system

Abstract

The system according to the embodiment aims to determine whether a user is a person being monitored or a general customer, and to provide appropriate service information based on that determination. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and determines whether the user is a person being monitored or a general customer. The generation unit creates a service guide based on the determination result obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately determined whether a user is a person being monitored or a general customer and provided service information based on that determination, and there is room for improvement.

[0005] The system according to the embodiment aims to determine whether a user is a person being monitored or a general customer, and to provide appropriate service information based on that determination. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and determines whether the user is a person being monitored or a general customer. The generation unit creates a service guide based on the determination result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can determine whether the user is a person being monitored or a general customer, and provide appropriate service information based on that determination. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI service according to an embodiment of the present invention is a system that uses a generation AI to determine whether a user is a monitored user or a general customer and distributes service information based on the result. The AI ​​service collects user information, and the generation AI analyzes the information to determine whether the user is a monitored user or a general customer. Based on the determination result, the generation AI generates appropriate service information and provides it to the user. For example, the AI ​​service collects information such as the user's name, age, gender, and past usage history. This information is input into the generation AI. The generation AI then analyzes the input information and determines whether the user is a monitored user or a general customer. For example, the generation AI identifies whether the user is a monitored user or a general customer based on past usage history or specific conditions. Based on the determination result, the generation AI generates appropriate service information. For example, it generates specific service information for monitored users and different service information for general customers. This service information is provided to the user. This allows the AI ​​service to provide appropriate service information to the user. For example, by providing specific discounts and benefits to monitored users and regular service information to general customers, user satisfaction can be improved. This allows the AI ​​service to provide appropriate service information to the user. For example, user satisfaction can be improved by providing specific discounts and special offers to those who are being monitored, and providing regular service information to general customers.

[0029] The AI ​​service according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects user information. The user information includes, but is not limited to, for example, name, age, gender, and past usage history. The collection unit collects information with, for example, the user's consent. The collection unit can also encrypt the collected information. For example, the collection unit obtains user consent using a checkbox or a consent form. The collection unit can also encrypt the information using encryption technology such as AES or RSA. The analysis unit analyzes the information collected by the collection unit and determines whether the user is a monitored user or a general customer. The analysis is performed using, for example, but is not limited to, a machine learning algorithm or a rule-based analysis method. For example, the analysis unit classifies users based on past usage history or specific conditions. The analysis unit can also estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis, and perform a detailed analysis if the user is relaxed and a brief analysis if the user is stressed. The generation unit generates a service guide based on the determination result obtained by the analysis unit. The generation is performed, for example, using a specific template, but is not limited to such an example. For example, the generation unit generates the service guide using a template engine. The generation unit can also generate a service guide customized based on user attributes. For example, the generation unit provides different service guides depending on the user's age and gender. This allows the AI ​​service according to the embodiment to collect and analyze user information and generate appropriate service guides. For example, by collecting and analyzing user information and generating appropriate service guides, it is possible to provide services quickly and accurately.

[0030] The collection unit may include a means for obtaining user consent. The collection unit may obtain user consent using, for example, a checkbox. For example, the collection unit may provide a checkbox on a web form and start information collection when the user selects the checkbox. The collection unit may also obtain user consent using a consent form. For example, the collection unit may provide the user with a paper or electronic consent form and confirm consent with a signature. The collection unit may also obtain user consent using an electronic signature. For example, the collection unit may confirm consent by having the user sign a digital signature using electronic signature technology. This improves the reliability of information collection by obtaining user consent. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may automate the process of obtaining user consent using AI.

[0031] The collection unit may include a means for encrypting the collected information. The collection unit may encrypt the information using, for example, AES (Advanced Encryption Standard). For example, the collection unit may encrypt the collected information using the AES algorithm and securely store it. The collection unit may also encrypt the information using RSA (Rivest-Shamir-Adleman). For example, the collection unit may encrypt the collected information using the RSA algorithm and securely transmit it. The collection unit may also hash the information using SHA (Secure Hash Algorithm). For example, the collection unit may hash the collected information using the SHA algorithm to prevent tampering. By encrypting the collected information, the security of the information is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the information encryption process using AI.

[0032] The generation unit can create a service guide using a specific template. The generation unit generates the service guide using, for example, a template engine. For example, the generation unit creates a service guide suitable for a user based on a pre-prepared template. The generation unit can also generate the service guide using a specific format or design. For example, the generation unit uses a unified design template to maintain consistency in the service guide. The generation unit can also generate the service guide using a template including specific items. For example, the generation unit uses a template including the user's name and benefit information to create a personalized service guide. In this way, the use of a specific template maintains consistency in the service guide. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can automate the template engine using a generation AI to efficiently generate the service guide.

[0033] The generation unit can create a personalized service guide based on the user's attributes. The generation unit customizes the service guide based on the user's age, for example. For example, the generation unit provides service guides for younger people and service guides for older people separately. The generation unit can also customize the service guide based on the user's gender. For example, the generation unit creates service guides for men and women, respectively. The generation unit can also customize the service guide based on the user's occupation or hobbies. For example, the generation unit provides service guides related to a specific occupation or special offer information according to a hobby. This improves user satisfaction by providing a service guide customized based on the user's attributes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user attribute information into the generation AI and generate a personalized service guide.

[0034] The collection unit can analyze the user's past usage history and select the most appropriate information collection method. The collection unit, for example, analyzes the user's past usage history and prioritizes collecting related information. For example, the collection unit collects related information based on services the user has frequently used in the past. The collection unit can also select the most effective information collection method from the user's past usage history. For example, the collection unit selects a method that the user has previously preferred, such as email or app notifications. The collection unit can also analyze the user's past usage history and determine the optimal timing for information collection. For example, the collection unit sets the optimal collection timing based on the time period in which the user received information in the past. This allows the optimal information collection method to be selected by analyzing the user's past usage history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history data into a generation AI, which can select the optimal information collection method.

[0035] When collecting information, the collection unit can select information based on the user's current areas of interest. The collection unit, for example, filters information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information related to topics that the user is currently interested in. The collection unit can also filter relevant information based on the user's recent search history. For example, the collection unit collects information related to keywords recently searched by the user. The collection unit can also collect relevant information based on the activities of online communities in which the user participates. For example, the collection unit analyzes posts in forums and groups in which the user participates and collects relevant information. This allows highly relevant information to be collected by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's area of ​​interest data into a generation AI, which then filters relevant information.

[0036] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit analyzes voice data acquired by a microphone and collects information. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. For example, the collection unit analyzes text data entered by the user and collects information. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit analyzes image data acquired by a camera and collects information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI, which can select the optimal collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting event information related to the user's current location. For example, the collection unit collects nearby event information based on the user's GPS data. The collection unit can also prioritize collecting nearby store information based on the user's geographical location. For example, the collection unit collects special offer information for nearby stores based on the user's location information. The collection unit can also prioritize collecting area-specific special offer information based on the user's location information. For example, the collection unit collects area-specific discount information based on the user's location information. In this way, highly relevant information can be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into a generation AI, and the generation AI can collect highly relevant information.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information based on the content of posts from accounts the user follows on social media. For example, the collection unit collects the latest posts from the accounts the user follows. The collection unit can also analyze the content of the user's social media posts and collect related information. For example, the collection unit analyzes the content of the user's posts and collects information on related topics. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on information shared by the user's friends. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI, which then collects related information.

[0039] When collecting information, the collection unit can individualize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the information collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the information collection method based on survey results provided by the user in the past. The collection unit can also adjust the type of information to collect by referring to the user's past feedback. For example, the collection unit selects the type of information to collect based on information the user has previously requested. The collection unit can also optimize the timing of information collection based on the user's feedback. For example, the collection unit collects information at times that the user has previously preferred. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI individualize the collection method.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral patterns. The analysis unit improves the accuracy of the analysis based on, for example, the user's past behavioral patterns. For example, the analysis unit analyzes the user's past access logs and purchase history to improve the accuracy of the analysis. The analysis unit can also analyze the user's past behavioral patterns and select the optimal analysis method. For example, the analysis unit selects the optimal analysis algorithm based on the user's past behavioral data. The analysis unit can also adjust the analysis results by referring to the user's past behavioral patterns. For example, the analysis unit corrects the analysis results based on the user's past behavioral data. This improves the accuracy of the analysis by taking the user's past behavioral patterns into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0041] The analysis unit can perform analysis based on the user's attribute information during analysis. The analysis unit adjusts the analysis criteria based on, for example, the user's age and gender. For example, the analysis unit sets different analysis criteria for younger users and older users. The analysis unit can also customize the analysis content based on the user's occupation or hobbies. For example, the analysis unit performs analysis related to a specific occupation. The analysis unit can also select the optimal analysis method based on the user's attribute information. For example, the analysis unit selects the optimal analysis algorithm based on the user's attribute data. This enables more appropriate analysis by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute data into a generation AI, which can then perform the analysis.

[0042] During analysis, the analysis unit can set analysis weights based on the user's usage frequency. For example, the analysis unit can assign higher analysis weights to services that the user uses frequently. For example, the analysis unit can perform more detailed analysis on services that the user accesses frequently. The analysis unit can also determine analysis priorities based on the user's usage frequency. For example, the analysis unit can prioritize analysis of services that the user uses frequently. The analysis unit can also adjust the accuracy of the analysis taking into account the user's usage frequency. For example, the analysis unit can perform more accurate analysis on services that the user uses frequently. This enables more appropriate analysis by weighting the analysis based on the user's usage frequency. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user usage frequency data into a generation AI, and the generation AI can set the analysis weights.

[0043] The analysis unit can perform analysis based on the user's geographical distribution during analysis. The analysis unit, for example, adjusts the analysis criteria based on the region where the user lives. For example, the analysis unit sets different analysis criteria for urban and rural areas. The analysis unit can also customize the content of the analysis taking the user's geographical distribution into consideration. For example, the analysis unit performs analysis related to a specific region. The analysis unit can also select the optimal analysis method based on the user's geographical distribution. For example, the analysis unit selects the optimal analysis algorithm based on the user's geographical data. This enables more appropriate analysis by taking the user's geographical distribution into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical data into a generation AI, which can then perform the analysis.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. The analysis unit, for example, improves the accuracy of the analysis based on related literature. For example, the analysis unit improves the accuracy of the analysis by referring to academic papers and technical reports. The analysis unit can also select the optimal analysis method by referring to related literature. For example, the analysis unit selects the optimal analysis algorithm based on related literature. The analysis unit can also adjust the results of the analysis by referring to related literature. For example, the analysis unit corrects the analysis results based on related literature. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0045] The analysis unit can perform analysis based on the user's market value during analysis. The analysis unit, for example, adjusts the analysis criteria based on the user's market value. For example, the analysis unit sets different analysis criteria based on purchasing power or influence. The analysis unit can also customize the content of the analysis taking the user's market value into consideration. For example, the analysis unit performs analysis related to a user with a specific market value. The analysis unit can also select the optimal analysis method based on the user's market value. For example, the analysis unit selects the optimal analysis algorithm based on the user's market value data. This enables more appropriate analysis by taking the user's market value into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's market value data into a generation AI, which can then perform the analysis.

[0046] When generating a service guide, the generation unit can set the level of detail of the guide based on the user's importance. For example, if the user is an important customer, the generation unit provides detailed service guides. For example, the generation unit creates detailed guides based on the user's purchase history and usage frequency. The generation unit can also determine the priority of the guide based on the user's importance. For example, the generation unit provides guides preferentially to important customers. The generation unit can also customize the content of the guide taking the user's importance into consideration. For example, the generation unit provides special benefit information to important customers. This enables more appropriate guidance by adjusting the level of detail of the guide based on the user's importance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user importance data into the generation AI and have the generation AI set the level of detail of the guide.

[0047] When generating a service guide, the generation unit can use different generation algorithms depending on the user's category. For example, if the user is being monitored, the generation unit applies a specific generation algorithm. For example, the generation unit generates a guide using a specific template for the monitored user. The generation unit can also apply a different generation algorithm if the user is a general customer. For example, the generation unit generates a guide using a different template for the general customer. The generation unit can also select an optimal generation algorithm based on the user's category. For example, the generation unit selects an optimal generation algorithm based on the user's age group and interests. This enables more appropriate guidance by applying different generation algorithms depending on the user's category. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input user category data into the generation AI and have the generation AI select an optimal generation algorithm.

[0048] When generating a service guide, the generation unit can improve the accuracy of generation based on the user's past guidance results. The generation unit improves the accuracy of generation based on, for example, the user's past guidance results. For example, the generation unit analyzes the user's reactions to guidance received in the past to improve the accuracy of generation. The generation unit can also analyze the user's past guidance results and select the optimal generation method. For example, the generation unit selects the optimal generation method based on the type of guidance the user has preferred in the past. The generation unit can also adjust the content of the generation by referring to the user's past guidance results. For example, the generation unit adjusts the content of the generation based on the content of guidance that the user has given a high rating in the past. This improves the accuracy of generation by referring to the user's past guidance results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past guidance result data into the generation AI and use the generation AI to improve the accuracy of generation.

[0049] When generating a service guide, the generation unit can set the priority of the guide based on the time of submission by the user. For example, if the user submits early, the generation unit prioritizes generating the service guide. For example, the generation unit prioritizes creating the guide based on data submitted early by the user. The generation unit can also adjust the priority of the guide based on the time of submission by the user. For example, the generation unit sets the priority of the guide based on the time of submission by the user. The generation unit can also quickly generate a service guide if the user submits late. For example, the generation unit quickly creates the guide based on data submitted late by the user. This enables more appropriate guidance by determining the priority of the guide based on the time of submission by the user. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and set the priority of the guide by the generation AI.

[0050] When generating a service guide, the generation unit can set the order of the guide based on the user's relevance. The generation unit, for example, adjusts the order of the guide so that the user receives highly relevant information preferentially. For example, the generation unit prioritizes the guidance of highly relevant information based on the user's interests and past behavior. The generation unit can also customize the content of the guide based on the user's relevance. For example, the generation unit adjusts the content of the guide according to the user's interests. The generation unit can also optimize the order of the guide taking the user's relevance into consideration. For example, the generation unit optimizes the order of the guide based on the user's past behavior data. This enables more appropriate guidance by adjusting the order of the guide based on the user's relevance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user relevance data into the generation AI and have the generation AI set the order of the guide.

[0051] When generating a service guide, the generation unit can set the use of technical terms in the guide according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides a guide that uses a lot of technical terms. For example, the generation unit creates a guide that uses a lot of technical terms based on the user's occupation and educational background. Furthermore, if the user is a beginner, the generation unit can also provide a guide in simple language. For example, the generation unit creates a guide in simple language according to the user's level of expertise. Furthermore, the generation unit can customize the content of the guide based on the user's level of expertise. For example, the generation unit adjusts the content of the guide according to the user's level of expertise. This enables more appropriate guidance by adjusting the use of technical terms in the guide according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI set the use of technical terms in the guide.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can analyze the user's past usage history and select the most appropriate information collection method. For example, the collection unit prioritizes collection of related information based on services that the user has frequently used in the past. The collection unit can also select the most effective information collection means from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and determine the optimal timing for information collection. In this way, the optimal information collection method can be selected by analyzing the user's past usage history.

[0054] The generation unit can create personalized service guides based on the user's attributes. For example, the generation unit customizes service guides based on the user's age. Service guides for young people and service guides for the elderly can be provided separately. The generation unit can also customize service guides based on the user's gender. Service guides for men and women can be created separately. Furthermore, the generation unit can customize service guides based on the user's occupation or hobbies. Service guides related to specific occupations or special offer information according to hobbies can be provided. This provides service guides customized based on the user's attributes, thereby improving user satisfaction.

[0055] When collecting information, the collection unit can select information based on the user's current areas of interest. For example, the collection unit filters information based on the user's current areas of interest. Information related to topics in which the user is currently interested can be preferentially collected. The collection unit can also filter relevant information based on the user's recent search history. Information related to keywords recently searched by the user can be collected. Furthermore, the collection unit can also collect relevant information based on the activities of online communities in which the user participates. Relevant information can be collected by analyzing the posts in forums and groups in which the user participates. In this way, highly relevant information can be collected by filtering information based on the user's areas of interest.

[0056] When generating a service guide, the generation unit can improve the accuracy of generation based on the user's past guidance results. For example, the generation unit improves the accuracy of generation based on the user's past guidance results. The generation unit can improve the accuracy of generation by analyzing the user's reactions to guidance received in the past. The generation unit can also analyze the user's past guidance results and select the optimal generation method. The optimal generation method can be selected based on the type of guidance that the user preferred in the past. Furthermore, the generation unit can adjust the content of the generation by referring to the user's past guidance results. The content of the generation can be adjusted based on the content of guidance that the user has given a high rating in the past. In this way, the accuracy of generation is improved by referring to the user's past guidance results.

[0057] When generating a service guide, the generation unit can set the use of technical terms in the guide according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides a guide that uses a lot of technical terms. The generation unit can create a guide that uses a lot of technical terms based on the user's occupation and educational background. In addition, if the user is a beginner, the generation unit can provide a guide in simple language. The generation unit can create a guide in simple language according to the user's level of expertise. Furthermore, the generation unit can customize the content of the guide based on the user's level of expertise. The content of the guide can be adjusted according to the user's level of expertise. This allows for more appropriate guidance by adjusting the use of technical terms in the guide according to the user's level of expertise.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user information. User information includes name, age, gender, and past usage history. The collection unit collects information with the user's consent and may also encrypt the collected information. For example, the collection unit obtains user consent using a checkbox or consent form, and encrypts the information using encryption technology such as AES or RSA. Step 2: The analysis unit analyzes the information collected by the collection unit and determines whether the user is a monitored person or a general customer. The analysis is performed using machine learning algorithms and rule-based analysis methods. For example, users can be classified based on past usage history or specific conditions, and the user's emotions can be estimated to adjust the analysis criteria. The system estimates the user's emotions using facial expression recognition and voice analysis, and performs a detailed analysis if the user is relaxed, and a brief analysis if the user is stressed. Step 3: The generator generates a service guide based on the results of the analysis. This is done using a specific template. For example, a template engine can be used to generate a service guide customized based on the user's attributes. Different service guides can be provided depending on the user's age and gender.

[0060] (Example 2) An AI service according to an embodiment of the present invention is a system that uses a generation AI to determine whether a user is a monitored user or a general customer and distributes service information based on the result. The AI ​​service collects user information, and the generation AI analyzes the information to determine whether the user is a monitored user or a general customer. Based on the determination result, the generation AI generates appropriate service information and provides it to the user. For example, the AI ​​service collects information such as the user's name, age, gender, and past usage history. This information is input into the generation AI. The generation AI then analyzes the input information and determines whether the user is a monitored user or a general customer. For example, the generation AI identifies whether the user is a monitored user or a general customer based on past usage history or specific conditions. Based on the determination result, the generation AI generates appropriate service information. For example, it generates specific service information for monitored users and different service information for general customers. This service information is provided to the user. This allows the AI ​​service to provide appropriate service information to the user. For example, by providing specific discounts and benefits to monitored users and regular service information to general customers, user satisfaction can be improved. This allows the AI ​​service to provide appropriate service information to the user. For example, user satisfaction can be improved by providing specific discounts and special offers to those who are being monitored, and providing regular service information to general customers.

[0061] The AI ​​service according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects user information. The user information includes, but is not limited to, for example, name, age, gender, and past usage history. The collection unit collects information with, for example, the user's consent. The collection unit can also encrypt the collected information. For example, the collection unit obtains user consent using a checkbox or a consent form. The collection unit can also encrypt the information using encryption technology such as AES or RSA. The analysis unit analyzes the information collected by the collection unit and determines whether the user is a monitored user or a general customer. The analysis is performed using, for example, but is not limited to, a machine learning algorithm or a rule-based analysis method. For example, the analysis unit classifies users based on past usage history or specific conditions. The analysis unit can also estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis, and perform a detailed analysis if the user is relaxed and a brief analysis if the user is stressed. The generation unit generates a service guide based on the determination result obtained by the analysis unit. The generation is performed, for example, using a specific template, but is not limited to such an example. For example, the generation unit generates the service guide using a template engine. The generation unit can also generate a service guide customized based on user attributes. For example, the generation unit provides different service guides depending on the user's age and gender. This allows the AI ​​service according to the embodiment to collect and analyze user information and generate appropriate service guides. For example, by collecting and analyzing user information and generating appropriate service guides, it is possible to provide services quickly and accurately.

[0062] The collection unit may include a means for obtaining user consent. The collection unit may obtain user consent using, for example, a checkbox. For example, the collection unit may provide a checkbox on a web form and start information collection when the user selects the checkbox. The collection unit may also obtain user consent using a consent form. For example, the collection unit may provide the user with a paper or electronic consent form and confirm consent with a signature. The collection unit may also obtain user consent using an electronic signature. For example, the collection unit may confirm consent by having the user sign a digital signature using electronic signature technology. This improves the reliability of information collection by obtaining user consent. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may automate the process of obtaining user consent using AI.

[0063] The collection unit may include a means for encrypting the collected information. The collection unit may encrypt the information using, for example, AES (Advanced Encryption Standard). For example, the collection unit may encrypt the collected information using the AES algorithm and securely store it. The collection unit may also encrypt the information using RSA (Rivest-Shamir-Adleman). For example, the collection unit may encrypt the collected information using the RSA algorithm and securely transmit it. The collection unit may also hash the information using SHA (Secure Hash Algorithm). For example, the collection unit may hash the collected information using the SHA algorithm to prevent tampering. By encrypting the collected information, the security of the information is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the information encryption process using AI.

[0064] The generation unit can create a service guide using a specific template. The generation unit generates the service guide using, for example, a template engine. For example, the generation unit creates a service guide suitable for a user based on a pre-prepared template. The generation unit can also generate the service guide using a specific format or design. For example, the generation unit uses a unified design template to maintain consistency in the service guide. The generation unit can also generate the service guide using a template including specific items. For example, the generation unit uses a template including the user's name and benefit information to create a personalized service guide. In this way, the use of a specific template maintains consistency in the service guide. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can automate the template engine using a generation AI to efficiently generate the service guide.

[0065] The generation unit can create a personalized service guide based on the user's attributes. The generation unit customizes the service guide based on the user's age, for example. For example, the generation unit provides service guides for younger people and service guides for older people separately. The generation unit can also customize the service guide based on the user's gender. For example, the generation unit creates service guides for men and women, respectively. The generation unit can also customize the service guide based on the user's occupation or hobbies. For example, the generation unit provides service guides related to a specific occupation or special offer information according to a hobby. This improves user satisfaction by providing a service guide customized based on the user's attributes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user attribute information into the generation AI and generate a personalized service guide.

[0066] The collection unit can estimate the user's emotions and set the timing of information collection based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the collection unit analyzes the user's facial expressions captured by a camera to determine whether the user is relaxed or stressed. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user to estimate the emotions. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI, and the generation AI can adjust the timing of information collection.

[0067] The collection unit can analyze the user's past usage history and select the most appropriate information collection method. The collection unit, for example, analyzes the user's past usage history and prioritizes collecting related information. For example, the collection unit collects related information based on services the user has frequently used in the past. The collection unit can also select the most effective information collection method from the user's past usage history. For example, the collection unit selects a method that the user has previously preferred, such as email or app notifications. The collection unit can also analyze the user's past usage history and determine the optimal timing for information collection. For example, the collection unit sets the optimal collection timing based on the time period in which the user received information in the past. This allows the optimal information collection method to be selected by analyzing the user's past usage history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history data into a generation AI, which can select the optimal information collection method.

[0068] When collecting information, the collection unit can select information based on the user's current areas of interest. The collection unit, for example, filters information based on the user's current areas of interest. For example, the collection unit prioritizes collecting information related to topics that the user is currently interested in. The collection unit can also filter relevant information based on the user's recent search history. For example, the collection unit collects information related to keywords recently searched by the user. The collection unit can also collect relevant information based on the activities of online communities in which the user participates. For example, the collection unit analyzes posts in forums and groups in which the user participates and collects relevant information. This allows highly relevant information to be collected by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's area of ​​interest data into a generation AI, which then filters relevant information.

[0069] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. For example, the collection unit analyzes voice data acquired by a microphone and collects information. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. For example, the collection unit analyzes text data entered by the user and collects information. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. For example, the collection unit analyzes image data acquired by a camera and collects information. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI, which can select the optimal collection means.

[0070] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the collection unit analyzes the user's facial expressions captured by a camera to determine whether the user is relaxed or stressed. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user to estimate the emotions. This enables more appropriate information to be collected by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI, and the generation AI can set the priority of the information.

[0071] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting event information related to the user's current location. For example, the collection unit collects nearby event information based on the user's GPS data. The collection unit can also prioritize collecting nearby store information based on the user's geographical location. For example, the collection unit collects special offer information for nearby stores based on the user's location information. The collection unit can also prioritize collecting area-specific special offer information based on the user's location information. For example, the collection unit collects area-specific discount information based on the user's location information. In this way, highly relevant information can be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into a generation AI, and the generation AI can collect highly relevant information.

[0072] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information based on the content of posts from accounts the user follows on social media. For example, the collection unit collects the latest posts from the accounts the user follows. The collection unit can also analyze the content of the user's social media posts and collect related information. For example, the collection unit analyzes the content of the user's posts and collects information on related topics. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on information shared by the user's friends. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI, which then collects related information.

[0073] When collecting information, the collection unit can individualize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the information collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the information collection method based on survey results provided by the user in the past. The collection unit can also adjust the type of information to collect by referring to the user's past feedback. For example, the collection unit selects the type of information to collect based on information the user has previously requested. The collection unit can also optimize the timing of information collection based on the user's feedback. For example, the collection unit collects information at times that the user has previously preferred. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI individualize the collection method.

[0074] The analysis unit can estimate the user's emotions and set analysis criteria based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions captured by a camera to determine whether the user is relaxed or stressed. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes text entered by the user to estimate emotions. This enables more appropriate analysis by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI, and the generation AI can set the analysis criteria.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral patterns. The analysis unit improves the accuracy of the analysis based on, for example, the user's past behavioral patterns. For example, the analysis unit analyzes the user's past access logs and purchase history to improve the accuracy of the analysis. The analysis unit can also analyze the user's past behavioral patterns and select the optimal analysis method. For example, the analysis unit selects the optimal analysis algorithm based on the user's past behavioral data. The analysis unit can also adjust the analysis results by referring to the user's past behavioral patterns. For example, the analysis unit corrects the analysis results based on the user's past behavioral data. This improves the accuracy of the analysis by taking the user's past behavioral patterns into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0076] The analysis unit can perform analysis based on the user's attribute information during analysis. The analysis unit adjusts the analysis criteria based on, for example, the user's age and gender. For example, the analysis unit sets different analysis criteria for younger users and older users. The analysis unit can also customize the analysis content based on the user's occupation or hobbies. For example, the analysis unit performs analysis related to a specific occupation. The analysis unit can also select the optimal analysis method based on the user's attribute information. For example, the analysis unit selects the optimal analysis algorithm based on the user's attribute data. This enables more appropriate analysis by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute data into a generation AI, which can then perform the analysis.

[0077] During analysis, the analysis unit can set analysis weights based on the user's usage frequency. For example, the analysis unit can assign higher analysis weights to services that the user uses frequently. For example, the analysis unit can perform more detailed analysis on services that the user accesses frequently. The analysis unit can also determine analysis priorities based on the user's usage frequency. For example, the analysis unit can prioritize analysis of services that the user uses frequently. The analysis unit can also adjust the accuracy of the analysis taking into account the user's usage frequency. For example, the analysis unit can perform more accurate analysis on services that the user uses frequently. This enables more appropriate analysis by weighting the analysis based on the user's usage frequency. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user usage frequency data into a generation AI, and the generation AI can set the analysis weights.

[0078] The analysis unit can estimate the user's emotions and set a display method for the analysis results based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expressions captured by a camera to determine whether the user is relaxed or stressed. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes text entered by the user to estimate the emotions. This enables a more appropriate display by adjusting the display method for the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI, and have the generation AI set the method for displaying the analysis results.

[0079] The analysis unit can perform analysis based on the user's geographical distribution during analysis. The analysis unit, for example, adjusts the analysis criteria based on the region where the user lives. For example, the analysis unit sets different analysis criteria for urban and rural areas. The analysis unit can also customize the content of the analysis taking the user's geographical distribution into consideration. For example, the analysis unit performs analysis related to a specific region. The analysis unit can also select the optimal analysis method based on the user's geographical distribution. For example, the analysis unit selects the optimal analysis algorithm based on the user's geographical data. This enables more appropriate analysis by taking the user's geographical distribution into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical data into a generation AI, which can then perform the analysis.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. The analysis unit, for example, improves the accuracy of the analysis based on related literature. For example, the analysis unit improves the accuracy of the analysis by referring to academic papers and technical reports. The analysis unit can also select the optimal analysis method by referring to related literature. For example, the analysis unit selects the optimal analysis algorithm based on related literature. The analysis unit can also adjust the results of the analysis by referring to related literature. For example, the analysis unit corrects the analysis results based on related literature. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0081] The analysis unit can perform analysis based on the user's market value during analysis. The analysis unit, for example, adjusts the analysis criteria based on the user's market value. For example, the analysis unit sets different analysis criteria based on purchasing power or influence. The analysis unit can also customize the content of the analysis taking the user's market value into consideration. For example, the analysis unit performs analysis related to a user with a specific market value. The analysis unit can also select the optimal analysis method based on the user's market value. For example, the analysis unit selects the optimal analysis algorithm based on the user's market value data. This enables more appropriate analysis by taking the user's market value into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's market value data into a generation AI, which can then perform the analysis.

[0082] The generation unit can estimate the user's emotion and set the presentation method of the service guide based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression captured by a camera and determines whether the user is relaxed or stressed. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes text entered by the user to estimate the emotion. This enables more appropriate guidance by adjusting the presentation method of the service guide based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI set the method of expressing the service information.

[0083] When generating a service guide, the generation unit can set the level of detail of the guide based on the user's importance. For example, if the user is an important customer, the generation unit provides detailed service guides. For example, the generation unit creates detailed guides based on the user's purchase history and usage frequency. The generation unit can also determine the priority of the guide based on the user's importance. For example, the generation unit provides guides preferentially to important customers. The generation unit can also customize the content of the guide taking the user's importance into consideration. For example, the generation unit provides special benefit information to important customers. This enables more appropriate guidance by adjusting the level of detail of the guide based on the user's importance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user importance data into the generation AI and have the generation AI set the level of detail of the guide.

[0084] When generating a service guide, the generation unit can use different generation algorithms depending on the user's category. For example, if the user is being monitored, the generation unit applies a specific generation algorithm. For example, the generation unit generates a guide using a specific template for the monitored user. The generation unit can also apply a different generation algorithm if the user is a general customer. For example, the generation unit generates a guide using a different template for the general customer. The generation unit can also select an optimal generation algorithm based on the user's category. For example, the generation unit selects an optimal generation algorithm based on the user's age group and interests. This enables more appropriate guidance by applying different generation algorithms depending on the user's category. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input user category data into the generation AI and have the generation AI select an optimal generation algorithm.

[0085] When generating a service guide, the generation unit can improve the accuracy of generation based on the user's past guidance results. The generation unit improves the accuracy of generation based on, for example, the user's past guidance results. For example, the generation unit analyzes the user's reactions to guidance received in the past to improve the accuracy of generation. The generation unit can also analyze the user's past guidance results and select the optimal generation method. For example, the generation unit selects the optimal generation method based on the type of guidance the user has preferred in the past. The generation unit can also adjust the content of the generation by referring to the user's past guidance results. For example, the generation unit adjusts the content of the generation based on the content of guidance that the user has given a high rating in the past. This improves the accuracy of generation by referring to the user's past guidance results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past guidance result data into the generation AI and use the generation AI to improve the accuracy of generation.

[0086] The generation unit can estimate the user's emotion and set the length of the service guidance based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the generation unit can analyze the user's facial expression captured by a camera and determine whether the user is relaxed or stressed. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit can analyze text entered by the user to estimate the emotion. This enables more appropriate guidance by adjusting the length of the service guidance based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI set the length of the service information.

[0087] When generating a service guide, the generation unit can set the priority of the guide based on the time of submission by the user. For example, if the user submits early, the generation unit prioritizes generating the service guide. For example, the generation unit prioritizes creating the guide based on data submitted early by the user. The generation unit can also adjust the priority of the guide based on the time of submission by the user. For example, the generation unit sets the priority of the guide based on the time of submission by the user. The generation unit can also quickly generate a service guide if the user submits late. For example, the generation unit quickly creates the guide based on data submitted late by the user. This enables more appropriate guidance by determining the priority of the guide based on the time of submission by the user. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and set the priority of the guide by the generation AI.

[0088] When generating a service guide, the generation unit can set the order of the guide based on the user's relevance. The generation unit, for example, adjusts the order of the guide so that the user receives highly relevant information preferentially. For example, the generation unit prioritizes the guidance of highly relevant information based on the user's interests and past behavior. The generation unit can also customize the content of the guide based on the user's relevance. For example, the generation unit adjusts the content of the guide according to the user's interests. The generation unit can also optimize the order of the guide taking the user's relevance into consideration. For example, the generation unit optimizes the order of the guide based on the user's past behavior data. This enables more appropriate guidance by adjusting the order of the guide based on the user's relevance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user relevance data into the generation AI and have the generation AI set the order of the guide.

[0089] When generating a service guide, the generation unit can set the use of technical terms in the guide according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides a guide that uses a lot of technical terms. For example, the generation unit creates a guide that uses a lot of technical terms based on the user's occupation and educational background. Furthermore, if the user is a beginner, the generation unit can also provide a guide in simple language. For example, the generation unit creates a guide in simple language according to the user's level of expertise. Furthermore, the generation unit can customize the content of the guide based on the user's level of expertise. For example, the generation unit adjusts the content of the guide according to the user's level of expertise. This enables more appropriate guidance by adjusting the use of technical terms in the guide according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI set the use of technical terms in the guide. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and determines whether the user is a person being monitored or a general customer. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates appropriate service information based on the determination result. The generated service information is provided to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine whether the user is a person being monitored or a general customer. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate service information based on the determination result. The generated service information is provided to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine whether the user is a person being monitored or a general customer. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate service information based on the determination result. The generated service information is provided to the user via the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414, and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine whether the user is a person being monitored or a general customer. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate service information based on the determination result. The generated service information is provided to the user through the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology, and perform a detailed analysis if the user is relaxed and a brief analysis if the user is stressed. The analysis unit can also estimate the user's emotions using voice analysis technology and adjust the depth of the analysis depending on the emotions. Furthermore, the analysis unit can estimate the user's emotions using text analysis technology and set analysis priorities based on the emotions. This enables flexible analysis according to the user's emotions.

[0092] The collection unit can analyze the user's past usage history and select the most appropriate information collection method. For example, the collection unit prioritizes collection of related information based on services that the user has frequently used in the past. The collection unit can also select the most effective information collection means from the user's past usage history. Furthermore, the collection unit can analyze the user's past usage history and determine the optimal timing for information collection. In this way, the optimal information collection method can be selected by analyzing the user's past usage history.

[0093] The collection unit can estimate the user's emotions and set the timing of information collection based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology, and collect information when the user is relaxed and refrain from collecting information when the user is stressed. The collection unit can also estimate the user's emotions using voice analysis technology and adjust the timing of information collection based on the emotions. Furthermore, the collection unit can estimate the user's emotions using text analysis technology and set priorities for information collection based on the emotions. This makes it possible to collect appropriate information according to the user's emotions.

[0094] The generation unit can create personalized service guides based on the user's attributes. For example, the generation unit customizes service guides based on the user's age. Service guides for young people and service guides for the elderly can be provided separately. The generation unit can also customize service guides based on the user's gender. Service guides for men and women can be created separately. Furthermore, the generation unit can customize service guides based on the user's occupation or hobbies. Service guides related to specific occupations or special offer information according to hobbies can be provided. This provides service guides customized based on the user's attributes, thereby improving user satisfaction.

[0095] When collecting information, the collection unit can select information based on the user's current areas of interest. For example, the collection unit filters information based on the user's current areas of interest. Information related to topics in which the user is currently interested can be preferentially collected. The collection unit can also filter relevant information based on the user's recent search history. Information related to keywords recently searched by the user can be collected. Furthermore, the collection unit can also collect relevant information based on the activities of online communities in which the user participates. Relevant information can be collected by analyzing the posts in forums and groups in which the user participates. In this way, highly relevant information can be collected by filtering information based on the user's areas of interest.

[0096] The analysis unit can estimate the user's emotions and set analysis criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology and determine whether the user is relaxed or stressed. The analysis unit can also estimate the user's emotions using voice analysis technology and adjust analysis criteria based on the emotions. Furthermore, the analysis unit can estimate the user's emotions using text analysis technology and set analysis priorities based on the emotions. This allows for more appropriate analysis by adjusting the analysis criteria based on the user's emotions.

[0097] When generating a service guide, the generation unit can improve the accuracy of generation based on the user's past guidance results. For example, the generation unit improves the accuracy of generation based on the user's past guidance results. The generation unit can improve the accuracy of generation by analyzing the user's reactions to guidance received in the past. The generation unit can also analyze the user's past guidance results and select the optimal generation method. The optimal generation method can be selected based on the type of guidance that the user preferred in the past. Furthermore, the generation unit can adjust the content of the generation by referring to the user's past guidance results. The content of the generation can be adjusted based on the content of guidance that the user has given a high rating in the past. In this way, the accuracy of generation is improved by referring to the user's past guidance results.

[0098] The collection unit can estimate the user's emotions and set the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology and determine whether the user is relaxed or stressed. The collection unit can also estimate the user's emotions using voice analysis technology and set the priority of information based on the emotions. Furthermore, the collection unit can estimate the user's emotions using text analysis technology and set the priority of information based on the emotions. In this way, by setting the priority of information based on the user's emotions, more appropriate information can be collected.

[0099] When generating a service guide, the generation unit can set the use of technical terms in the guide according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides a guide that uses a lot of technical terms. The generation unit can create a guide that uses a lot of technical terms based on the user's occupation and educational background. In addition, if the user is a beginner, the generation unit can provide a guide in simple language. The generation unit can create a guide in simple language according to the user's level of expertise. Furthermore, the generation unit can customize the content of the guide based on the user's level of expertise. The content of the guide can be adjusted according to the user's level of expertise. This allows for more appropriate guidance by adjusting the use of technical terms in the guide according to the user's level of expertise.

[0100] The generation unit can estimate the user's emotion and set the presentation method of the service information based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition technology and determine whether the user is relaxed or stressed. The generation unit can also estimate the user's emotion using voice analysis technology and adjust the presentation method of the service information based on the emotion. Furthermore, the generation unit can estimate the user's emotion using text analysis technology and set the presentation method of the service information based on the emotion. This allows for more appropriate guidance by adjusting the presentation method of the service information based on the user's emotion.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects user information. User information includes name, age, gender, and past usage history. The collection unit collects information with the user's consent and may also encrypt the collected information. For example, the collection unit obtains user consent using a checkbox or consent form, and encrypts the information using encryption technology such as AES or RSA. Step 2: The analysis unit analyzes the information collected by the collection unit and determines whether the user is a monitored person or a general customer. The analysis is performed using machine learning algorithms and rule-based analysis methods. For example, users can be classified based on past usage history or specific conditions, and the user's emotions can be estimated to adjust the analysis criteria. The system estimates the user's emotions using facial expression recognition and voice analysis, and performs a detailed analysis if the user is relaxed, and a brief analysis if the user is stressed. Step 3: The generator generates a service guide based on the results of the analysis. This is done using a specific template. For example, a template engine can be used to generate a service guide customized based on the user's attributes. Different service guides can be provided depending on the user's age and gender.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] 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.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and determines whether the user is a person being monitored or a general customer; a generation unit that generates a service guide based on the determination result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Provide a means to obtain user consent 2. The system of claim 1.

3. The collecting unit Have a means to encrypt collected information 2. The system of claim 1.

4. The generation unit Create a service announcement using a specific template 2. The system of claim 1.

5. The generation unit Create personalized service announcements based on user attributes 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and set the timing of information collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze the user's past usage history and select the most appropriate information collection method 2. The system of claim 1.

8. The collecting unit At the time of collection, filter information based on the user's current areas of interest 2. The system of claim 1.

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