system

Abstract

The system according to the embodiment aims to efficiently collect and analyze reviews from personas tailored to a target demographic. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs attributes of a target demographic. The generation unit generates a persona based on the attributes input by the reception unit. The collection unit collects reviews based on the persona generated by the generation unit. The analysis unit analyzes the reviews collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently collect and analyze reviews from personas tailored to target demographics.

[0005] The system according to the embodiment aims to efficiently collect and analyze reviews from personas tailored to a target demographic. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs attributes of a target demographic. The generation unit generates a persona based on the attributes input by the reception unit. The collection unit collects reviews based on the persona generated by the generation unit. The analysis unit analyzes the reviews collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze reviews from personas tailored to a target demographic. [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 virtual review platform according to an embodiment of the present invention is a system that uses a generative AI to generate personas with various attributes and automatically collect product reviews tailored to a target demographic. This system targets service providers, system developers, and others, and allows users to automatically collect product reviews from personas tailored to their target demographic. Specifically, users first input the attributes of the target demographic. Next, a generative AI generates a persona based on those attributes. The generated persona reviews the product and provides the results on the platform. This mechanism allows users to receive realistic feedback tailored to the target demographic. For example, when a user inputs attributes such as age, gender, occupation, and hobbies, the generative AI generates a persona based on those attributes. The generated persona reviews the product's usability, design, functionality, and other aspects and provides the results on the platform. This allows users to receive realistic feedback tailored to the target demographic. Furthermore, the present invention proposes a new development style, the market size of which is described as a specific market size. This allows the virtual review platform to automatically collect product reviews from personas tailored to the target demographic and provide analysis results.

[0029] A virtual review platform according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs attributes of a target demographic. The attributes of the target demographic include, but are not limited to, age, gender, occupation, and hobbies. For example, the reception unit provides a text box for inputting age. The reception unit may also provide radio buttons for selecting gender. The reception unit may also provide a drop-down menu for selecting occupation. For example, the reception unit may provide check boxes for inputting hobbies. The generation unit generates a persona using a generation AI based on the attributes input by the reception unit. The generation unit generates a virtual persona based on attributes such as age, gender, occupation, and hobbies. For example, the generation AI may receive a prompt such as "Please generate a persona based on these attributes" and generate the persona. The generation unit may also generate a persona using the generation AI based on a combination of attributes. For example, the generation unit may generate a persona based on a combination of age and gender. The collection unit collects product reviews written by the generated persona. The collection unit, for example, causes the generated persona to review the usability, design, functionality, etc. of the product. For example, the collection unit causes the generated persona to receive a prompt such as "Please rate the usability of this product" and perform the review. The collection unit can also cause the generated persona to receive a prompt such as "Please rate the design of this product" and perform the review. For example, the collection unit can cause the generated persona to receive a prompt such as "Please rate the functionality of this product" and perform the review. The analysis unit analyzes the collected reviews. For example, the analysis unit analyzes the collected reviews using text mining technology. The analysis unit can also analyze the collected reviews using statistical analysis technology. The analysis unit can also analyze the collected reviews using sentiment analysis technology. For example, the analysis unit extracts specific keywords from the collected reviews and analyzes their frequency. The provision unit provides the analysis results obtained by the analysis unit.The providing unit provides the analysis results in, for example, a report format. The providing unit can also provide the analysis results in, for example, a dashboard display. The providing unit can also provide the analysis results in a notification format. For example, the providing unit can also provide the analysis results in PDF format. As a result, the virtual review platform according to the embodiment can automatically collect product reviews from personas tailored to a target demographic and provide the analysis results.

[0030] The reception unit can input attributes including age, gender, occupation, and hobbies. The reception unit, for example, provides a text box for inputting age. For example, the reception unit can allow the user to input age in ranges such as teens, twenties, and thirties. The reception unit can also provide radio buttons for selecting gender. For example, the reception unit can allow the user to select gender from options such as male, female, and other. The reception unit can also provide a drop-down menu for selecting occupation. For example, the reception unit can allow the user to select occupation from options such as student, company employee, and freelance. The reception unit can also provide check boxes for inputting hobbies. For example, the reception unit can allow the user to select hobbies from options such as sports, reading, and traveling. This allows detailed attributes of the target demographic to be input. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the attribute data input by the user to a generation AI and cause the generation AI to analyze the attribute data.

[0031] The generation unit can generate a persona based on input attributes. The generation unit generates a virtual person image based on attributes such as age, gender, occupation, and hobbies. For example, the generation unit generates a virtual person image based on attributes such as age in her 20s, gender as female, occupation as an office worker, and hobby as reading. The generation unit can also generate a persona when the generation AI receives a prompt such as, "Please generate a persona based on these attributes." For example, the generation unit can generate a persona when the generation AI receives a prompt such as, "Please generate a persona for a woman in her 20s who is an office worker and whose hobby is reading." The generation unit can also generate a persona based on a combination of attributes using the generation AI. For example, the generation unit can generate a persona based on a combination of age and gender. This makes it possible to generate a persona based on input attributes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit can input attribute data entered by the user into the generation AI and cause the generation AI to generate a persona.

[0032] The collection unit can collect reviews of the usability, design, and functionality of a product by the generated persona. For example, the collection unit allows the generated persona to evaluate the usability of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the usability of this product" and perform the review. The collection unit can also allow the generated persona to evaluate the design of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the design of this product" and perform the review. The collection unit can also allow the generated persona to evaluate the functionality of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the functionality of this product" and perform the review. This makes it possible to collect detailed reviews by the generated persona. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the reviews performed by the generated persona into a generation AI and cause the generation AI to analyze the reviews.

[0033] The analysis unit can analyze the collected reviews. The analysis unit can analyze the collected reviews using, for example, text mining technology. For example, the analysis unit can extract specific keywords from the collected reviews and analyze their frequency. The analysis unit can also analyze the collected reviews using statistical analysis technology. For example, the analysis unit can extract specific patterns from the collected reviews and analyze their trends. The analysis unit can also analyze the collected reviews using sentiment analysis technology. For example, the analysis unit can extract emotional expressions from the collected reviews and analyze their sentiment scores. In this way, the collected reviews can be analyzed. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected reviews to a generation AI and have the generation AI analyze the reviews.

[0034] The providing unit can provide the analysis results. The providing unit can provide the analysis results, for example, in report format. For example, the providing unit can provide the analysis results in PDF format. The providing unit can also provide the analysis results in a dashboard display. For example, the providing unit can display the analysis results as graphs or charts. The providing unit can also provide the analysis results in notification format. For example, the providing unit can send the analysis results by email. In this way, the analysis results can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to execute a method for providing the results.

[0035] The reception unit can analyze past input history and suggest an appropriate attribute input method. For example, the reception unit automatically displays attributes that the user frequently input in the past as candidates. For example, the reception unit displays attributes such as age, gender, occupation, and hobbies that the user has input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest attributes to be used in a specific time period based on the user's past input history. For example, the reception unit suggests an optimal input method based on attributes that the user has input in a specific time period in the past. This makes it possible to suggest an optimal attribute input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to suggest an optimal input method.

[0036] When inputting attributes, the reception unit can customize input items based on the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying attributes related to the project the user is currently working on. For example, the reception unit can prioritize displaying attributes such as age, gender, occupation, and hobbies related to the project the user is currently working on. The reception unit can also automatically suggest related attributes based on the user's area of ​​interest. For example, the reception unit automatically suggests attributes that the user may be interested in. The reception unit can also dynamically change required attributes depending on the progress of the user's project. For example, the reception unit dynamically changes input items depending on the progress of the user's project. This makes it possible to customize input items based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and cause the generation AI to customize the input items.

[0037] The reception unit can prioritize inputting highly relevant attributes in consideration of the user's geographical location information when inputting attributes. For example, when the user is in a specific area, the reception unit can prioritize inputting attributes related to the area. For example, when the user is in a specific area, the reception unit can prioritize inputting attributes related to the area, such as age, gender, occupation, and hobbies. Furthermore, when the user is traveling, the reception unit can also prioritize inputting attributes related to the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting attributes related to the travel destination, such as age, gender, occupation, and hobbies. Furthermore, when the user is at home, the reception unit can prioritize inputting attributes related to the user's home. For example, when the user is at home, the reception unit can prioritize inputting attributes related to the user's home, such as age, gender, occupation, and hobbies. This allows highly relevant attributes to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI to determine the priority of highly relevant attributes.

[0038] The reception unit can analyze the user's social media activity when the attributes are input and suggest related attributes. The reception unit can, for example, suggest attributes related to topics frequently mentioned by the user on social media. For example, the reception unit can suggest attributes such as age, gender, occupation, and hobbies related to topics frequently mentioned by the user on social media. The reception unit can also analyze the attributes of the user's social media followers and suggest related attributes. For example, the reception unit can analyze the attributes of the user's social media followers and suggest related attributes such as age, gender, occupation, and hobbies. The reception unit can also analyze the content of the user's social media posts and suggest related attributes. For example, the reception unit can analyze the content of the user's social media posts and suggest related attributes such as age, gender, occupation, and hobbies. This makes it possible to suggest related attributes based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to suggest related attributes.

[0039] When generating a persona, the generation unit can adjust the level of detail of the generation based on the importance of the input attributes. For example, if there are many important attributes, the generation unit generates a detailed persona. For example, if there are many important attributes, such as age, gender, occupation, and hobbies, the generation unit generates a detailed persona. The generation unit can also generate a concise persona if there are few important attributes. For example, if there are few important attributes, such as age and gender, the generation unit generates a concise persona. The generation unit can also generate a persona that focuses on a specific attribute if that attribute is important. For example, if age is particularly important, the generation unit generates a persona that focuses on age. This allows the level of detail of the generation to be adjusted based on the importance of the input attributes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generative AI. For example, the generation unit can input attribute data input by a user into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0040] When generating a persona, the generation unit can apply different generation algorithms depending on the attribute category. The generation unit applies different generation algorithms based on, for example, an age attribute. For example, the generation unit applies different generation algorithms based on age attributes such as teens, twenties, and thirties. The generation unit can also apply different generation algorithms based on an occupation attribute. For example, the generation unit applies different generation algorithms based on occupation attributes such as student, company employee, and freelance. The generation unit can also apply different generation algorithms based on hobby attributes. For example, the generation unit applies different generation algorithms based on hobbies such as sports, reading, and traveling. This makes it possible to apply different generation algorithms depending on the attribute category. Some or all of the above-described processing in the generation unit may be performed using, or without, a generative AI. For example, the generation unit can input attribute data entered by a user into the generation AI and cause the generation AI to apply different generation algorithms.

[0041] When generating a persona, the generation unit can select an appropriate generation method by referring to the user's past persona generation history. The generation unit, for example, selects the optimal generation method based on personas previously generated by the user. For example, the generation unit selects the optimal generation method based on personas previously generated by the user having attributes such as age, gender, occupation, and hobbies. The generation unit can also select the most effective generation method from the user's past persona generation history. For example, the generation unit analyzes the user's past persona generation history and selects the optimal generation algorithm. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past persona generation history. For example, the generation unit adjusts parameters of the generation algorithm based on the user's past persona generation history. This makes it possible to select the optimal generation method based on the user's past persona generation history. Some or all of the above-described processing in the generation unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the generation unit can input the user's past persona generation history data into the generation AI and have the generation AI select the optimal generation method.

[0042] When generating a persona, the generation unit can generate a highly relevant persona by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation unit generates a persona related to that area. For example, when the user is in a specific area, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to that area. Furthermore, when the user is traveling, the generation unit can also generate a persona related to the travel destination. For example, when the user is traveling, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to the travel destination. Furthermore, when the user is at home, the generation unit can also generate a persona related to the user's home. For example, when the user is at home, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to the user's home. This makes it possible to generate a highly relevant persona based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed, for example, using generative AI, or may be performed without using generative AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a highly relevant persona.

[0043] The collection unit can adjust the level of detail of the reviews to be collected based on the persona's attributes during collection. For example, the collection unit collects detailed reviews when there are many important attributes. For example, the collection unit collects detailed reviews when there are many important attributes, such as age, gender, occupation, and hobbies. The collection unit can also collect concise reviews when there are few important attributes. For example, the collection unit collects concise reviews when there are few important attributes, such as age and gender. The collection unit can also collect reviews that focus on a particular attribute when that attribute is important. For example, the collection unit collects reviews that focus on age when age is particularly important. This makes it possible to adjust the level of detail of the reviews to be collected based on the persona's attributes. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input persona attribute data into the generation AI and cause the generation AI to adjust the level of detail of the reviews.

[0044] The collection unit can apply different collection algorithms depending on the category of the review when collecting reviews. For example, the collection unit applies a specific algorithm when collecting reviews related to the usability of a product. For example, the collection unit applies a specific collection algorithm when collecting reviews related to the usability of a product. The collection unit can also apply a different algorithm when collecting reviews related to the design of a product. For example, the collection unit applies a different collection algorithm when collecting reviews related to the design of a product. The collection unit can also apply a different algorithm when collecting reviews related to the functionality of a product. For example, the collection unit applies a different collection algorithm when collecting reviews related to the functionality of a product. This makes it possible to apply an optimal collection algorithm depending on the category of the review. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input review category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0045] The collection unit can select the optimal collection method by referring to the persona's past review history during collection. The collection unit, for example, selects the optimal collection method based on reviews previously collected by the persona. For example, the collection unit selects the optimal collection method based on reviews previously collected by the persona, including attributes such as age, gender, occupation, and hobbies. The collection unit can also select the most effective collection method from the persona's past review history. For example, the collection unit analyzes the persona's past review history and selects an optimal collection algorithm. The collection unit can also adjust parameters of the collection algorithm by referring to the persona's past review history. For example, the collection unit adjusts parameters of the collection algorithm based on the persona's past review history. This allows the optimal collection method to be selected based on the persona's past review history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the persona's past review history data into a generation AI and cause the generation AI to select the optimal collection method.

[0046] The collection unit can collect highly relevant reviews by taking into account the geographic location information of the persona during collection. For example, if the persona is in a specific area, the collection unit collects reviews related to that area. For example, if the persona is in a specific area, the collection unit collects reviews having attributes related to that area, such as age, gender, occupation, and hobbies. Furthermore, if the persona is traveling, the collection unit can also collect reviews related to the travel destination. For example, if the persona is traveling, the collection unit collects reviews having attributes related to the travel destination, such as age, gender, occupation, and hobbies. Furthermore, if the persona is at home, the collection unit can also collect reviews related to the persona's home. For example, if the persona is at home, the collection unit collects reviews having attributes related to the persona's home, such as age, gender, occupation, and hobbies. This makes it possible to collect highly relevant reviews based on the persona's geographic location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the persona's geographic location information into the generation AI and cause the generation AI to collect highly relevant reviews.

[0047] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected reviews. For example, if there are many critical reviews, the analysis unit performs a detailed analysis. For example, if there are many critical reviews among the collected reviews, the analysis unit performs a detailed analysis. Furthermore, if there are few critical reviews, the analysis unit can perform a brief analysis. For example, if there are few critical reviews among the collected reviews, the analysis unit can perform a brief analysis. Furthermore, if a specific review is important, the analysis unit can perform an analysis that focuses on that review. For example, if a specific review is important among the collected reviews, the analysis unit performs an analysis that focuses on that review. In this way, the level of detail of the analysis can be adjusted based on the importance of the collected reviews. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected review importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of the review during analysis. For example, the analysis unit applies a particular algorithm when analyzing reviews related to the usability of a product. For example, the analysis unit applies a particular analysis algorithm when analyzing reviews related to the usability of a product. The analysis unit can also apply a different algorithm when analyzing reviews related to the design of a product. For example, the analysis unit applies a different analysis algorithm when analyzing reviews related to the design of a product. The analysis unit can also apply an even different algorithm when analyzing reviews related to the functionality of a product. For example, the analysis unit applies an even different analysis algorithm when analyzing reviews related to the functionality of a product. This makes it possible to apply the optimal analysis algorithm depending on the category of the review. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input review category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0049] During analysis, the analysis unit can select the optimal analysis method by referring to past analysis results. The analysis unit, for example, selects the optimal analysis method based on past analysis results. For example, the analysis unit selects the optimal analysis method for collected reviews based on past analysis results. The analysis unit can also select the most effective analysis method from past analysis results. For example, the analysis unit analyzes past analysis results and selects the optimal analysis algorithm. The analysis unit can also adjust parameters of the analysis algorithm by referring to past analysis results. For example, the analysis unit adjusts parameters of the analysis algorithm based on past analysis results. This makes it possible to select the optimal analysis method based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and have the generation AI select the optimal analysis method.

[0050] The analysis unit may take the geographical distribution of reviews into consideration during analysis. For example, the analysis unit may prioritize analysis of reviews related to a specific region. For example, the analysis unit may prioritize analysis of reviews with attributes such as age, gender, occupation, and hobbies related to a specific region. The analysis unit may also compare and analyze reviews from different regions based on geographical distribution. For example, the analysis unit may compare and analyze reviews with attributes such as age, gender, occupation, and hobbies from different regions. The analysis unit may also perform an analysis that reflects the characteristics of each region by taking the geographical distribution into consideration. For example, the analysis unit may perform an analysis that reflects the characteristics of each region such as age, gender, occupation, and hobbies. This allows for an optimal analysis based on the geographical distribution of reviews. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input geographical distribution data of reviews into a generation AI and cause the generation AI to perform an optimal analysis.

[0051] The providing unit can select the optimal providing method by referring to the user's past usage history when providing the information. The providing unit selects the optimal providing method based on, for example, the user's past usage history. For example, the providing unit selects the optimal providing method based on the analysis results of collected reviews based on the user's past usage history. The providing unit can also select the most effective providing method from the user's past usage history. For example, the providing unit analyzes the user's past usage history and selects the optimal providing algorithm. The providing unit can also adjust parameters of the providing algorithm by referring to the user's past usage history. For example, the providing unit adjusts parameters of the providing algorithm based on the user's past usage history. This makes it possible to select the optimal providing method based on the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into a generation AI and cause the generation AI to select the optimal providing method.

[0052] The providing unit can customize the provided content based on the user's current project or area of ​​interest at the time of providing the content. For example, the providing unit prioritizes providing analysis results related to the project the user is currently working on. For example, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the project the user is currently working on. The providing unit can also automatically suggest related analysis results based on the user's area of ​​interest. For example, the providing unit automatically suggests analysis results having attributes such as age, gender, occupation, and hobbies related to the user's area of ​​interest. The providing unit can also dynamically change the required analysis results depending on the progress of the user's project. For example, the providing unit dynamically changes the analysis results to be provided depending on the progress of the user's project. This makes it possible to customize the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's project data and area of ​​interest data into a generation AI and cause the generation AI to customize the provided content.

[0053] When providing the analysis results, the providing unit can prioritize providing highly relevant analysis results by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit prioritizes providing analysis results related to that area. For example, when the user is in a specific area, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to that area. Furthermore, when the user is traveling, the providing unit can also prioritize providing analysis results related to the user's travel destination. For example, when the user is traveling, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the travel destination. Furthermore, when the user is at home, the providing unit can also prioritize providing analysis results related to the user's home. For example, when the user is at home, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the user's home. This makes it possible to prioritize providing highly relevant analysis results based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generating AI and cause the generating AI to provide highly relevant analysis results.

[0054] The providing unit may analyze the user's social media activities and provide related analysis results at the time of providing. The providing unit may provide, for example, analysis results related to topics frequently mentioned by the user on social media. For example, the providing unit may provide analysis results having attributes such as age, gender, occupation, and hobbies related to topics frequently mentioned by the user on social media. The providing unit may also analyze the attributes of the user's social media followers and provide related analysis results. For example, the providing unit may analyze the attributes of the user's social media followers and provide analysis results having related attributes such as age, gender, occupation, and hobbies. The providing unit may also analyze the content of the user's social media posts and provide related analysis results. For example, the providing unit may analyze the content of the user's social media posts and provide analysis results having related attributes such as age, gender, occupation, and hobbies. This makes it possible to provide related analysis results based on the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's social media data into a generation AI and cause the generation AI to provide related analysis results.

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

[0056] The reception unit can measure the input speed of the user and adjust the interface based on the input speed. For example, if the user is typing quickly, the reception unit can reduce the number of input items and provide a simple interface. Alternatively, if the user is typing slowly, the reception unit can provide detailed input options so that the user can input more slowly. Furthermore, the reception unit can suggest voice input or auto-completion functions according to the user's input speed. This makes it possible to adjust the interface according to the user's input speed.

[0057] The generation unit can refer to the user's past review history and generate a persona based on the content of the past review. For example, a persona with similar attributes can be generated based on the attributes of products that the user has previously given high ratings to. A persona can also be generated that avoids the attributes of products that the user has previously given low ratings to. Furthermore, the generation unit can extract specific trends from the user's past review history and generate a persona based on those trends. This makes it possible to generate an optimal persona based on the user's past review history.

[0058] The analysis unit can automatically classify collected reviews into those related to a specific theme and provide analysis results for each theme. For example, it can classify reviews related to the usability of a product and perform a detailed analysis based on that theme. It can also classify reviews related to design and provide analysis results based on that theme. It can also classify reviews related to functionality and provide analysis results based on that theme. This makes it possible to automatically classify reviews related to a specific theme and provide analysis results for each theme.

[0059] The reception unit can analyze the user's input content in real time and provide appropriate input assistance based on the input content. For example, when the user is inputting their age, it can automatically display candidates for occupations and hobbies related to their age. Also, when the user is inputting their gender, it can suggest attributes related to their gender. Furthermore, when the user is inputting their hobbies, it can provide detailed options related to their hobbies. In this way, it is possible to provide appropriate input assistance based on the user's input content.

[0060] The collection unit can automatically extract specific keywords from the collected reviews and classify the reviews based on those keywords. For example, reviews containing the keyword "easy to use" can be extracted and classified based on those keywords. Reviews containing the keyword "good design" can also be extracted and classified based on those keywords. Furthermore, reviews containing the keyword "rich in features" can also be extracted and classified based on those keywords. In this way, reviews can be automatically classified based on specific keywords.

[0061] The providing unit can analyze the user's past usage history and determine the optimal timing for provision based on the past usage history. For example, if the user has used the service during a specific time period in the past, the analysis results can be provided according to that time period. Also, if the user has used the service on a specific day of the week in the past, the analysis results can be provided according to that day of the week. Furthermore, if the user has used the service with a specific frequency in the past, the analysis results can be provided according to that frequency. This makes it possible to determine the optimal timing for provision based on the user's past usage history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit inputs the attributes of the target demographic. The attributes of the target demographic include age, gender, occupation, hobbies, etc. The reception unit provides, for example, a text box for inputting age, radio buttons for selecting gender, a drop-down menu for selecting occupation, and check boxes for inputting hobbies. Step 2: The generation unit uses the generation AI to generate a persona based on the attributes entered by the reception unit. The generation unit generates a virtual persona based on attributes such as age, gender, occupation, and hobbies. The generation unit generates a persona when the generation AI receives a prompt saying, "Please generate a persona based on these attributes." It is also possible to generate a persona based on a combination of attributes. Step 3: The collection department collects product reviews by the generated personas. The generated personas review the product's usability, design, functionality, etc. The collection department receives prompts such as "Please rate the usability of this product," "Please rate the design of this product," and "Please rate the functionality of this product," and the generated personas then conduct their reviews. Step 4: The analysis unit analyzes the collected reviews. The analysis unit analyzes the collected reviews using text mining, statistical analysis, and sentiment analysis techniques. For example, it extracts specific keywords from the collected reviews and analyzes their frequency. Step 5: The providing unit provides the analysis results obtained by the analysis unit. The providing unit can provide the analysis results in a report format, a dashboard display format, a notification format, or a PDF format.

[0064] (Example 2) A virtual review platform according to an embodiment of the present invention is a system that uses a generative AI to generate personas with various attributes and automatically collect product reviews tailored to a target demographic. This system targets service providers, system developers, and others, and allows users to automatically collect product reviews from personas tailored to their target demographic. Specifically, users first input the attributes of the target demographic. Next, a generative AI generates a persona based on those attributes. The generated persona reviews the product and provides the results on the platform. This mechanism allows users to receive realistic feedback tailored to the target demographic. For example, when a user inputs attributes such as age, gender, occupation, and hobbies, the generative AI generates a persona based on those attributes. The generated persona reviews the product's usability, design, functionality, and other aspects and provides the results on the platform. This allows users to receive realistic feedback tailored to the target demographic. Furthermore, the present invention proposes a new development style, the market size of which is described as a specific market size. This allows the virtual review platform to automatically collect product reviews from personas tailored to the target demographic and provide analysis results.

[0065] A virtual review platform according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs attributes of a target demographic. The attributes of the target demographic include, but are not limited to, age, gender, occupation, and hobbies. For example, the reception unit provides a text box for inputting age. The reception unit may also provide radio buttons for selecting gender. The reception unit may also provide a drop-down menu for selecting occupation. For example, the reception unit may provide check boxes for inputting hobbies. The generation unit generates a persona using a generation AI based on the attributes input by the reception unit. The generation unit generates a virtual persona based on attributes such as age, gender, occupation, and hobbies. For example, the generation AI may receive a prompt such as "Please generate a persona based on these attributes" and generate the persona. The generation unit may also generate a persona using the generation AI based on a combination of attributes. For example, the generation unit may generate a persona based on a combination of age and gender. The collection unit collects product reviews written by the generated persona. The collection unit, for example, causes the generated persona to review the usability, design, functionality, etc. of the product. For example, the collection unit causes the generated persona to receive a prompt such as "Please rate the usability of this product" and perform the review. The collection unit can also cause the generated persona to receive a prompt such as "Please rate the design of this product" and perform the review. For example, the collection unit can cause the generated persona to receive a prompt such as "Please rate the functionality of this product" and perform the review. The analysis unit analyzes the collected reviews. For example, the analysis unit analyzes the collected reviews using text mining technology. The analysis unit can also analyze the collected reviews using statistical analysis technology. The analysis unit can also analyze the collected reviews using sentiment analysis technology. For example, the analysis unit extracts specific keywords from the collected reviews and analyzes their frequency. The provision unit provides the analysis results obtained by the analysis unit.The providing unit provides the analysis results in, for example, a report format. The providing unit can also provide the analysis results in, for example, a dashboard display. The providing unit can also provide the analysis results in a notification format. For example, the providing unit can also provide the analysis results in PDF format. As a result, the virtual review platform according to the embodiment can automatically collect product reviews from personas tailored to a target demographic and provide the analysis results.

[0066] The reception unit can input attributes including age, gender, occupation, and hobbies. The reception unit, for example, provides a text box for inputting age. For example, the reception unit can allow the user to input age in ranges such as teens, twenties, and thirties. The reception unit can also provide radio buttons for selecting gender. For example, the reception unit can allow the user to select gender from options such as male, female, and other. The reception unit can also provide a drop-down menu for selecting occupation. For example, the reception unit can allow the user to select occupation from options such as student, company employee, and freelance. The reception unit can also provide check boxes for inputting hobbies. For example, the reception unit can allow the user to select hobbies from options such as sports, reading, and traveling. This allows detailed attributes of the target demographic to be input. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the attribute data input by the user to a generation AI and cause the generation AI to analyze the attribute data.

[0067] The generation unit can generate a persona based on input attributes. The generation unit generates a virtual person image based on attributes such as age, gender, occupation, and hobbies. For example, the generation unit generates a virtual person image based on attributes such as age in her 20s, gender as female, occupation as an office worker, and hobby as reading. The generation unit can also generate a persona when the generation AI receives a prompt such as, "Please generate a persona based on these attributes." For example, the generation unit can generate a persona when the generation AI receives a prompt such as, "Please generate a persona for a woman in her 20s who is an office worker and whose hobby is reading." The generation unit can also generate a persona based on a combination of attributes using the generation AI. For example, the generation unit can generate a persona based on a combination of age and gender. This makes it possible to generate a persona based on input attributes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit can input attribute data entered by the user into the generation AI and cause the generation AI to generate a persona.

[0068] The collection unit can collect reviews of the usability, design, and functionality of a product by the generated persona. For example, the collection unit allows the generated persona to evaluate the usability of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the usability of this product" and perform the review. The collection unit can also allow the generated persona to evaluate the design of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the design of this product" and perform the review. The collection unit can also allow the generated persona to evaluate the functionality of a product. For example, the collection unit allows the generated persona to receive a prompt such as "Please rate the functionality of this product" and perform the review. This makes it possible to collect detailed reviews by the generated persona. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the reviews performed by the generated persona into a generation AI and cause the generation AI to analyze the reviews.

[0069] The analysis unit can analyze the collected reviews. The analysis unit can analyze the collected reviews using, for example, text mining technology. For example, the analysis unit can extract specific keywords from the collected reviews and analyze their frequency. The analysis unit can also analyze the collected reviews using statistical analysis technology. For example, the analysis unit can extract specific patterns from the collected reviews and analyze their trends. The analysis unit can also analyze the collected reviews using sentiment analysis technology. For example, the analysis unit can extract emotional expressions from the collected reviews and analyze their sentiment scores. In this way, the collected reviews can be analyzed. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected reviews to a generation AI and have the generation AI analyze the reviews.

[0070] The providing unit can provide the analysis results. The providing unit can provide the analysis results, for example, in report format. For example, the providing unit can provide the analysis results in PDF format. The providing unit can also provide the analysis results in a dashboard display. For example, the providing unit can display the analysis results as graphs or charts. The providing unit can also provide the analysis results in notification format. For example, the providing unit can send the analysis results by email. In this way, the analysis results can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to execute a method for providing the results.

[0071] The reception unit can estimate the user's emotions and adjust the attribute input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, the reception unit reduces the number of input items, allowing the user to input quickly. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, the reception unit can display additional input items that the user may be interested in. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to allow the user to input attributes quickly. For example, the reception unit can use voice recognition technology to convert the user's dictation into text and treat it as an input item. This allows the attribute input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The reception unit can analyze past input history and suggest an appropriate attribute input method. For example, the reception unit automatically displays attributes that the user frequently input in the past as candidates. For example, the reception unit displays attributes such as age, gender, occupation, and hobbies that the user has input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest attributes to be used in a specific time period based on the user's past input history. For example, the reception unit suggests an optimal input method based on attributes that the user has input in a specific time period in the past. This makes it possible to suggest an optimal attribute input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to suggest an optimal input method.

[0073] When inputting attributes, the reception unit can customize input items based on the user's current project or area of ​​interest. For example, the reception unit can prioritize displaying attributes related to the project the user is currently working on. For example, the reception unit can prioritize displaying attributes such as age, gender, occupation, and hobbies related to the project the user is currently working on. The reception unit can also automatically suggest related attributes based on the user's area of ​​interest. For example, the reception unit automatically suggests attributes that the user may be interested in. The reception unit can also dynamically change required attributes depending on the progress of the user's project. For example, the reception unit dynamically changes input items depending on the progress of the user's project. This makes it possible to customize input items based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and cause the generation AI to customize the input items.

[0074] The reception unit can estimate the user's emotions and determine the priority of attributes to be input based on the estimated user emotions. For example, when the user is nervous, the reception unit allows the user to input important attributes with priority. For example, when the user is nervous, the reception unit allows the user to input important attributes with priority, such as age and gender. The reception unit can also allow the user to input detailed attributes with priority, when the user is relaxed. For example, when the user is relaxed, the reception unit allows the user to input detailed attributes such as hobbies and interests. The reception unit can also allow the user to input minimal attributes with priority, when the user is in a hurry. For example, when the user is in a hurry, the reception unit allows the user to input minimal attributes with priority, such as age and gender. This makes it possible to determine the priority of attributes to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The reception unit can prioritize inputting highly relevant attributes in consideration of the user's geographical location information when inputting attributes. For example, when the user is in a specific area, the reception unit can prioritize inputting attributes related to the area. For example, when the user is in a specific area, the reception unit can prioritize inputting attributes related to the area, such as age, gender, occupation, and hobbies. Furthermore, when the user is traveling, the reception unit can also prioritize inputting attributes related to the travel destination. For example, when the user is traveling, the reception unit can prioritize inputting attributes related to the travel destination, such as age, gender, occupation, and hobbies. Furthermore, when the user is at home, the reception unit can prioritize inputting attributes related to the user's home. For example, when the user is at home, the reception unit can prioritize inputting attributes related to the user's home, such as age, gender, occupation, and hobbies. This allows highly relevant attributes to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI to determine the priority of highly relevant attributes.

[0076] The reception unit can analyze the user's social media activity when the attributes are input and suggest related attributes. The reception unit can, for example, suggest attributes related to topics frequently mentioned by the user on social media. For example, the reception unit can suggest attributes such as age, gender, occupation, and hobbies related to topics frequently mentioned by the user on social media. The reception unit can also analyze the attributes of the user's social media followers and suggest related attributes. For example, the reception unit can analyze the attributes of the user's social media followers and suggest related attributes such as age, gender, occupation, and hobbies. The reception unit can also analyze the content of the user's social media posts and suggest related attributes. For example, the reception unit can analyze the content of the user's social media posts and suggest related attributes such as age, gender, occupation, and hobbies. This makes it possible to suggest related attributes based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to suggest related attributes.

[0077] The generation unit can estimate the user's emotions and adjust the persona generation method based on the estimated user emotions. For example, when the user is relaxed, the generation unit generates a detailed persona. For example, when the user is relaxed, the generation unit generates a persona with detailed attributes such as age, gender, occupation, and hobbies. Furthermore, when the user is in a hurry, the generation unit can generate a concise persona. For example, when the user is in a hurry, the generation unit generates a persona with concise attributes such as age and gender. Furthermore, when the user is excited, the generation unit can generate a visually stimulating persona. For example, when the user is excited, the generation unit generates a colorful and visually appealing persona. This allows the persona generation method to be adjusted 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. Some or all of the above-described processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the persona generation method.

[0078] When generating a persona, the generation unit can adjust the level of detail of the generation based on the importance of the input attributes. For example, if there are many important attributes, the generation unit generates a detailed persona. For example, if there are many important attributes, such as age, gender, occupation, and hobbies, the generation unit generates a detailed persona. The generation unit can also generate a concise persona if there are few important attributes. For example, if there are few important attributes, such as age and gender, the generation unit generates a concise persona. The generation unit can also generate a persona that focuses on a specific attribute if that attribute is important. For example, if age is particularly important, the generation unit generates a persona that focuses on age. This allows the level of detail of the generation to be adjusted based on the importance of the input attributes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generative AI. For example, the generation unit can input attribute data input by a user into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0079] When generating a persona, the generation unit can apply different generation algorithms depending on the attribute category. The generation unit applies different generation algorithms based on, for example, an age attribute. For example, the generation unit applies different generation algorithms based on age attributes such as teens, twenties, and thirties. The generation unit can also apply different generation algorithms based on an occupation attribute. For example, the generation unit applies different generation algorithms based on occupation attributes such as student, company employee, and freelance. The generation unit can also apply different generation algorithms based on hobby attributes. For example, the generation unit applies different generation algorithms based on hobbies such as sports, reading, and traveling. This makes it possible to apply different generation algorithms depending on the attribute category. Some or all of the above-described processing in the generation unit may be performed using, or without, a generative AI. For example, the generation unit can input attribute data entered by a user into the generation AI and cause the generation AI to apply different generation algorithms.

[0080] The generation unit can estimate the user's emotions and determine the priority of personas to be generated based on the estimated user emotions. For example, when the user is nervous, the generation unit prioritizes generating important personas. For example, when the user is nervous, the generation unit prioritizes generating personas with important attributes such as age and gender. The generation unit can also prioritize generating detailed personas when the user is relaxed. For example, when the user is relaxed, the generation unit prioritizes generating personas with detailed attributes such as hobbies and interests. The generation unit can also prioritize generating concise personas when the user is in a hurry. For example, when the user is in a hurry, the generation unit prioritizes generating personas with concise attributes such as age and gender. This makes it possible to determine the priority of personas to be generated 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. Some or all of the above-described processing in the generation unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI determine the priority of personas.

[0081] When generating a persona, the generation unit can select an appropriate generation method by referring to the user's past persona generation history. The generation unit, for example, selects the optimal generation method based on personas previously generated by the user. For example, the generation unit selects the optimal generation method based on personas previously generated by the user having attributes such as age, gender, occupation, and hobbies. The generation unit can also select the most effective generation method from the user's past persona generation history. For example, the generation unit analyzes the user's past persona generation history and selects the optimal generation algorithm. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past persona generation history. For example, the generation unit adjusts parameters of the generation algorithm based on the user's past persona generation history. This makes it possible to select the optimal generation method based on the user's past persona generation history. Some or all of the above-described processing in the generation unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the generation unit can input the user's past persona generation history data into the generation AI and have the generation AI select the optimal generation method.

[0082] When generating a persona, the generation unit can generate a highly relevant persona by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation unit generates a persona related to that area. For example, when the user is in a specific area, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to that area. Furthermore, when the user is traveling, the generation unit can also generate a persona related to the travel destination. For example, when the user is traveling, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to the travel destination. Furthermore, when the user is at home, the generation unit can also generate a persona related to the user's home. For example, when the user is at home, the generation unit generates a persona having attributes such as age, gender, occupation, and hobbies related to the user's home. This makes it possible to generate a highly relevant persona based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed, for example, using generative AI, or may be performed without using generative AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a highly relevant persona.

[0083] The collection unit can estimate the user's emotions and adjust the timing of review collection based on the estimated user emotions. For example, the collection unit delays the timing of review collection when the user is relaxed. For example, the collection unit delays the timing of review collection when the user is relaxed, allowing the user to provide a more detailed review. The collection unit can also accelerate the timing of review collection when the user is in a hurry. For example, the collection unit accelerates the timing of review collection when the user is in a hurry, allowing the user to provide a review quickly. The collection unit can also adjust the timing of review collection when the user is excited. For example, the collection unit adjusts the timing of review collection when the user is excited, allowing the user to provide an emotional review. This makes it possible to adjust the timing of review collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to adjust the timing of review collection.

[0084] The collection unit can adjust the level of detail of the reviews to be collected based on the persona's attributes during collection. For example, the collection unit collects detailed reviews when there are many important attributes. For example, the collection unit collects detailed reviews when there are many important attributes, such as age, gender, occupation, and hobbies. The collection unit can also collect concise reviews when there are few important attributes. For example, the collection unit collects concise reviews when there are few important attributes, such as age and gender. The collection unit can also collect reviews that focus on a particular attribute when that attribute is important. For example, the collection unit collects reviews that focus on age when age is particularly important. This makes it possible to adjust the level of detail of the reviews to be collected based on the persona's attributes. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input persona attribute data into the generation AI and cause the generation AI to adjust the level of detail of the reviews.

[0085] The collection unit can apply different collection algorithms depending on the category of the review when collecting reviews. For example, the collection unit applies a specific algorithm when collecting reviews related to the usability of a product. For example, the collection unit applies a specific collection algorithm when collecting reviews related to the usability of a product. The collection unit can also apply a different algorithm when collecting reviews related to the design of a product. For example, the collection unit applies a different collection algorithm when collecting reviews related to the design of a product. The collection unit can also apply a different algorithm when collecting reviews related to the functionality of a product. For example, the collection unit applies a different collection algorithm when collecting reviews related to the functionality of a product. This makes it possible to apply an optimal collection algorithm depending on the category of the review. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input review category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0086] The collection unit can estimate the user's emotions and determine the priority of reviews to be collected based on the estimated user emotions. For example, when the user is nervous, the collection unit prioritizes collecting important reviews. For example, when the user is nervous, the collection unit prioritizes collecting important reviews such as those about the product's usability and functionality. The collection unit can also prioritize collecting detailed reviews when the user is relaxed. For example, when the user is relaxed, the collection unit prioritizes collecting detailed reviews such as those about the product's design and usability. The collection unit can also prioritize collecting concise reviews when the user is in a hurry. For example, when the user is in a hurry, the collection unit prioritizes collecting concise reviews about the product's basic functions and performance. This makes it possible to determine the priority of reviews to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of reviews.

[0087] The collection unit can select the optimal collection method by referring to the persona's past review history during collection. The collection unit, for example, selects the optimal collection method based on reviews previously collected by the persona. For example, the collection unit selects the optimal collection method based on reviews previously collected by the persona, including attributes such as age, gender, occupation, and hobbies. The collection unit can also select the most effective collection method from the persona's past review history. For example, the collection unit analyzes the persona's past review history and selects an optimal collection algorithm. The collection unit can also adjust parameters of the collection algorithm by referring to the persona's past review history. For example, the collection unit adjusts parameters of the collection algorithm based on the persona's past review history. This allows the optimal collection method to be selected based on the persona's past review history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the persona's past review history data into a generation AI and cause the generation AI to select the optimal collection method.

[0088] The collection unit can collect highly relevant reviews by taking into account the geographic location information of the persona during collection. For example, if the persona is in a specific area, the collection unit collects reviews related to that area. For example, if the persona is in a specific area, the collection unit collects reviews having attributes related to that area, such as age, gender, occupation, and hobbies. Furthermore, if the persona is traveling, the collection unit can also collect reviews related to the travel destination. For example, if the persona is traveling, the collection unit collects reviews having attributes related to the travel destination, such as age, gender, occupation, and hobbies. Furthermore, if the persona is at home, the collection unit can also collect reviews related to the persona's home. For example, if the persona is at home, the collection unit collects reviews having attributes related to the persona's home, such as age, gender, occupation, and hobbies. This makes it possible to collect highly relevant reviews based on the persona's geographic location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the persona's geographic location information into the generation AI and cause the generation AI to collect highly relevant reviews.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis. For example, when the user is relaxed, the analysis unit performs a detailed analysis of the collected reviews. The analysis unit can also perform a concise analysis when the user is in a hurry. For example, when the user is in a hurry, the analysis unit performs a concise analysis of the collected reviews. The analysis unit can also perform a visually stimulating analysis when the user is excited. For example, when the user is excited, the analysis unit performs a visually stimulating analysis of the collected reviews. This allows the analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected reviews. For example, if there are many critical reviews, the analysis unit performs a detailed analysis. For example, if there are many critical reviews among the collected reviews, the analysis unit performs a detailed analysis. Furthermore, if there are few critical reviews, the analysis unit can perform a brief analysis. For example, if there are few critical reviews among the collected reviews, the analysis unit can perform a brief analysis. Furthermore, if a specific review is important, the analysis unit can perform an analysis that focuses on that review. For example, if a specific review is important among the collected reviews, the analysis unit performs an analysis that focuses on that review. In this way, the level of detail of the analysis can be adjusted based on the importance of the collected reviews. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collected review importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the review during analysis. For example, the analysis unit applies a particular algorithm when analyzing reviews related to the usability of a product. For example, the analysis unit applies a particular analysis algorithm when analyzing reviews related to the usability of a product. The analysis unit can also apply a different algorithm when analyzing reviews related to the design of a product. For example, the analysis unit applies a different analysis algorithm when analyzing reviews related to the design of a product. The analysis unit can also apply an even different algorithm when analyzing reviews related to the functionality of a product. For example, the analysis unit applies an even different analysis algorithm when analyzing reviews related to the functionality of a product. This makes it possible to apply the optimal analysis algorithm depending on the category of the review. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input review category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, when the user is nervous, the analysis unit provides a simple, highly visible display method. For example, when the user is nervous, the analysis unit displays the analysis results of the collected reviews in a simple, highly visible format. The analysis unit can also provide a display method including detailed information when the user is relaxed. For example, when the user is relaxed, the analysis unit displays the analysis results of the collected reviews in a format including detailed information. The analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. For example, when the user is in a hurry, the analysis unit displays the analysis results of the collected reviews in a format that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without using an AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the way the analysis results are displayed.

[0093] During analysis, the analysis unit can select the optimal analysis method by referring to past analysis results. The analysis unit, for example, selects the optimal analysis method based on past analysis results. For example, the analysis unit selects the optimal analysis method for collected reviews based on past analysis results. The analysis unit can also select the most effective analysis method from past analysis results. For example, the analysis unit analyzes past analysis results and selects the optimal analysis algorithm. The analysis unit can also adjust parameters of the analysis algorithm by referring to past analysis results. For example, the analysis unit adjusts parameters of the analysis algorithm based on past analysis results. This makes it possible to select the optimal analysis method based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and have the generation AI select the optimal analysis method.

[0094] The analysis unit may take the geographical distribution of reviews into consideration during analysis. For example, the analysis unit may prioritize analysis of reviews related to a specific region. For example, the analysis unit may prioritize analysis of reviews with attributes such as age, gender, occupation, and hobbies related to a specific region. The analysis unit may also compare and analyze reviews from different regions based on geographical distribution. For example, the analysis unit may compare and analyze reviews with attributes such as age, gender, occupation, and hobbies from different regions. The analysis unit may also perform an analysis that reflects the characteristics of each region by taking the geographical distribution into consideration. For example, the analysis unit may perform an analysis that reflects the characteristics of each region such as age, gender, occupation, and hobbies. This allows for an optimal analysis based on the geographical distribution of reviews. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input geographical distribution data of reviews into a generation AI and cause the generation AI to perform an optimal analysis.

[0095] The providing unit can estimate the user's emotions and adjust the display method of the analysis results to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is nervous, the providing unit displays the analysis results of the collected reviews in a simple, highly visible format. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit displays the analysis results of the collected reviews in a format including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit displays the analysis results of the collected reviews in a format that focuses on the main points. This makes it possible to adjust the display method of the analysis results to be provided depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the way in which the analysis results are displayed.

[0096] The providing unit can select the optimal providing method by referring to the user's past usage history when providing the information. The providing unit selects the optimal providing method based on, for example, the user's past usage history. For example, the providing unit selects the optimal providing method based on the analysis results of collected reviews based on the user's past usage history. The providing unit can also select the most effective providing method from the user's past usage history. For example, the providing unit analyzes the user's past usage history and selects the optimal providing algorithm. The providing unit can also adjust parameters of the providing algorithm by referring to the user's past usage history. For example, the providing unit adjusts parameters of the providing algorithm based on the user's past usage history. This makes it possible to select the optimal providing method based on the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into a generation AI and cause the generation AI to select the optimal providing method.

[0097] The providing unit can customize the provided content based on the user's current project or area of ​​interest at the time of providing the content. For example, the providing unit prioritizes providing analysis results related to the project the user is currently working on. For example, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the project the user is currently working on. The providing unit can also automatically suggest related analysis results based on the user's area of ​​interest. For example, the providing unit automatically suggests analysis results having attributes such as age, gender, occupation, and hobbies related to the user's area of ​​interest. The providing unit can also dynamically change the required analysis results depending on the progress of the user's project. For example, the providing unit dynamically changes the analysis results to be provided depending on the progress of the user's project. This makes it possible to customize the provided content based on the user's current project or area of ​​interest. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's project data and area of ​​interest data into a generation AI and cause the generation AI to customize the provided content.

[0098] The providing unit can estimate the user's emotions and determine the priority of the analysis results to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can prioritize providing important analysis results. For example, when the user is nervous, the providing unit can prioritize providing important analysis results from among the collected reviews. The providing unit can also prioritize providing detailed analysis results from among the collected reviews from among the user is relaxed. For example, when the user is relaxed, the providing unit can prioritize providing detailed analysis results from among the collected reviews. The providing unit can also prioritize providing concise analysis results from among the user is in a hurry. For example, when the user is in a hurry, the providing unit can prioritize providing concise analysis results from among the collected reviews. This makes it possible to determine the priority of the analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's emotional data into the generating AI and have the generating AI determine the priority of the analysis results.

[0099] When providing the analysis results, the providing unit can prioritize providing highly relevant analysis results by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit prioritizes providing analysis results related to that area. For example, when the user is in a specific area, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to that area. Furthermore, when the user is traveling, the providing unit can also prioritize providing analysis results related to the user's travel destination. For example, when the user is traveling, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the travel destination. Furthermore, when the user is at home, the providing unit can also prioritize providing analysis results related to the user's home. For example, when the user is at home, the providing unit prioritizes providing analysis results having attributes such as age, gender, occupation, and hobbies related to the user's home. This makes it possible to prioritize providing highly relevant analysis results based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generating AI and cause the generating AI to provide highly relevant analysis results.

[0100] The providing unit may analyze the user's social media activities and provide related analysis results at the time of providing. The providing unit may provide, for example, analysis results related to topics frequently mentioned by the user on social media. For example, the providing unit may provide analysis results having attributes such as age, gender, occupation, and hobbies related to topics frequently mentioned by the user on social media. The providing unit may also analyze the attributes of the user's social media followers and provide related analysis results. For example, the providing unit may analyze the attributes of the user's social media followers and provide analysis results having related attributes such as age, gender, occupation, and hobbies. The providing unit may also analyze the content of the user's social media posts and provide related analysis results. For example, the providing unit may analyze the content of the user's social media posts and provide analysis results having related attributes such as age, gender, occupation, and hobbies. This makes it possible to provide related analysis results based on the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's social media data into a generation AI and cause the generation AI to provide related analysis results. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for inputting attributes of the target demographic. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a persona using a generation AI. The collection unit is realized, for example, by the control unit 46A of the smart device 14 and collects product reviews by the generated persona. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected reviews. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting attributes of the target demographic. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a persona using a generation AI. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects product reviews by the generated persona. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected reviews. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for inputting attributes of the target demographic. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a persona using a generation AI. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects product reviews by the generated persona. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected reviews. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting attributes of the target demographic. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a persona using a generation AI. The collection unit is realized, for example, by the control unit 46A of the robot 414 and collects product reviews by the generated persona. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected reviews. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the analysis results.

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

[0102] The reception unit can measure the input speed of the user and adjust the interface based on the input speed. For example, if the user is typing quickly, the reception unit can reduce the number of input items and provide a simple interface. Alternatively, if the user is typing slowly, the reception unit can provide detailed input options so that the user can input more slowly. Furthermore, the reception unit can suggest voice input or auto-completion functions according to the user's input speed. This makes it possible to adjust the interface according to the user's input speed.

[0103] The generation unit can refer to the user's past review history and generate a persona based on the content of the past review. For example, a persona with similar attributes can be generated based on the attributes of products that the user has previously given high ratings to. A persona can also be generated that avoids the attributes of products that the user has previously given low ratings to. Furthermore, the generation unit can extract specific trends from the user's past review history and generate a persona based on those trends. This makes it possible to generate an optimal persona based on the user's past review history.

[0104] The collection unit can estimate the user's emotions and adjust the content of the review questions based on the estimated user emotions. For example, if the user is relaxed, detailed questions can be asked to collect more in-depth reviews. If the user is nervous, simple questions can be asked to quickly collect reviews. Furthermore, if the user is excited, questions that elicit emotional expressions can be asked. In this way, the content of the review questions can be adjusted according to the user's emotions.

[0105] The analysis unit can automatically classify collected reviews into those related to a specific theme and provide analysis results for each theme. For example, it can classify reviews related to the usability of a product and perform a detailed analysis based on that theme. It can also classify reviews related to design and provide analysis results based on that theme. It can also classify reviews related to functionality and provide analysis results based on that theme. This makes it possible to automatically classify reviews related to a specific theme and provide analysis results for each theme.

[0106] The providing unit can estimate the user's emotions and adjust the format of the analysis results to be provided based on the estimated user's emotions. For example, if the user is relaxed, the analysis results can be provided in a detailed report format. If the user is in a hurry, the analysis results can be provided in a concise format that summarizes the main points. Furthermore, if the user is excited, the analysis results can be provided using visually appealing graphs and charts. In this way, the format of the analysis results to be provided can be adjusted according to the user's emotions.

[0107] The reception unit can analyze the user's input content in real time and provide appropriate input assistance based on the input content. For example, when the user is inputting their age, it can automatically display candidates for occupations and hobbies related to their age. Also, when the user is inputting their gender, it can suggest attributes related to their gender. Furthermore, when the user is inputting their hobbies, it can provide detailed options related to their hobbies. In this way, it is possible to provide appropriate input assistance based on the user's input content.

[0108] The generation unit can estimate the user's emotions and dynamically change the attributes of the persona based on the estimated user's emotions. For example, if the user is relaxed, a persona with detailed attributes can be generated. If the user is in a hurry, a persona with concise attributes can be generated. Furthermore, if the user is excited, a persona with visually appealing attributes can be generated. This makes it possible to dynamically change the attributes of the persona according to the user's emotions.

[0109] The collection unit can automatically extract specific keywords from the collected reviews and classify the reviews based on those keywords. For example, reviews containing the keyword "easy to use" can be extracted and classified based on those keywords. Reviews containing the keyword "good design" can also be extracted and classified based on those keywords. Furthermore, reviews containing the keyword "rich in features" can also be extracted and classified based on those keywords. In this way, reviews can be automatically classified based on specific keywords.

[0110] The analysis unit can automatically extract emotional expressions from collected reviews and classify the reviews based on their emotional scores. For example, reviews expressing positive emotions can be extracted and classified based on their emotional scores. Reviews expressing negative emotions can also be extracted and classified based on their emotional scores. Furthermore, reviews expressing neutral emotions can also be extracted and classified based on their emotional scores. This makes it possible to automatically classify reviews based on their emotional expressions.

[0111] The providing unit can analyze the user's past usage history and determine the optimal timing for provision based on the past usage history. For example, if the user has used the service during a specific time period in the past, the analysis results can be provided according to that time period. Also, if the user has used the service on a specific day of the week in the past, the analysis results can be provided according to that day of the week. Furthermore, if the user has used the service with a specific frequency in the past, the analysis results can be provided according to that frequency. This makes it possible to determine the optimal timing for provision based on the user's past usage history.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit inputs the attributes of the target demographic. The attributes of the target demographic include age, gender, occupation, hobbies, etc. The reception unit provides, for example, a text box for inputting age, radio buttons for selecting gender, a drop-down menu for selecting occupation, and check boxes for inputting hobbies. Step 2: The generation unit uses the generation AI to generate a persona based on the attributes entered by the reception unit. The generation unit generates a virtual persona based on attributes such as age, gender, occupation, and hobbies. The generation unit generates a persona when the generation AI receives a prompt saying, "Please generate a persona based on these attributes." It is also possible to generate a persona based on a combination of attributes. Step 3: The collection department collects product reviews by the generated personas. The generated personas review the product's usability, design, functionality, etc. The collection department receives prompts such as "Please rate the usability of this product," "Please rate the design of this product," and "Please rate the functionality of this product," and the generated personas then conduct their reviews. Step 4: The analysis unit analyzes the collected reviews. The analysis unit analyzes the collected reviews using text mining, statistical analysis, and sentiment analysis techniques. For example, it extracts specific keywords from the collected reviews and analyzes their frequency. Step 5: The providing unit provides the analysis results obtained by the analysis unit. The providing unit can provide the analysis results in a report format, a dashboard display format, a notification format, or a PDF format.

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

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0120] The data processing device 12 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

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

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

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

Claims

1. a reception unit for inputting the attributes of the target demographic; a generation unit that generates a persona based on the attributes input by the reception unit; a collection unit that collects reviews by the personas generated by the generation unit; an analysis unit that analyzes the reviews collected by the collection unit; a providing unit that provides the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The reception unit Enter your attributes including age, gender, occupation, and hobbies The system of claim 1 .

3. The generation unit Generate personas based on input attributes The system of claim 1 .

4. The collecting unit Collect reviews of the product's usability, design, and functionality from the personas generated The system of claim 1 .

5. The analysis unit Analyze the collected reviews The system of claim 1 .

6. The providing unit Providing analytical results The system of claim 1 .

7. The reception unit Estimating user emotions and adjusting the attribute input interface based on the estimated user emotions The system of claim 1 .

8. The reception unit Analyzes past input history and suggests appropriate attribute input methods The system of claim 1 .

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