system

Abstract

The system according to the embodiment aims to improve the efficiency of responding to inquiries. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives an inquiry. The analysis unit analyzes the content of the inquiry received by the reception unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not sufficiently improved the efficiency of responding to inquiries, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of responding to inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives an inquiry. The analysis unit analyzes the content of the inquiry received by the reception unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of responding to inquiries. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An inquiry response speed-up system according to an embodiment of the present invention is a system that efficiently accepts, analyzes, and proposes inquiries. The system first accepts customer inquiries, then retrieves related data from a knowledge-based database and recommends effective proposal methods. This reduces the time it takes to respond to customer inquiries and also reduces the time it takes to perform internal business and service checks. For example, when a customer asks about how to use a product, the system records the question in a database and analyzes past inquiries and responses to derive the most appropriate answer. Furthermore, AI can suggest related products and services based on the inquiry. For example, when a customer asks about a specific product, it can suggest accessories and services related to that product. This mechanism reduces the time it takes to respond to customer inquiries. Because AI analyzes past data to derive the most appropriate answer, it can provide answers quickly. Furthermore, by recommending effective proposal methods, it is possible to provide more valuable information to customers. Furthermore, it also reduces the time it takes to perform internal business and service checks. The knowledge-based database centrally manages past inquiries and responses, allowing for quick access to necessary information. This improves internal business efficiency and shortens the time required to confirm services. For example, when an internal employee wants to check past responses to a specific inquiry, they can quickly retrieve the information from the database. Furthermore, when confirming services, they can quickly perform the confirmation work by utilizing the information stored in the database. In this way, a system that speeds up inquiry responses shortens the time required to respond to customer inquiries, and also shortens the time required for internal business and service confirmation. This improves overall business efficiency and customer satisfaction. As a result, the inquiry response acceleration system can efficiently accept, analyze, and propose inquiries.

[0029] The inquiry response speed-up system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives an inquiry. The inquiry may include, but is not limited to, a text-based question or a voice input. The reception unit may receive, for example, a text-based question and record the question in detail. The reception unit may also receive a voice input and convert it into text data using voice recognition technology. For example, the reception unit may analyze the voice using voice recognition software and save it as text. The analysis unit analyzes the inquiry received by the reception unit. For example, techniques such as natural language processing and data mining may be used for the analysis, but are not limited to these examples. For example, the analysis unit may analyze the inquiry using natural language processing technology and extract important keywords. The analysis unit may also analyze past inquiry data using data mining technology to find patterns. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposals may include, but are not limited to, product recommendations and service suggestions. For example, the proposal unit may recommend related products based on the analysis results. The suggestion unit can also suggest related services based on the analysis results. This allows the inquiry response acceleration system according to the embodiment to efficiently accept, analyze, and suggest inquiries. For example, the reception unit records the details of the inquiry and inputs the details into a knowledge-based database. The analysis unit analyzes past inquiry contents and responses to derive optimal responses. The suggestion unit suggests related products and services based on the inquiry contents. This allows the reception, analysis, and suggestion of inquiries to be efficiently performed. Furthermore, each element of the reception unit, analysis unit, and suggestion unit can include AI processing. For example, the reception unit can analyze the inquiry contents using AI and extract important information. The analysis unit can analyze past inquiry data using AI and find patterns. The suggestion unit can make optimal suggestions based on the analysis results using AI. This allows the reception, analysis, and suggestion of inquiries to be efficiently performed.

[0030] The inquiry response acceleration system includes a calling unit that calls data from a database. The calling unit calls data from the database. Data includes, for example, text data and numerical data, but is not limited to these examples. The calling unit, for example, calls text data and provides it to the analysis unit. The calling unit can also call numerical data and provide it to the analysis unit. For example, the calling unit calls the contents of past inquiries from the database and provides it to the analysis unit. This allows the calling unit to quickly call data from the database. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to select and call optimal data from the database. This allows the calling unit to quickly call data from the database.

[0031] The inquiry response acceleration system includes a recording unit that records past inquiry content. The recording unit records past inquiry content. Past inquiry content includes, for example, inquiries from the past year and inquiries of a specific category, but is not limited to these examples. The recording unit, for example, records inquiry content from the past year and stores it in a database. The recording unit can also record inquiry content of a specific category and store it in a database. For example, the recording unit records inquiry content related to products and stores it in a database. In this way, the recording unit records past inquiry content and can refer to it later. Furthermore, the recording unit can include AI processing. For example, the recording unit can automatically record inquiry content using AI and store it in a database. In this way, the recording unit records past inquiry content and can refer to it later.

[0032] The reception unit can specifically record the inquiry content and input it into a database that is a knowledge base. The reception unit can specifically record the inquiry content and input it into a database that is a knowledge base. Specific recording methods include, but are not limited to, detailed text recording and audio recording. For example, the reception unit can perform detailed text recording and input it into a database. The reception unit can also perform audio recording and input the audio data into a database. For example, the reception unit can use voice recognition technology to convert audio data into text data and input it into a database. This allows the reception unit to record the inquiry content in detail and input it into a database for easy later reference. Furthermore, the reception unit can include AI processing. For example, the reception unit can use AI to analyze the inquiry content, extract important information, and input it into a database. This allows the reception unit to record the inquiry content in detail and input it into a database for easy later reference.

[0033] The analysis unit can analyze past inquiry contents and answers to derive the most appropriate answer. The analysis unit analyzes past inquiry contents and answers to derive the most appropriate answer. Criteria for an appropriate answer include, but are not limited to, user satisfaction and answer accuracy, for example. The analysis unit selects an appropriate answer based on user satisfaction, for example. The analysis unit can also select an appropriate answer based on answer accuracy. For example, the analysis unit compares past inquiry contents and answers to select the most accurate answer. This allows the analysis unit to quickly derive the optimal answer by analyzing past inquiry contents and answers. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can use AI to analyze past inquiry data and find patterns. This allows the analysis unit to quickly derive the optimal answer by analyzing past inquiry contents and answers.

[0034] The suggestion unit can suggest related products and services based on the inquiry content. The suggestion unit suggests related products and services based on the inquiry content. Related products and services include, but are not limited to, products in the same category and complementary services. The suggestion unit, for example, suggests products in the same category. The suggestion unit can also suggest complementary services. For example, the suggestion unit suggests related accessories and services based on the inquiry content. In this way, the suggestion unit can provide valuable information to the user by suggesting related products and services based on the inquiry content. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the inquiry content using AI and make optimal suggestions. In this way, the suggestion unit can provide valuable information to the user by suggesting related products and services based on the inquiry content.

[0035] The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. Criteria for the optimal reception method include, but are not limited to, chatbots and telephone support. For example, the reception unit can automatically display inquiries that the user has frequently made in the past as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest a reception method to be used in a specific time period based on the user's past inquiry history. This allows the reception unit to select the optimal reception method by referring to the user's past inquiry history. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the past inquiry history using AI and select the optimal reception method. This allows the reception unit to select the optimal reception method by referring to the user's past inquiry history.

[0036] The reception unit may perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit may perform filtering based on the user's current situation and areas of interest at the time of reception. Methods for identifying the current situation and areas of interest include, but are not limited to, real-time behavioral data and past browsing history, for example. For example, when the user inputs their current situation, the reception unit may preferentially display related inquiry content. The reception unit may also filter related inquiry content based on the user's areas of interest. For example, the reception unit may suggest an optimal reception method based on the user's current situation and areas of interest. As a result, the reception unit can display more appropriate inquiry content by filtering based on the user's current situation and areas of interest. Furthermore, the reception unit may include AI processing. For example, the reception unit may use AI to analyze the user's current situation and areas of interest and perform optimal filtering. As a result, the reception unit can display more appropriate inquiry content by filtering based on the user's current situation and areas of interest.

[0037] The reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information when receiving the inquiries. The reception unit prioritizes receiving highly relevant inquiries by taking into account the user's geographical location information when receiving the inquiries. Methods for acquiring geographical location information include, but are not limited to, GPS data and IP addresses. For example, if a user makes an inquiry from a specific region, the reception unit prioritizes receiving inquiries related to that region. The reception unit can also filter related inquiry content based on the user's geographical location information. For example, the reception unit can propose an optimal reception method by taking into account the user's geographical location information. As a result, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the user's geographical location information using AI and select an optimal reception method. As a result, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity at the time of reception and receive related inquiries. The reception unit analyzes the user's social media activity at the time of reception and receives related inquiries. Methods of analyzing social media activity include, but are not limited to, analyzing post content and followers. The reception unit, for example, analyzes the user's social media activity and preferentially receives related inquiries. The reception unit can also identify areas of interest from the user's social media activity and suggest an optimal reception method. For example, the reception unit filters related inquiries based on the user's social media activity. As a result, the reception unit can analyze the user's social media activity and preferentially receive related inquiries. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the user's social media activity using AI and select an optimal reception method. As a result, the reception unit can analyze the user's social media activity and preferentially receive related inquiries.

[0039] The analysis unit can optimize the analysis algorithm by referring to past inquiry data during analysis. The analysis unit optimizes the analysis algorithm by referring to past inquiry data during analysis. Types of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis. For example, the analysis unit selects an optimal analysis algorithm based on the past inquiry data. The analysis unit can also analyze the past inquiry data to improve the accuracy of the analysis algorithm. For example, the analysis unit adjusts parameters of the analysis algorithm by referring to the past inquiry data. In this way, the analysis unit can optimize the analysis algorithm by referring to the past inquiry data. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the past inquiry data using AI and select an optimal analysis algorithm. In this way, the analysis unit can optimize the analysis algorithm by referring to the past inquiry data.

[0040] The analysis unit can apply different analysis methods depending on the category of the inquiry during analysis. The analysis unit applies different analysis methods depending on the category of the inquiry during analysis. Different analysis methods include, but are not limited to, clustering and classification, for example. For example, in the case of an inquiry about a product, the analysis unit can perform analysis by referring to a product database. Furthermore, in the case of an inquiry about a service, the analysis unit can also perform analysis by referring to a service database. For example, in the case of a technical inquiry, the analysis unit can perform analysis by referring to a technical database. In this way, the analysis unit can apply different analysis methods depending on the category of the inquiry, thereby enabling more appropriate analysis. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the category of the inquiry using AI and select the optimal analysis method. In this way, the analysis unit can apply different analysis methods depending on the category of the inquiry, thereby enabling more appropriate analysis.

[0041] The analysis unit can weight the analysis based on the time of submission of the inquiry during analysis. The analysis unit weights the analysis based on the time of submission of the inquiry during analysis. Weighting criteria include, for example, temporal importance and frequency, but are not limited to these examples. For example, the analysis unit prioritizes analysis of recent inquiries. The analysis unit can also prioritize analysis of inquiries submitted within a specific time period. For example, the analysis unit adjusts the weighting of the analysis based on past inquiry data. This allows the analysis unit to weight the analysis based on the time of submission of the inquiry, thereby enabling more appropriate analysis. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the time of submission of the inquiry using AI and perform optimal weighting. This allows the analysis unit to weight the analysis based on the time of submission of the inquiry, thereby enabling more appropriate analysis.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. Types of external data include, but are not limited to, data obtained from public databases and APIs. For example, the analysis unit performs the analysis by referring to an external product database. The analysis unit can also perform the analysis by referring to an external service database. For example, the analysis unit performs the analysis by referring to an external technology database. In this way, the analysis unit can improve the accuracy of the analysis by referring to the related external data. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the external data using AI and select an optimal analysis method. In this way, the analysis unit can improve the accuracy of the analysis by referring to the related external data.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the inquiry when making a suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the inquiry when making a suggestion. Examples of the level of detail of the proposal include, but are not limited to, a concise explanation or a detailed manual. For example, the suggestion unit makes a detailed suggestion for an important inquiry. The suggestion unit can also make a simple suggestion for a general inquiry. For example, the suggestion unit makes a suggestion that focuses on the main points for a quick response to an urgent inquiry. In this way, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the inquiry, thereby enabling a more appropriate suggestion. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the importance of the inquiry using AI and select an optimal level of detail of the suggestion. In this way, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the inquiry, thereby enabling a more appropriate suggestion.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the inquiry when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of the inquiry when making a suggestion. Examples of different suggestion algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, in the case of an inquiry about a product, the suggestion unit can make a suggestion based on a product database. Furthermore, in the case of an inquiry about a service, the suggestion unit can make a suggestion based on a service database. For example, in the case of a technical inquiry, the suggestion unit can make a suggestion based on a technology database. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of the inquiry, thereby enabling more appropriate suggestions. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the category of the inquiry using AI and select an optimal suggestion algorithm. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of the inquiry, thereby enabling more appropriate suggestions.

[0045] The suggestion unit, when making a proposal, can determine the priority of the proposal based on the time of submission of the inquiry. The suggestion unit, when making a proposal, determines the priority of the proposal based on the time of submission of the inquiry. Criteria for determining the priority include, for example, urgency and importance, but are not limited to these examples. For example, the suggestion unit can prioritize proposals for recent inquiries. The suggestion unit can also prioritize proposals for inquiries submitted during a specific time period. For example, the suggestion unit adjusts the priority of the proposal based on past inquiry data. This enables the suggestion unit to determine the priority of the proposal based on the time of submission of the inquiry, thereby enabling more appropriate proposals. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can use AI to analyze the time of submission of the inquiry and determine the optimal priority. This enables the suggestion unit to determine the priority of the proposal based on the time of submission of the inquiry, thereby enabling more appropriate proposals.

[0046] The suggestion unit can refer to the user's past purchase history when suggesting related products or services. The suggestion unit can refer to the user's past purchase history when suggesting related products or services. Methods for acquiring the past purchase history include, but are not limited to, a purchase database or a user profile, for example. The suggestion unit can suggest related products based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest optimal services. For example, the suggestion unit can make customized suggestions by referring to the user's past purchase history. This allows the suggestion unit to suggest more appropriate products or services by referring to the user's past purchase history. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can use AI to analyze the user's past purchase history and make optimal suggestions. This allows the suggestion unit to suggest more appropriate products or services by referring to the user's past purchase history.

[0047] The calling unit can select optimal data by referring to past inquiry data at the time of the call. The calling unit selects optimal data by referring to past inquiry data at the time of the call. Criteria for optimal data include, for example, the most recent data or highly relevant data, but are not limited to these examples. The calling unit selects optimal data based on, for example, past inquiry data. The calling unit can also analyze past inquiry data and select the most relevant data. For example, the calling unit adjusts data selection criteria by referring to past inquiry data. This allows the calling unit to select optimal data by referring to past inquiry data. Furthermore, the calling unit can include AI processing. For example, the calling unit can analyze past inquiry data using AI and select optimal data. This allows the calling unit to select optimal data by referring to past inquiry data.

[0048] The calling unit can apply different calling methods depending on the data category when making a call. The calling unit applies different calling methods depending on the data category when making a call. Different calling methods include, but are not limited to, SQL queries and API calls. For example, in the case of data related to a product, the calling unit can make a call by referring to a product database. In addition, in the case of data related to a service, the calling unit can make a call by referring to a service database. For example, in the case of technical data, the calling unit can make a call by referring to a technical database. In this way, the calling unit can apply different calling methods depending on the data category, thereby enabling more appropriate data to be called. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to analyze the data category and select the optimal calling method. In this way, the calling unit can apply different calling methods depending on the data category, thereby enabling more appropriate data to be called.

[0049] The calling unit can weight the calls based on the time of data submission at the time of the call. The calling unit weights the calls based on the time of data submission at the time of the call. Weighting criteria include, but are not limited to, temporal importance and frequency, for example. The calling unit, for example, prioritizes calling recent data. The calling unit can also prioritize calling data submitted within a specific time period. For example, the calling unit adjusts the weighting of the calls based on past data. This allows the calling unit to weight the calls based on the time of data submission, thereby enabling more appropriate data to be called. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to analyze the time of data submission and perform optimal weighting. This allows the calling unit to weight the calls based on the time of data submission, thereby enabling more appropriate data to be called.

[0050] The calling unit can improve the accuracy of the call by referring to related external data when making a call. The calling unit can improve the accuracy of the call by referring to related external data when making a call. Types of external data include, but are not limited to, data obtained from public databases and APIs. For example, the calling unit can call data by referring to an external product database. The calling unit can also call data by referring to an external service database. For example, the calling unit can call data by referring to an external technology database. In this way, the calling unit can improve the accuracy of the call by referring to the related external data. Furthermore, the calling unit can include AI processing. For example, the calling unit can analyze the external data using AI and call optimal data. In this way, the calling unit can improve the accuracy of the call by referring to the related external data.

[0051] The recording unit can optimize the recording algorithm by referring to past inquiry data during recording. The recording unit optimizes the recording algorithm by referring to past inquiry data during recording. Types of recording algorithms include, but are not limited to, machine learning algorithms and statistical analysis. For example, the recording unit selects an optimal recording algorithm based on the past inquiry data. The recording unit can also analyze the past inquiry data to improve the accuracy of the recording algorithm. For example, the recording unit adjusts parameters of the recording algorithm by referring to the past inquiry data. In this way, the recording unit can optimize the recording algorithm by referring to the past inquiry data. Furthermore, the recording unit can include AI processing. For example, the recording unit can analyze the past inquiry data using AI and select an optimal recording algorithm. In this way, the recording unit can optimize the recording algorithm by referring to the past inquiry data.

[0052] The recording unit can weight the records based on the time of submission of the inquiry when recording. The recording unit weights the records based on the time of submission of the inquiry when recording. Weighting criteria include, for example, temporal importance and frequency, but are not limited to these examples. For example, the recording unit prioritizes recording of recent inquiries. The recording unit can also prioritize recording of inquiries submitted within a specific time period. For example, the recording unit adjusts the weighting of the records based on past inquiry data. This allows the recording unit to weight the records based on the time of submission of the inquiry, thereby enabling more appropriate recording. Furthermore, the recording unit can include AI processing. For example, the recording unit can use AI to analyze the time of submission of the inquiry and perform optimal weighting. This allows the recording unit to weight the records based on the time of submission of the inquiry, thereby enabling more appropriate recording.

[0053] The function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data when inputting data into the database. The function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data when inputting data into the database. Types of input algorithms include, but are not limited to, machine learning algorithms and statistical analysis. The function of inputting data into a knowledge-based database can, for example, select an optimal input algorithm based on past inquiry data. The function of inputting data into a knowledge-based database can also analyze past inquiry data to improve the accuracy of the input algorithm. For example, the function of inputting data into a knowledge-based database can adjust parameters of the input algorithm by referring to past inquiry data. In this way, the function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data. Furthermore, the function of inputting data into a knowledge-based database can include AI processing. For example, the function of inputting data into a knowledge-based database can analyze past inquiry data using AI to select an optimal input algorithm. This allows the function of inputting data into a knowledge base database to optimize the input algorithm by referencing past inquiry data.

[0054] The function of inputting data into a knowledge-based database can weight the input based on the time of data submission when inputting the data into the database. The function of inputting data into a knowledge-based database can weight the input based on the time of data submission when inputting the data into the database. Weighting criteria include, but are not limited to, temporal importance and frequency, for example. The function of inputting data into a knowledge-based database can, for example, prioritize recent data input into the database. The function of inputting data into a knowledge-based database can also prioritize data submitted within a specific time period when inputting the data into the database. For example, the function of inputting data into a knowledge-based database can adjust the weighting of the input to the database based on past data. This allows the function of inputting data into a knowledge-based database to weight the input based on the time of data submission, thereby enabling more appropriate input. Furthermore, the function of inputting data into a knowledge-based database can include AI processing. For example, the function of inputting data into a knowledge-based database can use AI to analyze the time of data submission and perform optimal weighting. This allows the function of inputting data into a knowledge-based database to weight the input based on the time of data submission, making it possible to input data more appropriately.

[0055] The function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data during analysis. The function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data during analysis. Types of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis. The function that analyzes past inquiry contents and responses can, for example, select an optimal analysis algorithm based on the past inquiry data. The function that analyzes past inquiry contents and responses can also analyze the past inquiry data to improve the accuracy of the analysis algorithm. For example, the function that analyzes past inquiry contents and responses can adjust parameters of the analysis algorithm by referring to the past inquiry data. In this way, the function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data. Furthermore, the function that analyzes past inquiry contents and responses can include AI processing. For example, the function that analyzes past inquiry contents and responses can use AI to analyze the past inquiry data and select an optimal analysis algorithm. In this way, the function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data.

[0056] The function that analyzes past inquiry content and responses can weight the analysis based on the time of submission of the inquiry during analysis. The function that analyzes past inquiry content and responses can weight the analysis based on the time of submission of the inquiry during analysis. Weighting criteria include, but are not limited to, temporal importance and frequency. The function that analyzes past inquiry content and responses can, for example, prioritize the analysis of recent inquiries. The function that analyzes past inquiry content and responses can also prioritize the analysis of inquiries submitted within a specific time period. For example, the function that analyzes past inquiry content and responses can adjust the weighting of the analysis based on past inquiry data. This allows the function that analyzes past inquiry content and responses to weight the analysis based on the time of submission of the inquiry, enabling more appropriate analysis. Furthermore, the function that analyzes past inquiry content and responses can include AI processing. For example, the function that analyzes past inquiry content and responses can use AI to analyze the time of submission of the inquiry and perform optimal weighting. This allows the function that analyzes past inquiries and responses to perform more accurate analysis by weighting the analysis based on the time the inquiry was submitted.

[0057] The function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry when making a proposal. The function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry when making a proposal. Examples of the level of detail of the proposal include, but are not limited to, a brief explanation or a detailed manual. For example, the function for suggesting related products and services can provide detailed suggestions for important inquiries. Furthermore, the function for suggesting related products and services can provide simple suggestions for general inquiries. For example, the function for suggesting related products and services can provide suggestions that focus on the main points for urgent inquiries in order to respond quickly. In this way, the function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry, thereby enabling more appropriate suggestions. Furthermore, the function for suggesting related products and services can include AI processing. For example, the function for suggesting related products and services can use AI to analyze the importance of the inquiry and select the optimal level of detail of the proposal. In this way, the function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry, thereby enabling more appropriate suggestions.

[0058] The function for suggesting related products and services can prioritize the suggestions based on the time when the inquiry was submitted when making the suggestions. The function for suggesting related products and services can prioritize the suggestions based on the time when the inquiry was submitted when making the suggestions. Criteria for determining the priority include, but are not limited to, urgency and importance, for example. The function for suggesting related products and services can prioritize suggestions for recent inquiries, for example. The function for suggesting related products and services can also prioritize suggestions for inquiries submitted within a specific time period. For example, the function for suggesting related products and services can adjust the priority of suggestions based on past inquiry data. This allows the function for suggesting related products and services to prioritize the suggestions based on the time when the inquiry was submitted, thereby enabling more appropriate suggestions. Furthermore, the function for suggesting related products and services can include AI processing. For example, the function for suggesting related products and services can use AI to analyze the time when the inquiry was submitted and determine the optimal priority. This allows the function for suggesting related products and services to prioritize the suggestions based on the time when the inquiry was submitted, thereby enabling more appropriate suggestions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can refer to the user's past inquiry history and automatically route the inquiry to the most appropriate person depending on the content of the inquiry. For example, if a user has previously made an inquiry about a specific product, the inquiry can be routed to a person who is knowledgeable about that product. Also, if a user has made inquiries about the same problem multiple times in the past, the inquiry can be routed to a person who is familiar with solutions to that problem. Furthermore, if a user tends to make inquiries during a specific time period, the inquiry can be routed to a person who is available during that time period. This allows the reception unit to utilize the user's past inquiry history to provide faster and more appropriate responses.

[0061] When retrieving data from a database, the calling unit can evaluate the reliability of the data and preferentially call highly reliable data. For example, it can assign a reliability score to data in the database and preferentially call data with a high score. It can also evaluate reliability based on the frequency of data updates and the reliability of the source. Furthermore, it can evaluate the reliability of data based on user feedback and preferentially call highly reliable data. This allows the calling unit to quickly provide highly reliable data.

[0062] When recording the inquiry content, the recording unit can automatically detect input errors made by the user and make suggestions for correction. For example, if the user enters an incorrect product name, the recording unit can suggest the correct product name. Also, if the user enters incomplete information, the recording unit can suggest ways to complete the necessary information. Furthermore, if the user uses an ambiguous expression, the recording unit can suggest ways to convert it into a more specific expression. This allows the recording unit to record accurate and detailed inquiry content.

[0063] When analyzing the inquiry content, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the analysis can be performed by referring to an external product database. The analysis can also be performed by referring to an external service database. Furthermore, the analysis can be performed by referring to an external technology database. In this way, the analysis unit can improve the accuracy of the analysis by referring to related external data.

[0064] When making a suggestion, the suggestion unit can refer to the user's past purchase history to suggest related products and services. For example, the suggestion unit can suggest accessories related to products the user has previously purchased. The suggestion unit can also suggest new services related to services the user has previously used. Furthermore, the suggestion unit can make customized suggestions based on the user's purchase history. This allows the suggestion unit to suggest more appropriate products and services by referring to the user's past purchase history.

[0065] When retrieving data from a database, the invoking unit can apply different invoking methods depending on the category of the data. For example, in the case of data related to a product, the data can be retrieved by referencing a product database. In addition, in the case of data related to a service, the data can be retrieved by referencing a service database. Furthermore, in the case of technical data, the data can be retrieved by referencing a technical database. This allows the invoking unit to apply different invoking methods depending on the category of data, thereby enabling more appropriate data to be retrieved.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives an inquiry. The inquiry may include, for example, a text-based question or a voice input. The reception unit receives the text-based question and records it in detail. It can also receive voice input and convert it into text data using voice recognition technology. For example, the voice can be analyzed using voice recognition software and saved as text. Step 2: The analysis unit analyzes the inquiry received by the reception unit. For example, techniques such as natural language processing and data mining are used for the analysis. The analysis unit uses natural language processing technology to analyze the inquiry and extract important keywords. It can also use data mining technology to analyze past inquiry data and find patterns. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions include, for example, product recommendations and service suggestions. The suggestion unit can also recommend related products and suggest related services based on the analysis results.

[0068] (Example 2) An inquiry response speed-up system according to an embodiment of the present invention is a system that efficiently accepts, analyzes, and proposes inquiries. The system first accepts customer inquiries, then retrieves related data from a knowledge-based database and recommends effective proposal methods. This reduces the time it takes to respond to customer inquiries and also reduces the time it takes to perform internal business and service checks. For example, when a customer asks about how to use a product, the system records the question in a database and analyzes past inquiries and responses to derive the most appropriate answer. Furthermore, AI can suggest related products and services based on the inquiry. For example, when a customer asks about a specific product, it can suggest accessories and services related to that product. This mechanism reduces the time it takes to respond to customer inquiries. Because AI analyzes past data to derive the most appropriate answer, it can provide answers quickly. Furthermore, by recommending effective proposal methods, it is possible to provide more valuable information to customers. Furthermore, it also reduces the time it takes to perform internal business and service checks. The knowledge-based database centrally manages past inquiries and responses, allowing for quick access to necessary information. This improves internal business efficiency and shortens the time required to confirm services. For example, when an internal employee wants to check past responses to a specific inquiry, they can quickly retrieve the information from the database. Furthermore, when confirming services, they can quickly perform the confirmation work by utilizing the information stored in the database. In this way, a system that speeds up inquiry responses shortens the time required to respond to customer inquiries, and also shortens the time required for internal business and service confirmation. This improves overall business efficiency and customer satisfaction. As a result, the inquiry response acceleration system can efficiently accept, analyze, and propose inquiries.

[0069] The inquiry response speed-up system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives an inquiry. The inquiry may include, but is not limited to, a text-based question or a voice input. The reception unit may receive, for example, a text-based question and record the question in detail. The reception unit may also receive a voice input and convert it into text data using voice recognition technology. For example, the reception unit may analyze the voice using voice recognition software and save it as text. The analysis unit analyzes the inquiry received by the reception unit. For example, techniques such as natural language processing and data mining may be used for the analysis, but are not limited to these examples. For example, the analysis unit may analyze the inquiry using natural language processing technology and extract important keywords. The analysis unit may also analyze past inquiry data using data mining technology to find patterns. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposals may include, but are not limited to, product recommendations and service suggestions. For example, the proposal unit may recommend related products based on the analysis results. The suggestion unit can also suggest related services based on the analysis results. This allows the inquiry response acceleration system according to the embodiment to efficiently accept, analyze, and suggest inquiries. For example, the reception unit records the details of the inquiry and inputs the details into a knowledge-based database. The analysis unit analyzes past inquiry contents and responses to derive optimal responses. The suggestion unit suggests related products and services based on the inquiry contents. This allows the reception, analysis, and suggestion of inquiries to be efficiently performed. Furthermore, each element of the reception unit, analysis unit, and suggestion unit can include AI processing. For example, the reception unit can analyze the inquiry contents using AI and extract important information. The analysis unit can analyze past inquiry data using AI and find patterns. The suggestion unit can make optimal suggestions based on the analysis results using AI. This allows the reception, analysis, and suggestion of inquiries to be efficiently performed.

[0070] The inquiry response acceleration system includes a calling unit that calls data from a database. The calling unit calls data from the database. Data includes, for example, text data and numerical data, but is not limited to these examples. The calling unit, for example, calls text data and provides it to the analysis unit. The calling unit can also call numerical data and provide it to the analysis unit. For example, the calling unit calls the contents of past inquiries from the database and provides it to the analysis unit. This allows the calling unit to quickly call data from the database. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to select and call optimal data from the database. This allows the calling unit to quickly call data from the database.

[0071] The inquiry response acceleration system includes a recording unit that records past inquiry content. The recording unit records past inquiry content. Past inquiry content includes, for example, inquiries from the past year and inquiries of a specific category, but is not limited to these examples. The recording unit, for example, records inquiry content from the past year and stores it in a database. The recording unit can also record inquiry content of a specific category and store it in a database. For example, the recording unit records inquiry content related to products and stores it in a database. In this way, the recording unit records past inquiry content and can refer to it later. Furthermore, the recording unit can include AI processing. For example, the recording unit can automatically record inquiry content using AI and store it in a database. In this way, the recording unit records past inquiry content and can refer to it later.

[0072] The reception unit can specifically record the inquiry content and input it into a database that is a knowledge base. The reception unit can specifically record the inquiry content and input it into a database that is a knowledge base. Specific recording methods include, but are not limited to, detailed text recording and audio recording. For example, the reception unit can perform detailed text recording and input it into a database. The reception unit can also perform audio recording and input the audio data into a database. For example, the reception unit can use voice recognition technology to convert audio data into text data and input it into a database. This allows the reception unit to record the inquiry content in detail and input it into a database for easy later reference. Furthermore, the reception unit can include AI processing. For example, the reception unit can use AI to analyze the inquiry content, extract important information, and input it into a database. This allows the reception unit to record the inquiry content in detail and input it into a database for easy later reference.

[0073] The analysis unit can analyze past inquiry contents and answers to derive the most appropriate answer. The analysis unit analyzes past inquiry contents and answers to derive the most appropriate answer. Criteria for an appropriate answer include, but are not limited to, user satisfaction and answer accuracy, for example. The analysis unit selects an appropriate answer based on user satisfaction, for example. The analysis unit can also select an appropriate answer based on answer accuracy. For example, the analysis unit compares past inquiry contents and answers to select the most accurate answer. This allows the analysis unit to quickly derive the optimal answer by analyzing past inquiry contents and answers. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can use AI to analyze past inquiry data and find patterns. This allows the analysis unit to quickly derive the optimal answer by analyzing past inquiry contents and answers.

[0074] The suggestion unit can suggest related products and services based on the inquiry content. The suggestion unit suggests related products and services based on the inquiry content. Related products and services include, but are not limited to, products in the same category and complementary services. The suggestion unit, for example, suggests products in the same category. The suggestion unit can also suggest complementary services. For example, the suggestion unit suggests related accessories and services based on the inquiry content. In this way, the suggestion unit can provide valuable information to the user by suggesting related products and services based on the inquiry content. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the inquiry content using AI and make optimal suggestions. In this way, the suggestion unit can provide valuable information to the user by suggesting related products and services based on the inquiry content.

[0075] The reception unit can estimate a user's emotion and adjust the inquiry reception method based on the estimated user's emotion. The reception unit can estimate a user's emotion and adjust the inquiry reception method based on the estimated user's emotion. Methods for estimating a user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the reception unit can estimate a user's emotion using facial expression recognition technology. The reception unit can also estimate a user's emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of voice to estimate the user's emotion. This allows the reception unit to adjust the inquiry reception method according to the user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. On the other hand, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to enter the inquiry content quickly. This allows the reception unit to adjust the inquiry reception method according to the user's emotion, thereby enabling a more appropriate response. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0076] The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. Criteria for the optimal reception method include, but are not limited to, chatbots and telephone support. For example, the reception unit can automatically display inquiries that the user has frequently made in the past as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest a reception method to be used in a specific time period based on the user's past inquiry history. This allows the reception unit to select the optimal reception method by referring to the user's past inquiry history. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the past inquiry history using AI and select the optimal reception method. This allows the reception unit to select the optimal reception method by referring to the user's past inquiry history.

[0077] The reception unit may perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit may perform filtering based on the user's current situation and areas of interest at the time of reception. Methods for identifying the current situation and areas of interest include, but are not limited to, real-time behavioral data and past browsing history, for example. For example, when the user inputs their current situation, the reception unit may preferentially display related inquiry content. The reception unit may also filter related inquiry content based on the user's areas of interest. For example, the reception unit may suggest an optimal reception method based on the user's current situation and areas of interest. As a result, the reception unit can display more appropriate inquiry content by filtering based on the user's current situation and areas of interest. Furthermore, the reception unit may include AI processing. For example, the reception unit may use AI to analyze the user's current situation and areas of interest and perform optimal filtering. As a result, the reception unit can display more appropriate inquiry content by filtering based on the user's current situation and areas of interest.

[0078] The reception unit can estimate the user's emotion and determine the priority of inquiries to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of inquiries to be received based on the estimated user's emotion. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, the reception unit prioritizes inquiries when the user makes an urgent inquiry. The reception unit can also receive inquiries with normal priority when the user is relaxed. For example, the reception unit can increase the priority when the user is feeling stressed in order to respond quickly. This allows the reception unit to determine the priority of inquiries based on the user's emotion, thereby enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0079] The reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information when receiving the inquiries. The reception unit prioritizes receiving highly relevant inquiries by taking into account the user's geographical location information when receiving the inquiries. Methods for acquiring geographical location information include, but are not limited to, GPS data and IP addresses. For example, if a user makes an inquiry from a specific region, the reception unit prioritizes receiving inquiries related to that region. The reception unit can also filter related inquiry content based on the user's geographical location information. For example, the reception unit can propose an optimal reception method by taking into account the user's geographical location information. As a result, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the user's geographical location information using AI and select an optimal reception method. As a result, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity at the time of reception and receive related inquiries. The reception unit analyzes the user's social media activity at the time of reception and receives related inquiries. Methods of analyzing social media activity include, but are not limited to, analyzing post content and followers. The reception unit, for example, analyzes the user's social media activity and preferentially receives related inquiries. The reception unit can also identify areas of interest from the user's social media activity and suggest an optimal reception method. For example, the reception unit filters related inquiries based on the user's social media activity. As a result, the reception unit can analyze the user's social media activity and preferentially receive related inquiries. Furthermore, the reception unit can include AI processing. For example, the reception unit can analyze the user's social media activity using AI and select an optimal reception method. As a result, the reception unit can analyze the user's social media activity and preferentially receive related inquiries.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. Analysis methods include, but are not limited to, text mining and sentiment analysis. For example, when the user is stressed, the analysis unit can perform a quick analysis and provide a concise answer. Furthermore, when the user is relaxed, the analysis unit can perform a detailed analysis and provide a comprehensive answer. For example, when the user is in a hurry, the analysis unit prioritizes the most important information in the analysis. This allows the analysis unit to adjust the analysis method according to the user's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] The analysis unit can optimize the analysis algorithm by referring to past inquiry data during analysis. The analysis unit optimizes the analysis algorithm by referring to past inquiry data during analysis. Types of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis. For example, the analysis unit selects an optimal analysis algorithm based on the past inquiry data. The analysis unit can also analyze the past inquiry data to improve the accuracy of the analysis algorithm. For example, the analysis unit adjusts parameters of the analysis algorithm by referring to the past inquiry data. In this way, the analysis unit can optimize the analysis algorithm by referring to the past inquiry data. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the past inquiry data using AI and select an optimal analysis algorithm. In this way, the analysis unit can optimize the analysis algorithm by referring to the past inquiry data.

[0083] The analysis unit can apply different analysis methods depending on the category of the inquiry during analysis. The analysis unit applies different analysis methods depending on the category of the inquiry during analysis. Different analysis methods include, but are not limited to, clustering and classification, for example. For example, in the case of an inquiry about a product, the analysis unit can perform analysis by referring to a product database. Furthermore, in the case of an inquiry about a service, the analysis unit can also perform analysis by referring to a service database. For example, in the case of a technical inquiry, the analysis unit can perform analysis by referring to a technical database. In this way, the analysis unit can apply different analysis methods depending on the category of the inquiry, thereby enabling more appropriate analysis. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the category of the inquiry using AI and select the optimal analysis method. In this way, the analysis unit can apply different analysis methods depending on the category of the inquiry, thereby enabling more appropriate analysis.

[0084] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit estimates the user's emotions and determines the analysis priority based on the estimated user emotions. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, the analysis unit prioritizes analysis when the user is making an urgent inquiry. The analysis unit can also perform analysis with normal priority when the user is relaxed. For example, the analysis unit can increase the analysis priority when the user is feeling stressed in order to respond quickly. This allows the analysis unit to determine the analysis priority based on the user's emotions, thereby enabling a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] The analysis unit can weight the analysis based on the time of submission of the inquiry during analysis. The analysis unit weights the analysis based on the time of submission of the inquiry during analysis. Weighting criteria include, for example, temporal importance and frequency, but are not limited to these examples. For example, the analysis unit prioritizes analysis of recent inquiries. The analysis unit can also prioritize analysis of inquiries submitted within a specific time period. For example, the analysis unit adjusts the weighting of the analysis based on past inquiry data. This allows the analysis unit to weight the analysis based on the time of submission of the inquiry, thereby enabling more appropriate analysis. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the time of submission of the inquiry using AI and perform optimal weighting. This allows the analysis unit to weight the analysis based on the time of submission of the inquiry, thereby enabling more appropriate analysis.

[0086] The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. Types of external data include, but are not limited to, data obtained from public databases and APIs. For example, the analysis unit performs the analysis by referring to an external product database. The analysis unit can also perform the analysis by referring to an external service database. For example, the analysis unit performs the analysis by referring to an external technology database. In this way, the analysis unit can improve the accuracy of the analysis by referring to the related external data. Furthermore, the analysis unit can include AI processing. For example, the analysis unit can analyze the external data using AI and select an optimal analysis method. In this way, the analysis unit can improve the accuracy of the analysis by referring to the related external data.

[0087] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The way the suggestion is expressed includes, but is not limited to, text, images, videos, and the like. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby enabling more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The suggestion unit can adjust the level of detail of the proposal based on the importance of the inquiry when making a suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the inquiry when making a suggestion. Examples of the level of detail of the proposal include, but are not limited to, a concise explanation or a detailed manual. For example, the suggestion unit makes a detailed suggestion for an important inquiry. The suggestion unit can also make a simple suggestion for a general inquiry. For example, the suggestion unit makes a suggestion that focuses on the main points for a quick response to an urgent inquiry. In this way, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the inquiry, thereby enabling a more appropriate suggestion. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the importance of the inquiry using AI and select an optimal level of detail of the suggestion. In this way, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the inquiry, thereby enabling a more appropriate suggestion.

[0089] The suggestion unit can apply different suggestion algorithms depending on the category of the inquiry when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of the inquiry when making a suggestion. Examples of different suggestion algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, in the case of an inquiry about a product, the suggestion unit can make a suggestion based on a product database. Furthermore, in the case of an inquiry about a service, the suggestion unit can make a suggestion based on a service database. For example, in the case of a technical inquiry, the suggestion unit can make a suggestion based on a technology database. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of the inquiry, thereby enabling more appropriate suggestions. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can analyze the category of the inquiry using AI and select an optimal suggestion algorithm. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of the inquiry, thereby enabling more appropriate suggestions.

[0090] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The length of the suggestion can include, but is not limited to, short sentences and long sentences. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. For example, if the user is excited, the suggestion unit can make a suggestion with a visually stimulating effect. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion, thereby enabling more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] The suggestion unit, when making a proposal, can determine the priority of the proposal based on the time of submission of the inquiry. The suggestion unit, when making a proposal, determines the priority of the proposal based on the time of submission of the inquiry. Criteria for determining the priority include, for example, urgency and importance, but are not limited to these examples. For example, the suggestion unit can prioritize proposals for recent inquiries. The suggestion unit can also prioritize proposals for inquiries submitted during a specific time period. For example, the suggestion unit adjusts the priority of the proposal based on past inquiry data. This enables the suggestion unit to determine the priority of the proposal based on the time of submission of the inquiry, thereby enabling more appropriate proposals. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can use AI to analyze the time of submission of the inquiry and determine the optimal priority. This enables the suggestion unit to determine the priority of the proposal based on the time of submission of the inquiry, thereby enabling more appropriate proposals.

[0092] The suggestion unit can refer to the user's past purchase history when suggesting related products or services. The suggestion unit can refer to the user's past purchase history when suggesting related products or services. Methods for acquiring the past purchase history include, but are not limited to, a purchase database or a user profile, for example. The suggestion unit can suggest related products based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest optimal services. For example, the suggestion unit can make customized suggestions by referring to the user's past purchase history. This allows the suggestion unit to suggest more appropriate products or services by referring to the user's past purchase history. Furthermore, the suggestion unit can include AI processing. For example, the suggestion unit can use AI to analyze the user's past purchase history and make optimal suggestions. This allows the suggestion unit to suggest more appropriate products or services by referring to the user's past purchase history.

[0093] The calling unit can estimate the user's emotion and adjust the data calling method based on the estimated user's emotion. The calling unit can estimate the user's emotion and adjust the data calling method based on the estimated user's emotion. Examples of data calling methods include, but are not limited to, using a cache or acquiring real-time data. For example, the calling unit can quickly call data when the user is stressed. The calling unit can also call detailed data when the user is relaxed. For example, the calling unit can prioritize calling the most important data when the user is in a hurry. This allows the calling unit to adjust the data calling method according to the user's emotion, thereby enabling more appropriate data to be called. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calling unit can be performed using an AI, for example, or without an AI. For example, the calling unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0094] The calling unit can select optimal data by referring to past inquiry data at the time of the call. The calling unit selects optimal data by referring to past inquiry data at the time of the call. Criteria for optimal data include, for example, the most recent data or highly relevant data, but are not limited to these examples. The calling unit selects optimal data based on, for example, past inquiry data. The calling unit can also analyze past inquiry data and select the most relevant data. For example, the calling unit adjusts data selection criteria by referring to past inquiry data. This allows the calling unit to select optimal data by referring to past inquiry data. Furthermore, the calling unit can include AI processing. For example, the calling unit can analyze past inquiry data using AI and select optimal data. This allows the calling unit to select optimal data by referring to past inquiry data.

[0095] The calling unit can apply different calling methods depending on the data category when making a call. The calling unit applies different calling methods depending on the data category when making a call. Different calling methods include, but are not limited to, SQL queries and API calls. For example, in the case of data related to a product, the calling unit can make a call by referring to a product database. In addition, in the case of data related to a service, the calling unit can make a call by referring to a service database. For example, in the case of technical data, the calling unit can make a call by referring to a technical database. In this way, the calling unit can apply different calling methods depending on the data category, thereby enabling more appropriate data to be called. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to analyze the data category and select the optimal calling method. In this way, the calling unit can apply different calling methods depending on the data category, thereby enabling more appropriate data to be called.

[0096] The calling unit can estimate the user's emotion and determine the priority of the data to be called based on the estimated user's emotion. The calling unit can estimate the user's emotion and determine the priority of the data to be called based on the estimated user's emotion. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, when the user has an urgent inquiry, the calling unit can call data with a normal priority when the user is relaxed. For example, when the user is feeling stressed, the calling unit can increase the priority of the data to respond quickly. This allows the calling unit to determine the priority of data according to the user's emotion, thereby enabling more appropriate data to be called. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calling unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0097] The calling unit can weight the calls based on the time of data submission at the time of the call. The calling unit weights the calls based on the time of data submission at the time of the call. Weighting criteria include, but are not limited to, temporal importance and frequency, for example. The calling unit, for example, prioritizes calling recent data. The calling unit can also prioritize calling data submitted within a specific time period. For example, the calling unit adjusts the weighting of the calls based on past data. This allows the calling unit to weight the calls based on the time of data submission, thereby enabling more appropriate data to be called. Furthermore, the calling unit can include AI processing. For example, the calling unit can use AI to analyze the time of data submission and perform optimal weighting. This allows the calling unit to weight the calls based on the time of data submission, thereby enabling more appropriate data to be called.

[0098] The calling unit can improve the accuracy of the call by referring to related external data when making a call. The calling unit can improve the accuracy of the call by referring to related external data when making a call. Types of external data include, but are not limited to, data obtained from public databases and APIs. For example, the calling unit can call data by referring to an external product database. The calling unit can also call data by referring to an external service database. For example, the calling unit can call data by referring to an external technology database. In this way, the calling unit can improve the accuracy of the call by referring to the related external data. Furthermore, the calling unit can include AI processing. For example, the calling unit can analyze the external data using AI and call optimal data. In this way, the calling unit can improve the accuracy of the call by referring to the related external data.

[0099] The recording unit can estimate the user's emotion and adjust the recording method based on the estimated user's emotion. The recording unit can estimate the user's emotion and adjust the recording method based on the estimated user's emotion. Examples of recording methods include, but are not limited to, detailed text recording and audio recording. For example, the recording unit can provide a concise recording method when the user is stressed. The recording unit can also provide a detailed recording method when the user is relaxed. For example, the recording unit can provide a quick recording method when the user is in a hurry. This allows the recording unit to adjust the recording method according to the user's emotion, enabling more appropriate recording. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, an AI. For example, the recording unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The recording unit can optimize the recording algorithm by referring to past inquiry data during recording. The recording unit optimizes the recording algorithm by referring to past inquiry data during recording. Types of recording algorithms include, but are not limited to, machine learning algorithms and statistical analysis. For example, the recording unit selects an optimal recording algorithm based on the past inquiry data. The recording unit can also analyze the past inquiry data to improve the accuracy of the recording algorithm. For example, the recording unit adjusts parameters of the recording algorithm by referring to the past inquiry data. In this way, the recording unit can optimize the recording algorithm by referring to the past inquiry data. Furthermore, the recording unit can include AI processing. For example, the recording unit can analyze the past inquiry data using AI and select an optimal recording algorithm. In this way, the recording unit can optimize the recording algorithm by referring to the past inquiry data.

[0101] The recording unit can estimate the user's emotions and determine the recording priority based on the estimated user emotions. The recording unit estimates the user's emotions and determines the recording priority based on the estimated user emotions. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, the recording unit prioritizes recording when the user makes an urgent inquiry. The recording unit can also record at a normal priority when the user is relaxed. For example, the recording unit can increase the recording priority when the user is stressed in order to respond quickly. This allows the recording unit to determine the recording priority according to the user's emotions, enabling more appropriate recording. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0102] The recording unit can weight the records based on the time of submission of the inquiry when recording. The recording unit weights the records based on the time of submission of the inquiry when recording. Weighting criteria include, for example, temporal importance and frequency, but are not limited to these examples. For example, the recording unit prioritizes recording of recent inquiries. The recording unit can also prioritize recording of inquiries submitted within a specific time period. For example, the recording unit adjusts the weighting of the records based on past inquiry data. This allows the recording unit to weight the records based on the time of submission of the inquiry, thereby enabling more appropriate recording. Furthermore, the recording unit can include AI processing. For example, the recording unit can use AI to analyze the time of submission of the inquiry and perform optimal weighting. This allows the recording unit to weight the records based on the time of submission of the inquiry, thereby enabling more appropriate recording.

[0103] The function for inputting data into a knowledge-based database can estimate a user's emotion and adjust the input method to the database based on the estimated user emotion. The function for inputting data into a knowledge-based database can estimate a user's emotion and adjust the input method to the database based on the estimated user emotion. Input methods include, but are not limited to, manual input and automatic input. The function for inputting data into a knowledge-based database can provide a simple input method when the user is stressed. The function for inputting data into a knowledge-based database can also provide a detailed input method when the user is relaxed. For example, the function for inputting data into a knowledge-based database can provide a method that allows quick input when the user is in a hurry. This allows the function for inputting data into a knowledge-based database to adjust the input method to the database according to the user's emotion, enabling more appropriate input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the function of inputting data into a knowledge base database may be performed using, for example, AI, or may be performed without using AI. For example, the function of inputting data into a knowledge base database may input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0104] The function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data when inputting data into the database. The function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data when inputting data into the database. Types of input algorithms include, but are not limited to, machine learning algorithms and statistical analysis. The function of inputting data into a knowledge-based database can, for example, select an optimal input algorithm based on past inquiry data. The function of inputting data into a knowledge-based database can also analyze past inquiry data to improve the accuracy of the input algorithm. For example, the function of inputting data into a knowledge-based database can adjust parameters of the input algorithm by referring to past inquiry data. In this way, the function of inputting data into a knowledge-based database can optimize the input algorithm by referring to past inquiry data. Furthermore, the function of inputting data into a knowledge-based database can include AI processing. For example, the function of inputting data into a knowledge-based database can analyze past inquiry data using AI to select an optimal input algorithm. This allows the function of inputting data into a knowledge base database to optimize the input algorithm by referencing past inquiry data.

[0105] The function for inputting data into a knowledge-based database can estimate a user's emotions and determine the priority of input to the database based on the estimated user emotions. The function for inputting data into a knowledge-based database can estimate a user's emotions and determine the priority of input to the database based on the estimated user emotions. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, the function for inputting data into a knowledge-based database can prioritize input to the database when the user has an urgent inquiry. The function for inputting data into a knowledge-based database can also input data into the database at normal priority when the user is relaxed. For example, the function for inputting data into a knowledge-based database can increase the priority of input to the database when the user is stressed in order to respond quickly. This allows the function for inputting data into a knowledge-based database to determine the priority of input to the database based on the user's emotions, enabling more appropriate input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the function of inputting data into a knowledge-based database may be performed using AI, or may be performed without using AI. For example, the function of inputting data into a knowledge-based database may input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0106] The function of inputting data into a knowledge-based database can weight the input based on the time of data submission when inputting the data into the database. The function of inputting data into a knowledge-based database can weight the input based on the time of data submission when inputting the data into the database. Weighting criteria include, but are not limited to, temporal importance and frequency, for example. The function of inputting data into a knowledge-based database can, for example, prioritize recent data input into the database. The function of inputting data into a knowledge-based database can also prioritize data submitted within a specific time period when inputting the data into the database. For example, the function of inputting data into a knowledge-based database can adjust the weighting of the input to the database based on past data. This allows the function of inputting data into a knowledge-based database to weight the input based on the time of data submission, thereby enabling more appropriate input. Furthermore, the function of inputting data into a knowledge-based database can include AI processing. For example, the function of inputting data into a knowledge-based database can use AI to analyze the time of data submission and perform optimal weighting. This allows the function of inputting data into a knowledge-based database to weight the input based on the time of data submission, making it possible to input data more appropriately.

[0107] The function for analyzing past inquiries and responses can estimate a user's emotions and adjust the analysis method based on the estimated user emotions. The function for analyzing past inquiries and responses can estimate a user's emotions and adjust the analysis method based on the estimated user emotions. Analysis methods include, but are not limited to, text mining and sentiment analysis. For example, when a user is stressed, the function for analyzing past inquiries and responses can quickly analyze and provide a concise answer. Furthermore, when a user is relaxed, the function for analyzing past inquiries and responses can perform a detailed analysis and provide a comprehensive answer. For example, when a user is in a hurry, the function for analyzing past inquiries and responses can prioritize the most important information. This allows the function for analyzing past inquiries and responses to adjust the analysis method according to the user's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the function of analyzing past inquiry contents and responses may be performed using, for example, AI, or may be performed without using AI. For example, the function of analyzing past inquiry contents and responses may input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0108] The function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data during analysis. The function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data during analysis. Types of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis. The function that analyzes past inquiry contents and responses can, for example, select an optimal analysis algorithm based on the past inquiry data. The function that analyzes past inquiry contents and responses can also analyze the past inquiry data to improve the accuracy of the analysis algorithm. For example, the function that analyzes past inquiry contents and responses can adjust parameters of the analysis algorithm by referring to the past inquiry data. In this way, the function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data. Furthermore, the function that analyzes past inquiry contents and responses can include AI processing. For example, the function that analyzes past inquiry contents and responses can use AI to analyze the past inquiry data and select an optimal analysis algorithm. In this way, the function that analyzes past inquiry contents and responses can optimize the analysis algorithm by referring to the past inquiry data.

[0109] The function for analyzing past inquiries and responses can estimate a user's emotions and determine the analysis priority based on the estimated user emotions. The function for analyzing past inquiries and responses can estimate a user's emotions and determine the analysis priority based on the estimated user emotions. Criteria for determining the priority include, but are not limited to, urgency and importance. For example, the function for analyzing past inquiries and responses can prioritize analysis when the user has made an urgent inquiry. The function for analyzing past inquiries and responses can also perform analysis at normal priority when the user is relaxed. For example, the function for analyzing past inquiries and responses can increase the analysis priority when the user is stressed in order to respond quickly. This allows the function for analyzing past inquiries and responses to determine the analysis priority based on the user's emotions, enabling more appropriate analysis. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the function of analyzing past inquiry contents and responses may be performed using, for example, AI, or may be performed without using AI. For example, the function of analyzing past inquiry contents and responses may input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0110] The function that analyzes past inquiry content and responses can weight the analysis based on the time of submission of the inquiry during analysis. The function that analyzes past inquiry content and responses can weight the analysis based on the time of submission of the inquiry during analysis. Weighting criteria include, but are not limited to, temporal importance and frequency. The function that analyzes past inquiry content and responses can, for example, prioritize the analysis of recent inquiries. The function that analyzes past inquiry content and responses can also prioritize the analysis of inquiries submitted within a specific time period. For example, the function that analyzes past inquiry content and responses can adjust the weighting of the analysis based on past inquiry data. This allows the function that analyzes past inquiry content and responses to weight the analysis based on the time of submission of the inquiry, enabling more appropriate analysis. Furthermore, the function that analyzes past inquiry content and responses can include AI processing. For example, the function that analyzes past inquiry content and responses can use AI to analyze the time of submission of the inquiry and perform optimal weighting. This allows the function that analyzes past inquiries and responses to perform more accurate analysis by weighting the analysis based on the time the inquiry was submitted.

[0111] The function for suggesting related products and services can estimate a user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. The function for suggesting related products and services can estimate a user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. Examples of ways to present the suggestions include, but are not limited to, text, images, and videos. For example, when a user is stressed, the function for suggesting related products and services can provide simple and easy-to-understand suggestions. Furthermore, when a user is relaxed, the function for suggesting related products and services can provide detailed suggestions. For example, when a user is in a hurry, the function for suggesting related products and services can provide suggestions that focus on the main points. This allows the function for suggesting related products and services to adjust the way the suggestions are presented based on the user's emotions, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the function of suggesting related products and services may be performed using, or without, AI. For example, the function of suggesting related products and services may input user emotion data into a generation AI and have the generation AI estimate the emotion.

[0112] The function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry when making a proposal. The function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry when making a proposal. Examples of the level of detail of the proposal include, but are not limited to, a brief explanation or a detailed manual. For example, the function for suggesting related products and services can provide detailed suggestions for important inquiries. Furthermore, the function for suggesting related products and services can provide simple suggestions for general inquiries. For example, the function for suggesting related products and services can provide suggestions that focus on the main points for urgent inquiries in order to respond quickly. In this way, the function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry, thereby enabling more appropriate suggestions. Furthermore, the function for suggesting related products and services can include AI processing. For example, the function for suggesting related products and services can use AI to analyze the importance of the inquiry and select the optimal level of detail of the proposal. In this way, the function for suggesting related products and services can adjust the level of detail of the proposal based on the importance of the inquiry, thereby enabling more appropriate suggestions.

[0113] The function for suggesting related products and services can estimate a user's emotions and adjust the length of the suggestions based on the estimated user emotions. The function for suggesting related products and services can estimate a user's emotions and adjust the length of the suggestions based on the estimated user emotions. The length of the suggestions can include, but is not limited to, short sentences or long sentences. For example, when a user is in a hurry, the function for suggesting related products and services can provide short, to-the-point suggestions. Furthermore, when a user is relaxed, the function for suggesting related products and services can provide longer suggestions with detailed explanations. For example, when a user is excited, the function for suggesting related products and services can provide suggestions with visually stimulating effects. This allows the function for suggesting related products and services to adjust the length of the suggestions based on the user's emotions, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the function of suggesting related products and services may be performed using, or without, AI. For example, the function of suggesting related products and services may input user emotion data into a generation AI and have the generation AI estimate the emotion.

[0114] The function for suggesting related products and services can prioritize the suggestions based on the time when the inquiry was submitted when making the suggestions. The function for suggesting related products and services can prioritize the suggestions based on the time when the inquiry was submitted when making the suggestions. Criteria for determining the priority include, but are not limited to, urgency and importance, for example. The function for suggesting related products and services can prioritize suggestions for recent inquiries, for example. The function for suggesting related products and services can also prioritize suggestions for inquiries submitted within a specific time period. For example, the function for suggesting related products and services can adjust the priority of suggestions based on past inquiry data. This allows the function for suggesting related products and services to prioritize the suggestions based on the time when the inquiry was submitted, thereby enabling more appropriate suggestions. Furthermore, the function for suggesting related products and services can include AI processing. For example, the function for suggesting related products and services can use AI to analyze the time when the inquiry was submitted and determine the optimal priority. This allows the function for suggesting related products and services to prioritize the suggestions based on the time when the inquiry was submitted, thereby enabling more appropriate suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, calling unit, and recording unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts text-based questions or voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing or data mining technology. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products and services based on the analysis results. The calling unit is realized by the specific processing unit 290 of the data processing device 12 and calls data from the database 24. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the contents of past inquiries and stores them in the database 24. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, calling unit, and recording unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing and data mining techniques. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products and services based on the analysis results. The calling unit is realized by the specific processing unit 290 of the data processing device 12 and calls data from the database 24. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the contents of past inquiries and stores them in the database 24. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, calling unit, and recording unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing and data mining techniques. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products and services based on the analysis results. The calling unit is realized by the specific processing unit 290 of the data processing device 12 and calls data from the database 24. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the contents of past inquiries and stores them in the database 24. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, calling unit, and recording unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing and data mining techniques. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products and services based on the analysis results. The calling unit is realized by the specific processing unit 290 of the data processing device 12 and calls data from the database 24. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the contents of past inquiries and stores them in the database 24.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The reception unit can refer to the user's past inquiry history and automatically route the inquiry to the most appropriate person depending on the content of the inquiry. For example, if a user has previously made an inquiry about a specific product, the inquiry can be routed to a person who is knowledgeable about that product. Also, if a user has made inquiries about the same problem multiple times in the past, the inquiry can be routed to a person who is familiar with solutions to that problem. Furthermore, if a user tends to make inquiries during a specific time period, the inquiry can be routed to a person who is available during that time period. This allows the reception unit to utilize the user's past inquiry history to provide faster and more appropriate responses.

[0117] When retrieving data from a database, the calling unit can evaluate the reliability of the data and preferentially call highly reliable data. For example, it can assign a reliability score to data in the database and preferentially call data with a high score. It can also evaluate reliability based on the frequency of data updates and the reliability of the source. Furthermore, it can evaluate the reliability of data based on user feedback and preferentially call highly reliable data. This allows the calling unit to quickly provide highly reliable data.

[0118] When recording the inquiry content, the recording unit can automatically detect input errors made by the user and make suggestions for correction. For example, if the user enters an incorrect product name, the recording unit can suggest the correct product name. Also, if the user enters incomplete information, the recording unit can suggest ways to complete the necessary information. Furthermore, if the user uses an ambiguous expression, the recording unit can suggest ways to convert it into a more specific expression. This allows the recording unit to record accurate and detailed inquiry content.

[0119] The reception unit can estimate the user's emotions and dynamically adjust the priority of the inquiry content based on the estimated user emotions. For example, if the user is expressing strong dissatisfaction, the inquiry can be processed with the highest priority. On the other hand, if the user is relaxed, the inquiry can be processed with normal priority. Furthermore, if the user requires an urgent response, the priority can be increased to respond quickly. This allows the reception unit to adjust the priority of the inquiry content according to the user's emotions, enabling more appropriate responses.

[0120] When analyzing the content of an inquiry, the analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can perform a quick analysis and provide a concise answer. If the user is relaxed, the analysis unit can perform a detailed analysis and provide a comprehensive answer. Furthermore, if the user is in a hurry, the analysis unit can prioritize the most important information. This allows the analysis unit to adjust the depth of the analysis according to the user's emotions, enabling more appropriate analysis.

[0121] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand suggestion can be made. If the user is relaxed, a detailed suggestion can be made. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be made. In this way, the suggestion unit can adjust the way suggestions are expressed according to the user's emotions, thereby enabling more appropriate suggestions.

[0122] The reception unit can estimate the user's emotions and customize the inquiry reception method based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter the inquiry content. This allows the reception unit to customize the inquiry reception method according to the user's emotions, enabling more appropriate responses.

[0123] When analyzing the inquiry content, the analysis unit can improve the accuracy of the analysis by referring to related external data. For example, the analysis can be performed by referring to an external product database. The analysis can also be performed by referring to an external service database. Furthermore, the analysis can be performed by referring to an external technology database. In this way, the analysis unit can improve the accuracy of the analysis by referring to related external data.

[0124] When making a suggestion, the suggestion unit can refer to the user's past purchase history to suggest related products and services. For example, the suggestion unit can suggest accessories related to products the user has previously purchased. The suggestion unit can also suggest new services related to services the user has previously used. Furthermore, the suggestion unit can make customized suggestions based on the user's purchase history. This allows the suggestion unit to suggest more appropriate products and services by referring to the user's past purchase history.

[0125] When retrieving data from a database, the invoking unit can apply different invoking methods depending on the category of the data. For example, in the case of data related to a product, the data can be retrieved by referencing a product database. In addition, in the case of data related to a service, the data can be retrieved by referencing a service database. Furthermore, in the case of technical data, the data can be retrieved by referencing a technical database. This allows the invoking unit to apply different invoking methods depending on the category of data, thereby enabling more appropriate data to be retrieved.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The reception unit receives an inquiry. The inquiry may include, for example, a text-based question or a voice input. The reception unit receives the text-based question and records it in detail. It can also receive voice input and convert it into text data using voice recognition technology. For example, the voice can be analyzed using voice recognition software and saved as text. Step 2: The analysis unit analyzes the inquiry received by the reception unit. For example, techniques such as natural language processing and data mining are used for the analysis. The analysis unit uses natural language processing technology to analyze the inquiry and extract important keywords. It can also use data mining technology to analyze past inquiry data and find patterns. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions include, for example, product recommendations and service suggestions. The suggestion unit can also recommend related products and suggest related services based on the analysis results.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0130] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] 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.

[0191] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0193] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0197] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving inquiries; an analysis unit that analyzes the inquiry content received by the reception unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. Provides a calling section that calls data from the database 2. The system of claim 1.

3. Equipped with a recording unit that records past inquiries 2. The system of claim 1.

4. The reception unit Record the specific details of the inquiry and enter them into a knowledge base database 2. The system of claim 1.

5. The analysis unit Analyze past inquiries and responses to find the most appropriate answer 2. The system of claim 1.

6. The proposal unit To suggest relevant products and services based on your inquiry 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the way inquiries are received based on the estimated user emotions 2. The system of claim 1.

8. The reception unit When accepting a call, the system selects the most appropriate method of acceptance by referring to the user's past inquiry history.

2. The system of claim 1.

9. The reception unit At the time of check-in, filtering is performed based on the user's current situation and interests.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inquiries based on the estimated user emotions.

2. The system of claim 1.

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