feedback. By considering the interrelationships between data, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into AI and have AI perform correlation analysis.

[0044] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a procedure analysis algorithm to product installation procedures. For example, the analysis unit can apply a problem-solving algorithm to troubleshooting information. The analysis unit can also apply a natural language processing algorithm to frequently asked questions. This allows for efficient data analysis by applying the appropriate analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply the appropriate analysis method.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis schedule based on the data submission date. This allows for the prioritization of the analysis of the latest information by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis process. For example, the analysis unit can refer to literature related to product troubleshooting information. For example, the analysis unit can refer to literature related to product installation procedures. The analysis unit can also refer to literature related to frequently asked questions. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0047] The generation unit can adjust the level of detail of the Yes-No map generation based on the importance of the data. For example, the generation unit can generate a detailed Yes-No map based on high-importance data. For example, the generation unit can generate a simplified Yes-No map based on low-importance data. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the data. This allows for the generation of detailed Yes-No maps based on important data by adjusting the level of detail of the generation based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generation.

[0048] The generation unit can apply different generation algorithms depending on the data category when generating Yes-No maps. For example, the generation unit can apply a procedure generation algorithm to product installation instructions. For example, the generation unit can apply a problem-solving generation algorithm to troubleshooting information. The generation unit can also apply a natural language processing generation algorithm to frequently asked questions. This allows for efficient generation of Yes-No maps by applying the appropriate generation algorithm according to the data category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into the AI ​​and have the AI ​​apply the appropriate generation algorithm.

[0049] The generation unit can determine the generation priority based on the data submission timing when generating Yes-No maps. For example, the generation unit can prioritize generating Yes-No maps based on recently submitted data. For example, the generation unit can postpone the generation of Yes-No maps based on older data. The generation unit can also dynamically adjust the generation schedule based on the data submission timing. This allows the latest information to be reflected in the Yes-No maps preferentially by determining the generation priority based on the data submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the data submission timing into the AI ​​and have the AI ​​determine the generation priority.

[0050] The generation unit can improve the accuracy of Yes-No map generation by referring to relevant literature for the data. For example, the generation unit can refer to literature related to product troubleshooting information. For example, the generation unit can refer to literature related to product installation procedures. The generation unit can also refer to literature related to frequently asked questions. By referring to relevant literature for the data, the accuracy of Yes-No map generation can be improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0051] The response unit can adjust the level of detail in its response based on the importance of the Yes-No map. For example, the response unit can provide a detailed response based on a highly important Yes-No map. For example, the response unit can provide a simplified response based on a less important Yes-No map. The response unit can also dynamically adjust the level of detail in its response according to the importance of the Yes-No map. This allows for the priority provision of important information by adjusting the level of detail in the response based on the importance of the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the importance of the Yes-No map into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the response.

[0052] The response unit can apply different response algorithms depending on the category in the Yes-No map when responding. For example, the response unit can apply a procedure response algorithm to product installation instructions. For example, the response unit can apply a problem-solving response algorithm to troubleshooting information. The response unit can also apply a natural language processing response algorithm to frequently asked questions. This allows for efficient response provision by applying the appropriate response algorithm according to the category in the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the categories in the Yes-No map into the AI ​​and have the AI ​​apply the appropriate response algorithm.

[0053] The response unit can determine the priority of responses based on the submission timing of the Yes-No maps. For example, the response unit may prioritize responses based on recently generated Yes-No maps. For example, the response unit may postpone responses based on older Yes-No maps. The response unit can also dynamically adjust the response schedule based on the submission timing of the Yes-No maps. This allows for the priority provision of the latest information by prioritizing responses based on the submission timing of the Yes-No maps. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the submission timing of the Yes-No maps into the AI ​​and have the AI ​​perform the determination of response priorities.

[0054] The response unit can improve the accuracy of its response by referring to relevant literature in the Yes-No map when providing a response. For example, the response unit can provide a response by referring to literature related to product troubleshooting information. For example, the response unit can provide a response by referring to literature related to product installation procedures. The response unit can also provide a response by referring to literature related to frequently asked questions. This improves the accuracy of the response by referring to relevant literature in the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0055] The response unit can provide a response based on the latest information, taking into account updates to the Yes-No map. For example, the response unit can provide a response based on information about the latest product version. For example, the response unit can provide a response based on recently updated troubleshooting procedures. The response unit can also provide a response based on the latest FAQ information. This allows the response unit to provide a response based on the latest information by taking into account updates to the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input Yes-No map update information into the AI ​​and have the AI ​​execute a response based on the latest information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] A customer support system can refer to a user's past inquiry history and customize responses based on that history. For example, it can provide more detailed explanations to users who have repeatedly inquired about the same problem in the past. It can also provide concise and prompt responses to users who have previously requested quick responses. Furthermore, it can provide additional relevant information based on past inquiries. This enables customized responses based on a user's past inquiry history, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's past inquiry history into the AI ​​and have the AI ​​perform the response customization.

[0058] A customer support system can analyze a user's past behavior data and prepare responses in advance for anticipated problems. For example, it can prepare the latest troubleshooting information for a user who has frequently reported problems with a particular product in the past. For a user who has repeatedly asked a particular question in the past, it can prepare detailed answers to that question in advance. It can also provide additional relevant information based on past behavior data. This enables preparation based on the user's past behavior data, allowing for quick and appropriate responses. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's past behavior data into AI and have the AI ​​perform the preparation.

[0059] A customer support system can use a user's geographical location to provide region-specific responses. For example, it can provide region-specific troubleshooting information for product issues that only occur in a particular area. It can also provide information on product usage and regulations specific to each region. Furthermore, it can provide contact information for regional support centers. This enables appropriate responses based on the user's geographical location, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's geographical location information into the AI ​​and have the AI ​​execute region-specific responses.

[0060] A customer support system can use a user's device information to provide device-specific responses. For example, it can provide device-specific troubleshooting information for problems that only occur on a particular device. It can also provide information on device-specific setup and usage. Furthermore, it can provide contact information for device-specific support centers. This enables appropriate responses based on the user's device information, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's device information into the AI ​​and have the AI ​​execute device-specific responses.

[0061] A customer support system can analyze a user's usage history and prioritize providing information about frequently used features. For example, it can prioritize providing detailed information and troubleshooting information about a particular feature to users who frequently use that feature. It can also provide the latest update information about frequently used features. Furthermore, it can provide FAQs about frequently used features. This enables appropriate responses based on the user's usage history, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's usage history into AI and prioritize providing information about frequently used features.

[0062] A customer support system can analyze user feedback and improve the quality of responses based on that feedback. For example, it can provide similar responses based on positive feedback from users. Based on negative feedback, it can improve the content and format of responses. It can also adjust the timing and tone of responses based on feedback. This enables appropriate responses based on user feedback and can improve customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user feedback into an AI and have the AI ​​perform improvements to the quality of responses based on that feedback.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reading unit reads company data. This company data includes financial data, customer data, product data, etc. The reading unit can also read specifications and manuals provided by the company. For example, it can read technical specifications, operation manuals, user guides, etc. Step 2: The analysis unit analyzes the loaded data. The analysis unit analyzes specifications and manuals using natural language processing technology and text analysis algorithms. Step 3: The generation unit generates a Yes-No map based on the analyzed data. The generation unit performs a binary choice mapping and generates a Yes-No map in a visual display format. Step 4: The response unit provides a response based on the generated Yes-No map. The response unit can provide text responses, voice responses, and graphical feedback.

[0065] (Example of form 2) The customer support system according to an embodiment of the present invention is a system that utilizes AI to read corporate data, automatically generates Yes-No maps, and provides an accurate and reliable response system. This customer support system provides a rule-based, accurate response system by having the AI ​​read and understand specifications and manuals provided by the company and automatically generate Yes-No maps. This prevents the AI ​​from providing misinformation and improves the accuracy and reliability of customer support. For example, the customer support system has the AI ​​read and understand specifications and manuals provided by the company. For example, the AI ​​analyzes manuals that include product installation procedures, frequently asked questions, and troubleshooting information. Next, based on the read data, the AI ​​automatically generates Yes-No maps and creates response rules. For example, it generates troubleshooting procedures for when a product does not start as a Yes-No map. This allows the AI ​​to provide appropriate responses to specific questions. Furthermore, the rule-based responses using Yes-No maps provide accurate customer support AI. For example, if a customer inquires about a product installation error, the AI ​​provides appropriate troubleshooting procedures based on the Yes-No map. This allows the customer to receive quick and accurate support. This mechanism prevents the AI ​​from providing misinformation and improves the accuracy and reliability of customer support. Furthermore, it can quickly respond to frequently changing information, improving the quality and efficiency of customer support. For example, when a new product version is released, the AI ​​can read the new specifications and manuals and update the Yes-No map, providing support based on the latest information. Thus, this invention provides an accurate and reliable customer support system by using AI to read corporate data and automatically generate Yes-No maps. This improves the accuracy and reliability of customer responses and increases customer satisfaction. As a result, the customer support system can efficiently read and analyze corporate data, generate Yes-No maps, and provide accurate responses.

[0066] The customer support system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a response unit. The reading unit reads corporate data. Corporate data includes, but is not limited to, financial data, customer data, and product data. The reading unit reads, for example, specifications and manuals provided by the company. For example, the reading unit can read technical specifications, operation manuals, user guides, etc. The analysis unit analyzes the read data. The analysis unit analyzes the specifications and manuals using, for example, natural language processing technology. For example, the analysis unit can analyze the data using a text analysis algorithm. The generation unit generates a Yes-No map based on the analyzed data. For example, the generation unit generates a Yes-No map by mapping binary choices. For example, the generation unit can generate a Yes-No map in a visual display format. The response unit provides a response based on the generated Yes-No map. The response unit provides, for example, a text response. For example, the response unit can also provide a voice response. The response unit can also provide graphical feedback. As a result, the customer support system according to the embodiment can efficiently read and analyze corporate data, generate a Yes-No map, and provide accurate responses. Some or all of the above-described processes in the reading unit, analysis unit, generation unit, and response unit may be performed using AI, for example, or without AI. For example, the reading unit can input corporate data into the AI ​​and have the AI ​​perform data reading. The analysis unit can input the read data into the AI ​​and have the AI ​​perform data analysis. The generation unit can input the analyzed data into the AI ​​and have the AI ​​perform the generation of a Yes-No map. The response unit can input the generated Yes-No map into the AI ​​and have the AI ​​perform the provision of a response.

[0067] The data entry unit reads corporate data. Corporate data includes, but is not limited to, financial data, customer data, and product data. The data entry unit can read specifications and manuals provided by companies. Specifically, it can read technical specifications, operation manuals, user guides, etc. This data is often obtained from the company's internal systems or cloud storage. The data entry unit automatically recognizes the format and structure of the data and imports it in an appropriate manner. For example, it can analyze manuals in PDF format or financial data in Excel format and extract the necessary information. Furthermore, the data entry unit has a function to check the integrity of the data and issue warnings if there are inconsistencies or missing data. This allows the data entry unit to read corporate data efficiently and accurately. The data entry unit can also preprocess the data using AI. For example, it can analyze text data using natural language processing technology and extract important keywords and phrases. This allows subsequent analysis and generation units to process the data efficiently. Furthermore, the data entry unit is designed to respond quickly to data updates and additions and has a function to update data in real time. This enables analysis and responses based on the latest data at all times.

[0068] The analysis unit analyzes the loaded data. For example, it can analyze specifications and manuals using natural language processing technology. Specifically, it can analyze data using text analysis algorithms. For instance, the analysis unit can extract important keywords and phrases from a document and evaluate their relationships. Furthermore, it can analyze the structure of the document and classify its content by chapter and section. This makes it easier to understand the entire document. The analysis unit can use AI to understand the meaning of data and perform contextual analysis. For example, it can use machine learning algorithms to learn patterns from past data and perform highly accurate analysis on new data. Furthermore, the analysis unit can analyze data correlations and discover hidden patterns and trends. This can provide insights useful for corporate decision-making. The analysis unit also has a function to visually display the data analysis results, presenting the results clearly using graphs and charts. This allows users to intuitively understand the analysis results. Furthermore, the analysis unit can analyze data in real time and provide results immediately. This enables rapid decision-making.

[0069] The generation unit generates Yes / No maps based on the analyzed data. For example, it can map binary choices and generate Yes / No maps. Specifically, based on the analysis results, the generation unit provides Yes or No options for problems and questions that users may face. For example, in product troubleshooting, it can present the next action to take in a Yes / No format when a specific symptom occurs. The generation unit can generate Yes / No maps in a visual display format, making them easy for users to understand. Furthermore, the generation unit can automate the generation of Yes / No maps using AI. For example, it can use machine learning algorithms to learn the optimal Yes / No map from past data and apply it to new data. This allows the generation unit to always provide Yes / No maps based on the latest information. The generation unit also has a function to continuously improve Yes / No maps based on user feedback. This enables flexible responses to meet user needs. Additionally, the generation unit can simulate multiple scenarios and select the most appropriate Yes / No map. This allows it to provide users with the best possible solutions.

[0070] The response unit provides responses based on the generated Yes-No map. For example, it provides text responses. Specifically, when a user selects an option according to the Yes-No map, it generates an appropriate text response based on that selection. For example, in product troubleshooting, if a user selects a specific problem, it provides a text solution to that problem. The response unit can also provide voice responses. For example, it can use speech synthesis technology to provide text responses in voice. This allows users to obtain information without using their hands. The response unit can also provide graphical feedback. For example, it can make solutions easier for users to understand by showing them with diagrams or videos. Furthermore, the response unit can improve the accuracy of its responses using AI. For example, it can use natural language generation technology to generate more natural responses to user questions. This allows users to receive more satisfying support. The response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. For example, it can evaluate the solutions provided and revise the responses based on that evaluation. The response unit can also reliably transmit information using multiple communication methods. For example, information can be provided to users quickly and reliably by using a combination of text messages, emails, and chatbots. This allows the response unit to provide users with fast and accurate responses, improving the quality of customer support.

[0071] The reading unit can read specifications and manuals provided by companies. For example, the reading unit can read technical specifications. For example, the reading unit can read operation manuals. The reading unit can also read user guides. This allows for accurate reading of specifications and manuals provided by companies. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input specifications and manuals provided by companies into AI and have the AI ​​perform the data reading.

[0072] The analysis unit can analyze the loaded specifications and manuals. For example, the analysis unit can analyze the specifications and manuals using natural language processing technology. For example, the analysis unit can analyze data using text analysis algorithms. The analysis unit can also analyze data using data analysis algorithms. This allows for accurate analysis of the loaded specifications and manuals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the loaded specifications and manuals into an AI and have the AI ​​perform the data analysis.

[0073] The generation unit can generate a Yes / No map based on the analyzed data. For example, the generation unit can perform a binary choice mapping and generate a Yes / No map. For example, the generation unit can generate a Yes / No map in a visual display format. The generation unit can also generate a Yes / No map based on the data structure. This allows for the generation of an accurate Yes / No map based on the analyzed data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed data into AI and have the AI ​​perform the generation of the Yes / No map.

[0074] The response unit can provide a response based on the generated Yes-No map. For example, the response unit can provide a text response. For example, the response unit can also provide a voice response. The response unit can also provide graphical feedback. This allows for the provision of an accurate response based on the generated Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the generated Yes-No map into the AI ​​and have the AI ​​perform the task of providing the response.

[0075] The generation unit can update the Yes-No map. For example, the generation unit can update the Yes-No map by adding or deleting data. For example, the generation unit can update the Yes-No map based on the update frequency. The generation unit can also update the Yes-No map based on changes in the data. This allows the Yes-No map to be updated with the latest information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data changes to the AI ​​and have the AI ​​perform the Yes-No map update.

[0076] The data loading unit can estimate the user's emotions and adjust the timing of loading corporate data based on the estimated emotions. For example, if the user is stressed, the AI ​​can load the corporate data quickly and provide an immediate response. For example, if the user is relaxed, the AI ​​can load the corporate data at a normal speed and provide detailed information. Also, if the user is in a hurry, the AI ​​can prioritize loading the most important data and provide a quick response. This allows the timing of loading corporate data to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data loading unit may be performed using AI or not using AI. For example, the data loading unit can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0077] The data loading unit can prioritize data based on its importance when loading corporate data. For example, the loading unit can prioritize loading high-priority data (e.g., product troubleshooting information) and postpone loading low-priority data (e.g., general FAQs). The loading unit can also dynamically change the loading order according to the importance of the data. This allows important data to be loaded preferentially by prioritizing based on data importance. Some or all of the above processing in the loading unit may be performed using AI, or not. For example, the loading unit can input the data importance into the AI ​​and have the AI ​​perform the prioritization.

[0078] The data reading unit can apply different reading algorithms depending on the data format when reading corporate data. For example, in the case of text data, the reading unit can apply a natural language processing algorithm. For example, in the case of image data, the reading unit can apply an image recognition algorithm. Furthermore, in the case of audio data, the reading unit can apply a speech recognition algorithm. This allows for efficient data reading by applying the appropriate reading algorithm according to the data format. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the data format into the AI ​​and have the AI ​​execute the application of the appropriate reading algorithm.

[0079] The data loading unit can estimate the user's emotions and determine the priority of data to load based on the estimated emotions. For example, if the user is feeling anxious, the loading unit may prioritize loading highly reliable data to provide reassurance. For example, if the user is excited, the loading unit may prioritize loading the most relevant data so that the AI ​​can respond quickly. Alternatively, if the user is relaxed, the loading unit may load all data equally so that the AI ​​can provide detailed information. This allows for the loading of more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the loading unit may be performed using or without AI. For example, the loading unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0080] The data loading unit can adjust the loading frequency when loading corporate data, taking into account the data update frequency. For example, the loading unit can periodically load data that is frequently updated (e.g., product version information). For example, the loading unit can load data that is not updated frequently (e.g., basic product specifications) as needed. The loading unit can also dynamically adjust the loading schedule based on the data update frequency. This ensures that the latest information is always maintained by adjusting the loading frequency based on the data update frequency. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can input the data update frequency into the AI ​​and have the AI ​​adjust the loading frequency.

[0081] The data loading unit can prioritize loading reliable data by considering the data source when loading corporate data. For example, the loading unit can prioritize loading official corporate documents. For example, the loading unit can postpone loading data from less reliable sources. The loading unit can also dynamically change the loading priority based on the data source. This allows for the provision of reliable information by prioritizing the loading of reliable data based on the data source. Some or all of the above processing in the loading unit may be performed using AI, for example, or not using AI. For example, the loading unit can input the data source into AI and have AI perform a reliability assessment.

[0082] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is stressed, the AI ​​can provide a concise analysis result. For example, if the user is relaxed, the AI ​​can provide a detailed analysis result. Also, if the user is in a hurry, the AI ​​can provide an analysis result that focuses on the most important information. In this way, by adjusting the level of detail of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0083] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between product troubleshooting information and installation procedures. For example, the analysis unit can analyze the interrelationships between frequently asked questions and product specifications. Furthermore, the analysis unit can analyze the interrelationships between troubleshooting procedures and user feedback. By considering the interrelationships between data, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into AI and have AI perform correlation analysis.

[0084] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply a procedure analysis algorithm to product installation procedures. For example, the analysis unit can apply a problem-solving algorithm to troubleshooting information. The analysis unit can also apply a natural language processing algorithm to frequently asked questions. This allows for efficient data analysis by applying the appropriate analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​apply the appropriate analysis method.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis schedule based on the data submission date. This allows for the prioritization of the analysis of the latest information by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis process. For example, the analysis unit can refer to literature related to product troubleshooting information. For example, the analysis unit can refer to literature related to product installation procedures. The analysis unit can also refer to literature related to frequently asked questions. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating the Yes-No map based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a Yes-No map that proceeds at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a Yes-No map that emphasizes the shortest route. Also, if the user is excited, the generation unit can generate a Yes-No map with visually stimulating effects. In this way, by adjusting the method of generating the Yes-No map according to the user's emotions, a more appropriate Yes-No map can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0089] The generation unit can adjust the level of detail of the Yes-No map generation based on the importance of the data. For example, the generation unit can generate a detailed Yes-No map based on high-importance data. For example, the generation unit can generate a simplified Yes-No map based on low-importance data. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the data. This allows for the generation of detailed Yes-No maps based on important data by adjusting the level of detail of the generation based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generation.

[0090] The generation unit can apply different generation algorithms depending on the data category when generating Yes-No maps. For example, the generation unit can apply a procedure generation algorithm to product installation instructions. For example, the generation unit can apply a problem-solving generation algorithm to troubleshooting information. The generation unit can also apply a natural language processing generation algorithm to frequently asked questions. This allows for efficient generation of Yes-No maps by applying the appropriate generation algorithm according to the data category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into the AI ​​and have the AI ​​apply the appropriate generation algorithm.

[0091] The generation unit can estimate the user's emotions and adjust the display method of the Yes-No map based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the generation unit can provide a concise display method. In this way, by adjusting the display method of the Yes-No map according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0092] The generation unit can determine the generation priority based on the data submission timing when generating Yes-No maps. For example, the generation unit can prioritize generating Yes-No maps based on recently submitted data. For example, the generation unit can postpone the generation of Yes-No maps based on older data. The generation unit can also dynamically adjust the generation schedule based on the data submission timing. This allows the latest information to be reflected in the Yes-No maps preferentially by determining the generation priority based on the data submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the data submission timing into the AI ​​and have the AI ​​determine the generation priority.

[0093] The generation unit can improve the accuracy of Yes-No map generation by referring to relevant literature for the data. For example, the generation unit can refer to literature related to product troubleshooting information. For example, the generation unit can refer to literature related to product installation procedures. The generation unit can also refer to literature related to frequently asked questions. By referring to relevant literature for the data, the accuracy of Yes-No map generation can be improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0094] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is nervous, the response unit can provide a calm response. For example, if the user is relaxed, the response unit can provide a cheerful response. Also, if the user is in a hurry, the response unit can provide a quick and concise response. In this way, by adjusting the way the response is expressed according to the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0095] The response unit can adjust the level of detail in its response based on the importance of the Yes-No map. For example, the response unit can provide a detailed response based on a highly important Yes-No map. For example, the response unit can provide a simplified response based on a less important Yes-No map. The response unit can also dynamically adjust the level of detail in its response according to the importance of the Yes-No map. This allows for the priority provision of important information by adjusting the level of detail in the response based on the importance of the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the importance of the Yes-No map into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the response.

[0096] The response unit can apply different response algorithms depending on the category in the Yes-No map when responding. For example, the response unit can apply a procedure response algorithm to product installation instructions. For example, the response unit can apply a problem-solving response algorithm to troubleshooting information. The response unit can also apply a natural language processing response algorithm to frequently asked questions. This allows for efficient response provision by applying the appropriate response algorithm according to the category in the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the categories in the Yes-No map into the AI ​​and have the AI ​​apply the appropriate response algorithm.

[0097] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is nervous, the response unit can provide a short, to-the-point response. For example, if the user is relaxed, the response unit can provide a longer response that includes a detailed explanation. Also, if the user is in a hurry, the response unit can provide a quick and concise response. By adjusting the length of the response according to the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0098] The response unit can determine the priority of responses based on the submission timing of the Yes-No maps. For example, the response unit may prioritize responses based on recently generated Yes-No maps. For example, the response unit may postpone responses based on older Yes-No maps. The response unit can also dynamically adjust the response schedule based on the submission timing of the Yes-No maps. This allows for the priority provision of the latest information by prioritizing responses based on the submission timing of the Yes-No maps. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the submission timing of the Yes-No maps into the AI ​​and have the AI ​​perform the determination of response priorities.

[0099] The response unit can improve the accuracy of its response by referring to relevant literature in the Yes-No map when providing a response. For example, the response unit can provide a response by referring to literature related to product troubleshooting information. For example, the response unit can provide a response by referring to literature related to product installation procedures. The response unit can also provide a response by referring to literature related to frequently asked questions. This improves the accuracy of the response by referring to relevant literature in the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input relevant literature into the AI ​​and have the AI ​​perform the literature lookup.

[0100] The response unit can provide a response based on the latest information, taking into account updates to the Yes-No map. For example, the response unit can provide a response based on information about the latest product version. For example, the response unit can provide a response based on recently updated troubleshooting procedures. The response unit can also provide a response based on the latest FAQ information. This allows the response unit to provide a response based on the latest information by taking into account updates to the Yes-No map. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input Yes-No map update information into the AI ​​and have the AI ​​execute a response based on the latest information.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] A customer support system can estimate a user's emotions and adjust the tone of its response based on those emotions. For example, if a user is angry, the response unit can provide a calm and composed response. If a user is sad, the response unit can provide a gentle response. If a user is happy, the response unit can provide a bright and cheerful response. This allows for responses in an appropriate tone according to the user's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0103] A customer support system can refer to a user's past inquiry history and customize responses based on that history. For example, it can provide more detailed explanations to users who have repeatedly inquired about the same problem in the past. It can also provide concise and prompt responses to users who have previously requested quick responses. Furthermore, it can provide additional relevant information based on past inquiries. This enables customized responses based on a user's past inquiry history, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's past inquiry history into the AI ​​and have the AI ​​perform the response customization.

[0104] A customer support system can estimate a user's emotions and adjust the content of its response based on those emotions. For example, if a user is feeling anxious, the response unit can provide reassurance by offering words of encouragement along with detailed explanations. If a user is excited, the response unit can provide a quick and concise response. If a user is relaxed, the response unit can also provide a response that includes detailed information. This enables responses that are appropriate to the user's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0105] A customer support system can estimate a user's emotions and adjust the timing of its response based on those emotions. For example, if a user is in a hurry, the response unit can provide a quick response. If a user is relaxed, the response unit can provide a response at a normal speed. If a user is stressed, the response unit can provide an immediate response. This allows for responses at the appropriate time according to the user's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0106] A customer support system can estimate a user's emotions and adjust the format of its response based on those emotions. For example, if a user is nervous, the response unit can provide a response in a simple, easy-to-read text format. If a user is relaxed, the response unit can provide a response in a graphical format with detailed information. If a user is in a hurry, the response unit can provide a concise response that gets straight to the point. This allows for responses in an appropriate format according to the user's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0107] A customer support system can analyze a user's past behavior data and prepare responses in advance for anticipated problems. For example, it can prepare the latest troubleshooting information for a user who has frequently reported problems with a particular product in the past. For a user who has repeatedly asked a particular question in the past, it can prepare detailed answers to that question in advance. It can also provide additional relevant information based on past behavior data. This enables preparation based on the user's past behavior data, allowing for quick and appropriate responses. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's past behavior data into AI and have the AI ​​perform the preparation.

[0108] A customer support system can use a user's geographical location to provide region-specific responses. For example, it can provide region-specific troubleshooting information for product issues that only occur in a particular area. It can also provide information on product usage and regulations specific to each region. Furthermore, it can provide contact information for regional support centers. This enables appropriate responses based on the user's geographical location, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's geographical location information into the AI ​​and have the AI ​​execute region-specific responses.

[0109] A customer support system can use a user's device information to provide device-specific responses. For example, it can provide device-specific troubleshooting information for problems that only occur on a particular device. It can also provide information on device-specific setup and usage. Furthermore, it can provide contact information for device-specific support centers. This enables appropriate responses based on the user's device information, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's device information into the AI ​​and have the AI ​​execute device-specific responses.

[0110] A customer support system can analyze a user's usage history and prioritize providing information about frequently used features. For example, it can prioritize providing detailed information and troubleshooting information about a particular feature to users who frequently use that feature. It can also provide the latest update information about frequently used features. Furthermore, it can provide FAQs about frequently used features. This enables appropriate responses based on the user's usage history, thereby improving customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the user's usage history into AI and prioritize providing information about frequently used features.

[0111] A customer support system can analyze user feedback and improve the quality of responses based on that feedback. For example, it can provide similar responses based on positive feedback from users. Based on negative feedback, it can improve the content and format of responses. It can also adjust the timing and tone of responses based on feedback. This enables appropriate responses based on user feedback and can improve customer satisfaction. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user feedback into an AI and have the AI ​​perform improvements to the quality of responses based on that feedback.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reading unit reads company data. This company data includes financial data, customer data, product data, etc. The reading unit can also read specifications and manuals provided by the company. For example, it can read technical specifications, operation manuals, user guides, etc. Step 2: The analysis unit analyzes the loaded data. The analysis unit analyzes specifications and manuals using natural language processing technology and text analysis algorithms. Step 3: The generation unit generates a Yes-No map based on the analyzed data. The generation unit performs a binary choice mapping and generates a Yes-No map in a visual display format. Step 4: The response unit provides a response based on the generated Yes-No map. The response unit can provide text responses, voice responses, and graphical feedback.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and response unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reading unit is implemented by the computer 36 of the smart device 14 and reads corporate data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the read data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a Yes-No map. The response unit is implemented by the control unit 46A of the smart device 14 and provides a response based on the generated Yes-No map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart glasses 214 and reads corporate data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a Yes-No map. The response unit is implemented by the control unit 46A of the smart glasses 214 and provides a response based on the generated Yes-No map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the headset terminal 314 and reads corporate data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a Yes-No map. The response unit is implemented by the control unit 46A of the headset terminal 314 and provides a response based on the generated Yes-No map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and response unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the robot 414 and reads corporate data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a Yes-No map. The response unit is implemented by the control unit 46A of the robot 414 and provides a response based on the generated Yes-No map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A reading unit that reads company data, An analysis unit analyzes the data read by the aforementioned reading unit, A generation unit that generates a Yes-No map based on the data analyzed by the analysis unit, A response unit that provides a response based on the Yes-No map generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned reading unit, Read the specifications and manuals provided by the company. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the loaded specifications and manuals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a Yes-No map based on the analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The response unit is Provide a response based on the generated Yes-No map. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Update the Yes-No map The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reading unit, It estimates user sentiment and adjusts the timing of loading corporate data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reading unit, When loading corporate data, prioritize based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reading unit, When loading corporate data, different loading algorithms are applied depending on the data format. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reading unit, It estimates the user's emotions and determines the priority of data to load based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reading unit, When loading company data, adjust the loading frequency considering the data update frequency. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reading unit, When loading corporate data, prioritize loading highly reliable data, taking into account the data source. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust the method of generating the Yes / No map based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a Yes / No map, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating Yes / No maps, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's sentiment and adjusts how the Yes / No map is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating Yes-No maps, the generation priority is determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating Yes / No maps, we improve the accuracy of the generation by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response unit is When responding, adjust the level of detail in the response based on the importance of the Yes-No map. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is When responding, apply a different response algorithm depending on the category in the Yes-No map. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is When responding, prioritize responses based on when the Yes-No map was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The response unit is When responding, refer to relevant literature in the Yes-No map to improve the accuracy of your response. The system described in Appendix 1, characterized by the features described herein. (Note 31) The response unit is When responding, the system takes into account updates to the Yes-No map to provide a response based on the latest information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reading unit that reads company data, An analysis unit analyzes the data read by the aforementioned reading unit, A generation unit that generates a Yes-No map based on the data analyzed by the analysis unit, A response unit that provides a response based on the Yes-No map generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned reading unit, Read the specifications and manuals provided by the company. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the loaded specifications and manuals. The system according to feature 1.

4. The generating unit is Generate a Yes-No map based on the analyzed data. The system according to feature 1.

5. The response unit is Provide a response based on the generated Yes-No map. The system according to feature 1.

6. The generating unit is Update the Yes-No map The system according to feature 1.

7. The aforementioned reading unit, It estimates user sentiment and adjusts the timing of loading corporate data based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned reading unit, When loading corporate data, prioritize based on the importance of the data. The system according to feature 1.

9. The aforementioned reading unit, When loading corporate data, different loading algorithms are applied depending on the data format. The system according to feature 1.

10. The aforementioned reading unit, It estimates the user's emotions and determines the priority of data to load based on the estimated user emotions. The system according to feature 1.

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