System

Abstract

To provide a system for collecting relevant information based on user-entered free-text and delivering it to an operator.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a hearing unit, and a provision unit. The reception unit receives free-text entered from a user. The analysis unit analyzes the content of the free-text received by the reception unit and identifies a service or category. The hearing unit identifies missing information based on the content analyzed by the analysis unit and performs a hearing with the user. The provision unit delivers the information obtained by the hearing unit to an operator.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques are prone to insufficient information or inappropriate classification based on free text input from users, leaving room for improvement.

[0005] The system according to the embodiment aims to collect appropriate information based on free text input from the user and provide it to the operator. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a hearing unit, and a provision unit. The reception unit receives free text input from a user. The analysis unit analyzes the content of the free text received by the reception unit and identifies a service or category. The hearing unit identifies missing information based on the content analyzed by the analysis unit and hears from the user. The provision unit provides the information obtained by the hearing unit to an operator. Effect of the Invention

[0007] An embodiment of the system can collect and provide pertinent information to an operator based on free text input from the user. [Brief description 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. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a 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 (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information analysis system according to the embodiment of the present invention is a system that automatically analyzes the contents of free text entered by the user, identifies the corresponding service or category, and analyzes the missing information. In this system, the user enters the contents of the incoming message in free text, and the AI ​​analyzes the contents to identify the corresponding service or category, and further identifies the missing information. If necessary, the AI ​​will conduct a hearing of the user about the missing information, and the information will be delivered to the operator in a complete state, aiming to promote self-resolution and improve CX (customer experience) by improving EX (experience) and shortening ART (average response time). For example, if a user enters content such as "I can't connect to the Internet," this information is entered into the AI, which analyzes it and classifies it into the category of Internet connection services. Next, the AI ​​identifies missing information such as the status of the connected device and the presence or absence of error messages, and asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the AI, and finally the information is delivered to the operator in a complete state. This allows the operator to respond quickly, promoting self-resolution and improving CX by improving EX and shortening ART. This enables the information analysis system to analyze the user's free text input, identify appropriate services and categories, and complete missing information, enabling operators to respond more efficiently.

[0029] The information analysis system according to the embodiment includes a reception unit, an analysis unit, a hearing unit, and a provision unit. The reception unit receives free text input from a user. The free text input from a user includes, for example, sentences, words, phrases, etc., but is not limited to such examples. The reception unit, for example, receives the free text input by the user and transmits it to the analysis unit. The analysis unit uses AI to analyze the content of the free text received by the reception unit and identifies the corresponding service or category. The analysis unit analyzes the content of the free text using, for example, natural language processing technology or machine learning algorithm. The analysis unit, for example, tokenizes the content of the free text and analyzes the meaning of each token. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit classifies the content of the free text into a category of Internet connection services. The hearing unit identifies missing information based on the content analyzed by the analysis unit, and hears from the user as necessary. The hearing unit asks the user specific questions based on the missing information identified by the analysis unit, for example. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The providing unit provides the information obtained by the hearing unit to the operator. The providing unit includes, for example, an interface for providing information to the operator. The providing unit provides, for example, text information, image information, links, and the like to the operator. In this way, the information analysis system according to the embodiment analyzes the free text input by the user, identifies appropriate services and categories, and complements missing information, thereby enabling the operator to respond efficiently.

[0030] The reception unit receives free text input from the user. The free text input from the user includes, but is not limited to, sentences, words, phrases, etc. The reception unit, for example, receives the free text input by the user and transmits it to the analysis unit. Specifically, the reception unit receives input from the user through an interface such as a web form or a chatbot. This allows the user to input problems or questions in natural language. The reception unit has a communication protocol for transmitting the input text to the analysis unit in real time, and can minimize data delays and losses. Furthermore, the reception unit can temporarily store the user's input content and retransmit or reanalyze it as necessary. For example, even if the user interrupts input midway or a communication error occurs, the reception unit can hold the input content and transmit it again. This can improve the user's convenience and increase the reliability of the system.

[0031] The analysis unit uses AI to analyze the content of the free text accepted by the acceptance unit and identifies the corresponding service or category. The analysis unit analyzes the content of the free text using, for example, natural language processing technology or machine learning algorithm. Specifically, the analysis unit tokenizes the content of the free text and analyzes the meaning of each token. Tokenization is a process of dividing a sentence into words and phrases, which makes it easier to understand the structure of the text. The analysis unit analyzes the meaning of each token based on the tokenized text and identifies the meaning according to the context. For example, the analysis unit analyzes the frequency of occurrence and co-occurrence of words to identify the subject and intention of the text. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit uses a model that has been trained in advance to classify the content of the free text into the category of Internet connection services. The model is trained based on a large amount of text data and can classify text with high accuracy. Furthermore, the analysis unit can continuously learn the content input by the user and improve the analysis accuracy. This allows the analysis unit to quickly identify appropriate services and categories in response to various input contents of the user.

[0032] The hearing unit identifies the missing information based on the contents analyzed by the analysis unit, and hears the user as necessary. The hearing unit, for example, asks a specific question to the user based on the missing information identified by the analysis unit. Specifically, the hearing unit analyzes the contents of the user's input, and automatically generates an additional question when the necessary information is missing. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The hearing unit analyzes the contents of the user's answer and checks whether all the necessary information is available. If there is missing information, the question is generated again and asked to the user. This allows the hearing unit to efficiently collect necessary information from the user and provide it to the analysis unit and the providing unit. Furthermore, the hearing unit can learn the contents of the user's answer and reflect it in the next and subsequent question generation. This allows the hearing unit to flexibly respond to the needs of the user, and improve the efficiency and accuracy of the entire system.

[0033] The providing unit provides the information obtained by the hearing unit to the operator. The providing unit includes, for example, an interface for providing information to the operator. Specifically, the providing unit organizes and visually displays the information collected from the user so that the operator can quickly and accurately grasp the information. For example, the providing unit provides text information, image information, links, and the like to the operator. The text information includes the contents of the user's input and additional information collected by the hearing unit. The image information displays photos and screenshots provided by the user. The links provide access to related web pages and manuals. This allows the operator to grasp at a glance the information needed to quickly respond to the user's problems and questions. Furthermore, the providing unit can collect feedback from the operator and use it to improve the system. For example, the operator can make comments and evaluations on the provided information, which allows the providing unit to continuously improve the accuracy and usefulness of the information. This allows the providing unit to support the operator's efficient response and increase the user's satisfaction.

[0034] The analysis unit can analyze the content of the free text and identify the corresponding service or category. The analysis unit can analyze the content of the free text using, for example, natural language processing technology. The natural language processing technology includes, for example, technologies such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can, for example, tokenize the content of the free text and analyze the meaning of each token. For example, the analysis unit can divide the content of the free text into words and analyze the meaning of each word. The analysis unit can also classify the content of the free text and identify the corresponding service or category. For example, the analysis unit can classify the content of the free text into categories of Internet connection services. As a result, the analysis unit can analyze the content of the free text and identify the appropriate service or category. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the content of the free text to AI, and the AI ​​can output the analysis result.

[0035] The hearing unit can identify missing information based on the analyzed content and ask a question to the user. The hearing unit, for example, asks a specific question to the user based on the missing information identified by the analysis unit. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The hearing unit, for example, reanalyzes the answer from the user and complements the necessary information. For example, the hearing unit analyzes the content of the answer given by the user and identifies the missing information. As a result, the hearing unit can identify the missing information and complement the necessary information by asking a specific question to the user. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the hearing unit can input the user's answer to an AI, and the AI ​​can identify the missing information.

[0036] The providing unit can provide information to the operator. The providing unit includes, for example, an interface for providing information to the operator. The providing unit provides, for example, text information, image information, links, and the like to the operator. For example, the providing unit provides the user's free text input contents, analysis results, missing information, hearing results, and the like to the operator. In this way, the providing unit enables a quick response by providing information to the operator. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs information to be provided to the operator to AI, and the AI ​​can organize and output the information.

[0037] The analysis unit can perform the analysis using an algorithm. The analysis unit performs the analysis using, for example, a natural language processing algorithm or a machine learning algorithm. The natural language processing algorithm includes, for example, techniques such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The machine learning algorithm includes, for example, techniques such as a classification algorithm, a clustering algorithm, and a regression algorithm. For example, the analysis unit tokenizes the content of the free text and analyzes the meaning of each token. For example, the analysis unit divides the content of the free text into words and analyzes the meaning of each word. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit classifies the content of the free text into a category of Internet connection services. As a result, the analysis unit uses a specific algorithm to improve the accuracy of the analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the content of the free text to AI, which can output the analysis result.

[0038] The hearing unit can reanalyze the answer from the user and complement the information. The hearing unit, for example, reanalyzes the answer from the user and complements the necessary information. For example, the hearing unit analyzes the content of the answer given by the user and identifies the missing information. The hearing unit analyzes the answer from the user using, for example, natural language processing technology or machine learning algorithm. The natural language processing technology includes, for example, technologies such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The machine learning algorithm includes, for example, technologies such as classification algorithm, clustering algorithm, and regression algorithm. Thereby, the hearing unit can complement the necessary information by reanalyzing the answer from the user. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input the answer from the user to AI, and the AI ​​can identify the missing information.

[0039] The reception unit can analyze the user's past incoming call history and suggest the optimal input method. The reception unit, for example, analyzes the user's past incoming call history and suggests the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past incoming call history. Furthermore, the reception unit can also suggest a similar input method by referring to the contents entered by the user in the past. In this way, the reception unit can suggest the optimal input method by analyzing the user's past incoming call history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past incoming call history data into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception unit can guide the input contents based on the user's current situation and field of interest when inputting free text. For example, the reception unit guides the input contents based on the user's current situation and field of interest when inputting free text. For example, when the user inputs the current situation, the reception unit presents related questions to guide the input. The reception unit can also suggest appropriate input contents based on the user's field of interest. Furthermore, when the user inputs about a specific topic, the reception unit can also provide related information to support the input. Thereby, the reception unit promotes appropriate input by guiding the input contents based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data and field of interest data to AI, and the AI ​​can guide the input contents.

[0041] The reception unit can preferentially accept input contents based on the geographical location information of the user when inputting free text. For example, when inputting free text, the reception unit preferentially accepts input contents with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit allows the user to preferentially input information related to the area. In addition, when the user is moving, the reception unit can also allow the user to input related information based on the current location. Furthermore, when the user is in a specific place, the reception unit can also allow the user to preferentially input information related to the place. Thereby, the reception unit can preferentially accept input contents with high relevance by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to AI, and the AI ​​can preferentially accept input contents with high relevance.

[0042] The reception unit can analyze the social media activity of the user when inputting free text and accept related input contents. The reception unit can analyze the social media activity of the user when inputting free text, for example, and accept related input contents. The reception unit can suggest related input contents based on information shared by the user on social media, for example. The reception unit can also identify topics of interest from the user's social media activity and guide the input contents. Furthermore, the reception unit can cause the user to input related information based on the content mentioned by the user on social media. In this way, the reception unit can accept related input contents by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can accept related input contents.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the free text during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the free text during analysis. The analysis unit, for example, performs a detailed analysis on free text with high importance. The analysis unit can also perform a simplified analysis on free text with low importance. Furthermore, the analysis unit can set an appropriate level of detail of the analysis according to the content of the free text. Thereby, the analysis unit can provide an appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the free text to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the free text during analysis. The analysis unit applies different analysis algorithms depending on the category of the free text during analysis, for example. The analysis unit applies a specialized analysis algorithm to free text of technical content, for example. The analysis unit can also apply a general-purpose analysis algorithm to free text of general content. Furthermore, the analysis unit can select an optimal analysis algorithm for free text belonging to a specific category. As a result, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the category of the free text. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the free text to AI, and the AI ​​can select and apply an optimal analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission time of the free text during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the free text during the analysis. The analysis unit, for example, preferentially analyzes free text submitted earlier. The analysis unit can also postpone free text submitted later. Furthermore, the analysis unit can set an appropriate analysis priority according to the submission time. Thereby, the analysis unit can perform analysis in an appropriate order by determining the priority of the analysis based on the submission time of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the free text to AI, and the AI ​​can determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the free text during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the free text during analysis. The analysis unit, for example, prioritizes analysis of highly relevant free text. The analysis unit can also postpone analysis of less relevant free text. Furthermore, the analysis unit can set an appropriate analysis order according to the content of the free text. As a result, the analysis unit can prioritize analysis of highly relevant content by adjusting the analysis order based on the relevance of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the free text to AI, and the AI ​​can adjust the order of analysis.

[0047] The hearing unit can select the optimal question by analyzing the user's past answer history during the hearing. For example, the hearing unit selects the optimal question by analyzing the user's past answer history during the hearing. The hearing unit selects a related question based on the content of the user's past answers, for example. The hearing unit can also predict and select the optimal question from the user's past answer history. Furthermore, the hearing unit can also select a detailed question by referring to the content of the user's past answers. Thereby, the hearing unit can select the optimal question by analyzing the user's past answer history. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit inputs the user's past answer history data into AI, and the AI ​​can select the optimal question.

[0048] The hearing unit can customize the question content based on the user's current situation at the time of hearing. The hearing unit, for example, customizes the question content based on the user's current situation at the time of hearing. The hearing unit customizes related questions, for example, when the user inputs the current situation. The hearing unit can also select appropriate questions based on the user's current situation. Furthermore, when the user is in a specific situation, the hearing unit can also customize questions according to the situation. Thereby, the hearing unit can ask appropriate questions by customizing the question content based on the user's current situation. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit can input the user's current situation data to AI, and the AI ​​can customize the question content.

[0049] The hearing unit can select the optimal question in consideration of the geographical location information of the user at the time of hearing. For example, the hearing unit selects the optimal question in consideration of the geographical location information of the user at the time of hearing. For example, when the user is in a specific area, the hearing unit selects a question related to the area. In addition, when the user is moving, the hearing unit can also select an appropriate question based on the current location. Furthermore, when the user is in a specific place, the hearing unit can also select a question related to the place. Thereby, the hearing unit can select the optimal question by considering the geographical location information of the user. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit inputs the geographical location information data of the user to AI, and the AI ​​can select the optimal question.

[0050] The hearing unit can analyze the user's social media activity at the time of hearing and suggest the question content. The hearing unit, for example, analyzes the user's social media activity at the time of hearing and suggests the question content. The hearing unit can, for example, suggest related questions based on information shared by the user on social media. The hearing unit can also identify topics of interest from the user's social media activity and suggest the question content. Furthermore, the hearing unit can also suggest related questions based on the content mentioned by the user on social media. In this way, the hearing unit can suggest related question content by analyzing the user's social media activity. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit can input the user's social media activity data into AI, and the AI ​​can suggest the question content.

[0051] The providing unit can provide optimal information by referring to the user's past incoming history when providing information. For example, the providing unit can provide optimal information by referring to the user's past incoming history when providing information. The providing unit can provide, for example, information that the user has used frequently in the past with priority. The providing unit can also provide related information from the user's past incoming history. Furthermore, the providing unit can also provide optimal information based on the content that the user has input in the past. Thereby, the providing unit can provide optimal information by referring to the user's past incoming history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's past incoming history data into AI, and the AI ​​can provide optimal information.

[0052] The providing unit can customize the information content based on the user's current situation when providing information. For example, the providing unit customizes the information content based on the user's current situation when providing information. For example, when the user inputs the current situation, the providing unit customizes and provides related information. The providing unit can also provide appropriate information based on the user's current situation. Furthermore, when the user is in a specific situation, the providing unit can also customize and provide information according to the situation. Thereby, the providing unit can provide appropriate information by customizing the information content based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to AI, and the AI ​​can customize the information content.

[0053] The providing unit can provide optimal information in consideration of the geographical location information of the user when providing information. The providing unit, for example, provides optimal information in consideration of the geographical location information of the user when providing information. For example, when the user is in a specific area, the providing unit provides information related to the area. In addition, when the user is moving, the providing unit can also provide appropriate information based on the current location. Furthermore, when the user is in a specific place, the providing unit can also provide information related to the place. Thereby, the providing unit can provide optimal information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to AI, and the AI ​​can provide optimal information.

[0054] The providing unit can analyze the user's social media activity and suggest information content when providing information. For example, the providing unit analyzes the user's social media activity and suggests information content when providing information. The providing unit provides related information based on information shared by the user on social media, for example. The providing unit can also identify topics of interest from the user's social media activity and suggest information content. Furthermore, the providing unit can provide related information based on content mentioned by the user on social media. Thereby, the providing unit can suggest related information content by analyzing the user's social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can suggest information content.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When accepting input contents from a user, the reception unit can predict the input contents by referring to the user's past input history. For example, the effort required for input can be reduced by preferentially presenting phrases and words that the user has used frequently in the past. The reception unit can also automatically generate related questions from the user's past input contents and present them to the user. Furthermore, the reception unit can provide an automatic completion function for input contents based on the user's past input history. In this way, the reception unit can improve input efficiency by utilizing the user's past input history.

[0057] When analyzing the content of the free text, the analysis unit can perform the analysis while taking into account the context of the user's input content. For example, the analysis unit can analyze the context before and after the free text to identify a more accurate meaning. The analysis unit can also complement related information based on the context of the user's input content. Furthermore, the analysis unit can also identify an appropriate category or service based on the context of the user's input content. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the context of the user's input content.

[0058] When asking a question to the user, the hearing unit can analyze the content of the user's answer in real time and dynamically generate the next question. For example, the next required information is identified based on the content of the user's answer, and an appropriate question is generated. The hearing unit can also adjust the order of questions based on the content of the user's answer. Furthermore, by analyzing the content of the user's answer in real time, the hearing unit can omit unnecessary questions and realize efficient hearing. In this way, the hearing unit can efficiently collect information by analyzing the content of the user's answer in real time.

[0059] The providing unit can adjust the display order based on the importance of the information when providing information to the operator. For example, the providing unit can preferentially display information with a high degree of importance, allowing the operator to respond quickly. The providing unit can also adjust the display format based on the importance of the information. Furthermore, the providing unit can provide a summary of the information in order to reduce the operator's workload. In this way, the providing unit can support the operator in responding efficiently by adjusting the display order based on the importance of the information.

[0060] The analysis unit can evaluate the reliability of the user's input content when analyzing the content of the free text. For example, the analysis unit evaluates the consistency and accuracy of the user's input content, and prioritizes the analysis of highly reliable information. The analysis unit can also adjust the reliability of the analysis result based on the reliability of the user's input content. Furthermore, by evaluating the reliability of the user's input content, the analysis unit can eliminate inaccurate information and improve the accuracy of the analysis. In this way, the analysis unit can provide a highly reliable analysis result by evaluating the reliability of the user's input content.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives free text input from a user. The free text input from a user may include sentences, words, phrases, etc. The reception unit receives the free text input by the user and transmits it to the analysis unit. Step 2: The analysis unit uses AI to analyze the content of the free text received by the reception unit and identify the relevant services and categories. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the content of the free text, and performs tokenization and semantic analysis. It also classifies the content of the free text and identifies the relevant services and categories. Step 3: The hearing unit identifies missing information based on the content analyzed by the analysis unit, and hears from the user as necessary. The hearing unit asks the user specific questions based on the missing information identified by the analysis unit. For example, the hearing unit may ask the user a question such as "Please tell me the status of the connected devices." The information answered by the user is analyzed again by the hearing unit. Step 4: The providing unit provides the information obtained by the hearing unit to the operator. The providing unit has an interface for providing information to the operator, and provides text information, image information, links, and the like.

[0063] (Example 2) The information analysis system according to the embodiment of the present invention is a system that automatically analyzes the contents of free text entered by the user, identifies the corresponding service or category, and analyzes the missing information. In this system, the user enters the contents of the incoming message in free text, and the AI ​​analyzes the contents to identify the corresponding service or category, and further identifies the missing information. If necessary, the AI ​​will conduct a hearing of the user about the missing information, and the information will be delivered to the operator in a complete state, aiming to promote self-resolution and improve CX (customer experience) by improving EX (experience) and shortening ART (average response time). For example, if a user enters content such as "I can't connect to the Internet," this information is entered into the AI, which analyzes it and classifies it into the category of Internet connection services. Next, the AI ​​identifies missing information such as the status of the connected device and the presence or absence of error messages, and asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the AI, and finally the information is delivered to the operator in a complete state. This allows the operator to respond quickly, promoting self-resolution and improving CX by improving EX and shortening ART. This enables the information analysis system to analyze the user's free text input, identify appropriate services and categories, and complete missing information, enabling operators to respond more efficiently.

[0064] The information analysis system according to the embodiment includes a reception unit, an analysis unit, a hearing unit, and a provision unit. The reception unit receives free text input from a user. The free text input from a user includes, for example, sentences, words, phrases, etc., but is not limited to such examples. The reception unit, for example, receives the free text input by the user and transmits it to the analysis unit. The analysis unit uses AI to analyze the content of the free text received by the reception unit and identifies the corresponding service or category. The analysis unit analyzes the content of the free text using, for example, natural language processing technology or machine learning algorithm. The analysis unit, for example, tokenizes the content of the free text and analyzes the meaning of each token. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit classifies the content of the free text into a category of Internet connection services. The hearing unit identifies missing information based on the content analyzed by the analysis unit, and hears from the user as necessary. The hearing unit asks the user specific questions based on the missing information identified by the analysis unit, for example. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The providing unit provides the information obtained by the hearing unit to the operator. The providing unit includes, for example, an interface for providing information to the operator. The providing unit provides, for example, text information, image information, links, and the like to the operator. In this way, the information analysis system according to the embodiment analyzes the free text input by the user, identifies appropriate services and categories, and complements missing information, thereby enabling the operator to respond efficiently.

[0065] The reception unit receives free text input from the user. The free text input from the user includes, but is not limited to, sentences, words, phrases, etc. The reception unit, for example, receives the free text input by the user and transmits it to the analysis unit. Specifically, the reception unit receives input from the user through an interface such as a web form or a chatbot. This allows the user to input problems or questions in natural language. The reception unit has a communication protocol for transmitting the input text to the analysis unit in real time, and can minimize data delays and losses. Furthermore, the reception unit can temporarily store the user's input content and retransmit or reanalyze it as necessary. For example, even if the user interrupts input midway or a communication error occurs, the reception unit can hold the input content and transmit it again. This can improve the user's convenience and increase the reliability of the system.

[0066] The analysis unit uses AI to analyze the content of the free text accepted by the acceptance unit and identifies the corresponding service or category. The analysis unit analyzes the content of the free text using, for example, natural language processing technology or machine learning algorithm. Specifically, the analysis unit tokenizes the content of the free text and analyzes the meaning of each token. Tokenization is a process of dividing a sentence into words and phrases, which makes it easier to understand the structure of the text. The analysis unit analyzes the meaning of each token based on the tokenized text and identifies the meaning according to the context. For example, the analysis unit analyzes the frequency of occurrence and co-occurrence of words to identify the subject and intention of the text. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit uses a model that has been trained in advance to classify the content of the free text into the category of Internet connection services. The model is trained based on a large amount of text data and can classify text with high accuracy. Furthermore, the analysis unit can continuously learn the content input by the user and improve the analysis accuracy. This allows the analysis unit to quickly identify appropriate services and categories in response to various input contents of the user.

[0067] The hearing unit identifies the missing information based on the contents analyzed by the analysis unit, and hears the user as necessary. The hearing unit, for example, asks a specific question to the user based on the missing information identified by the analysis unit. Specifically, the hearing unit analyzes the contents of the user's input, and automatically generates an additional question when the necessary information is missing. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The hearing unit analyzes the contents of the user's answer and checks whether all the necessary information is available. If there is missing information, the question is generated again and asked to the user. This allows the hearing unit to efficiently collect necessary information from the user and provide it to the analysis unit and the providing unit. Furthermore, the hearing unit can learn the contents of the user's answer and reflect it in the next and subsequent question generation. This allows the hearing unit to flexibly respond to the needs of the user, and improve the efficiency and accuracy of the entire system.

[0068] The providing unit provides the information obtained by the hearing unit to the operator. The providing unit includes, for example, an interface for providing information to the operator. Specifically, the providing unit organizes and visually displays the information collected from the user so that the operator can quickly and accurately grasp the information. For example, the providing unit provides text information, image information, links, and the like to the operator. The text information includes the contents of the user's input and additional information collected by the hearing unit. The image information displays photos and screenshots provided by the user. The links provide access to related web pages and manuals. This allows the operator to grasp at a glance the information needed to quickly respond to the user's problems and questions. Furthermore, the providing unit can collect feedback from the operator and use it to improve the system. For example, the operator can make comments and evaluations on the provided information, which allows the providing unit to continuously improve the accuracy and usefulness of the information. This allows the providing unit to support the operator's efficient response and increase the user's satisfaction.

[0069] The analysis unit can analyze the content of the free text and identify the corresponding service or category. The analysis unit can analyze the content of the free text using, for example, natural language processing technology. The natural language processing technology includes, for example, technologies such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can, for example, tokenize the content of the free text and analyze the meaning of each token. For example, the analysis unit can divide the content of the free text into words and analyze the meaning of each word. The analysis unit can also classify the content of the free text and identify the corresponding service or category. For example, the analysis unit can classify the content of the free text into categories of Internet connection services. As a result, the analysis unit can analyze the content of the free text and identify the appropriate service or category. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the content of the free text to AI, and the AI ​​can output the analysis result.

[0070] The hearing unit can identify missing information based on the analyzed content and ask a question to the user. The hearing unit, for example, asks a specific question to the user based on the missing information identified by the analysis unit. For example, the hearing unit asks the user a question such as "Please tell me the status of the connected device." The information answered by the user is analyzed again by the hearing unit. The hearing unit, for example, reanalyzes the answer from the user and complements the necessary information. For example, the hearing unit analyzes the content of the answer given by the user and identifies the missing information. As a result, the hearing unit can identify the missing information and complement the necessary information by asking a specific question to the user. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the hearing unit can input the user's answer to an AI, and the AI ​​can identify the missing information.

[0071] The providing unit can provide information to the operator. The providing unit includes, for example, an interface for providing information to the operator. The providing unit provides, for example, text information, image information, links, and the like to the operator. For example, the providing unit provides the user's free text input contents, analysis results, missing information, hearing results, and the like to the operator. In this way, the providing unit enables a quick response by providing information to the operator. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs information to be provided to the operator to AI, and the AI ​​can organize and output the information.

[0072] The analysis unit can perform the analysis using an algorithm. The analysis unit performs the analysis using, for example, a natural language processing algorithm or a machine learning algorithm. The natural language processing algorithm includes, for example, techniques such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The machine learning algorithm includes, for example, techniques such as a classification algorithm, a clustering algorithm, and a regression algorithm. For example, the analysis unit tokenizes the content of the free text and analyzes the meaning of each token. For example, the analysis unit divides the content of the free text into words and analyzes the meaning of each word. In addition, the analysis unit classifies the content of the free text and identifies the corresponding service or category. For example, the analysis unit classifies the content of the free text into a category of Internet connection services. As a result, the analysis unit uses a specific algorithm to improve the accuracy of the analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the content of the free text to AI, which can output the analysis result.

[0073] The hearing unit can reanalyze the answer from the user and complement the information. The hearing unit, for example, reanalyzes the answer from the user and complements the necessary information. For example, the hearing unit analyzes the content of the answer given by the user and identifies the missing information. The hearing unit analyzes the answer from the user using, for example, natural language processing technology or machine learning algorithm. The natural language processing technology includes, for example, technologies such as tokenization, morphological analysis, grammatical analysis, and semantic analysis. The machine learning algorithm includes, for example, technologies such as classification algorithm, clustering algorithm, and regression algorithm. Thereby, the hearing unit can complement the necessary information by reanalyzing the answer from the user. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit can input the answer from the user to AI, and the AI ​​can identify the missing information.

[0074] The reception unit can estimate the user's emotion and adjust the input timing of the free text based on the user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input timing of the free text based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the reception unit can delay the timing of prompting the user to input, allowing the user to relax. In addition, when the user is relaxed, the reception unit can also advance the timing of prompting the user to input, promoting smooth input. Furthermore, when the user is in a hurry, the reception unit can immediately prompt the user to input, enabling a quick response. Thereby, the reception unit adjusts the input timing according to the user's emotion, thereby reducing the user's stress and promoting smooth input. A part or all of the above-mentioned 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 emotion data to an AI, which can estimate the emotion and adjust the input timing.

[0075] The reception unit can analyze the user's past incoming call history and suggest the optimal input method. The reception unit, for example, analyzes the user's past incoming call history and suggests the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past incoming call history. Furthermore, the reception unit can also suggest a similar input method by referring to the contents entered by the user in the past. In this way, the reception unit can suggest the optimal input method by analyzing the user's past incoming call history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past incoming call history data into AI, and the AI ​​can suggest the optimal input method.

[0076] The reception unit can guide the input contents based on the user's current situation and field of interest when inputting free text. For example, the reception unit guides the input contents based on the user's current situation and field of interest when inputting free text. For example, when the user inputs the current situation, the reception unit presents related questions to guide the input. The reception unit can also suggest appropriate input contents based on the user's field of interest. Furthermore, when the user inputs about a specific topic, the reception unit can also provide related information to support the input. Thereby, the reception unit promotes appropriate input by guiding the input contents based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data and field of interest data to AI, and the AI ​​can guide the input contents.

[0077] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input contents based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the reception unit allows the user to input important information preferentially when the user is stressed. In addition, the reception unit can also allow the user to input detailed information when the user is relaxed. Furthermore, the reception unit can also allow the user to input minimum information preferentially when the user is in a hurry. In this way, the reception unit determines the priority of the input contents according to the user's emotion, thereby allowing important information to be input preferentially. Part or all of the above-mentioned 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 emotion data to an AI, and the AI ​​can determine the priority of the input contents.

[0078] The reception unit can preferentially accept input contents based on the geographical location information of the user when inputting free text. For example, when inputting free text, the reception unit preferentially accepts input contents with high relevance in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit allows the user to preferentially input information related to the area. In addition, when the user is moving, the reception unit can also allow the user to input related information based on the current location. Furthermore, when the user is in a specific place, the reception unit can also allow the user to preferentially input information related to the place. Thereby, the reception unit can preferentially accept input contents with high relevance by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to AI, and the AI ​​can preferentially accept input contents with high relevance.

[0079] The reception unit can analyze the social media activity of the user when inputting free text and accept related input contents. The reception unit can analyze the social media activity of the user when inputting free text, for example, and accept related input contents. The reception unit can suggest related input contents based on information shared by the user on social media, for example. The reception unit can also identify topics of interest from the user's social media activity and guide the input contents. Furthermore, the reception unit can cause the user to input related information based on the content mentioned by the user on social media. In this way, the reception unit can accept related input contents by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can accept related input contents.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit provides a simple and highly visible analysis result when the user is stressed. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide an analysis result that covers the main points when the user is in a hurry. Thereby, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis according to the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI, and the AI ​​can adjust the expression method of the analysis result.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the free text during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the free text during analysis. The analysis unit, for example, performs a detailed analysis on free text with high importance. The analysis unit can also perform a simplified analysis on free text with low importance. Furthermore, the analysis unit can set an appropriate level of detail of the analysis according to the content of the free text. Thereby, the analysis unit can provide an appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the free text to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the free text during analysis. The analysis unit applies different analysis algorithms depending on the category of the free text during analysis, for example. The analysis unit applies a specialized analysis algorithm to free text of technical content, for example. The analysis unit can also apply a general-purpose analysis algorithm to free text of general content. Furthermore, the analysis unit can select an optimal analysis algorithm for free text belonging to a specific category. As a result, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the category of the free text. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the free text to AI, and the AI ​​can select and apply an optimal analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit provides a short and to the point analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a quickly understandable analysis result when the user is in a hurry. Thereby, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis according to the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the submission time of the free text during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the free text during the analysis. The analysis unit, for example, preferentially analyzes free text submitted earlier. The analysis unit can also postpone free text submitted later. Furthermore, the analysis unit can set an appropriate analysis priority according to the submission time. Thereby, the analysis unit can perform analysis in an appropriate order by determining the priority of the analysis based on the submission time of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the free text to AI, and the AI ​​can determine the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the free text during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the free text during analysis. The analysis unit, for example, prioritizes analysis of highly relevant free text. The analysis unit can also postpone analysis of less relevant free text. Furthermore, the analysis unit can set an appropriate analysis order according to the content of the free text. As a result, the analysis unit can prioritize analysis of highly relevant content by adjusting the analysis order based on the relevance of the free text. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the free text to AI, and the AI ​​can adjust the order of analysis.

[0086] The hearing unit can estimate the user's emotion and adjust the hearing method based on the user's emotion. The hearing unit, for example, estimates the user's emotion and adjusts the hearing method based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the hearing unit performs hearing in a gentle tone. In addition, when the user is relaxed, the hearing unit can also ask detailed questions. Furthermore, when the user is in a hurry, the hearing unit can also ask brief questions. Thereby, the hearing unit can perform hearing appropriate for the user by adjusting the hearing method according to the user's emotion. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the hearing unit can input the user's emotional data into the AI, allowing the AI ​​to adjust the way it hears.

[0087] The hearing unit can select the optimal question by analyzing the user's past answer history during the hearing. For example, the hearing unit selects the optimal question by analyzing the user's past answer history during the hearing. The hearing unit selects a related question based on the content of the user's past answers, for example. The hearing unit can also predict and select the optimal question from the user's past answer history. Furthermore, the hearing unit can also select a detailed question by referring to the content of the user's past answers. Thereby, the hearing unit can select the optimal question by analyzing the user's past answer history. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, AI, or may be performed without using AI. For example, the hearing unit inputs the user's past answer history data into AI, and the AI ​​can select the optimal question.

[0088] The hearing unit can customize the question content based on the user's current situation at the time of hearing. The hearing unit, for example, customizes the question content based on the user's current situation at the time of hearing. The hearing unit customizes related questions, for example, when the user inputs the current situation. The hearing unit can also select appropriate questions based on the user's current situation. Furthermore, when the user is in a specific situation, the hearing unit can also customize questions according to the situation. Thereby, the hearing unit can ask appropriate questions by customizing the question content based on the user's current situation. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit can input the user's current situation data to AI, and the AI ​​can customize the question content.

[0089] The hearing unit can estimate the user's emotion and determine the hearing priority based on the user's emotion. The hearing unit, for example, estimates the user's emotion and determines the hearing priority based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the hearing unit prioritizes important questions when the user is stressed. In addition, the hearing unit can also prioritize detailed questions when the user is relaxed. Furthermore, the hearing unit can also prioritize brief questions when the user is in a hurry. Thereby, the hearing unit can prioritize important questions by determining the hearing priority according to the user's emotion. A part or all of the above-mentioned processing in the hearing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the hearing unit can input the user's emotional data into the AI, which can then determine hearing priorities.

[0090] The hearing unit can select the optimal question in consideration of the geographical location information of the user at the time of hearing. For example, the hearing unit selects the optimal question in consideration of the geographical location information of the user at the time of hearing. For example, when the user is in a specific area, the hearing unit selects a question related to the area. In addition, when the user is moving, the hearing unit can also select an appropriate question based on the current location. Furthermore, when the user is in a specific place, the hearing unit can also select a question related to the place. Thereby, the hearing unit can select the optimal question by considering the geographical location information of the user. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit inputs the geographical location information data of the user to AI, and the AI ​​can select the optimal question.

[0091] The hearing unit can analyze the user's social media activity at the time of hearing and suggest the question content. The hearing unit, for example, analyzes the user's social media activity at the time of hearing and suggests the question content. The hearing unit can, for example, suggest related questions based on information shared by the user on social media. The hearing unit can also identify topics of interest from the user's social media activity and suggest the question content. Furthermore, the hearing unit can also suggest related questions based on the content mentioned by the user on social media. In this way, the hearing unit can suggest related question content by analyzing the user's social media activity. Part or all of the above-mentioned processing in the hearing unit may be performed, for example, using AI, or may be performed without using AI. For example, the hearing unit can input the user's social media activity data into AI, and the AI ​​can suggest the question content.

[0092] The providing unit can estimate the user's emotion and adjust the method of providing information based on the user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing information based on the estimated user's emotion. The estimation of emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the providing unit provides simple and highly visible information. In addition, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide information that covers the main points. Thereby, the providing unit can provide appropriate information for the user by adjusting the method of providing information according to the user's emotion. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the AI, and the AI ​​can adjust the method of providing information.

[0093] The providing unit can provide optimal information by referring to the user's past incoming history when providing information. For example, the providing unit can provide optimal information by referring to the user's past incoming history when providing information. The providing unit can provide, for example, information that the user has used frequently in the past with priority. The providing unit can also provide related information from the user's past incoming history. Furthermore, the providing unit can also provide optimal information based on the content that the user has input in the past. Thereby, the providing unit can provide optimal information by referring to the user's past incoming history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's past incoming history data into AI, and the AI ​​can provide optimal information.

[0094] The providing unit can customize the information content based on the user's current situation when providing information. For example, the providing unit customizes the information content based on the user's current situation when providing information. For example, when the user inputs the current situation, the providing unit customizes and provides related information. The providing unit can also provide appropriate information based on the user's current situation. Furthermore, when the user is in a specific situation, the providing unit can also customize and provide information according to the situation. Thereby, the providing unit can provide appropriate information by customizing the information content based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to AI, and the AI ​​can customize the information content.

[0095] The providing unit can estimate the user's emotion and determine the priority of information provision based on the user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of information provision based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the providing unit provides important information preferentially when the user is stressed. In addition, the providing unit can also provide detailed information preferentially when the user is relaxed. Furthermore, the providing unit can also provide minimum information preferentially when the user is in a hurry. Thereby, the providing unit can provide important information preferentially by determining the priority of information provision according to the user's emotion. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI, and the AI ​​can determine the priority of information provision.

[0096] The providing unit can provide optimal information in consideration of the geographical location information of the user when providing information. The providing unit, for example, provides optimal information in consideration of the geographical location information of the user when providing information. For example, when the user is in a specific area, the providing unit provides information related to the area. In addition, when the user is moving, the providing unit can also provide appropriate information based on the current location. Furthermore, when the user is in a specific place, the providing unit can also provide information related to the place. Thereby, the providing unit can provide optimal information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to AI, and the AI ​​can provide optimal information.

[0097] The providing unit can analyze the user's social media activity and suggest information content when providing information. For example, the providing unit analyzes the user's social media activity and suggests information content when providing information. The providing unit provides related information based on information shared by the user on social media, for example. The providing unit can also identify topics of interest from the user's social media activity and suggest information content. Furthermore, the providing unit can provide related information based on content mentioned by the user on social media. Thereby, the providing unit can suggest related information content by analyzing the user's social media activity. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can suggest information content.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When accepting input contents from a user, the reception unit can predict the input contents by referring to the user's past input history. For example, the effort required for input can be reduced by preferentially presenting phrases and words that the user has used frequently in the past. The reception unit can also automatically generate related questions from the user's past input contents and present them to the user. Furthermore, the reception unit can provide an automatic completion function for input contents based on the user's past input history. In this way, the reception unit can improve input efficiency by utilizing the user's past input history.

[0100] When analyzing the content of the free text, the analysis unit can perform the analysis while taking into account the context of the user's input content. For example, the analysis unit can analyze the context before and after the free text to identify a more accurate meaning. The analysis unit can also complement related information based on the context of the user's input content. Furthermore, the analysis unit can also identify an appropriate category or service based on the context of the user's input content. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the context of the user's input content.

[0101] When asking a question to the user, the hearing unit can analyze the content of the user's answer in real time and dynamically generate the next question. For example, the next required information is identified based on the content of the user's answer, and an appropriate question is generated. The hearing unit can also adjust the order of questions based on the content of the user's answer. Furthermore, by analyzing the content of the user's answer in real time, the hearing unit can omit unnecessary questions and realize efficient hearing. In this way, the hearing unit can efficiently collect information by analyzing the content of the user's answer in real time.

[0102] The providing unit can adjust the display order based on the importance of the information when providing information to the operator. For example, the providing unit can preferentially display information with a high degree of importance, allowing the operator to respond quickly. The providing unit can also adjust the display format based on the importance of the information. Furthermore, the providing unit can provide a summary of the information in order to reduce the operator's workload. In this way, the providing unit can support the operator in responding efficiently by adjusting the display order based on the importance of the information.

[0103] The analysis unit can evaluate the reliability of the user's input content when analyzing the content of the free text. For example, the analysis unit evaluates the consistency and accuracy of the user's input content, and prioritizes the analysis of highly reliable information. The analysis unit can also adjust the reliability of the analysis result based on the reliability of the user's input content. Furthermore, by evaluating the reliability of the user's input content, the analysis unit can eliminate inaccurate information and improve the accuracy of the analysis. In this way, the analysis unit can provide a highly reliable analysis result by evaluating the reliability of the user's input content.

[0104] The reception unit can estimate the user's emotions and customize the input interface based on the user's emotions. For example, when the user is stressed, a simple and intuitive interface is provided to reduce the burden of input. When the user is relaxed, detailed input options can be provided to promote input according to the user's needs. Furthermore, when the user is in a hurry, an interface that allows quick input can be provided. In this way, the reception unit can customize the input interface according to the user's emotions to reduce the user's stress and promote smooth input.

[0105] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the user's emotions. For example, if the user is feeling stressed, a simple and highly visible analysis result can be provided to make it easier to understand. If the user is relaxed, detailed analysis results can be provided to deepen the user's understanding. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be provided to make it easier for the user to understand. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the way in which the analysis results are presented according to the user's emotions.

[0106] The hearing unit can estimate the user's emotions and adjust the timing of hearing based on the user's emotions. For example, when the user is stressed, the timing of hearing can be delayed to allow the user to relax. Also, when the user is relaxed, the timing of hearing can be advanced to promote smooth information gathering. Furthermore, when the user is in a hurry, the hearing unit can immediately hear the user to enable a quick response. In this way, the hearing unit can reduce the user's stress and efficiently gather information by adjusting the timing of hearing according to the user's emotions.

[0107] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the user's emotions. For example, when the user is feeling stressed, the timing of providing information can be delayed to allow the user to relax. Also, when the user is relaxed, the timing of providing information can be advanced to promote smooth information provision. Furthermore, when the user is in a hurry, the information can be provided immediately to enable a quick response. In this way, the providing unit can reduce the user's stress and provide information smoothly by adjusting the timing of providing information according to the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the format of information provision based on the user's emotions. For example, when the user is feeling stressed, simple and highly visible information is provided. When the user is relaxed, detailed information can be provided. Furthermore, when the user is in a hurry, information that focuses on the main points can be provided. In this way, the providing unit can provide appropriate information for the user by adjusting the format of information provision according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives free text input from a user. The free text input from a user may include sentences, words, phrases, etc. The reception unit receives the free text input by the user and transmits it to the analysis unit. Step 2: The analysis unit uses AI to analyze the content of the free text received by the reception unit and identify the relevant services and categories. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the content of the free text, and performs tokenization and semantic analysis. It also classifies the content of the free text and identifies the relevant services and categories. Step 3: The hearing unit identifies missing information based on the content analyzed by the analysis unit, and hears from the user as necessary. The hearing unit asks the user specific questions based on the missing information identified by the analysis unit. For example, the hearing unit may ask the user a question such as "Please tell me the status of the connected devices." The information answered by the user is analyzed again by the hearing unit. Step 4: The providing unit provides the information obtained by the hearing unit to the operator. The providing unit has an interface for providing information to the operator, and provides text information, image information, links, and the like.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, analysis unit, hearing unit, and provision 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 control unit 46A of the smart device 14 and receives free text input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the free text using AI to identify the corresponding service or category. The hearing unit is realized, for example, by the control unit 46A of the smart device 14 and hears from the user based on the missing information identified by the analysis unit. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the information obtained by the hearing unit to the operator. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, analysis unit, hearing unit, and provision 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 control unit 46A of the smart glasses 214 and receives free text input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the free text using AI to identify the corresponding service or category. The hearing unit is realized, for example, by the control unit 46A of the smart glasses 214 and hears the user based on the missing information identified by the analysis unit. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the information obtained by the hearing unit to the operator. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, analysis unit, hearing unit, and provision 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 control unit 46A of the headset type terminal 314 and receives free text input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the free text using AI to identify the corresponding service or category. The hearing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and hears the user based on the missing information identified by the analysis unit. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the information obtained by the hearing unit to the operator. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned reception unit, analysis unit, hearing unit, and provision 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 control unit 46A of the robot 414 and receives free text input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the free text using AI to identify the corresponding service or category. The hearing unit is realized, for example, by the control unit 46A of the robot 414 and hears from the user based on the missing information identified by the analysis unit. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the information obtained by the hearing unit to the operator. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a reception unit for receiving free text input from a user; an analysis unit that analyzes the content of the free text received by the reception unit and identifies a service or a category; a hearing unit that identifies missing information based on the content analyzed by the analysis unit and hears from a user; A providing unit that provides the information obtained by the hearing unit to an operator. A system characterized by: (Appendix 2) The analysis unit is Analyze the content of free text to identify applicable services and categories 2. The system of claim 1, (Appendix 3) The hearing section includes: Identify missing information based on the analyzed content and ask questions to the user 2. The system of claim 1, (Appendix 4) The providing unit is Providing information to operators 2. The system of claim 1, (Appendix 5) The analysis unit is Perform analysis using algorithms 2. The system of claim 1, (Appendix 6) The hearing section includes: Re-analyze user responses and supplement the information 2. The system of claim 1, (Appendix 7) The reception unit is Estimate user emotions and adjust the timing of free text input based on the user's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past call history and suggest input methods 2. The system of claim 1, (Appendix 9) The reception unit is When entering free text, guides the user based on their current context and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize inputs based on user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering free text, input is given priority based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The reception unit is When entering free text, it analyzes the user's social media activity and accepts relevant input. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust analysis presentation based on user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of free text 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, different parsing algorithms are applied depending on the category of the free text 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust parsing length based on user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the analysis priority will be determined based on the time of free text submission. 2. The system of claim 1, (Appendix 18) The analysis unit is During parsing, adjust the order of parsing based on free text relevance 2. The system of claim 1, (Appendix 19) The hearing section includes: Infer user emotions and adjust listening methods based on user emotions 2. The system of claim 1, (Appendix 20) The hearing section includes: When conducting interviews, the most appropriate questions are selected by analyzing the user's past answer history. 2. The system of claim 1, (Appendix 21) The hearing section includes: When conducting interviews, tailor questions to the user based on their current situation 2. The system of claim 1, (Appendix 22) The hearing section includes: Estimate user emotions and prioritize hearings based on user emotions 2. The system of claim 1, (Appendix 23) The hearing section includes: When conducting interviews, select the most appropriate questions based on the user's geographic location. 2. The system of claim 1, (Appendix 24) The hearing section includes: When conducting interviews, suggest questions based on users' social media activity 2. The system of claim 1, (Appendix 25) The providing unit is Estimate the user's emotions and adjust the way information is presented based on the user's emotions 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, the most appropriate information is provided by referring to the user's past reception history. 2. The system of claim 1, (Appendix 27) The providing unit is When providing information, tailor the information content based on the user's current situation 2. The system of claim 1, (Appendix 28) The providing unit is Estimate user emotions and prioritize information provision based on the user emotions 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, provide optimal information by taking into account the user's geographic location information 2. The system of claim 1, (Appendix 30) The providing unit is When providing information, the content of the information is suggested based on the user's social media activity. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 free text input from a user; an analysis unit that analyzes the content of the free text received by the reception unit and identifies a service or a category; a hearing unit that identifies missing information based on the content analyzed by the analysis unit and hears from a user; A providing unit that provides the information obtained by the hearing unit to an operator. A system characterized by:

2. The analysis unit is Perform analysis using algorithms 2. The system of claim 1 .

3. The hearing section includes: Re-analyze user responses and supplement the information 2. The system of claim 1 .

4. The reception unit is Estimate user emotions and adjust the timing of free text input based on the user's emotions 2. The system of claim 1 .

5. The reception unit is Analyze the user's past call history and suggest input methods 2. The system of claim 1 .

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