System

Abstract

An object of the system according to the embodiment is to efficiently formulate business ideas and marketing strategies based on generation Z's preferences and behaviors.SOLUTION: A system includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives information on a new product from a user. The analysis unit analyzes the information received by the reception unit and specifies a design, a price range, and an advertisement method suitable for the generation Z. The proposal unit provides a business idea or marketing advice based on the information specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently develop business ideas and marketing strategies based on the preferences and behavior of Gen Z.

[0005] The system of the embodiment aims to efficiently develop business ideas and marketing strategies based on the preferences and behavior of Generation Z. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about new products from users. The analysis unit analyzes the information received by the reception unit and identifies designs, price ranges, and advertising methods suitable for Generation Z. The proposal unit provides business ideas and marketing advice based on the information identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently develop business ideas and marketing strategies based on the preferences and behavior of Generation Z. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention provides business ideas and marketing advice based on the preferences and behavior of Gen Z. In this system, a user inputs information about a new product, and the AI ​​system analyzes the information to identify designs, price ranges, and promotional methods that appeal to Gen Z. The system then provides business ideas and marketing advice. For example, when a user launches a new fashion item, the AI ​​system can input the product's design and price range, and the system can suggest designs and price ranges that are popular with Gen Z. The system can also suggest effective promotional methods using social media and collaborations with influencers. This maximizes product sales and improves Gen Z customer satisfaction. This allows companies to efficiently target Gen Z and increase product sales. Furthermore, receiving advice based on Gen Z's preferences and behavior can improve customer satisfaction.

[0029] An AI system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about new products from a user. The information about new products includes, but is not limited to, product features, target markets, and price ranges. For example, when a user releases a new fashion item, the reception unit can input the product's design and price range. The analysis unit analyzes the information received by the reception unit and identifies a design, price range, and promotion method suitable for Generation Z. The analysis unit compares the input information with, for example, a database of Generation Z's preferences and behaviors. For example, the analysis unit analyzes past purchasing behavior and trend information in the database to identify designs, price ranges, and promotion methods preferred by Generation Z. The proposal unit provides business ideas and marketing advice based on the information identified by the analysis unit. For example, the proposal unit provides advice on designs that resonate with Generation Z, effective promotion methods, and appropriate pricing. For example, the proposal unit can suggest effective promotion methods using social media and collaborations with influencers. As a result, the AI ​​system of the embodiment can analyze information from users and provide business ideas and marketing advice that will resonate with Generation Z.

[0030] The reception unit can receive information on product features, target markets, and price ranges. Product features include, but are not limited to, product functions, designs, and materials. Target markets include, but are not limited to, age groups, regions, and consumer interests. Price ranges include, but are not limited to, low, mid, and high price ranges. For example, when a user releases a new fashion item, the reception unit can input the design and price range of the product. By receiving detailed product information, more accurate analysis is possible.

[0031] The analysis unit can reference a database of Gen Z's preferences and behaviors and compare them with the input information. Gen Z's preferences and behaviors include, but are not limited to, purchasing behavior, the social media they use, and their preferred designs. The database can include, but is not limited to, past purchasing data, trend information, and market research data. The analysis unit analyzes the past purchasing behavior and trend information in the database to identify, for example, Gen Z's preferred designs, price ranges, and advertising methods. This enables analysis based on Gen Z's preferences and behaviors.

[0032] The suggestion unit can propose designs suitable for Generation Z. Designs suitable for Generation Z include, but are not limited to, trendy colors, shapes, and functions. The suggestion unit, for example, proposes designs that resonate with Generation Z. For example, the suggestion unit can propose designs and colors that are popular with Generation Z. This makes it possible to propose designs that resonate with Generation Z.

[0033] The proposal unit can propose effective advertising methods. Examples of effective advertising methods include, but are not limited to, the media to be used, the advertising format, and the target demographic. The proposal unit can propose, for example, effective advertising methods that utilize social media. For example, the proposal unit can propose collaborations with influencers and advertising campaigns that resonate with the target demographic. This makes it possible to propose effective advertising methods.

[0034] The proposal unit can propose optimal pricing. Examples of optimal pricing include, but are not limited to, cost analysis, competitor pricing, and consumer willingness to pay. The proposal unit, for example, proposes a price range that appeals to Generation Z. For example, the proposal unit can propose appropriate pricing based on competitor pricing and market demand. This enables optimal pricing to be proposed.

[0035] The reception unit can analyze the user's past input history and select the optimal information reception method. The past input history includes, for example, past input data, frequency, content trends, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This enables efficient information reception by selecting the optimal information reception method based on the user's past input history.

[0036] When receiving product information, the reception unit can filter the product information based on the user's current business situation and areas of interest. Examples of business situations include, but are not limited to, sales status, market share, and competitor trends. Examples of areas of interest include, but are not limited to, specific product categories, technical fields, and marketing techniques. The reception unit, for example, receives only highly relevant product information based on the user's current business situation. The reception unit can also preferentially receive interesting product information based on the user's areas of interest. The reception unit can also combine the user's business situation and areas of interest to filter optimal product information. In this way, highly relevant information can be received by filtering information based on the user's business situation and areas of interest.

[0037] When receiving product information, the reception unit can select the optimal reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the reception unit can receive the product information using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the product information using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the product information using image recognition technology. This allows for efficient information reception by selecting the optimal reception means depending on the user's input method.

[0038] When receiving product information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. Examples of highly relevant information include, but are not limited to, the user's current location, local trends, and local events. For example, if the user is in a specific area, the reception unit can prioritize receiving product information related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving product information related to the user's travel destination. Furthermore, the reception unit can filter optimal product information based on the user's current location. This enables efficient information reception by preferentially receiving highly relevant information based on the user's geographical location information.

[0039] When receiving product information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, but is not limited to, for example, the content of posts, the reactions of followers, and the platform used. For example, the reception unit preferentially receives product information in which the user has shown interest on social media. The reception unit can also analyze the content of posts made by the user on social media and receive related product information. The reception unit can also receive related product information by referring to the activities of the user's friends on social media. This allows for efficient information reception by receiving related information based on the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving product information. Past feedback includes, but is not limited to, user ratings, comments, and improvement requests. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the reception method. This enables efficient information reception by customizing the reception method based on the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales forecasts, market demand, and competitor trends. Examples of the level of detail of the analysis include, but are not limited to, data granularity, analysis depth, and report format. For example, the analysis unit performs a detailed analysis of products with high importance. The analysis unit can also perform a concise analysis of products with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the product. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the product.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. Product categories include, but are not limited to, for example, electronic devices, clothing, and food. Analysis algorithms include, but are not limited to, for example, clustering, regression analysis, and classification. For example, in the case of fashion items, the analysis unit performs analysis based on design and trends. In addition, in the case of electronic devices, the analysis unit can also perform analysis based on function and performance. In addition, in the case of food, the analysis unit can also perform analysis based on taste and nutritional value. This makes it possible to perform analysis according to the product category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback. For example, the analysis unit can improve the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission time of the product. Examples of the submission time of the product include, but are not limited to, seasonality, market trends, competitor trends, etc. Examples of the analysis priority include, but are not limited to, the importance of information, urgency, and user interest. For example, the analysis unit prioritizes analysis of products whose submission time is approaching. Furthermore, the analysis unit can also postpone analysis of products whose submission time is further away. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission time of the product. This enables efficient analysis by determining the analysis priority according to the submission time of the product.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. Examples of product relevance include, but are not limited to, commonality in product lines, overlapping target markets, and technical relevance. Examples of the order of analysis include, but are not limited to, priority, dependency, and time constraints. For example, the analysis unit prioritizes analysis of highly related products. The analysis unit can also postpone analysis of less related products. The analysis unit can also dynamically adjust the order of analysis based on the relevance of products. This enables efficient analysis by adjusting the order of analysis based on the relevance of products.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications, years of experience, and past achievements. Examples of the use of technical terms include, but are not limited to, term definitions, frequency of use, and level of detail in explanations. For example, if the user has technical expertise, the analysis unit performs an analysis that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0047] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales forecasts, market demand, and competitor trends. Examples of the level of detail of the proposal include, but are not limited to, data granularity, analysis depth, and report format. For example, the proposal unit makes detailed proposals for products with high importance. The proposal unit can also make concise proposals for products with low importance. The proposal unit can also dynamically adjust the level of detail of the proposal depending on the importance of the product. This enables efficient proposals by adjusting the level of detail of the proposal depending on the importance of the product.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. Product categories include, but are not limited to, for example, electronic devices, clothing, and food. Suggestion algorithms include, but are not limited to, for example, recommendation systems, clustering, and classification. For example, in the case of fashion items, the suggestion unit makes suggestions based on design and trends. In addition, in the case of electronic devices, the suggestion unit can also make suggestions based on function and performance. In addition, in the case of food, the suggestion unit can also make suggestions based on taste and nutritional value. This makes it possible to make suggestions according to the product category.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, past datasets, proposal reports, feedback, etc. The suggestion unit can improve the accuracy of the current proposal, for example, based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and propose an optimal proposal method. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the product. Examples of the submission time of the product include, but are not limited to, seasonality, market trends, competitor trends, etc. Examples of the priority of the proposal include, but are not limited to, the importance of the information, urgency, and user interest. For example, the suggestion unit prioritizes the proposal for products whose submission time is approaching. Furthermore, the suggestion unit can also postpone the proposal for products whose submission time is further away. Furthermore, the suggestion unit can dynamically adjust the priority of the proposal depending on the submission time of the product. This enables efficient proposals by determining the priority of the proposal depending on the submission time of the product.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the products. Examples of the relevance of the products include, but are not limited to, commonality in product lines, overlapping target markets, and technical relevance. Examples of the order of proposals include, but are not limited to, priority, dependency, and time constraints. For example, the suggestion unit prioritizes proposals for highly relevant products. The suggestion unit can also postpone proposals for less relevant products. The suggestion unit can also dynamically adjust the order of proposals according to the relevance of the products. This enables efficient proposals by adjusting the order of proposals according to the relevance of the products.

[0052] When making a proposal, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications, years of experience, and past achievements. Examples of the use of technical terms include, but are not limited to, term definitions, frequency of use, and level of detail in explanations. For example, if the user has technical expertise, the suggestion unit may make a proposal that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit may make a proposal that is concise and easy to understand. Furthermore, the suggestion unit may dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past purchase history and automatically suggest new product information that the user may be interested in. For example, new products of similar designs or brands can be suggested based on data on fashion items the user has previously purchased. Also, if the user frequently purchases products in a specific price range, new products in that price range can be suggested preferentially. Furthermore, if the user tends to purchase specific items in a specific season, new products suited to that season can be suggested. This makes it possible to make more personalized product suggestions based on the user's purchase history.

[0055] The analysis unit can analyze a user's social media activity and identify trends and topics that the user is likely to be interested in. For example, it can analyze the content of posts that the user frequently likes or comments on, and suggest products and services related to that content. It can also analyze the content of posts by influencers the user follows, and suggest products and services introduced by those influencers. It can also analyze the activities of online communities and groups in which the user participates, and suggest products and services that are popular in those communities. This enables more relevant product suggestions to be made based on the user's social media activity.

[0056] The suggestion unit can customize the suggestions by reflecting the user's past feedback. For example, it can prioritize suggestions of products with specific designs or functions based on feedback provided by the user in the past. Also, if the user has given a high rating to a previously suggested product, it can suggest similar products based on that rating. Furthermore, if the user has requested improvements to a previously suggested product, it can also suggest products that meet those requests. This makes it possible to make more personalized suggestions based on the user's past feedback.

[0057] The suggestion unit can suggest products related to local trends and events based on the user's geographical location information. For example, if the user is in a specific area, it can suggest products and services that are popular in that area. Also, if the user is traveling, it can suggest products related to tourist attractions and events at the user's travel destination. Furthermore, it can suggest products that are suitable for the local season and climate based on the user's current location. This allows for more relevant product suggestions based on the user's geographical location information.

[0058] The suggestion unit can adjust the use of technical terms in the proposed content according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide detailed suggestions using a lot of technical terms. On the other hand, if the user does not have technical expertise, the suggestion unit can provide concise and easy-to-understand suggestions. Furthermore, the suggestion unit can dynamically adjust the level of detail in the explanation of the proposed content according to the user's level of expertise. This allows for more appropriate suggestions according to the user's level of expertise.

[0059] The analysis unit can improve the accuracy of the current analysis by referring to the user's past analysis results. For example, the current analysis algorithm can be adjusted based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and propose the optimal analysis method. Furthermore, the analysis unit can dynamically adjust the level of detail by referring to the user's past analysis results. This makes it possible to perform more accurate analysis by referring to the user's past analysis results.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives new product information from the user. The new product information includes the product's features, target market, price range, etc. For example, when a user releases a new fashion item, they can input the product's design and price range. Step 2: The analysis unit analyzes the information received by the reception unit and identifies designs, price ranges, and advertising methods suitable for Gen Z. The analysis unit references a database of Gen Z preferences and behavior and compares it with the input information. For example, the analysis unit analyzes past purchasing behavior and trend information in the database. Step 3: The proposal department provides business ideas and marketing advice based on the information identified by the analysis department. The proposal department offers advice on design that resonates with Gen Z, effective advertising methods, appropriate pricing, and more. For example, they can suggest effective advertising methods using social media and collaborations with influencers.

[0062] (Example 2) An AI system according to an embodiment of the present invention provides business ideas and marketing advice based on the preferences and behavior of Gen Z. In this system, a user inputs information about a new product, and the AI ​​system analyzes the information to identify designs, price ranges, and promotional methods that appeal to Gen Z. The system then provides business ideas and marketing advice. For example, when a user launches a new fashion item, the AI ​​system can input the product's design and price range, and the system can suggest designs and price ranges that are popular with Gen Z. The system can also suggest effective promotional methods using social media and collaborations with influencers. This maximizes product sales and improves Gen Z customer satisfaction. This allows companies to efficiently target Gen Z and increase product sales. Furthermore, receiving advice based on Gen Z's preferences and behavior can improve customer satisfaction.

[0063] An AI system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about new products from a user. The information about new products includes, but is not limited to, product features, target markets, and price ranges. For example, when a user releases a new fashion item, the reception unit can input the product's design and price range. The analysis unit analyzes the information received by the reception unit and identifies a design, price range, and promotion method suitable for Generation Z. The analysis unit compares the input information with, for example, a database of Generation Z's preferences and behaviors. For example, the analysis unit analyzes past purchasing behavior and trend information in the database to identify designs, price ranges, and promotion methods preferred by Generation Z. The proposal unit provides business ideas and marketing advice based on the information identified by the analysis unit. For example, the proposal unit provides advice on designs that resonate with Generation Z, effective promotion methods, and appropriate pricing. For example, the proposal unit can suggest effective promotion methods using social media and collaborations with influencers. As a result, the AI ​​system of the embodiment can analyze information from users and provide business ideas and marketing advice that will resonate with Generation Z.

[0064] The reception unit can receive information on product features, target markets, and price ranges. Product features include, but are not limited to, product functions, designs, and materials. Target markets include, but are not limited to, age groups, regions, and consumer interests. Price ranges include, but are not limited to, low, mid, and high price ranges. For example, when a user releases a new fashion item, the reception unit can input the design and price range of the product. By receiving detailed product information, more accurate analysis is possible.

[0065] The analysis unit can reference a database of Gen Z's preferences and behaviors and compare them with the input information. Gen Z's preferences and behaviors include, but are not limited to, purchasing behavior, the social media they use, and their preferred designs. The database can include, but is not limited to, past purchasing data, trend information, and market research data. The analysis unit analyzes the past purchasing behavior and trend information in the database to identify, for example, Gen Z's preferred designs, price ranges, and advertising methods. This enables analysis based on Gen Z's preferences and behaviors.

[0066] The suggestion unit can propose designs suitable for Generation Z. Designs suitable for Generation Z include, but are not limited to, trendy colors, shapes, and functions. The suggestion unit, for example, proposes designs that resonate with Generation Z. For example, the suggestion unit can propose designs and colors that are popular with Generation Z. This makes it possible to propose designs that resonate with Generation Z.

[0067] The proposal unit can propose effective advertising methods. Examples of effective advertising methods include, but are not limited to, the media to be used, the advertising format, and the target demographic. The proposal unit can propose, for example, effective advertising methods that utilize social media. For example, the proposal unit can propose collaborations with influencers and advertising campaigns that resonate with the target demographic. This makes it possible to propose effective advertising methods.

[0068] The proposal unit can propose optimal pricing. Examples of optimal pricing include, but are not limited to, cost analysis, competitor pricing, and consumer willingness to pay. The proposal unit, for example, proposes a price range that appeals to Generation Z. For example, the proposal unit can propose appropriate pricing based on competitor pricing and market demand. This enables optimal pricing to be proposed.

[0069] The reception unit can estimate the user's emotions and adjust the timing of receiving product information based on the estimated user emotions. User emotions include, but are not limited to, stress, excitement, fatigue, etc. For example, if the user is feeling stressed, the reception unit can receive product information during a time when the user is able to relax. Furthermore, if the user is excited, the reception unit can immediately receive product information. Furthermore, if the user is tired, the reception unit can receive product information after a break. This allows for more appropriate information reception by adjusting the timing of information reception 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The reception unit can analyze the user's past input history and select the optimal information reception method. The past input history includes, for example, past input data, frequency, content trends, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This enables efficient information reception by selecting the optimal information reception method based on the user's past input history.

[0071] When receiving product information, the reception unit can filter the product information based on the user's current business situation and areas of interest. Examples of business situations include, but are not limited to, sales status, market share, and competitor trends. Examples of areas of interest include, but are not limited to, specific product categories, technical fields, and marketing techniques. The reception unit, for example, receives only highly relevant product information based on the user's current business situation. The reception unit can also preferentially receive interesting product information based on the user's areas of interest. The reception unit can also combine the user's business situation and areas of interest to filter optimal product information. In this way, highly relevant information can be received by filtering information based on the user's business situation and areas of interest.

[0072] When receiving product information, the reception unit can select the optimal reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the reception unit can receive the product information using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the product information using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the product information using image recognition technology. This allows for efficient information reception by selecting the optimal reception means depending on the user's input method.

[0073] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. Information priority includes, but is not limited to, the importance, urgency, and user interest of the information. For example, when the user is stressed, the reception unit postpones information of low importance. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving information of high importance. This allows for more appropriate information reception by determining the priority of information according to the user's emotions. The emotion estimation is realized 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.

[0074] When receiving product information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. Examples of highly relevant information include, but are not limited to, the user's current location, local trends, and local events. For example, if the user is in a specific area, the reception unit can prioritize receiving product information related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving product information related to the user's travel destination. Furthermore, the reception unit can filter optimal product information based on the user's current location. This enables efficient information reception by preferentially receiving highly relevant information based on the user's geographical location information.

[0075] When receiving product information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, but is not limited to, for example, the content of posts, the reactions of followers, and the platform used. For example, the reception unit preferentially receives product information in which the user has shown interest on social media. The reception unit can also analyze the content of posts made by the user on social media and receive related product information. The reception unit can also receive related product information by referring to the activities of the user's friends on social media. This allows for efficient information reception by receiving related information based on the user's social media activity.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback when receiving product information. Past feedback includes, but is not limited to, user ratings, comments, and improvement requests. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also analyze the user's feedback and customize the reception method. This enables efficient information reception by customizing the reception method based on the user's past feedback.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Methods for presenting the analysis include, but are not limited to, graph displays, text reports, and interactive dashboards. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales forecasts, market demand, and competitor trends. Examples of the level of detail of the analysis include, but are not limited to, data granularity, analysis depth, and report format. For example, the analysis unit performs a detailed analysis of products with high importance. The analysis unit can also perform a concise analysis of products with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the product. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the product.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. Product categories include, but are not limited to, for example, electronic devices, clothing, and food. Analysis algorithms include, but are not limited to, for example, clustering, regression analysis, and classification. For example, in the case of fashion items, the analysis unit performs analysis based on design and trends. In addition, in the case of electronic devices, the analysis unit can also perform analysis based on function and performance. In addition, in the case of food, the analysis unit can also perform analysis based on taste and nutritional value. This makes it possible to perform analysis according to the product category.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback. For example, the analysis unit can improve the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Examples of the length of the analysis include, but are not limited to, the number of pages in the report, the depth of the analysis, and the format of the report. For example, the analysis unit can perform a short, concise analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. The analysis unit can also perform a visually stimulating analysis when the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. 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.

[0082] During analysis, the analysis unit can determine the analysis priority based on the submission time of the product. Examples of the submission time of the product include, but are not limited to, seasonality, market trends, competitor trends, etc. Examples of the analysis priority include, but are not limited to, the importance of information, urgency, and user interest. For example, the analysis unit prioritizes analysis of products whose submission time is approaching. Furthermore, the analysis unit can also postpone analysis of products whose submission time is further away. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission time of the product. This enables efficient analysis by determining the analysis priority according to the submission time of the product.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. Examples of product relevance include, but are not limited to, commonality in product lines, overlapping target markets, and technical relevance. Examples of the order of analysis include, but are not limited to, priority, dependency, and time constraints. For example, the analysis unit prioritizes analysis of highly related products. The analysis unit can also postpone analysis of less related products. The analysis unit can also dynamically adjust the order of analysis based on the relevance of products. This enables efficient analysis by adjusting the order of analysis based on the relevance of products.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications, years of experience, and past achievements. Examples of the use of technical terms include, but are not limited to, term definitions, frequency of use, and level of detail in explanations. For example, if the user has technical expertise, the analysis unit performs an analysis that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also perform a concise and easy-to-understand analysis. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0085] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. Examples of ways to present the suggestion include, but are not limited to, graph displays, text reports, and interactive dashboards. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. For example, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. For example, the suggestion unit can provide visually stimulating suggestions when the user is excited. This allows for more appropriate suggestions to be provided by adjusting the way the suggestion is presented according to the user's emotion. The 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.

[0086] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales forecasts, market demand, and competitor trends. Examples of the level of detail of the proposal include, but are not limited to, data granularity, analysis depth, and report format. For example, the proposal unit makes detailed proposals for products with high importance. The proposal unit can also make concise proposals for products with low importance. The proposal unit can also dynamically adjust the level of detail of the proposal depending on the importance of the product. This enables efficient proposals by adjusting the level of detail of the proposal depending on the importance of the product.

[0087] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. Product categories include, but are not limited to, for example, electronic devices, clothing, and food. Suggestion algorithms include, but are not limited to, for example, recommendation systems, clustering, and classification. For example, in the case of fashion items, the suggestion unit makes suggestions based on design and trends. In addition, in the case of electronic devices, the suggestion unit can also make suggestions based on function and performance. In addition, in the case of food, the suggestion unit can also make suggestions based on taste and nutritional value. This makes it possible to make suggestions according to the product category.

[0088] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, past datasets, proposal reports, feedback, etc. The suggestion unit can improve the accuracy of the current proposal, for example, based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and propose an optimal proposal method. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. Examples of the length of the suggestions include, but are not limited to, the number of pages in the report, the depth of the analysis, and the format of the report. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows for adjusting the length of the suggestions according to the user's emotions to provide more appropriate suggestions. 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.

[0090] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the product. Examples of the submission time of the product include, but are not limited to, seasonality, market trends, competitor trends, etc. Examples of the priority of the proposal include, but are not limited to, the importance of the information, urgency, and user interest. For example, the suggestion unit prioritizes the proposal for products whose submission time is approaching. Furthermore, the suggestion unit can also postpone the proposal for products whose submission time is further away. Furthermore, the suggestion unit can dynamically adjust the priority of the proposal depending on the submission time of the product. This enables efficient proposals by determining the priority of the proposal depending on the submission time of the product.

[0091] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the products. Examples of the relevance of the products include, but are not limited to, commonality in product lines, overlapping target markets, and technical relevance. Examples of the order of proposals include, but are not limited to, priority, dependency, and time constraints. For example, the suggestion unit prioritizes proposals for highly relevant products. The suggestion unit can also postpone proposals for less relevant products. The suggestion unit can also dynamically adjust the order of proposals according to the relevance of the products. This enables efficient proposals by adjusting the order of proposals according to the relevance of the products.

[0092] When making a proposal, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications, years of experience, and past achievements. Examples of the use of technical terms include, but are not limited to, term definitions, frequency of use, and level of detail in explanations. For example, if the user has technical expertise, the suggestion unit may make a proposal that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit may make a proposal that is concise and easy to understand. Furthermore, the suggestion unit may dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. This allows for more appropriate proposals to be provided by adjusting the use of technical terms in the proposal according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, and suggestion 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 allows a user to input information about new products. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information by referring to a database related to the preferences and behaviors of Generation Z. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides business ideas and marketing advice based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and suggestion 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 allows a user to input information about new products. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information by referring to a database related to the preferences and behaviors of Generation Z. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides business ideas and marketing advice based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, and suggestion 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 allows the user to input information about new products. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information by referring to a database related to the preferences and behaviors of Generation Z. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides business ideas and marketing advice based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, and suggestion 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 allows a user to input information about new products. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information by referring to a database related to the preferences and behaviors of Generation Z. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides business ideas and marketing advice based on the analysis results. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's past purchase history and automatically suggest new product information that the user may be interested in. For example, new products of similar designs or brands can be suggested based on data on fashion items the user has previously purchased. Also, if the user frequently purchases products in a specific price range, new products in that price range can be suggested preferentially. Furthermore, if the user tends to purchase specific items in a specific season, new products suited to that season can be suggested. This makes it possible to make more personalized product suggestions based on the user's purchase history.

[0095] The analysis unit can analyze a user's social media activity and identify trends and topics that the user is likely to be interested in. For example, it can analyze the content of posts that the user frequently likes or comments on, and suggest products and services related to that content. It can also analyze the content of posts by influencers the user follows, and suggest products and services introduced by those influencers. It can also analyze the activities of online communities and groups in which the user participates, and suggest products and services that are popular in those communities. This enables more relevant product suggestions to be made based on the user's social media activity.

[0096] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, suggestions can be made at a time when the user is able to relax. Also, if the user is excited, suggestions can be made immediately. Furthermore, if the user is tired, suggestions can be made after a break. This allows for more effective suggestions by adjusting the timing of suggestions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The suggestion unit can customize the suggestions by reflecting the user's past feedback. For example, it can prioritize suggestions of products with specific designs or functions based on feedback provided by the user in the past. Also, if the user has given a high rating to a previously suggested product, it can suggest similar products based on that rating. Furthermore, if the user has requested improvements to a previously suggested product, it can also suggest products that meet those requests. This makes it possible to make more personalized suggestions based on the user's past feedback.

[0098] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result that focuses on the main points can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the level of analysis detail according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] The suggestion unit can suggest products related to local trends and events based on the user's geographical location information. For example, if the user is in a specific area, it can suggest products and services that are popular in that area. Also, if the user is traveling, it can suggest products related to tourist attractions and events at the user's travel destination. Furthermore, it can suggest products that are suitable for the local season and climate based on the user's current location. This allows for more relevant product suggestions based on the user's geographical location information.

[0100] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user emotions. For example, if the user is feeling stressed, less important analyses can be postponed. Also, if the user is relaxed, detailed analyses can be prioritized. Furthermore, if the user is in a hurry, more important analyses can be prioritized. By adjusting the order of analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The suggestion unit can adjust the use of technical terms in the proposed content according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide detailed suggestions using a lot of technical terms. On the other hand, if the user does not have technical expertise, the suggestion unit can provide concise and easy-to-understand suggestions. Furthermore, the suggestion unit can dynamically adjust the level of detail in the explanation of the proposed content according to the user's level of expertise. This allows for more appropriate suggestions according to the user's level of expertise.

[0102] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be made. If the user is in a hurry, concise suggestions that focus on the main points can be made. Furthermore, if the user is excited, visually stimulating suggestions can be made. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The analysis unit can improve the accuracy of the current analysis by referring to the user's past analysis results. For example, the current analysis algorithm can be adjusted based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and propose the optimal analysis method. Furthermore, the analysis unit can dynamically adjust the level of detail by referring to the user's past analysis results. This makes it possible to perform more accurate analysis by referring to the user's past analysis results.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives new product information from the user. The new product information includes the product's features, target market, price range, etc. For example, when a user releases a new fashion item, they can input the product's design and price range. Step 2: The analysis unit analyzes the information received by the reception unit and identifies designs, price ranges, and advertising methods suitable for Gen Z. The analysis unit references a database of Gen Z preferences and behavior and compares it with the input information. For example, the analysis unit analyzes past purchasing behavior and trend information in the database. Step 3: The proposal department provides business ideas and marketing advice based on the information identified by the analysis department. The proposal department offers advice on design that resonates with Gen Z, effective advertising methods, appropriate pricing, and more. For example, they can suggest effective advertising methods using social media and collaborations with influencers.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 that receives information about new products from users; an analysis unit that analyzes the information received by the reception unit and identifies a design, price range, and advertising method suitable for Generation Z; a proposal unit that provides business ideas and marketing advice based on the information identified by the analysis unit. A system characterized by:

2. The reception unit Accept information on product features, target market, and price range The system of claim 1 .

3. The analysis unit Consult a database of Gen Z preferences and behaviors and compare them with the information you provide The system of claim 1 .

4. The proposal unit Proposing designs suitable for Generation Z The system of claim 1 .

5. The proposal unit Propose effective advertising methods The system of claim 1 .

6. The proposal unit Propose optimal pricing The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of receiving product information based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past input history and select the optimal method of receiving information The system of claim 1 .

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