System

Abstract

An object of the system according to the embodiment is to effectively analyze the preferences and trends of consumers and reflect them in the development of new products.SOLUTION: A system includes an analysis part, an idea generation part, and a process optimization part. The analysis unit analyzes purchase data of consumers, posts on social media, and evaluations on review sites. The idea generation unit generates an idea of the new product based on a result of the analysis by the analysis unit. The process optimization unit optimizes the product development process based on the idea of the new product generated by the idea generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively analyze consumer preferences and trends and reflect them in new product development, leaving room for improvement.

[0005] The system according to the embodiment aims to effectively analyze consumer preferences and trends and reflect them in the development of new products. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an idea generation unit, and a process optimization unit. The analysis unit analyzes consumer purchasing data, social media posts, and reviews on review sites. The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. The process optimization unit optimizes the product development process based on the new product ideas generated by the idea generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively analyze consumer preferences and trends and reflect them in the development of new products. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A product development system according to an embodiment of the present invention is a system in which a generation AI analyzes consumer preferences and trends, generates new product ideas, and optimizes the product development process. This allows the product development system to perform a detailed analysis of consumer preferences and trends and develop new products based on the results.

[0029] A product development system according to an embodiment includes an analysis unit, an idea generation unit, and a process optimization unit. The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit analyzes consumer purchasing histories to identify popular products. The analysis unit can also analyze social media posts to understand consumer preferences and trends. The analysis unit can also analyze review site ratings to identify consumer satisfaction and dissatisfaction. The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit generates ideas for new organic snacks for health-conscious consumers. The idea generation unit can also propose specific product concepts and features based on the analysis results. The idea generation unit can also identify target markets and generate product ideas suitable for those markets. The process optimization unit optimizes the product development process based on the new product ideas generated by the idea generation unit. For example, the process optimization unit collects consumer feedback in the early stages of product development and reflects it in product improvements. The process optimization unit can also propose optimal actions at each stage of the development process to support efficient product development. The process optimization unit also optimizes the development schedule and enables rapid product launch. This allows the product development system according to the embodiment to perform detailed analysis of consumer preferences and trends and develop new products based on the analysis. For example, the analysis unit identifies popular products based on consumer purchasing data. The idea generation unit generates new product ideas based on the results. The process optimization unit optimizes the product development process based on the ideas.

[0030] The analysis unit can include consumer lifestyle data in the analysis. For example, the analysis unit uses a generative AI to analyze consumer exercise habit data and identify health-conscious trends. For example, it predicts products preferred by health-conscious consumers based on data from a fitness app. The analysis unit also analyzes consumer eating pattern data to identify food preferences and trends. For example, it predicts the foods and ingredients preferred by consumers based on data from a food log app. The analysis unit also analyzes the consumer's overall lifestyle (e.g., sleep patterns, stress levels) to predict overall preferences and trends. For example, it suggests products that suit the consumer's lifestyle based on data from a wearable device. In this way, by including consumer lifestyle data in the analysis, it is possible to predict preferences and trends in more detail.

[0031] The analysis unit can include consumer voice messages or video reviews in the analysis. For example, the analysis unit uses a generative AI to analyze consumer voice messages and identify emotions and preferences. For example, it uses voice recognition technology to predict preferences from the content of consumer speech. The analysis unit also analyzes video reviews, infers emotions from the consumer's facial expressions and tone, and predicts preferences and trends based on that data. For example, it combines the content of the video reviews with facial expression analysis for analysis. The analysis unit also converts the voice messages and video reviews into text data and analyzes consumer preferences and trends based on that data. For example, it automatically converts the content of the voice or video into text and uses it for analysis. In this way, by including consumer voice messages and video reviews in the analysis, more detailed preferences and trends can be predicted.

[0032] The analysis unit can analyze consumer data from different regions or cultural spheres to identify global trends. For example, the analysis unit uses the generation AI to analyze consumer data from different regions to identify regional preferences and trends. For example, it compares and analyzes consumer data from Asia and Europe. The analysis unit also analyzes consumer data from each cultural sphere to identify preferences and trends based on cultural background. For example, it predicts products and services that are popular in specific cultural spheres. The analysis unit also integrates and analyzes global consumer data to identify worldwide trends. For example, it extracts common trends based on data from multiple regions or cultural spheres. This makes it possible to analyze consumer data from different regions or cultural spheres and identify global trends.

[0033] The idea generation unit can analyze past product success stories and generate new product ideas based on those patterns. For example, the idea generation unit uses a generative AI to analyze past product success stories and generate new product ideas based on those patterns. For example, it extracts commonalities between successful products and proposes new product concepts. The idea generation unit also identifies features and designs preferred by consumers based on data from success stories and generates new product ideas based on those. For example, it incorporates popular functions and designs. The idea generation unit also analyzes past product success stories and generates new product ideas based on that knowledge. For example, it analyzes success factors and reflects them in new products. This makes it possible to analyze past product success stories and generate new product ideas based on those patterns.

[0034] The idea generation unit can analyze trends in different industries and propose new products that combine ideas from different industries. For example, the idea generation unit uses a generation AI to analyze trend data from different industries and propose new products that combine ideas from different industries. For example, it can propose a product that combines the technology field with the consumer market. The idea generation unit can also analyze successful cases from different industries and generate new product ideas based on that knowledge. For example, it can propose a product that applies medical technology to everyday life. The idea generation unit can also integrate and analyze trends from different industries and propose products that meet new market needs. For example, it can propose a product that combines needs from different industries. This makes it possible to analyze trends from different industries and propose new products that combine ideas from different industries.

[0035] The idea generation unit can generate new product ideas based on consumer life events. For example, the generation AI analyzes consumer life event data and generates new product ideas based on that data. For example, it can propose products related to marriage and childbirth. The idea generation unit can also identify consumer needs based on life events and generate new product ideas accordingly. For example, it can propose childcare products for after childbirth. The idea generation unit can also analyze consumer life event data in real time and generate new product ideas based on the results. For example, it can propose new ideas every time a life event occurs. This makes it possible to generate new product ideas based on consumer life events.

[0036] The process optimization unit can analyze past data on the development process and propose an optimal development schedule. For example, the process optimization unit uses a generation AI to analyze past development process data and propose an optimal development schedule. For example, it analyzes the success factors of past projects and reflects them in the schedule. The process optimization unit also proposes an efficient schedule based on data from each stage of the development process. For example, it optimizes the time required for specific tasks and resource allocation. The process optimization unit also analyzes past development data in real time and proposes an optimal schedule based on the results. For example, it dynamically adjusts the schedule based on past data. This makes it possible to analyze past data on the development process and propose an optimal development schedule.

[0037] The Process Optimization Department can analyze development processes in different industries and introduce the optimal process. For example, the Process Optimization Department uses generative AI to analyze development process data from different industries and introduce the optimal process. For example, it can propose a development process that combines the technology field with the consumer market. The Process Optimization Department can also analyze successful cases from different industries and optimize the development process based on that knowledge. For example, it can propose a development process that applies medical technology to everyday life. The Process Optimization Department can also perform an integrated analysis of development processes from different industries and propose processes that meet new market needs. For example, it can propose a development process that combines the needs of different industries. This makes it possible to analyze development processes in different industries and introduce the optimal process.

[0038] The process optimization department can collect feedback in real time at each stage of the development process and make improvements immediately. For example, the process optimization department uses generative AI to collect feedback in real time at each stage of the development process and make improvements immediately based on the results. For example, it collects consumer feedback during the prototype testing stage and identifies areas for improvement. The process optimization department also uses the feedback data to make improvement suggestions at each stage of the development process that will make consumers feel positive. For example, it adjusts the design and functions based on consumer feedback. The process optimization department also collects consumer feedback data in real time and makes improvement suggestions at each stage of the development process based on the results. For example, it updates the areas for improvement every time consumer feedback changes. This allows feedback to be collected in real time at each stage of the development process and make improvements immediately.

[0039] The process optimization department can analyze successful cases of past marketing campaigns and propose new strategies based on those patterns. For example, the process optimization department uses generative AI to analyze successful cases of past marketing campaigns and propose new strategies based on those patterns. For example, it extracts commonalities between successful campaigns and proposes a new strategy. The process optimization department can also identify marketing messages and methods preferred by consumers based on data from successful cases and propose a new strategy based on that. For example, it can incorporate popular messages and methods. The process optimization department can also analyze successful cases of past campaigns and propose a new marketing strategy based on that knowledge. For example, it can analyze success factors and reflect them in a new strategy. This makes it possible to analyze successful cases of past marketing campaigns and propose a new strategy based on their patterns.

[0040] The process optimization unit can analyze marketing data from different regions or cultural spheres and propose global strategies. For example, the process optimization unit uses generative AI to analyze marketing data from different regions and propose strategies for each region. For example, it compares and analyzes marketing data from Asia and Europe. The process optimization unit also analyzes marketing data from each cultural sphere and proposes strategies based on cultural backgrounds. For example, it proposes marketing methods that are effective in specific cultural spheres. The process optimization unit also integrates and analyzes global marketing data and proposes worldwide strategies. For example, it proposes a common strategy based on data from multiple regions and cultural spheres. This makes it possible to analyze marketing data from different regions and cultural spheres and propose global strategies.

[0041] The process optimization unit can propose marketing strategies based on consumer life events. For example, the process optimization unit uses generative AI to analyze consumer life event data and propose marketing strategies based on that data. For example, it proposes marketing campaigns related to marriage and childbirth. The process optimization unit also identifies consumer needs based on life events and proposes marketing strategies accordingly. For example, it proposes a marketing strategy for childcare products after childbirth. The process optimization unit also analyzes consumer life event data in real time and proposes marketing strategies based on the results. For example, it proposes a new strategy each time a life event occurs. This makes it possible to propose marketing strategies based on consumer life events.

[0042] The process optimization department can analyze past product improvement data and make new improvement proposals based on those patterns. For example, the process optimization department uses generative AI to analyze past product improvement data and make new improvement proposals based on those patterns. For example, it extracts common points between successful improvements and proposes new improvements. The process optimization department can also identify features and designs preferred by consumers based on the improvement data and make new improvement proposals based on those. For example, it can incorporate popular functions and designs. The process optimization department can also analyze past improvement data and make new improvement proposals based on that knowledge. For example, it can analyze success factors and reflect them in new improvements. This makes it possible to analyze past product improvement data and make new improvement proposals based on those patterns.

[0043] The process optimization department can analyze trend data from different industries and propose product improvements that combine ideas from different industries. For example, the process optimization department uses generative AI to analyze trend data from different industries and propose product improvements that combine ideas from different industries. For example, it proposes improvements that combine the technology field with the consumer market. The process optimization department also analyzes successful cases from different industries and proposes product improvements based on that knowledge. For example, it proposes improvements that apply medical technology to everyday life. The process optimization department also performs an integrated analysis of trends from different industries and proposes product improvements that meet new market needs. For example, it proposes improvements that combine needs from different industries. This makes it possible to analyze trend data from different industries and propose product improvements that combine ideas from different industries.

[0044] The process optimization unit can make product improvement proposals based on consumer life events. For example, the process optimization unit uses generative AI to analyze consumer life event data and make product improvement proposals based on that data. For example, it proposes product improvements related to marriage and childbirth. The process optimization unit also identifies consumer needs based on life events and makes product improvement proposals accordingly. For example, it proposes improvements to childcare products after childbirth. The process optimization unit also analyzes consumer life event data in real time and makes product improvement proposals based on the results. For example, it proposes new improvements each time a life event occurs. This makes it possible to make product improvement proposals based on consumer life events.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit may analyze consumer purchasing histories to identify popular products. The analysis unit may also analyze social media posts to understand consumer preferences and trends. The analysis unit may also analyze review site ratings to identify consumer satisfaction and dissatisfaction. The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit may generate ideas for new organic snacks for health-conscious consumers. The idea generation unit may also propose specific product concepts and features based on the analysis results. The idea generation unit may also identify target markets and generate product ideas suitable for those markets. The process optimization unit optimizes the product development process based on the new product ideas generated by the idea generation unit. For example, the process optimization unit may collect consumer feedback in the early stages of product development and reflect it in product improvements. The process optimization unit may also propose optimal actions at each stage of the development process to support efficient product development. The process optimization unit may also optimize the development schedule and bring products to market more quickly. As a result, the product development system according to the embodiment can perform detailed analysis of consumer preferences and trends and develop new products based on the results. For example, the analysis unit identifies popular products based on consumer purchasing data. The idea generation unit generates new product ideas based on the results. The process optimization unit optimizes the product development process based on the ideas.

[0047] The analysis unit can include consumer lifestyle data in the analysis. For example, the analysis unit uses a generative AI to analyze consumer exercise habit data and identify health-conscious trends. For example, it predicts products preferred by health-conscious consumers based on data from a fitness app. The analysis unit also analyzes consumer eating pattern data to identify food preferences and trends. For example, it predicts the foods and ingredients preferred by consumers based on data from a food log app. The analysis unit also analyzes the consumer's overall lifestyle (e.g., sleep patterns, stress levels) to predict overall preferences and trends. For example, it suggests products that suit the consumer's lifestyle based on data from a wearable device. In this way, by including consumer lifestyle data in the analysis, it is possible to predict preferences and trends in more detail.

[0048] The analysis unit can include consumer voice messages or video reviews in the analysis. For example, the analysis unit uses a generative AI to analyze consumer voice messages and identify emotions and preferences. For example, it uses voice recognition technology to predict preferences from the content of consumer speech. The analysis unit also analyzes video reviews, infers emotions from the consumer's facial expressions and tone, and predicts preferences and trends based on that data. For example, it combines the content of the video reviews with facial expression analysis for analysis. The analysis unit also converts the voice messages and video reviews into text data and analyzes consumer preferences and trends based on that data. For example, it automatically converts the content of the voice or video into text and uses it for analysis. In this way, by including consumer voice messages and video reviews in the analysis, more detailed preferences and trends can be predicted.

[0049] The analysis unit can analyze consumer data from different regions or cultural spheres to identify global trends. For example, the analysis unit uses the generation AI to analyze consumer data from different regions to identify regional preferences and trends. For example, it compares and analyzes consumer data from Asia and Europe. The analysis unit also analyzes consumer data from each cultural sphere to identify preferences and trends based on cultural background. For example, it predicts products and services that are popular in specific cultural spheres. The analysis unit also integrates and analyzes global consumer data to identify worldwide trends. For example, it extracts common trends based on data from multiple regions or cultural spheres. This makes it possible to analyze consumer data from different regions or cultural spheres and identify global trends.

[0050] The idea generation unit can analyze past product success stories and generate new product ideas based on those patterns. For example, the idea generation unit uses a generative AI to analyze past product success stories and generate new product ideas based on those patterns. For example, it extracts commonalities between successful products and proposes new product concepts. The idea generation unit also identifies features and designs preferred by consumers based on data from success stories and generates new product ideas based on those. For example, it incorporates popular functions and designs. The idea generation unit also analyzes past product success stories and generates new product ideas based on that knowledge. For example, it analyzes success factors and reflects them in new products. This makes it possible to analyze past product success stories and generate new product ideas based on those patterns.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit analyzes consumer purchasing history to identify which products are popular. The analysis unit can also analyze social media posts to understand consumer preferences and trends. The analysis unit can also analyze review site ratings to identify consumer satisfaction and dissatisfaction. Step 2: The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit generates ideas for new organic snacks for health-conscious consumers. The idea generation unit can also propose specific product concepts and features based on the analysis results. The idea generation unit can also identify target markets and generate product ideas suitable for those markets. Step 3: The Process Optimization Department optimizes the product development process based on the new product ideas generated by the Idea Generation Department. For example, the Process Optimization Department collects consumer feedback in the early stages of product development and reflects it in product improvements. The Process Optimization Department can also suggest optimal actions at each stage of the development process to support efficient product development. The Process Optimization Department can also optimize the development schedule and quickly bring products to market.

[0053] (Example 2) A product development system according to an embodiment of the present invention is a system in which a generation AI analyzes consumer preferences and trends, generates new product ideas, and optimizes the product development process. This allows the product development system to perform a detailed analysis of consumer preferences and trends and develop new products based on the results.

[0054] A product development system according to an embodiment includes an analysis unit, an idea generation unit, and a process optimization unit. The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit analyzes consumer purchasing histories to identify popular products. The analysis unit can also analyze social media posts to understand consumer preferences and trends. The analysis unit can also analyze review site ratings to identify consumer satisfaction and dissatisfaction. The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit generates ideas for new organic snacks for health-conscious consumers. The idea generation unit can also propose specific product concepts and features based on the analysis results. The idea generation unit can also identify target markets and generate product ideas suitable for those markets. The process optimization unit optimizes the product development process based on the new product ideas generated by the idea generation unit. For example, the process optimization unit collects consumer feedback in the early stages of product development and reflects it in product improvements. The process optimization unit can also propose optimal actions at each stage of the development process to support efficient product development. The process optimization unit also optimizes the development schedule and enables rapid product launch. This allows the product development system according to the embodiment to perform detailed analysis of consumer preferences and trends and develop new products based on the analysis. For example, the analysis unit identifies popular products based on consumer purchasing data. The idea generation unit generates new product ideas based on the results. The process optimization unit optimizes the product development process based on the ideas.

[0055] The analysis unit analyzes consumer emotions and can predict preferences and trends based on changes in emotions. For example, the generation AI in the analysis unit analyzes social media posts and comments on review sites to identify consumer emotions. For example, it predicts products and trends that consumers prefer based on posts with a high percentage of positive emotions. The analysis unit also combines and analyzes consumer purchase history and emotional data to predict fluctuations in preferences based on changes in emotions. For example, it takes into account changes in emotions related to specific seasons or events. The generation AI in the analysis unit also analyzes consumer emotional data in real time and dynamically predicts preferences and trends according to changes in emotions. For example, if a consumer's emotions change suddenly, it can identify new trends based on those changes. This makes it possible to predict preferences and trends based on changes in consumer emotions.

[0056] The analysis unit can include consumer lifestyle data in the analysis. For example, the analysis unit uses a generative AI to analyze consumer exercise habit data and identify health-conscious trends. For example, it predicts products preferred by health-conscious consumers based on data from a fitness app. The analysis unit also analyzes consumer eating pattern data to identify food preferences and trends. For example, it predicts the foods and ingredients preferred by consumers based on data from a food log app. The analysis unit also analyzes the consumer's overall lifestyle (e.g., sleep patterns, stress levels) to predict overall preferences and trends. For example, it suggests products that suit the consumer's lifestyle based on data from a wearable device. In this way, by including consumer lifestyle data in the analysis, it is possible to predict preferences and trends in more detail.

[0057] The analysis unit can use the emotion estimation function to predict changes in preferences based on changes in emotions. For example, the analysis unit predicts changes in preferences in response to changes in emotions based on the generation AI's emotion estimation data. For example, it preferentially suggests products for which the consumer has positive emotions. The analysis unit also uses the emotion estimation function to monitor changes in the consumer's emotions in real time and predict changes in preferences based on that data. For example, it updates the preference prediction every time the consumer's emotions change. The analysis unit also analyzes the consumer's emotion estimation data and predicts trends based on changes in emotions. For example, if the consumer's emotions change suddenly, it identifies a new trend based on those changes. This makes it possible to predict changes in preferences based on changes in emotions using the consumer emotion estimation function.

[0058] The analysis unit can include consumer voice messages or video reviews in the analysis. For example, the analysis unit uses a generative AI to analyze consumer voice messages and identify emotions and preferences. For example, it uses voice recognition technology to predict preferences from the content of consumer speech. The analysis unit also analyzes video reviews, infers emotions from the consumer's facial expressions and tone, and predicts preferences and trends based on that data. For example, it combines the content of the video reviews with facial expression analysis for analysis. The analysis unit also converts the voice messages and video reviews into text data and analyzes consumer preferences and trends based on that data. For example, it automatically converts the content of the voice or video into text and uses it for analysis. In this way, by including consumer voice messages and video reviews in the analysis, more detailed preferences and trends can be predicted.

[0059] The analysis unit can analyze consumer data from different regions or cultural spheres to identify global trends. For example, the analysis unit uses the generation AI to analyze consumer data from different regions to identify regional preferences and trends. For example, it compares and analyzes consumer data from Asia and Europe. The analysis unit also analyzes consumer data from each cultural sphere to identify preferences and trends based on cultural background. For example, it predicts products and services that are popular in specific cultural spheres. The analysis unit also integrates and analyzes global consumer data to identify worldwide trends. For example, it extracts common trends based on data from multiple regions or cultural spheres. This makes it possible to analyze consumer data from different regions or cultural spheres and identify global trends.

[0060] The analysis unit can use the emotion estimation function to analyze changes in preferences based on consumer emotions in real time. For example, the analysis unit uses a generation AI to analyze consumer emotion estimation data in real time and predict changes in preferences based on changes in emotions. For example, the analysis unit updates the preference predictions every time the consumer's emotions change. The analysis unit also uses the emotion estimation function to monitor changes in consumer emotions in real time and predict changes in preferences based on the data. For example, if there is a sudden change in consumer emotions, the analysis unit identifies a new trend based on the change. The analysis unit also analyzes the consumer emotion estimation data and predicts trends based on changes in emotions. For example, if the consumer's emotions change in a positive direction, the analysis unit suggests new products based on the change. In this way, the emotion estimation function can be used to analyze changes in preferences based on consumer emotions in real time.

[0061] The idea generation unit generates new product ideas based on consumer emotional data and can propose products that are likely to resonate emotionally. For example, the idea generation unit uses a generative AI to analyze consumer emotional data and generate new product ideas that are likely to resonate emotionally. For example, it proposes product concepts based on themes that evoke strong positive emotions. The idea generation unit also proposes product features and designs that consumers can easily relate to based on the emotional data. For example, it determines colors and shapes based on consumers' emotional reactions. The idea generation unit also analyzes consumer emotional data in real time and generates new product ideas based on the results. For example, it proposes new ideas every time consumers' emotions change. This makes it possible to generate new product ideas based on consumer emotional data and propose products that are likely to resonate emotionally.

[0062] The idea generation unit can analyze past product success stories and generate new product ideas based on those patterns. For example, the idea generation unit uses a generative AI to analyze past product success stories and generate new product ideas based on those patterns. For example, it extracts commonalities between successful products and proposes new product concepts. The idea generation unit also identifies features and designs preferred by consumers based on data from success stories and generates new product ideas based on those. For example, it incorporates popular functions and designs. The idea generation unit also analyzes past product success stories and generates new product ideas based on that knowledge. For example, it analyzes success factors and reflects them in new products. This makes it possible to analyze past product success stories and generate new product ideas based on those patterns.

[0063] The idea generation unit can use the emotion estimation function to generate new product ideas that elicit a positive emotional response. For example, the idea generation unit uses a generation AI to generate new product ideas that elicit a positive emotional response based on consumer emotion estimation data. For example, the idea generation unit proposes a product concept based on a theme that evokes strong positive emotions. The idea generation unit also uses the emotion estimation function to propose product features and designs that evoke positive emotions in consumers. For example, it determines colors and shapes based on consumers' emotional responses. The idea generation unit also analyzes consumer emotion estimation data in real time and generates new product ideas based on the results. For example, it proposes new ideas every time consumers' emotions change. In this way, the consumer emotion estimation function can be used to generate new product ideas that elicit a positive emotional response.

[0064] The idea generation unit can analyze trends in different industries and propose new products that combine ideas from different industries. For example, the idea generation unit uses a generation AI to analyze trend data from different industries and propose new products that combine ideas from different industries. For example, it can propose a product that combines the technology field with the consumer market. The idea generation unit can also analyze successful cases from different industries and generate new product ideas based on that knowledge. For example, it can propose a product that applies medical technology to everyday life. The idea generation unit can also integrate and analyze trends from different industries and propose products that meet new market needs. For example, it can propose a product that combines needs from different industries. This makes it possible to analyze trends from different industries and propose new products that combine ideas from different industries.

[0065] The idea generation unit can generate new product ideas based on consumer life events. For example, the generation AI analyzes consumer life event data and generates new product ideas based on that data. For example, it can propose products related to marriage and childbirth. The idea generation unit can also identify consumer needs based on life events and generate new product ideas accordingly. For example, it can propose childcare products for after childbirth. The idea generation unit can also analyze consumer life event data in real time and generate new product ideas based on the results. For example, it can propose new ideas every time a life event occurs. This makes it possible to generate new product ideas based on consumer life events.

[0066] The idea generation unit can use the emotion estimation function to generate new product ideas based on consumer emotions in real time. For example, the idea generation unit uses a generation AI to generate new product ideas that elicit a positive emotional response in real time based on consumer emotion estimation data. For example, the idea generation unit proposes a product concept based on a theme that evokes strong positive emotions. The idea generation unit also uses the emotion estimation function to propose product features and designs that evoke positive emotions in consumers in real time. For example, the color and shape are determined based on the consumer's emotional response. The idea generation unit also analyzes consumer emotion estimation data in real time and generates new product ideas based on the results. For example, new ideas are proposed every time the consumer's emotions change. In this way, the emotion estimation function can be used to generate new product ideas based on consumer emotions in real time.

[0067] The process optimization department can collect consumer emotional data at each stage of the development process and make improvement suggestions based on those emotions. For example, the process optimization department uses generative AI to collect consumer emotional data at each stage of the development process and make improvement suggestions based on that data. For example, it collects consumer emotional responses during the prototype testing stage and identifies areas for improvement. The process optimization department also uses the emotional data to make improvement suggestions that will make consumers feel positive at each stage of the development process. For example, it adjusts the design and functions based on the consumer emotional responses. The process optimization department also collects consumer emotional data in real time and makes improvement suggestions at each stage of the development process based on the results. For example, it updates the improvements every time consumer emotions change. This makes it possible to collect consumer emotional data at each stage of the development process and make improvement suggestions based on emotions.

[0068] The process optimization unit can analyze past data on the development process and propose an optimal development schedule. For example, the process optimization unit uses a generation AI to analyze past development process data and propose an optimal development schedule. For example, it analyzes the success factors of past projects and reflects them in the schedule. The process optimization unit also proposes an efficient schedule based on data from each stage of the development process. For example, it optimizes the time required for specific tasks and resource allocation. The process optimization unit also analyzes past development data in real time and proposes an optimal schedule based on the results. For example, it dynamically adjusts the schedule based on past data. This makes it possible to analyze past data on the development process and propose an optimal development schedule.

[0069] The process optimization unit can use the consumer emotion estimation function to make suggestions that elicit a positive emotional response at each stage of the development process. For example, the process optimization unit uses the generative AI to make suggestions that elicit a positive emotional response at each stage of the development process based on consumer emotion estimation data. For example, it adjusts the prototype design. The process optimization unit also uses the emotion estimation function to make improvement suggestions that will elicit positive emotions from consumers at each stage of the development process. For example, it adjusts functions and designs based on consumer emotional responses. The process optimization unit also collects consumer emotion estimation data in real time and makes improvement suggestions at each stage of the development process based on the results. For example, it updates the improvements every time consumer emotions change. In this way, the consumer emotion estimation function can be used to make suggestions that elicit a positive emotional response at each stage of the development process.

[0070] The Process Optimization Department can analyze development processes in different industries and introduce the optimal process. For example, the Process Optimization Department uses generative AI to analyze development process data from different industries and introduce the optimal process. For example, it can propose a development process that combines the technology field with the consumer market. The Process Optimization Department can also analyze successful cases from different industries and optimize the development process based on that knowledge. For example, it can propose a development process that applies medical technology to everyday life. The Process Optimization Department can also perform an integrated analysis of development processes from different industries and propose processes that meet new market needs. For example, it can propose a development process that combines the needs of different industries. This makes it possible to analyze development processes in different industries and introduce the optimal process.

[0071] The process optimization department can collect feedback in real time at each stage of the development process and make improvements immediately. For example, the process optimization department uses generative AI to collect feedback in real time at each stage of the development process and make improvements immediately based on the results. For example, it collects consumer feedback during the prototype testing stage and identifies areas for improvement. The process optimization department also uses the feedback data to make improvement suggestions at each stage of the development process that will make consumers feel positive. For example, it adjusts the design and functions based on consumer feedback. The process optimization department also collects consumer feedback data in real time and makes improvement suggestions at each stage of the development process based on the results. For example, it updates the areas for improvement every time consumer feedback changes. This allows feedback to be collected in real time at each stage of the development process and make improvements immediately.

[0072] The process optimization unit can use the emotion estimation function to propose optimal actions based on consumer emotions at each stage of the development process. For example, the process optimization unit uses generative AI to make suggestions that will elicit a positive emotional response at each stage of the development process based on consumer emotion estimation data. For example, it adjusts the prototype design. The process optimization unit also uses the emotion estimation function to propose improvements that will evoke positive emotions in consumers at each stage of the development process. For example, it adjusts functions and designs based on consumer emotional responses. The process optimization unit also collects consumer emotion estimation data in real time and makes improvement suggestions at each stage of the development process based on the results. For example, it updates the improvements every time consumer emotions change. This makes it possible to use the emotion estimation function to propose optimal actions based on consumer emotions at each stage of the development process.

[0073] The process optimization unit can generate marketing messages that are easy to empathize with emotionally based on consumer emotional data. For example, the process optimization unit uses a generation AI to analyze consumer emotional data and generate marketing messages that are easy to empathize with emotionally. For example, it creates a message based on a theme that evokes strong positive emotions. The process optimization unit also suggests features and designs for marketing messages that consumers can easily empathize with based on the emotional data. For example, it determines colors and tones based on the consumer's emotional reactions. The process optimization unit also analyzes consumer emotional data in real time and generates marketing messages based on the results. For example, it suggests new messages every time the consumer's emotions change. This makes it possible to generate marketing messages that are easy to empathize with emotionally based on consumer emotional data.

[0074] The process optimization department can analyze successful cases of past marketing campaigns and propose new strategies based on those patterns. For example, the process optimization department uses generative AI to analyze successful cases of past marketing campaigns and propose new strategies based on those patterns. For example, it extracts commonalities between successful campaigns and proposes a new strategy. The process optimization department can also identify marketing messages and methods preferred by consumers based on data from successful cases and propose a new strategy based on that. For example, it can incorporate popular messages and methods. The process optimization department can also analyze successful cases of past campaigns and propose a new marketing strategy based on that knowledge. For example, it can analyze success factors and reflect them in a new strategy. This makes it possible to analyze successful cases of past marketing campaigns and propose a new strategy based on their patterns.

[0075] The process optimization unit can use the emotion estimation function to propose marketing strategies that elicit positive emotional responses. For example, the process optimization unit uses generative AI to propose marketing strategies that elicit positive emotional responses based on consumer emotion estimation data. For example, a strategy is created based on a theme that evokes strong positive emotions. The process optimization unit also uses the emotion estimation function to propose marketing messages and methods that will evoke positive emotions in consumers. For example, colors and tones are determined based on the consumer's emotional responses. The process optimization unit also analyzes consumer emotion estimation data in real time and proposes marketing strategies based on the results. For example, a new strategy is proposed every time the consumer's emotions change. In this way, the emotion estimation function can be used to propose marketing strategies that elicit positive emotional responses.

[0076] The process optimization unit can analyze marketing data from different regions or cultural spheres and propose global strategies. For example, the process optimization unit uses generative AI to analyze marketing data from different regions and propose strategies for each region. For example, it compares and analyzes marketing data from Asia and Europe. The process optimization unit also analyzes marketing data from each cultural sphere and proposes strategies based on cultural backgrounds. For example, it proposes marketing methods that are effective in specific cultural spheres. The process optimization unit also integrates and analyzes global marketing data and proposes worldwide strategies. For example, it proposes a common strategy based on data from multiple regions and cultural spheres. This makes it possible to analyze marketing data from different regions and cultural spheres and propose global strategies.

[0077] The process optimization unit can propose marketing strategies based on consumer life events. For example, the process optimization unit uses generative AI to analyze consumer life event data and propose marketing strategies based on that data. For example, it proposes marketing campaigns related to marriage and childbirth. The process optimization unit also identifies consumer needs based on life events and proposes marketing strategies accordingly. For example, it proposes a marketing strategy for childcare products after childbirth. The process optimization unit also analyzes consumer life event data in real time and proposes marketing strategies based on the results. For example, it proposes a new strategy each time a life event occurs. This makes it possible to propose marketing strategies based on consumer life events.

[0078] The process optimization unit can use the emotion estimation function to propose marketing strategies based on consumer emotions in real time. For example, the process optimization unit uses generative AI to propose marketing strategies that elicit positive emotional responses in real time based on consumer emotion estimation data. For example, a strategy is created based on themes that evoke strong positive emotions. The process optimization unit also uses the emotion estimation function to propose marketing messages and methods that evoke positive emotions in consumers in real time. For example, it determines colors and tones based on consumers' emotional responses. The process optimization unit also analyzes consumer emotion estimation data in real time and proposes marketing strategies based on the results. For example, it proposes a new strategy every time consumer emotions change. In this way, the emotion estimation function can be used to propose marketing strategies based on consumer emotions in real time.

[0079] The process optimization department can make product improvement proposals that resonate with consumers emotionally based on consumer emotional data. For example, the process optimization department uses generative AI to analyze consumer emotional data and make product improvement proposals that resonate with consumers emotionally. For example, it improves product design and functions based on feedback that shows strong positive emotions. The process optimization department also uses emotional data to propose product features and designs that consumers can easily relate to. For example, it adjusts colors and shapes based on consumers' emotional reactions. The process optimization department also analyzes consumer emotional data in real time and makes product improvement proposals based on the results. For example, it updates the improvements every time consumers' emotions change. This makes it possible to make product improvement proposals that resonate with consumers emotionally based on consumer emotional data.

[0080] The process optimization department can analyze past product improvement data and make new improvement proposals based on those patterns. For example, the process optimization department uses generative AI to analyze past product improvement data and make new improvement proposals based on those patterns. For example, it extracts common points between successful improvements and proposes new improvements. The process optimization department can also identify features and designs preferred by consumers based on the improvement data and make new improvement proposals based on those. For example, it can incorporate popular functions and designs. The process optimization department can also analyze past improvement data and make new improvement proposals based on that knowledge. For example, it can analyze success factors and reflect them in new improvements. This makes it possible to analyze past product improvement data and make new improvement proposals based on those patterns.

[0081] The process optimization unit can use the emotion estimation function to make product improvement proposals that elicit a positive emotional response. For example, the process optimization unit uses generative AI to make product improvement proposals that elicit a positive emotional response based on consumer emotion estimation data. For example, it improves product design and functions based on feedback that evokes strong positive emotions. The process optimization unit also uses the emotion estimation function to propose product features and designs that evoke positive emotions from consumers. For example, it adjusts colors and shapes based on consumers' emotional responses. The process optimization unit also analyzes consumer emotion estimation data in real time and makes product improvement proposals based on the results. For example, it updates the improvements every time consumer emotions change. This makes it possible to use the emotion estimation function to make product improvement proposals that elicit a positive emotional response.

[0082] The process optimization department can analyze trend data from different industries and propose product improvements that combine ideas from different industries. For example, the process optimization department uses generative AI to analyze trend data from different industries and propose product improvements that combine ideas from different industries. For example, it proposes improvements that combine the technology field with the consumer market. The process optimization department also analyzes successful cases from different industries and proposes product improvements based on that knowledge. For example, it proposes improvements that apply medical technology to everyday life. The process optimization department also performs an integrated analysis of trends from different industries and proposes product improvements that meet new market needs. For example, it proposes improvements that combine needs from different industries. This makes it possible to analyze trend data from different industries and propose product improvements that combine ideas from different industries.

[0083] The process optimization unit can make product improvement proposals based on consumer life events. For example, the process optimization unit uses generative AI to analyze consumer life event data and make product improvement proposals based on that data. For example, it proposes product improvements related to marriage and childbirth. The process optimization unit also identifies consumer needs based on life events and makes product improvement proposals accordingly. For example, it proposes improvements to childcare products after childbirth. The process optimization unit also analyzes consumer life event data in real time and makes product improvement proposals based on the results. For example, it proposes new improvements each time a life event occurs. This makes it possible to make product improvement proposals based on consumer life events.

[0084] The process optimization unit can use the emotion estimation function to make product improvement suggestions based on consumer emotions in real time. For example, the process optimization unit uses generative AI to make product improvement suggestions in real time that elicit positive emotional responses based on consumer emotion estimation data. For example, the process optimization unit improves product design and functions based on feedback that evokes strong positive emotions. The process optimization unit also uses the emotion estimation function to suggest product features and designs that evoke positive emotions in consumers in real time. For example, the process optimization unit adjusts colors and shapes based on consumers' emotional responses. The process optimization unit also analyzes consumer emotion estimation data in real time and makes product improvement suggestions based on the results. For example, the improvements can be updated every time consumer emotions change. This allows the emotion estimation function to make product improvement suggestions based on consumer emotions in real time.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit may analyze consumer purchasing histories to identify popular products. The analysis unit may also analyze social media posts to understand consumer preferences and trends. The analysis unit may also analyze review site ratings to identify consumer satisfaction and dissatisfaction. The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit may generate ideas for new organic snacks for health-conscious consumers. The idea generation unit may also propose specific product concepts and features based on the analysis results. The idea generation unit may also identify target markets and generate product ideas suitable for those markets. The process optimization unit optimizes the product development process based on the new product ideas generated by the idea generation unit. For example, the process optimization unit may collect consumer feedback in the early stages of product development and reflect it in product improvements. The process optimization unit may also propose optimal actions at each stage of the development process to support efficient product development. The process optimization unit may also optimize the development schedule and bring products to market more quickly. As a result, the product development system according to the embodiment can perform detailed analysis of consumer preferences and trends and develop new products based on the results. For example, the analysis unit identifies popular products based on consumer purchasing data. The idea generation unit generates new product ideas based on the results. The process optimization unit optimizes the product development process based on the ideas.

[0087] The analysis unit analyzes consumer emotions and can predict preferences and trends based on changes in emotions. For example, the generation AI in the analysis unit analyzes social media posts and comments on review sites to identify consumer emotions. For example, it predicts products and trends that consumers prefer based on posts with a high percentage of positive emotions. The analysis unit also combines and analyzes consumer purchase history and emotional data to predict fluctuations in preferences based on changes in emotions. For example, it takes into account changes in emotions related to specific seasons or events. The generation AI in the analysis unit also analyzes consumer emotional data in real time and dynamically predicts preferences and trends according to changes in emotions. For example, if a consumer's emotions change suddenly, it can identify new trends based on those changes. This makes it possible to predict preferences and trends based on changes in consumer emotions.

[0088] The analysis unit can include consumer lifestyle data in the analysis. For example, the analysis unit uses a generative AI to analyze consumer exercise habit data and identify health-conscious trends. For example, it predicts products preferred by health-conscious consumers based on data from a fitness app. The analysis unit also analyzes consumer eating pattern data to identify food preferences and trends. For example, it predicts the foods and ingredients preferred by consumers based on data from a food log app. The analysis unit also analyzes the consumer's overall lifestyle (e.g., sleep patterns, stress levels) to predict overall preferences and trends. For example, it suggests products that suit the consumer's lifestyle based on data from a wearable device. In this way, by including consumer lifestyle data in the analysis, it is possible to predict preferences and trends in more detail.

[0089] The analysis unit can use the emotion estimation function to predict changes in preferences based on changes in emotions. For example, the analysis unit predicts changes in preferences in response to changes in emotions based on the generation AI's emotion estimation data. For example, it preferentially suggests products for which the consumer has positive emotions. The analysis unit also uses the emotion estimation function to monitor changes in the consumer's emotions in real time and predict changes in preferences based on that data. For example, it updates the preference prediction every time the consumer's emotions change. The analysis unit also analyzes the consumer's emotion estimation data and predicts trends based on changes in emotions. For example, if the consumer's emotions change suddenly, it identifies a new trend based on those changes. This makes it possible to predict changes in preferences based on changes in emotions using the consumer emotion estimation function.

[0090] The analysis unit can include consumer voice messages or video reviews in the analysis. For example, the analysis unit uses a generative AI to analyze consumer voice messages and identify emotions and preferences. For example, it uses voice recognition technology to predict preferences from the content of consumer speech. The analysis unit also analyzes video reviews, infers emotions from the consumer's facial expressions and tone, and predicts preferences and trends based on that data. For example, it combines the content of the video reviews with facial expression analysis for analysis. The analysis unit also converts the voice messages and video reviews into text data and analyzes consumer preferences and trends based on that data. For example, it automatically converts the content of the voice or video into text and uses it for analysis. In this way, by including consumer voice messages and video reviews in the analysis, more detailed preferences and trends can be predicted.

[0091] The analysis unit can analyze consumer data from different regions or cultural spheres to identify global trends. For example, the analysis unit uses the generation AI to analyze consumer data from different regions to identify regional preferences and trends. For example, it compares and analyzes consumer data from Asia and Europe. The analysis unit also analyzes consumer data from each cultural sphere to identify preferences and trends based on cultural background. For example, it predicts products and services that are popular in specific cultural spheres. The analysis unit also integrates and analyzes global consumer data to identify worldwide trends. For example, it extracts common trends based on data from multiple regions or cultural spheres. This makes it possible to analyze consumer data from different regions or cultural spheres and identify global trends.

[0092] The analysis unit can use the emotion estimation function to analyze changes in preferences based on consumer emotions in real time. For example, the analysis unit uses a generation AI to analyze consumer emotion estimation data in real time and predict changes in preferences based on changes in emotions. For example, the analysis unit updates the preference predictions every time the consumer's emotions change. The analysis unit also uses the emotion estimation function to monitor changes in consumer emotions in real time and predict changes in preferences based on the data. For example, if there is a sudden change in consumer emotions, the analysis unit identifies a new trend based on the change. The analysis unit also analyzes the consumer emotion estimation data and predicts trends based on changes in emotions. For example, if the consumer's emotions change in a positive direction, the analysis unit suggests new products based on the change. In this way, the emotion estimation function can be used to analyze changes in preferences based on consumer emotions in real time.

[0093] The idea generation unit generates new product ideas based on consumer emotional data and can propose products that are likely to resonate emotionally. For example, the idea generation unit uses a generative AI to analyze consumer emotional data and generate new product ideas that are likely to resonate emotionally. For example, it proposes product concepts based on themes that evoke strong positive emotions. The idea generation unit also proposes product features and designs that consumers can easily relate to based on the emotional data. For example, it determines colors and shapes based on consumers' emotional reactions. The idea generation unit also analyzes consumer emotional data in real time and generates new product ideas based on the results. For example, it proposes new ideas every time consumers' emotions change. This makes it possible to generate new product ideas based on consumer emotional data and propose products that are likely to resonate emotionally.

[0094] The idea generation unit can analyze past product success stories and generate new product ideas based on those patterns. For example, the idea generation unit uses a generative AI to analyze past product success stories and generate new product ideas based on those patterns. For example, it extracts commonalities between successful products and proposes new product concepts. The idea generation unit also identifies features and designs preferred by consumers based on data from success stories and generates new product ideas based on those. For example, it incorporates popular functions and designs. The idea generation unit also analyzes past product success stories and generates new product ideas based on that knowledge. For example, it analyzes success factors and reflects them in new products. This makes it possible to analyze past product success stories and generate new product ideas based on those patterns.

[0095] The idea generation unit can use the emotion estimation function to generate new product ideas that elicit a positive emotional response. For example, the idea generation unit uses a generation AI to generate new product ideas that elicit a positive emotional response based on consumer emotion estimation data. For example, the idea generation unit proposes a product concept based on a theme that evokes strong positive emotions. The idea generation unit also uses the emotion estimation function to propose product features and designs that evoke positive emotions in consumers. For example, it determines colors and shapes based on consumers' emotional responses. The idea generation unit also analyzes consumer emotion estimation data in real time and generates new product ideas based on the results. For example, it proposes new ideas every time consumers' emotions change. In this way, the consumer emotion estimation function can be used to generate new product ideas that elicit a positive emotional response.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The analysis unit analyzes consumer purchasing data, social media posts, and review site ratings. For example, the analysis unit analyzes consumer purchasing history to identify which products are popular. The analysis unit can also analyze social media posts to understand consumer preferences and trends. The analysis unit can also analyze review site ratings to identify consumer satisfaction and dissatisfaction. Step 2: The idea generation unit generates new product ideas based on the results of the analysis by the analysis unit. For example, the idea generation unit generates ideas for new organic snacks for health-conscious consumers. The idea generation unit can also propose specific product concepts and features based on the analysis results. The idea generation unit can also identify target markets and generate product ideas suitable for those markets. Step 3: The Process Optimization Department optimizes the product development process based on the new product ideas generated by the Idea Generation Department. For example, the Process Optimization Department collects consumer feedback in the early stages of product development and reflects it in product improvements. The Process Optimization Department can also suggest optimal actions at each stage of the development process to support efficient product development. The Process Optimization Department can also optimize the development schedule and quickly bring products to market.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0124] 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.

[0125] 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.

[0126] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

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

[0134] 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.

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

[0136] 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).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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, in order to avoid confusion and to 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.

[0164] 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. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes consumer purchasing data, social media posts, and ratings on review sites; an idea generation unit that generates ideas for new products based on the results of the analysis by the analysis unit; a process optimization unit that optimizes a product development process based on the new product ideas generated by the idea generation unit. A system characterized by:

2. The analysis unit Analyze consumer sentiment and predict preferences and trends based on changes in sentiment 2. The system of claim 1.

3. The analysis unit Include consumer lifestyle data in your analysis 2. The system of claim 1.

4. The analysis unit Predicting preference shifts based on changes in consumer sentiment 2. The system of claim 1.

5. The analysis unit Include consumer voice messages or video reviews in your analysis 2. The system of claim 1.

6. The analysis unit Analyze consumer data from different regions or cultures to identify global trends 2. The system of claim 1.

7. The analysis unit Analyzing changes in consumer preferences based on emotions in real time 2. The system of claim 1.

8. The idea generation unit Generate new product ideas based on consumer sentiment data and propose products that resonate with people emotionally.

2. The system of claim 1.

Images