System

Abstract

An object of a system according to an embodiment is to generate a persona based on a sense of values of a user and efficiently utilize the persona.SOLUTION: A system includes an input unit, a generation unit, a classification unit, a selection unit, and a utilization unit. The input unit inputs a sense of values of a user. The generation unit analyzes the values input by the input unit and generates a persona. The classification unit categorizes the persona generated by the generation unit. The selection unit selects the persona categorized by the classification unit. The utilization unit utilizes the persona selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 do not adequately generate and utilize personas based on users' values, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a persona based on a user's values ​​and to utilize it efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, a classification unit, a selection unit, and an utilization unit. The input unit inputs a user's values. The generation unit analyzes the values ​​input by the input unit and generates a persona. The classification unit categorizes the personas generated by the generation unit. The selection unit selects the personas categorized by the classification unit. The utilization unit utilizes the persona selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a persona based on the user's values ​​and utilize it efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A persona generation system according to an embodiment of the present invention generates personas that reflect a user's values ​​and share them on a platform. In the persona generation system, a user inputs their values ​​and characteristics, and a generation AI analyzes the information to generate a persona. The generated persona is then analyzed and categorized by the generation AI before being uploaded to the platform. A user can select a desired persona from the genre and use it for a specific purpose. For example, in the persona generation system, a user inputs details of their values ​​and characteristics. For example, the user inputs information such as "I am sociable and enjoy the outdoors." This information is input to the generation AI. The persona generation system then uses the generation AI to analyze the input information and generate a persona. The generation AI analyzes the user's values ​​and characteristics and generates a persona based on them. For example, a persona such as "a sociable person who enjoys the outdoors" is generated. The persona generation system then analyzes and categorized the generated persona before uploading it to the platform. The generation AI analyzes the persona's characteristics and classifies it into an appropriate genre. For example, a persona can be categorized into the "outdoor enthusiast" genre. The persona generation system then allows the user to select the persona they want from the genre and use it according to their needs. For example, when formulating a marketing strategy, selecting the "outdoor enthusiast" persona and using that persona as a reference can lead to a more effective approach. This allows the persona generation system to efficiently generate, classify, select, and utilize personas that reflect the user's values. This allows the persona generation system to accurately grasp the personas of the target market and formulate an efficient strategy. For example, when formulating a marketing strategy for a new product, referring to personas in a specific genre can lead to a more effective approach. Furthermore, users can obtain a more personalized experience by selecting a persona that matches their values.

[0029] A persona generation system according to an embodiment includes an input unit, a generation unit, a classification unit, a selection unit, and an utilization unit. The input unit inputs a user's values. The user's values ​​include, but are not limited to, personal beliefs, preferences, and priorities. For example, the input unit allows the user to input details of their own values ​​and characteristics. The generation unit uses a generation AI to analyze the values ​​input by the input unit and generate a persona. The generation AI analyzes the user's values ​​and characteristics using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a persona based on the values ​​and characteristics. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona such as "a sociable person who loves the outdoors" based on the values ​​and characteristics. The classification unit categorizes the persona generated by the generation unit. The classification unit uses the generation AI to analyze the characteristics of the persona and classify it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and classifies it into the "outdoor lover" genre. The selection unit selects personas categorized by the classification unit. The selection unit, for example, selects a persona that the user wants from a genre. The utilization unit utilizes the personas selected by the selection unit. The utilization unit, for example, utilizes the selected personas in a marketing strategy. This allows the persona generation system according to the embodiment to efficiently generate, classify, select, and utilize personas that reflect the user's values.

[0030] The generation unit can analyze the user's values ​​using a generation AI and generate a persona. The generation unit, for example, uses a generation AI to analyze the user's values ​​and generate a persona. The generation AI, for example, uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's values ​​and characteristics and generate a persona based on the values ​​and characteristics. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona that is "sociable and likes the outdoors" based on the values ​​and characteristics. In this way, the generation AI can be used to generate a persona that accurately reflects the user's values.

[0031] The classification unit can categorize the personas generated by the generation AI. The classification unit categorizes the generated personas using, for example, the generation AI. The generation AI, for example, analyzes the characteristics of the persona and categorizes it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and categorizes it into the genre "outdoor enthusiast." In this way, the use of the generation AI improves the accuracy of categorizing the personas into categories.

[0032] The selection unit can select a desired persona from a genre. For example, the selection unit allows a user to select a desired persona from a genre. For example, when formulating a marketing strategy, the selection unit can select a persona of "outdoor enthusiasts" and use that persona as a reference to enable a more effective approach. This allows the user to select an appropriate persona from a genre.

[0033] The utilization department can utilize the selected personas in a marketing strategy. The utilization department, for example, utilizes the selected personas in a marketing strategy. The utilization department, for example, can accurately grasp the personas of the target market and formulate an efficient strategy. In this way, by utilizing the selected personas in a marketing strategy, it is possible to accurately grasp the personas of the target market and formulate an efficient strategy.

[0034] The utilization department can utilize the selected persona in sales materials. The utilization department, for example, utilizes the selected persona in sales materials. The utilization department can increase the effectiveness of sales activities by utilizing the persona in, for example, product introduction materials or presentation materials. In this way, by utilizing the selected persona in sales materials, the effectiveness of sales activities can be increased.

[0035] The utilization unit can utilize the selected persona in a review. The utilization unit, for example, utilizes the selected persona in a review. The utilization unit, for example, utilizes the persona to obtain feedback from users, which can be useful for improving products and services. In this way, by utilizing the selected persona in a review, feedback from users can be obtained.

[0036] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, values ​​that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest values ​​that will be used in a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method by analyzing the user's past input history.

[0037] The input unit can filter the input content based on the user's current situation and areas of interest when inputting values. For example, when the user inputs values, the input unit filters the input content based on the user's current situation and areas of interest. For example, when the user inputs their current situation, related values ​​are suggested based on that. It is also possible to set the user's areas of interest in advance and filter the input content based on those areas. It is also possible to preferentially display values ​​related to a specific situation when the user is in that situation. In this way, by filtering the input content based on the user's current situation and areas of interest, more relevant values ​​can be input.

[0038] The input unit can select the optimum input means depending on the user's input method when inputting values. For example, when inputting values, the input unit selects the optimum input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the values ​​are input using voice recognition technology. Also, if the user selects text input, keyboard input can be supported. Also, if the user selects image input, the values ​​can be input using image analysis technology. In this way, by selecting the optimum input means depending on the user's input method, the input of values ​​is made more efficient.

[0039] The input unit can prioritize inputting highly relevant values ​​in consideration of the user's geographical location information when inputting values. For example, the input unit prioritizes inputting highly relevant values ​​in consideration of the user's geographical location information when inputting values. For example, when the user is in a specific area, values ​​related to that area can be prioritized. Also, when the user is traveling, values ​​related to the travel destination can be prioritized. Also, when the user is at home, values ​​related to the home can be prioritized. In this way, highly relevant values ​​can be prioritized by taking the user's geographical location information into consideration.

[0040] The input unit can analyze the user's social media activity and input related values ​​when values ​​are input. For example, the input unit can analyze the user's social media activity and input related values ​​when values ​​are input. For example, the input unit can automatically input values ​​shared by the user on social media. It can also analyze the content of the user's social media posts and input related values. It can also input related values ​​by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently input related values ​​by analyzing the user's social media activity.

[0041] The input unit can customize the input method by reflecting the user's past feedback when inputting values. For example, the input unit customizes the input method by reflecting the user's past feedback when inputting values. For example, the input method is customized based on feedback provided by the user in the past. The input unit can also suggest the optimal input means based on the user's past feedback. The input interface can also be improved by analyzing the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, allowing for more appropriate input.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of values ​​when generating a persona. For example, the generation unit adjusts the level of detail of the generation based on the importance of values ​​when generating a persona. For example, a detailed persona is generated based on important values. A simplified persona can also be generated based on general values. The level of detail of the generation can also be adjusted according to the importance of the user's values. In this way, by adjusting the level of detail of the generation based on the importance of values, a more appropriate persona can be generated.

[0043] The generation unit can apply different generation algorithms depending on the value category when generating a persona. For example, the generation unit applies different generation algorithms depending on the value category when generating a persona. For example, an algorithm that generates a sociable persona based on a sociable value can be applied. Also, an algorithm that generates an outdoorsy persona can be applied based on a value of loving the outdoors. Also, the optimal generation algorithm can be selected depending on the category of the user's value. In this way, by applying different generation algorithms depending on the value category, a more appropriate persona can be generated.

[0044] The generation unit can improve the accuracy of generation when generating a persona by referring to the user's past generation results. For example, when generating a persona, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation accuracy is improved based on personas generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. The generation unit can also generate a more accurate persona by referring to the user's past generation results. In this way, the generation accuracy can be improved by referring to the user's past generation results.

[0045] The generation unit can determine the generation priority based on the time of submission of values ​​when generating a persona. For example, the generation unit determines the generation priority based on the time of submission of values ​​when generating a persona. For example, the generation unit generates values ​​that were submitted most recently with priority. Values ​​that were submitted earlier can also be postponed. The generation priority can also be determined based on the time of submission. In this way, by determining the generation priority based on the time of submission of values, it is possible to generate a persona that preferentially reflects the most recent values.

[0046] The generation unit can adjust the order of generation based on the relevance of values ​​when generating a persona. For example, the generation unit adjusts the order of generation based on the relevance of values ​​when generating a persona. For example, highly relevant values ​​are generated with priority. Also, values ​​with low relevance can be postponed. The order of generation can also be adjusted based on the relevance of values. In this way, by adjusting the order of generation based on the relevance of values, it is possible to generate a persona that reflects highly relevant values ​​with priority.

[0047] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a persona. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating a persona. For example, for a user with high level of expertise, a persona that uses a lot of technical terminology can be generated. Also, for a user with low level of expertise, a persona that uses simple language can be generated. The use of technical terminology in the generation can also be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a persona that is easy for the user to understand can be generated.

[0048] The classification unit can improve the accuracy of classification by taking into account the interrelationships between personas when classifying. For example, the classification unit improves the accuracy of classification by taking into account the interrelationships between personas when classifying. For example, the classification unit analyzes the interrelationships between personas and classifies highly related personas into the same genre. The classification accuracy can also be improved by taking into account the interrelationships between personas. Furthermore, optimal classification can be performed based on the interrelationships between personas. In this way, the classification accuracy can be improved by taking into account the interrelationships between personas.

[0049] The classification unit can perform classification taking into account attribute information of the persona creator when classifying. For example, the classification unit performs classification taking into account attribute information of the persona creator when classifying. For example, classification is performed taking into account the age and gender of the persona creator. Classification can also be performed taking into account the occupation and hobbies of the persona creator. Optimal classification can also be performed based on the attribute information of the persona creator. As a result, more appropriate classification is possible by taking into account attribute information of the persona creator.

[0050] The classification unit can weight the classification based on the frequency of generation of the persona during classification. For example, the classification unit weights the classification based on the frequency of generation of the persona during classification. For example, personas with a high generation frequency are prioritized for classification. Personas with a low generation frequency can also be postponed. Classification can also be weighted based on the frequency of generation. Thus, weighting the classification based on the frequency of generation of the persona enables more appropriate classification.

[0051] The classification unit can perform classification taking into account the geographical distribution of personas. For example, the classification unit performs classification taking into account the geographical distribution of personas. For example, the classification unit analyzes the geographical distribution of personas and performs classification by region. It is also possible to classify highly related personas into the same genre taking into account the geographical distribution. It is also possible to perform optimal classification based on the geographical distribution. This allows for more appropriate classification by taking into account the geographical distribution of personas.

[0052] The classification unit can improve the accuracy of classification by referring to literature related to the persona during classification. The classification unit, for example, improves the accuracy of classification by referring to literature related to the persona during classification. For example, the classification unit improves the accuracy of classification by referring to literature related to the persona. Furthermore, optimal classification can be performed based on the related literature. Furthermore, the accuracy of classification can be improved by analyzing literature related to the persona. In this way, the accuracy of classification can be improved by referring to literature related to the persona.

[0053] The classification unit can take into consideration the market value of the persona when classifying. For example, the classification unit takes into consideration the market value of the persona when classifying. For example, personas with high market value are classified first. Personas with low market value can also be put off until later. The classification priority can also be determined based on market value. This allows for more appropriate classification by taking into consideration the market value of the persona.

[0054] The selection unit can adjust the level of detail of the selection based on the importance of the persona at the time of selection. For example, the selection unit adjusts the level of detail of the selection based on the importance of the persona at the time of selection. For example, important personas can be selected with priority. Or general personas can be selected later. The level of detail of the selection can also be adjusted based on the importance of the persona. In this way, adjusting the level of detail of the selection based on the importance of the persona enables more appropriate selection.

[0055] The selection unit can apply different selection algorithms depending on the category of the persona when making a selection. For example, the selection unit applies different selection algorithms depending on the category of the persona when making a selection. For example, an algorithm that gives priority to selecting sociable personas can be applied. Alternatively, an algorithm that gives priority to selecting personas who like the outdoors can be applied. Furthermore, the optimal selection algorithm can be selected depending on the category of the persona. In this way, applying different selection algorithms depending on the category of the persona enables more appropriate selection.

[0056] The selection unit can improve the accuracy of selection by referring to the user's past selection results when making a selection. For example, the selection unit improves the accuracy of selection by referring to the user's past selection results when making a selection. For example, the selection accuracy is improved based on personas selected by the user in the past. The selection unit can also analyze the user's past selection results and improve the selection algorithm. The selection unit can also make a more accurate selection by referring to the user's past selection results. In this way, the selection accuracy can be improved by referring to the user's past selection results.

[0057] The selection unit can determine the priority of selection based on the time of submission of the persona at the time of selection. For example, the selection unit determines the priority of selection based on the time of submission of the persona at the time of selection. For example, the selection unit preferentially selects the most recently submitted persona. Also, it is possible to postpone the persona that was submitted earlier. It is also possible to determine the priority of selection based on the time of submission. In this way, by determining the priority of selection based on the time of submission of the persona, it is possible to preferentially select the most recent persona.

[0058] The selection unit can adjust the selection order based on the relevance of the personas at the time of selection. For example, the selection unit adjusts the selection order based on the relevance of the personas at the time of selection. For example, highly relevant personas are selected preferentially. Also, personas with low relevance can be postponed. Also, the selection order can be adjusted based on the relevance of the personas. In this way, by adjusting the selection order based on the relevance of the personas, highly relevant personas can be selected preferentially.

[0059] The selection unit can adjust the use of technical terminology in the selection according to the user's level of expertise when making a selection. The selection unit, for example, adjusts the use of technical terminology in the selection according to the user's level of expertise when making a selection. For example, a user with high level of expertise can be provided with selection options that use a lot of technical terminology. Also, a user with low level of expertise can be provided with selection options that use simple language. The use of technical terminology in the selection can also be adjusted according to the user's level of expertise. In this way, adjusting the use of technical terminology in the selection according to the user's level of expertise enables a selection that is easy for the user to understand.

[0060] The utilization unit can adjust the level of detail of utilization based on the importance of the persona at the time of utilization. For example, the utilization unit adjusts the level of detail of utilization based on the importance of the persona at the time of utilization. For example, important personas can be utilized with priority. Or general personas can be deferred. The level of detail of utilization can also be adjusted based on the importance of the persona. In this way, by adjusting the level of detail of utilization based on the importance of the persona, more appropriate utilization becomes possible.

[0061] The utilization unit can apply different utilization algorithms depending on the category of the persona during utilization. For example, the utilization unit applies different utilization algorithms depending on the category of the persona during utilization. For example, an algorithm that prioritizes utilization of sociable personas can be applied. Alternatively, an algorithm that prioritizes utilization of personas that like the outdoors can be applied. Furthermore, the optimal utilization algorithm can be selected depending on the category of the persona. In this way, more appropriate utilization can be achieved by applying different utilization algorithms depending on the category of the persona.

[0062] The utilization unit can improve the accuracy of utilization by referring to the user's past utilization results during utilization. For example, the utilization unit improves the accuracy of utilization by referring to the user's past utilization results during utilization. For example, the utilization accuracy is improved based on personas that the user has used in the past. The utilization unit can also analyze the user's past utilization results and improve the utilization algorithm. The utilization unit can also perform more accurate utilization by referring to the user's past utilization results. In this way, the utilization accuracy can be improved by referring to the user's past utilization results.

[0063] The utilization department can determine the priority of utilization based on the time of submission of the persona at the time of utilization. For example, the utilization department determines the priority of utilization based on the time of submission of the persona at the time of utilization. For example, the utilization department gives priority to the most recently submitted persona. It is also possible to postpone the use of personas that have been submitted earlier. It is also possible to determine the priority of utilization based on the time of submission. In this way, by determining the priority of utilization based on the time of submission of the persona, it is possible to preferentially utilize the most recent persona.

[0064] The utilization unit can adjust the order of utilization based on the relevance of personas at the time of utilization. The utilization unit, for example, adjusts the order of utilization based on the relevance of personas at the time of utilization. For example, highly relevant personas are utilized preferentially. Also, personas with low relevance can be postponed. Also, the order of utilization can be adjusted based on the relevance of personas. In this way, by adjusting the order of utilization based on the relevance of personas, highly relevant personas can be utilized preferentially.

[0065] The utilization unit can adjust the use of technical terms in the utilization according to the user's level of expertise during utilization. For example, the utilization unit can provide utilization options that use a lot of technical terms to a user with high expertise. Also, it can provide utilization options that use simpler words to a user with low expertise. It can also adjust the use of technical terms in the utilization according to the user's level of expertise. In this way, by adjusting the use of technical terms in the utilization according to the user's level of expertise, utilization that is easy for the user to understand can be achieved.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The persona generation system can further include a behavior acquisition unit that acquires a user's behavior history. The behavior acquisition unit collects the user's website browsing history, app usage history, etc., and analyzes the user's behavior patterns based on this information. For example, it can identify the categories of websites the user frequently visits and apps they use, and reflect this information in persona generation. The behavior acquisition unit can also acquire the user's location information and analyze behavior patterns in specific locations. This makes it possible to generate a more detailed and accurate persona based on the user's behavior history.

[0068] The classification unit can classify the generated personas by time of day. For example, by classifying personas generated in the morning and personas generated in the evening separately, it is possible to reflect the differences in user behavior patterns and values ​​between time periods. It is also possible to classify personas for weekends and weekdays separately. This makes it possible to classify personas according to time of day and day of the week, allowing for the development of more accurate marketing strategies.

[0069] The utilization unit can utilize the selected persona for advertisement delivery. For example, it can deliver targeted advertisements based on a specific persona and display advertisements tailored to the user's interests. It can also adjust the content and display timing of advertisements based on the persona. In this way, by utilizing the selected persona for advertisement delivery, a more effective advertising strategy can be realized.

[0070] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation accuracy can be improved based on personas generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0071] The selection unit can improve the accuracy of the selection by referring to the user's past selection results. For example, the selection accuracy can be improved based on personas selected by the user in the past. The selection unit can also analyze the user's past selection results and improve the selection algorithm. This makes it possible to improve the accuracy of the selection by referring to the user's past selection results.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The input unit inputs the user's values. The user's values ​​include, but are not limited to, personal beliefs, preferences, priorities, etc. The input unit allows the user to input details of their own values ​​and characteristics, for example. Step 2: The generation unit uses a generation AI to analyze the values ​​input by the input unit and generate a persona. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's values ​​and characteristics and generate a persona based on them. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona such as "a sociable person who likes the outdoors" based on them. Step 3: The classification unit categorizes the personas generated by the generation unit. The classification unit uses the generation AI to analyze the characteristics of the persona and classify it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and classifies it into the genre of "outdoor enthusiasts." Step 4: The selection unit selects the personas categorized by the classification unit. For example, the selection unit selects the persona that the user wants from the genre. Step 5: The utilization unit utilizes the personas selected by the selection unit. For example, the utilization unit utilizes the selected personas in a marketing strategy.

[0074] (Example 2) A persona generation system according to an embodiment of the present invention generates personas that reflect a user's values ​​and share them on a platform. In the persona generation system, a user inputs their values ​​and characteristics, and a generation AI analyzes the information to generate a persona. The generated persona is then analyzed and categorized by the generation AI before being uploaded to the platform. A user can select a desired persona from the genre and use it for a specific purpose. For example, in the persona generation system, a user inputs details of their values ​​and characteristics. For example, the user inputs information such as "I am sociable and enjoy the outdoors." This information is input to the generation AI. The persona generation system then uses the generation AI to analyze the input information and generate a persona. The generation AI analyzes the user's values ​​and characteristics and generates a persona based on them. For example, a persona such as "a sociable person who enjoys the outdoors" is generated. The persona generation system then analyzes and categorized the generated persona before uploading it to the platform. The generation AI analyzes the persona's characteristics and classifies it into an appropriate genre. For example, a persona can be categorized into the "outdoor enthusiast" genre. The persona generation system then allows the user to select the persona they want from the genre and use it according to their needs. For example, when formulating a marketing strategy, selecting the "outdoor enthusiast" persona and using that persona as a reference can lead to a more effective approach. This allows the persona generation system to efficiently generate, classify, select, and utilize personas that reflect the user's values. This allows the persona generation system to accurately grasp the personas of the target market and formulate an efficient strategy. For example, when formulating a marketing strategy for a new product, referring to personas in a specific genre can lead to a more effective approach. Furthermore, users can obtain a more personalized experience by selecting a persona that matches their values.

[0075] A persona generation system according to an embodiment includes an input unit, a generation unit, a classification unit, a selection unit, and an utilization unit. The input unit inputs a user's values. The user's values ​​include, but are not limited to, personal beliefs, preferences, and priorities. For example, the input unit allows the user to input details of their own values ​​and characteristics. The generation unit uses a generation AI to analyze the values ​​input by the input unit and generate a persona. The generation AI analyzes the user's values ​​and characteristics using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a persona based on the values ​​and characteristics. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona such as "a sociable person who loves the outdoors" based on the values ​​and characteristics. The classification unit categorizes the persona generated by the generation unit. The classification unit uses the generation AI to analyze the characteristics of the persona and classify it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and classifies it into the "outdoor lover" genre. The selection unit selects personas categorized by the classification unit. The selection unit, for example, selects a persona that the user wants from a genre. The utilization unit utilizes the personas selected by the selection unit. The utilization unit, for example, utilizes the selected personas in a marketing strategy. This allows the persona generation system according to the embodiment to efficiently generate, classify, select, and utilize personas that reflect the user's values.

[0076] The generation unit can analyze the user's values ​​using a generation AI and generate a persona. The generation unit, for example, uses a generation AI to analyze the user's values ​​and generate a persona. The generation AI, for example, uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's values ​​and characteristics and generate a persona based on the values ​​and characteristics. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona that is "sociable and likes the outdoors" based on the values ​​and characteristics. In this way, the generation AI can be used to generate a persona that accurately reflects the user's values.

[0077] The classification unit can categorize the personas generated by the generation AI. The classification unit categorizes the generated personas using, for example, the generation AI. The generation AI, for example, analyzes the characteristics of the persona and categorizes it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and categorizes it into the genre "outdoor enthusiast." In this way, the use of the generation AI improves the accuracy of categorizing the personas into categories.

[0078] The selection unit can select a desired persona from a genre. For example, the selection unit allows a user to select a desired persona from a genre. For example, when formulating a marketing strategy, the selection unit can select a persona of "outdoor enthusiasts" and use that persona as a reference to enable a more effective approach. This allows the user to select an appropriate persona from a genre.

[0079] The utilization department can utilize the selected personas in a marketing strategy. The utilization department, for example, utilizes the selected personas in a marketing strategy. The utilization department, for example, can accurately grasp the personas of the target market and formulate an efficient strategy. In this way, by utilizing the selected personas in a marketing strategy, it is possible to accurately grasp the personas of the target market and formulate an efficient strategy.

[0080] The utilization department can utilize the selected persona in sales materials. The utilization department, for example, utilizes the selected persona in sales materials. The utilization department can increase the effectiveness of sales activities by utilizing the persona in, for example, product introduction materials or presentation materials. In this way, by utilizing the selected persona in sales materials, the effectiveness of sales activities can be increased.

[0081] The utilization unit can utilize the selected persona in a review. The utilization unit, for example, utilizes the selected persona in a review. The utilization unit, for example, utilizes the persona to obtain feedback from users, which can be useful for improving products and services. In this way, by utilizing the selected persona in a review, feedback from users can be obtained.

[0082] The input unit can estimate the user's emotions and adjust the input method of values ​​based on the estimated user emotions. For example, the input unit can estimate the user's emotions and adjust the input method of values ​​based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, if the user is in a hurry, voice input can be prioritized to enable quick input of values. This allows for more appropriate input by adjusting the input method of values ​​according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, values ​​that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest values ​​that will be used in a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method by analyzing the user's past input history.

[0084] The input unit can filter the input content based on the user's current situation and areas of interest when inputting values. For example, when the user inputs values, the input unit filters the input content based on the user's current situation and areas of interest. For example, when the user inputs their current situation, related values ​​are suggested based on that. It is also possible to set the user's areas of interest in advance and filter the input content based on those areas. It is also possible to preferentially display values ​​related to a specific situation when the user is in that situation. In this way, by filtering the input content based on the user's current situation and areas of interest, more relevant values ​​can be input.

[0085] The input unit can select the optimum input means depending on the user's input method when inputting values. For example, when inputting values, the input unit selects the optimum input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the values ​​are input using voice recognition technology. Also, if the user selects text input, keyboard input can be supported. Also, if the user selects image input, the values ​​can be input using image analysis technology. In this way, by selecting the optimum input means depending on the user's input method, the input of values ​​is made more efficient.

[0086] The input unit can estimate the user's emotions and determine the priority of values ​​to be input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of values ​​to be input based on the estimated user emotions. For example, if the user is feeling stressed, important values ​​are input preferentially. Also, if the user is relaxed, detailed values ​​can be input. Also, if the user is in a hurry, the most important values ​​can be input preferentially. In this way, by determining the priority of values ​​according to the user's emotions, important values ​​can be input preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The input unit can prioritize inputting highly relevant values ​​in consideration of the user's geographical location information when inputting values. For example, the input unit prioritizes inputting highly relevant values ​​in consideration of the user's geographical location information when inputting values. For example, when the user is in a specific area, values ​​related to that area can be prioritized. Also, when the user is traveling, values ​​related to the travel destination can be prioritized. Also, when the user is at home, values ​​related to the home can be prioritized. In this way, highly relevant values ​​can be prioritized by taking the user's geographical location information into consideration.

[0088] The input unit can analyze the user's social media activity and input related values ​​when values ​​are input. For example, the input unit can analyze the user's social media activity and input related values ​​when values ​​are input. For example, the input unit can automatically input values ​​shared by the user on social media. It can also analyze the content of the user's social media posts and input related values. It can also input related values ​​by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently input related values ​​by analyzing the user's social media activity.

[0089] The input unit can customize the input method by reflecting the user's past feedback when inputting values. For example, the input unit customizes the input method by reflecting the user's past feedback when inputting values. For example, the input method is customized based on feedback provided by the user in the past. The input unit can also suggest the optimal input means based on the user's past feedback. The input interface can also be improved by analyzing the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, allowing for more appropriate input.

[0090] The generation unit can estimate the user's emotions and adjust the persona generation method based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the persona generation method based on the estimated user emotions. For example, if the user is relaxed, a persona that progresses at a leisurely pace can be generated. If the user is in a hurry, a persona that emphasizes the shortest route can be generated. If the user is excited, a persona that adds visually stimulating effects can be generated. In this way, by adjusting the persona generation method according to the user's emotions, a more appropriate persona can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The generation unit can adjust the level of detail of the generation based on the importance of values ​​when generating a persona. For example, the generation unit adjusts the level of detail of the generation based on the importance of values ​​when generating a persona. For example, a detailed persona is generated based on important values. A simplified persona can also be generated based on general values. The level of detail of the generation can also be adjusted according to the importance of the user's values. In this way, by adjusting the level of detail of the generation based on the importance of values, a more appropriate persona can be generated.

[0092] The generation unit can apply different generation algorithms depending on the value category when generating a persona. For example, the generation unit applies different generation algorithms depending on the value category when generating a persona. For example, an algorithm that generates a sociable persona based on a sociable value can be applied. Also, an algorithm that generates an outdoorsy persona can be applied based on a value of loving the outdoors. Also, the optimal generation algorithm can be selected depending on the category of the user's value. In this way, by applying different generation algorithms depending on the value category, a more appropriate persona can be generated.

[0093] The generation unit can improve the accuracy of generation when generating a persona by referring to the user's past generation results. For example, when generating a persona, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation accuracy is improved based on personas generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. The generation unit can also generate a more accurate persona by referring to the user's past generation results. In this way, the generation accuracy can be improved by referring to the user's past generation results.

[0094] The generation unit can estimate the user's emotions and adjust the persona generation speed based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the persona generation speed based on the estimated user emotions. For example, if the user is in a hurry, the generation speed can be increased. Also, if the user is relaxed, the generation speed can be decreased. The generation speed can also be adjusted according to the user's emotions. This makes it possible to generate a persona that meets the user's needs by adjusting the persona generation speed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0095] The generation unit can determine the generation priority based on the time of submission of values ​​when generating a persona. For example, the generation unit determines the generation priority based on the time of submission of values ​​when generating a persona. For example, the generation unit generates values ​​that were submitted most recently with priority. Values ​​that were submitted earlier can also be postponed. The generation priority can also be determined based on the time of submission. In this way, by determining the generation priority based on the time of submission of values, it is possible to generate a persona that preferentially reflects the most recent values.

[0096] The generation unit can adjust the order of generation based on the relevance of values ​​when generating a persona. For example, the generation unit adjusts the order of generation based on the relevance of values ​​when generating a persona. For example, highly relevant values ​​are generated with priority. Also, values ​​with low relevance can be postponed. The order of generation can also be adjusted based on the relevance of values. In this way, by adjusting the order of generation based on the relevance of values, it is possible to generate a persona that reflects highly relevant values ​​with priority.

[0097] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a persona. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating a persona. For example, for a user with high level of expertise, a persona that uses a lot of technical terminology can be generated. Also, for a user with low level of expertise, a persona that uses simple language can be generated. The use of technical terminology in the generation can also be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a persona that is easy for the user to understand can be generated.

[0098] The classification unit can estimate the user's emotion and adjust the classification criteria based on the estimated user's emotion. The classification unit, for example, estimates the user's emotion and adjusts the classification criteria based on the estimated user's emotion. For example, if the user is relaxed, detailed classification criteria can be applied. On the other hand, if the user is in a hurry, simplified classification criteria can be applied. The classification criteria can also be adjusted according to the user's emotion. This allows for more appropriate classification by adjusting the classification criteria according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The classification unit can improve the accuracy of classification by taking into account the interrelationships between personas when classifying. For example, the classification unit improves the accuracy of classification by taking into account the interrelationships between personas when classifying. For example, the classification unit analyzes the interrelationships between personas and classifies highly related personas into the same genre. The classification accuracy can also be improved by taking into account the interrelationships between personas. Furthermore, optimal classification can be performed based on the interrelationships between personas. In this way, the classification accuracy can be improved by taking into account the interrelationships between personas.

[0100] The classification unit can perform classification taking into account attribute information of the persona creator when classifying. For example, the classification unit performs classification taking into account attribute information of the persona creator when classifying. For example, classification is performed taking into account the age and gender of the persona creator. Classification can also be performed taking into account the occupation and hobbies of the persona creator. Optimal classification can also be performed based on the attribute information of the persona creator. As a result, more appropriate classification is possible by taking into account attribute information of the persona creator.

[0101] The classification unit can weight the classification based on the frequency of generation of the persona during classification. For example, the classification unit weights the classification based on the frequency of generation of the persona during classification. For example, personas with a high generation frequency are prioritized for classification. Personas with a low generation frequency can also be postponed. Classification can also be weighted based on the frequency of generation. Thus, weighting the classification based on the frequency of generation of the persona enables more appropriate classification.

[0102] The classification unit can estimate the user's emotion and adjust the order in which the classification results are displayed based on the estimated user's emotion. The classification unit, for example, estimates the user's emotion and adjusts the order in which the classification results are displayed based on the estimated user's emotion. For example, if the user is relaxed, detailed classification results can be displayed preferentially. On the other hand, if the user is in a hurry, simplified classification results can be displayed preferentially. The display order of the classification results can also be adjusted according to the user's emotion. This allows for a display that is easy for the user to view by adjusting the order in which the classification results are displayed according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The classification unit can perform classification taking into account the geographical distribution of personas. For example, the classification unit performs classification taking into account the geographical distribution of personas. For example, the classification unit analyzes the geographical distribution of personas and performs classification by region. It is also possible to classify highly related personas into the same genre taking into account the geographical distribution. It is also possible to perform optimal classification based on the geographical distribution. This allows for more appropriate classification by taking into account the geographical distribution of personas.

[0104] The classification unit can improve the accuracy of classification by referring to literature related to the persona during classification. The classification unit, for example, improves the accuracy of classification by referring to literature related to the persona during classification. For example, the classification unit improves the accuracy of classification by referring to literature related to the persona. Furthermore, optimal classification can be performed based on the related literature. Furthermore, the accuracy of classification can be improved by analyzing literature related to the persona. In this way, the accuracy of classification can be improved by referring to literature related to the persona.

[0105] The classification unit can take into consideration the market value of the persona when classifying. For example, the classification unit takes into consideration the market value of the persona when classifying. For example, personas with high market value are classified first. Personas with low market value can also be put off until later. The classification priority can also be determined based on market value. This allows for more appropriate classification by taking into consideration the market value of the persona.

[0106] The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the selection method based on the estimated user's emotion. For example, if the user is relaxed, detailed selection options can be provided. On the other hand, if the user is in a hurry, simplified selection options can be provided. The selection method can also be adjusted according to the user's emotion. This allows for more appropriate selection by adjusting the selection method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The selection unit can adjust the level of detail of the selection based on the importance of the persona at the time of selection. For example, the selection unit adjusts the level of detail of the selection based on the importance of the persona at the time of selection. For example, important personas can be selected with priority. Or general personas can be selected later. The level of detail of the selection can also be adjusted based on the importance of the persona. In this way, adjusting the level of detail of the selection based on the importance of the persona enables more appropriate selection.

[0108] The selection unit can apply different selection algorithms depending on the category of the persona when making a selection. For example, the selection unit applies different selection algorithms depending on the category of the persona when making a selection. For example, an algorithm that gives priority to selecting sociable personas can be applied. Alternatively, an algorithm that gives priority to selecting personas who like the outdoors can be applied. Furthermore, the optimal selection algorithm can be selected depending on the category of the persona. In this way, applying different selection algorithms depending on the category of the persona enables more appropriate selection.

[0109] The selection unit can improve the accuracy of selection by referring to the user's past selection results when making a selection. For example, the selection unit improves the accuracy of selection by referring to the user's past selection results when making a selection. For example, the selection accuracy is improved based on personas selected by the user in the past. The selection unit can also analyze the user's past selection results and improve the selection algorithm. The selection unit can also make a more accurate selection by referring to the user's past selection results. In this way, the selection accuracy can be improved by referring to the user's past selection results.

[0110] The selection unit can estimate the user's emotion and determine the selection priority based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and determines the selection priority based on the estimated user's emotion. For example, if the user is relaxed, detailed selection options can be displayed preferentially. On the other hand, if the user is in a hurry, simplified selection options can be displayed preferentially. The selection priority can also be determined according to the user's emotion. This enables more appropriate selection by determining the selection priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The selection unit can determine the priority of selection based on the time of submission of the persona at the time of selection. For example, the selection unit determines the priority of selection based on the time of submission of the persona at the time of selection. For example, the selection unit preferentially selects the most recently submitted persona. Also, it is possible to postpone the persona that was submitted earlier. It is also possible to determine the priority of selection based on the time of submission. In this way, by determining the priority of selection based on the time of submission of the persona, it is possible to preferentially select the most recent persona.

[0112] The selection unit can adjust the selection order based on the relevance of the personas at the time of selection. For example, the selection unit adjusts the selection order based on the relevance of the personas at the time of selection. For example, highly relevant personas are selected preferentially. Also, personas with low relevance can be postponed. Also, the selection order can be adjusted based on the relevance of the personas. In this way, by adjusting the selection order based on the relevance of the personas, highly relevant personas can be selected preferentially.

[0113] The selection unit can adjust the use of technical terminology in the selection according to the user's level of expertise when making a selection. The selection unit, for example, adjusts the use of technical terminology in the selection according to the user's level of expertise when making a selection. For example, a user with high level of expertise can be provided with selection options that use a lot of technical terminology. Also, a user with low level of expertise can be provided with selection options that use simple language. The use of technical terminology in the selection can also be adjusted according to the user's level of expertise. In this way, adjusting the use of technical terminology in the selection according to the user's level of expertise enables a selection that is easy for the user to understand.

[0114] The utilization unit can estimate the user's emotion and adjust the utilization method based on the estimated user's emotion. The utilization unit, for example, estimates the user's emotion and adjusts the utilization method based on the estimated user's emotion. For example, if the user is relaxed, a detailed utilization method can be provided. Alternatively, if the user is in a hurry, a simplified utilization method can be provided. The utilization method can also be adjusted according to the user's emotion. This enables more appropriate utilization by adjusting the utilization method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0115] The utilization unit can adjust the level of detail of utilization based on the importance of the persona at the time of utilization. For example, the utilization unit adjusts the level of detail of utilization based on the importance of the persona at the time of utilization. For example, important personas can be utilized with priority. Or general personas can be deferred. The level of detail of utilization can also be adjusted based on the importance of the persona. In this way, by adjusting the level of detail of utilization based on the importance of the persona, more appropriate utilization becomes possible.

[0116] The utilization unit can apply different utilization algorithms depending on the category of the persona during utilization. For example, the utilization unit applies different utilization algorithms depending on the category of the persona during utilization. For example, an algorithm that prioritizes utilization of sociable personas can be applied. Alternatively, an algorithm that prioritizes utilization of personas that like the outdoors can be applied. Furthermore, the optimal utilization algorithm can be selected depending on the category of the persona. In this way, more appropriate utilization can be achieved by applying different utilization algorithms depending on the category of the persona.

[0117] The utilization unit can improve the accuracy of utilization by referring to the user's past utilization results during utilization. For example, the utilization unit improves the accuracy of utilization by referring to the user's past utilization results during utilization. For example, the utilization accuracy is improved based on personas that the user has used in the past. The utilization unit can also analyze the user's past utilization results and improve the utilization algorithm. The utilization unit can also perform more accurate utilization by referring to the user's past utilization results. In this way, the utilization accuracy can be improved by referring to the user's past utilization results.

[0118] The utilization unit can estimate the user's emotions and determine utilization priorities based on the estimated user emotions. The utilization unit, for example, estimates the user's emotions and determines utilization priorities based on the estimated user emotions. For example, if the user is relaxed, detailed utilization options can be displayed preferentially. On the other hand, if the user is in a hurry, simplified utilization options can be displayed preferentially. The utilization priorities can also be determined according to the user's emotions. This enables more appropriate utilization by determining utilization priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The utilization department can determine the priority of utilization based on the time of submission of the persona at the time of utilization. For example, the utilization department determines the priority of utilization based on the time of submission of the persona at the time of utilization. For example, the utilization department gives priority to the most recently submitted persona. It is also possible to postpone the use of personas that have been submitted earlier. It is also possible to determine the priority of utilization based on the time of submission. In this way, by determining the priority of utilization based on the time of submission of the persona, it is possible to preferentially utilize the most recent persona.

[0120] The utilization unit can adjust the order of utilization based on the relevance of personas at the time of utilization. The utilization unit, for example, adjusts the order of utilization based on the relevance of personas at the time of utilization. For example, highly relevant personas are utilized preferentially. Also, personas with low relevance can be postponed. Also, the order of utilization can be adjusted based on the relevance of personas. In this way, by adjusting the order of utilization based on the relevance of personas, highly relevant personas can be utilized preferentially.

[0121] The utilization unit can adjust the use of technical terms in the utilization according to the user's level of expertise during utilization. For example, the utilization unit can provide utilization options that use a lot of technical terms to a user with high expertise. Also, it can provide utilization options that use simpler words to a user with low expertise. It can also adjust the use of technical terms in the utilization according to the user's level of expertise. In this way, by adjusting the use of technical terms in the utilization according to the user's level of expertise, utilization that is easy for the user to understand can be achieved. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, generation unit, classification unit, selection unit, and utilization 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 input unit is realized by the reception device 38 of the smart device 14, through which the user inputs their values ​​and characteristics in detail. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input values ​​using a generation AI to generate a persona. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and categorizes the generated personas. The selection unit is realized, for example, by the control unit 46A of the smart device 14, and allows the user to select a desired persona from a genre. The utilization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and utilizes the selected persona in a marketing strategy. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, generation unit, classification unit, selection unit, and utilization 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 input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs their values ​​and characteristics by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input values ​​using a generation AI to generate a persona. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and categorizes the generated personas. The selection unit is realized, for example, by the control unit 46A of the smart glasses 214, and the user selects a desired persona from a genre. The utilization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and utilizes the selected persona in a marketing strategy. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, generation unit, classification unit, selection unit, and utilization unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs their values ​​and characteristics by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input values ​​using a generation AI to generate a persona. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and categorizes the generated personas. The selection unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and the user selects a desired persona from a genre. The utilization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and utilizes the selected persona in a marketing strategy. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, generation unit, classification unit, selection unit, and utilization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs their values ​​and characteristics by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input values ​​using a generation AI to generate a persona. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and categorizes the generated personas. The selection unit is realized, for example, by the control unit 46A of the robot 414, and the user selects a desired persona from a genre. The utilization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and utilizes the selected persona in a marketing strategy.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The persona generation system can further include a behavior acquisition unit that acquires a user's behavior history. The behavior acquisition unit collects the user's website browsing history, app usage history, etc., and analyzes the user's behavior patterns based on this information. For example, it can identify the categories of websites the user frequently visits and apps they use, and reflect this information in persona generation. The behavior acquisition unit can also acquire the user's location information and analyze behavior patterns in specific locations. This makes it possible to generate a more detailed and accurate persona based on the user's behavior history.

[0124] The generation unit can estimate the user's emotions and adjust the persona generation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed persona can be generated, and if the user is in a hurry, a simplified persona can be generated. Also, if the user is excited, a visually stimulating persona can be generated. In this way, by adjusting the persona generation method according to the user's emotions, a more appropriate persona can be generated.

[0125] The classification unit can classify the generated personas by time of day. For example, by classifying personas generated in the morning and personas generated in the evening separately, it is possible to reflect the differences in user behavior patterns and values ​​between time periods. It is also possible to classify personas for weekends and weekdays separately. This makes it possible to classify personas according to time of day and day of the week, allowing for the development of more accurate marketing strategies.

[0126] The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. For example, if the user is relaxed, detailed selection options can be provided, and if the user is in a hurry, simplified selection options can be provided. Also, if the user is feeling stressed, the options can be narrowed down and displayed. In this way, adjusting the selection method according to the user's emotion enables a more appropriate selection.

[0127] The utilization unit can utilize the selected persona for advertisement delivery. For example, it can deliver targeted advertisements based on a specific persona and display advertisements tailored to the user's interests. It can also adjust the content and display timing of advertisements based on the persona. In this way, by utilizing the selected persona for advertisement delivery, a more effective advertising strategy can be realized.

[0128] The input unit can estimate the user's emotions and adjust the input method for values ​​based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. This allows the input method for values ​​to be adjusted according to the user's emotions, enabling more appropriate input.

[0129] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation accuracy can be improved based on personas generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. This makes it possible to improve the accuracy of generation by referring to the user's past generation results.

[0130] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed classification criteria can be applied, and if the user is in a hurry, simplified classification criteria can be applied. Also, if the user is feeling stressed, the classification criteria can be relaxed and displayed. In this way, adjusting the classification criteria according to the user's emotions enables more appropriate classification.

[0131] The selection unit can improve the accuracy of the selection by referring to the user's past selection results. For example, the selection accuracy can be improved based on personas selected by the user in the past. The selection unit can also analyze the user's past selection results and improve the selection algorithm. This makes it possible to improve the accuracy of the selection by referring to the user's past selection results.

[0132] The utilization unit can estimate the user's emotions and adjust the utilization method based on the estimated user's emotions. For example, if the user is relaxed, detailed utilization methods can be provided, and if the user is in a hurry, simplified utilization methods can be provided. Also, if the user is feeling stressed, the utilization methods can be narrowed down and displayed. This allows for more appropriate utilization by adjusting the utilization method according to the user's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The input unit inputs the user's values. The user's values ​​include, but are not limited to, personal beliefs, preferences, priorities, etc. The input unit allows the user to input details of their own values ​​and characteristics, for example. Step 2: The generation unit uses a generation AI to analyze the values ​​input by the input unit and generate a persona. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's values ​​and characteristics and generate a persona based on them. For example, the generation AI analyzes the user's values ​​and characteristics and generates a persona such as "a sociable person who likes the outdoors" based on them. Step 3: The classification unit categorizes the personas generated by the generation unit. The classification unit uses the generation AI to analyze the characteristics of the persona and classify it into an appropriate genre. For example, the generation AI analyzes the characteristics of the persona and classifies it into the genre of "outdoor enthusiasts." Step 4: The selection unit selects the personas categorized by the classification unit. For example, the selection unit selects the persona that the user wants from the genre. Step 5: The utilization unit utilizes the personas selected by the selection unit. For example, the utilization unit utilizes the selected personas in a marketing strategy.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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. an input unit for inputting a user's values; a generation unit that analyzes the values ​​input by the input unit and generates a persona; a classification unit that categorizes the personas generated by the generation unit; a selection unit that selects personas categorized by the classification unit; and an utilization unit that utilizes the persona selected by the selection unit. A system characterized by:

2. The generation unit Analyze user values ​​and generate personas using generative AI The system of claim 1 .

3. The classification unit Classifying personas generated by generative AI into genres The system of claim 1 .

4. The selection unit Select the persona you want from the genre The system of claim 1 .

5. The utilization part is Use the selected personas in your marketing strategy The system of claim 1 .

6. The utilization part is Use the selected personas in your sales materials The system of claim 1 .

7. The utilization part is Use the selected personas for reviews The system of claim 1 .

8. The input unit Estimate the user's emotions and adjust the input method of values ​​based on the estimated user emotions The system of claim 1 .

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