system

Abstract

The system according to the embodiment aims to enable new business teams to grasp market trends quickly and at low cost and generate a direction for business development. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects market data. The analysis unit analyzes the market data collected by the collection unit. The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. The provision unit provides advice based on the direction generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that market research and analysis required to enter an existing market is time-consuming and costly.

[0005] The system according to the embodiment aims to enable new business teams to grasp market trends quickly and at low cost and generate a direction for business development. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects market data. The analysis unit analyzes the market data collected by the collection unit. The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. The provision unit provides advice based on the direction generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment enables new business teams to grasp market trends quickly and at low cost and generate directions for business development. [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 market forecast generation system according to an embodiment of the present invention uses a market forecast generation AI to reduce the time and cost required for new business teams to enter existing markets. This market forecast generation system aims to grasp market trends and generate business development directions. While traditional methods involve market research and analysis conducted by personnel, this system differentiates itself by providing consistent, large-scale analysis and expert advice. For example, the system collects market data, including past sales data, consumer behavior data, and competitor trends. This data is then input into the generation AI, which then analyzes the collected market data. The generation AI quickly processes large amounts of data and extracts market trends and patterns. For example, it analyzes products with increased sales during specific seasons and changes in consumer preferences. Based on the analysis results, the generation AI generates business development directions. Specifically, it provides advice to new business teams on which markets to enter, what products and services to offer, and what marketing strategies to adopt. This allows new business teams to enter the market quickly and effectively. Furthermore, the generation AI updates its advice in real time in response to market fluctuations. For example, if a competitor launches a new product or consumer preferences change suddenly, the generative AI will provide advice based on the latest data. This allows new business teams to always develop their business based on the latest information. This system allows new business teams to significantly reduce the time and cost required for market research and analysis. In addition, receiving consistent, large-scale analysis and expert advice enables more effective business development. In this way, the market prediction generation system allows new business teams to significantly reduce the time and cost required for market research and analysis, allowing them to enter the market quickly and effectively.

[0029] A market forecast generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects market data. Examples of market data include, but are not limited to, past sales data, consumer behavior data, and competitor trends. The collection unit can collect publicly available data on the Internet using, for example, web scraping technology. The collection unit can also acquire market data from specific data sources using an API (Application Programming Interface). For example, the collection unit can execute a script to automatically extract data from a specific website. The collection unit can also acquire necessary information from a database through the API. The analysis unit analyzes the market data collected by the collection unit. For example, the analysis unit can analyze the market data using a machine learning algorithm. For example, the analysis unit can predict sales using regression analysis. The analysis unit can also analyze consumer behavior patterns using a clustering algorithm. The analysis unit can also extract complex data patterns using deep learning technology. For example, the analysis unit can predict changes in consumer preferences using a neural network. The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. For example, the generation unit can determine which market to enter based on the analysis results. The generation unit can also propose what products and services to offer. Furthermore, the generation unit can generate what marketing strategy to adopt. For example, the generation unit can analyze the trends of competitors in a specific market and propose an optimal marketing strategy based on the results. The provision unit provides advice based on the direction generated by the generation unit. For example, the provision unit can present a specific action plan to a new business team. The provision unit can also update the advice in real time in response to market fluctuations. For example, if a competitor releases a new product, the provision unit can update the advice based on that information.As a result, the market forecast generation system according to the embodiment can consistently perform a range of operations from collecting and analyzing market data to generating business development directions and providing advice.

[0030] The collection unit can collect market data using web scraping or an API. The collection unit, for example, uses web scraping technology to collect public data on the Internet. For example, the collection unit can execute a script to automatically extract data from a specific website. The collection unit can also obtain market data from a specific data source using an API (Application Programming Interface). For example, the collection unit can obtain necessary information from a database through an API. This allows for efficient collection of market data by using web scraping or an API. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a web scraping script into a generation AI and have the generation AI generate the script.

[0031] The analysis unit can analyze market data using machine learning or deep learning. The analysis unit analyzes market data using, for example, a machine learning algorithm. For example, the analysis unit can predict sales using regression analysis. The analysis unit can also analyze consumer behavior patterns using a clustering algorithm. Furthermore, the analysis unit can extract complex data patterns using deep learning technology. For example, the analysis unit can predict changes in consumer preferences using a neural network. This enables highly accurate market data analysis using machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input training data for the machine learning algorithm to the generation AI and cause the generation AI to train the algorithm.

[0032] The generation unit can generate a marketing strategy based on the analysis results. For example, the generation unit can determine which market to enter based on the analysis results. The generation unit can also suggest what products and services to offer. Furthermore, the generation unit can generate what marketing strategy to adopt. For example, the generation unit can analyze the trends of competitors in a specific market and suggest an optimal marketing strategy based on the results. This enables effective business development by generating a marketing strategy based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate a marketing strategy.

[0033] The provision unit can provide the generated marketing strategy to the new business team. For example, the provision unit can present a specific action plan to the new business team. The provision unit can also update the advice in real time in response to market fluctuations. For example, when a competitor releases a new product, the provision unit can update the advice based on that information. In this way, providing the generated marketing strategy to the new business team enables rapid and effective business development. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the generated marketing strategy to a generation AI and have the generation AI provide the advice.

[0034] The providing unit can update the advice in real time in response to market fluctuations. For example, when a competitor releases a new product, the providing unit can update the advice based on that information. Furthermore, even when consumer preferences change suddenly, the providing unit can provide advice based on the latest data. This allows advice to be updated in real time in response to market fluctuations, making it possible to always develop business based on the latest information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the latest market data into the generating AI and cause the generating AI to update the advice.

[0035] The collection unit can analyze past market data collection history and select the optimal collection method. For example, the collection unit can identify the most effective collection method from the past collection history and use that method preferentially. The collection unit can also analyze the past collection history and optimize the collection frequency. Furthermore, the collection unit can prioritize collection from a specific data source based on the past collection history. In this way, by analyzing the past collection history, the optimal collection method can be selected and market data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data to the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting market data, the collection unit can filter the market data based on a specific industry or region. For example, the collection unit can filter the market data so as to collect only data related to a specific industry. The collection unit can also filter the market data so as to collect only data related to a specific region. Furthermore, the collection unit can filter the data based on both the industry and the region to collect the most relevant data. In this way, highly relevant market data can be collected by filtering the data based on a specific industry or region. Some or all of the above-described processing in the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input filtering conditions to a generation AI and have the generation AI perform filtering.

[0037] When collecting market data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting market data related to the user's current location. The collection unit can also collect highly relevant data based on the user's past location information. Furthermore, the collection unit can collect highly relevant data by taking into account the user's future travel plans. In this way, highly relevant market data can be preferentially collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] When collecting market data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related market data. The collection unit can also analyze the activities of the user's followers and friends on social media and collect related market data. Furthermore, the collection unit can analyze the user's social media trends and collect related market data. In this way, highly relevant market data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to sales data. The analysis unit can also apply a clustering algorithm to consumer behavior data. Furthermore, the analysis unit can apply a trend analysis algorithm to competitor trend data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select the analysis algorithm to apply.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze trends based on past data. Furthermore, the analysis unit can prioritize analysis of data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of the most relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0043] The generation unit can adjust the level of detail of the generation based on the importance of the analysis results during generation. For example, the generation unit generates a detailed business development direction based on an analysis result with high importance. The generation unit can also generate a simplified business development direction based on an analysis result with low importance. Furthermore, the generation unit can generate a business development direction with a moderate level of detail based on an analysis result with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the analysis result, an efficient business development direction can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0044] The generation unit can apply different generation algorithms depending on the business category during generation. For example, the generation unit can apply an innovation algorithm to business development related to product development. The generation unit can also apply a targeting algorithm to business development related to marketing. Furthermore, the generation unit can apply an optimization algorithm to business development related to sales strategy. In this way, by applying different generation algorithms depending on the business category, it is possible to generate an optimal business development direction. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the business category into the generation AI and cause the generation AI to select the generation algorithm to be applied.

[0045] At the time of generation, the generation unit can determine the generation priority based on the time when the analysis results were collected. The generation unit, for example, prioritizes generating a business development direction based on the latest analysis results. The generation unit can also generate a business development direction based on past analysis results. Furthermore, the generation unit can generate a business development direction based on analysis results from a specific period. In this way, by determining the generation priority based on the time when the analysis results were collected, it is possible to generate a business development direction based on the latest data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and cause the generation AI to determine the generation priority.

[0046] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. The generation unit, for example, prioritizes generating a business development direction based on the most relevant analysis result. The generation unit can also postpone generating a business development direction based on a less relevant analysis result. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the analysis results. As a result, by adjusting the order of generation based on the relevance of the analysis results, an efficient business development direction can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of generation.

[0047] When providing advice, the providing unit can select the optimal provision method by referring to the user's past business development history. The providing unit, for example, provides optimal advice based on the user's past business development history. The providing unit can also provide advice by referring to the user's past success cases. Furthermore, the providing unit can analyze the user's past failure cases and provide advice including areas for improvement. In this way, optimal advice can be provided by referring to the user's past business development history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past business development history into the generation AI and cause the generation AI to select the optimal provision method.

[0048] When providing advice, the providing unit can customize the means of advice based on the user's current business situation. For example, the providing unit can provide advice for increasing sales based on the user's current sales situation. The providing unit can also provide advice including areas for improvement based on the user's current marketing strategy. Furthermore, the providing unit can analyze the user's current competitive situation and provide advice for increasing competitive advantage. In this way, by customizing the means of advice based on the user's current business situation, more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current business situation data into the generating AI and cause the generating AI to customize the means of advice.

[0049] When providing advice, the providing unit can select the optimal advice method by taking into account the user's geographical location information. The providing unit provides advice based on market information related to the user's current location, for example. The providing unit can also provide highly relevant advice based on the user's past location information. Furthermore, the providing unit can also provide optimal advice by taking into account the user's future travel plans. In this way, highly relevant advice can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal advice method.

[0050] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, analyzes the content posted by the user on social media and provides relevant advice. The providing unit can also analyze the activity of the user's followers and friends on social media and provide relevant advice. Furthermore, the providing unit can analyze the user's social media trends and provide relevant advice. In this way, highly relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest a means of providing advice.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's purchase history and select market data collection targets based on past purchase patterns. For example, the collection unit can identify product categories that the user has purchased in the past and prioritize collecting market data related to those categories. The collection unit can also analyze the user's purchase frequency and collect data related to frequently purchased products. Furthermore, the collection unit can take into account the time of the user's purchases and collect data related to specific seasons or events. In this way, by selecting market data collection targets based on the user's purchase history, more relevant data can be collected.

[0053] The generation unit can customize the business development direction to be generated taking into account the user's business goals. For example, if the user aims to increase sales, the generation unit can propose a business development direction specialized for increasing sales. Also, if the user aims to expand market share, the generation unit can propose a business development direction specialized for expanding market share. Furthermore, if the user aims to enter a new market, the generation unit can propose a business development direction specialized for entering a new market. In this way, customizing the business development direction according to the user's business goals enables more effective business development.

[0054] The collection unit can analyze the user's social media activities and collect related market data. For example, the collection unit can analyze the content of the user's posts on social media and collect related market data. The collection unit can also analyze the activities of the user's followers and friends on social media and collect related market data. Furthermore, the collection unit can analyze the user's social media trends and collect related market data. In this way, highly relevant market data can be collected by analyzing the user's social media activities.

[0055] The generation unit can customize the business development direction to be generated by referring to the user's past business development history. For example, the generation unit can refer to the user's past success stories and propose similar business development directions. The generation unit can also analyze the user's past failure stories and propose business development directions that include areas for improvement. Furthermore, the generation unit can propose an optimal business development direction based on the user's past business development history. This allows for more effective business development by referring to the user's past business development history.

[0056] The collection unit can prioritize collection of highly relevant market data by taking into account the user's geographical location information. For example, the collection unit prioritizes collection of market data related to the user's current location. The collection unit can also collect highly relevant data based on the user's past location information. Furthermore, the collection unit can also collect highly relevant data by taking into account the user's future travel plans. In this way, highly relevant market data can be prioritized by taking into account the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects market data. Market data includes past sales data, consumer behavior data, competitor trends, and so on. The collection unit can use web scraping technology to collect publicly available data on the Internet. It can also use APIs to obtain market data from specific data sources. For example, it can run a script to automatically extract data from a specific website, or it can obtain the required information from a database through an API. Step 2: The analysis unit analyzes the market data collected by the collection unit. The analysis unit can analyze the market data using machine learning algorithms. For example, regression analysis can be used to predict sales, or clustering algorithms can be used to analyze consumer behavior patterns. Furthermore, deep learning techniques can be used to extract complex data patterns. For example, neural networks can be used to predict changes in consumer preferences. Step 3: The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. Based on the analysis results, the generation unit can determine which market to enter and propose what products and services to offer. Furthermore, the generation unit can also generate what marketing strategy should be adopted. For example, it can analyze the trends of competitors in a specific market and propose the optimal marketing strategy based on that. Step 4: The delivery department provides advice based on the direction generated by the generation department. The delivery department can present specific action plans to the new business team. It can also update advice in real time in response to market fluctuations. For example, if a competitor launches a new product, the advice can be updated based on that information.

[0059] (Example 2) A market forecast generation system according to an embodiment of the present invention uses a market forecast generation AI to reduce the time and cost required for new business teams to enter existing markets. This market forecast generation system aims to grasp market trends and generate business development directions. While traditional methods involve market research and analysis conducted by personnel, this system differentiates itself by providing consistent, large-scale analysis and expert advice. For example, the system collects market data, including past sales data, consumer behavior data, and competitor trends. This data is then input into the generation AI, which then analyzes the collected market data. The generation AI quickly processes large amounts of data and extracts market trends and patterns. For example, it analyzes products with increased sales during specific seasons and changes in consumer preferences. Based on the analysis results, the generation AI generates business development directions. Specifically, it provides advice to new business teams on which markets to enter, what products and services to offer, and what marketing strategies to adopt. This allows new business teams to enter the market quickly and effectively. Furthermore, the generation AI updates its advice in real time in response to market fluctuations. For example, if a competitor launches a new product or consumer preferences change suddenly, the generative AI will provide advice based on the latest data. This allows new business teams to always develop their business based on the latest information. This system allows new business teams to significantly reduce the time and cost required for market research and analysis. In addition, receiving consistent, large-scale analysis and expert advice enables more effective business development. In this way, the market prediction generation system allows new business teams to significantly reduce the time and cost required for market research and analysis, allowing them to enter the market quickly and effectively.

[0060] A market forecast generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects market data. Examples of market data include, but are not limited to, past sales data, consumer behavior data, and competitor trends. The collection unit can collect publicly available data on the Internet using, for example, web scraping technology. The collection unit can also acquire market data from specific data sources using an API (Application Programming Interface). For example, the collection unit can execute a script to automatically extract data from a specific website. The collection unit can also acquire necessary information from a database through the API. The analysis unit analyzes the market data collected by the collection unit. For example, the analysis unit can analyze the market data using a machine learning algorithm. For example, the analysis unit can predict sales using regression analysis. The analysis unit can also analyze consumer behavior patterns using a clustering algorithm. The analysis unit can also extract complex data patterns using deep learning technology. For example, the analysis unit can predict changes in consumer preferences using a neural network. The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. For example, the generation unit can determine which market to enter based on the analysis results. The generation unit can also propose what products and services to offer. Furthermore, the generation unit can generate what marketing strategy to adopt. For example, the generation unit can analyze the trends of competitors in a specific market and propose an optimal marketing strategy based on the results. The provision unit provides advice based on the direction generated by the generation unit. For example, the provision unit can present a specific action plan to a new business team. The provision unit can also update the advice in real time in response to market fluctuations. For example, if a competitor releases a new product, the provision unit can update the advice based on that information.As a result, the market forecast generation system according to the embodiment can consistently perform a range of operations from collecting and analyzing market data to generating business development directions and providing advice.

[0061] The collection unit can collect market data using web scraping or an API. The collection unit, for example, uses web scraping technology to collect public data on the Internet. For example, the collection unit can execute a script to automatically extract data from a specific website. The collection unit can also obtain market data from a specific data source using an API (Application Programming Interface). For example, the collection unit can obtain necessary information from a database through an API. This allows for efficient collection of market data by using web scraping or an API. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a web scraping script into a generation AI and have the generation AI generate the script.

[0062] The analysis unit can analyze market data using machine learning or deep learning. The analysis unit analyzes market data using, for example, a machine learning algorithm. For example, the analysis unit can predict sales using regression analysis. The analysis unit can also analyze consumer behavior patterns using a clustering algorithm. Furthermore, the analysis unit can extract complex data patterns using deep learning technology. For example, the analysis unit can predict changes in consumer preferences using a neural network. This enables highly accurate market data analysis using machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input training data for the machine learning algorithm to the generation AI and cause the generation AI to train the algorithm.

[0063] The generation unit can generate a marketing strategy based on the analysis results. For example, the generation unit can determine which market to enter based on the analysis results. The generation unit can also suggest what products and services to offer. Furthermore, the generation unit can generate what marketing strategy to adopt. For example, the generation unit can analyze the trends of competitors in a specific market and suggest an optimal marketing strategy based on the results. This enables effective business development by generating a marketing strategy based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate a marketing strategy.

[0064] The provision unit can provide the generated marketing strategy to the new business team. For example, the provision unit can present a specific action plan to the new business team. The provision unit can also update the advice in real time in response to market fluctuations. For example, when a competitor releases a new product, the provision unit can update the advice based on that information. In this way, providing the generated marketing strategy to the new business team enables rapid and effective business development. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the generated marketing strategy to a generation AI and have the generation AI provide the advice.

[0065] The providing unit can update the advice in real time in response to market fluctuations. For example, when a competitor releases a new product, the providing unit can update the advice based on that information. Furthermore, even when consumer preferences change suddenly, the providing unit can provide advice based on the latest data. This allows advice to be updated in real time in response to market fluctuations, making it possible to always develop business based on the latest information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the latest market data into the generating AI and cause the generating AI to update the advice.

[0066] The collection unit can estimate the user's emotions and adjust the timing of market data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing so that the user can receive data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can also advance the collection timing to provide data quickly. Furthermore, if the user is in a hurry, the collection unit can immediately set the collection timing to quickly collect data. This allows the market data collection timing to be adjusted according to the user's emotions, thereby collecting data at the optimal timing for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0067] The collection unit can analyze past market data collection history and select the optimal collection method. For example, the collection unit can identify the most effective collection method from the past collection history and use that method preferentially. The collection unit can also analyze the past collection history and optimize the collection frequency. Furthermore, the collection unit can prioritize collection from a specific data source based on the past collection history. In this way, by analyzing the past collection history, the optimal collection method can be selected and market data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data to the generation AI and have the generation AI select the optimal collection method.

[0068] When collecting market data, the collection unit can filter the market data based on a specific industry or region. For example, the collection unit can filter the market data so as to collect only data related to a specific industry. The collection unit can also filter the market data so as to collect only data related to a specific region. Furthermore, the collection unit can filter the data based on both the industry and the region to collect the most relevant data. In this way, highly relevant market data can be collected by filtering the data based on a specific industry or region. Some or all of the above-described processing in the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input filtering conditions to a generation AI and have the generation AI perform filtering.

[0069] The collection unit can estimate the user's emotions and prioritize the market data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important data and prioritizes collecting more important data. The collection unit can also collect all data equally when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important data. Thus, by prioritizing market data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the collected data.

[0070] When collecting market data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting market data related to the user's current location. The collection unit can also collect highly relevant data based on the user's past location information. Furthermore, the collection unit can collect highly relevant data by taking into account the user's future travel plans. In this way, highly relevant market data can be preferentially collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0071] When collecting market data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related market data. The collection unit can also analyze the activities of the user's followers and friends on social media and collect related market data. Furthermore, the collection unit can analyze the user's social media trends and collect related market data. In this way, highly relevant market data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to sales data. The analysis unit can also apply a clustering algorithm to consumer behavior data. Furthermore, the analysis unit can apply a trend analysis algorithm to competitor trend data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI select the analysis algorithm to apply.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also analyze trends based on past data. Furthermore, the analysis unit can prioritize analysis of data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of the most relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0078] The generation unit can estimate the user's emotions and adjust the business development direction to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can suggest a long-term business development direction. Furthermore, if the user is in a hurry, the generation unit can also suggest a short-term business development direction. Furthermore, if the user is excited, the generation unit can suggest an innovative business development direction. This allows optimal business development for the user by adjusting the business development direction according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the business development direction.

[0079] The generation unit can adjust the level of detail of the generation based on the importance of the analysis results during generation. For example, the generation unit generates a detailed business development direction based on an analysis result with high importance. The generation unit can also generate a simplified business development direction based on an analysis result with low importance. Furthermore, the generation unit can generate a business development direction with a moderate level of detail based on an analysis result with medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the analysis result, an efficient business development direction can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0080] The generation unit can apply different generation algorithms depending on the business category during generation. For example, the generation unit can apply an innovation algorithm to business development related to product development. The generation unit can also apply a targeting algorithm to business development related to marketing. Furthermore, the generation unit can apply an optimization algorithm to business development related to sales strategy. In this way, by applying different generation algorithms depending on the business category, it is possible to generate an optimal business development direction. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the business category into the generation AI and cause the generation AI to select the generation algorithm to be applied.

[0081] The generation unit can estimate the user's emotions and adjust the length of the business development to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short-term business development direction. Furthermore, if the user is relaxed, the generation unit can generate a long-term business development direction. Furthermore, if the user is excited, the generation unit can generate an innovative business development direction. This allows the user to optimally develop the business by adjusting the length of the business development according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the business development.

[0082] At the time of generation, the generation unit can determine the generation priority based on the time when the analysis results were collected. The generation unit, for example, prioritizes generating a business development direction based on the latest analysis results. The generation unit can also generate a business development direction based on past analysis results. Furthermore, the generation unit can generate a business development direction based on analysis results from a specific period. In this way, by determining the generation priority based on the time when the analysis results were collected, it is possible to generate a business development direction based on the latest data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and cause the generation AI to determine the generation priority.

[0083] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. The generation unit, for example, prioritizes generating a business development direction based on the most relevant analysis result. The generation unit can also postpone generating a business development direction based on a less relevant analysis result. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the analysis results. As a result, by adjusting the order of generation based on the relevance of the analysis results, an efficient business development direction can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of generation.

[0084] The providing unit can estimate the user's emotions and adjust the advice providing method based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the providing unit can also provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can also provide concise advice that focuses on the main points. By adjusting the advice providing method according to the user's emotions, optimal advice can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the advice providing method.

[0085] When providing advice, the providing unit can select the optimal provision method by referring to the user's past business development history. The providing unit, for example, provides optimal advice based on the user's past business development history. The providing unit can also provide advice by referring to the user's past success cases. Furthermore, the providing unit can analyze the user's past failure cases and provide advice including areas for improvement. In this way, optimal advice can be provided by referring to the user's past business development history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past business development history into the generation AI and cause the generation AI to select the optimal provision method.

[0086] When providing advice, the providing unit can customize the means of advice based on the user's current business situation. For example, the providing unit can provide advice for increasing sales based on the user's current sales situation. The providing unit can also provide advice including areas for improvement based on the user's current marketing strategy. Furthermore, the providing unit can analyze the user's current competitive situation and provide advice for increasing competitive advantage. In this way, by customizing the means of advice based on the user's current business situation, more effective advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current business situation data into the generating AI and cause the generating AI to customize the means of advice.

[0087] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit postpones less important advice and prioritizes more important advice. The providing unit can also provide all advice equally when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can provide the most important advice first. This allows important advice to be provided preferentially by determining the priority of advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of advice.

[0088] When providing advice, the providing unit can select the optimal advice method by taking into account the user's geographical location information. The providing unit provides advice based on market information related to the user's current location, for example. The providing unit can also provide highly relevant advice based on the user's past location information. Furthermore, the providing unit can also provide optimal advice by taking into account the user's future travel plans. In this way, highly relevant advice can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal advice method.

[0089] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, analyzes the content posted by the user on social media and provides relevant advice. The providing unit can also analyze the activity of the user's followers and friends on social media and provide relevant advice. Furthermore, the providing unit can analyze the user's social media trends and provide relevant advice. In this way, highly relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest a means of providing advice. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect public data on the Internet using web scraping technology via the control unit 46A of the smart device 14. The collection unit can also acquire market data using an API via the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the market data using a machine learning algorithm via the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a business development direction based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit can provide advice based on the direction generated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect public data on the Internet using web scraping technology via the control unit 46A of the smart glasses 214. The collection unit can also acquire market data using an API via the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the market data using a machine learning algorithm via the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a business development direction based on the analysis result via the specific processing unit 290 of the data processing device 12. The provision unit can provide advice based on the direction generated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect public data on the Internet using web scraping technology via the control unit 46A of the headset type terminal 314. The collection unit can also acquire market data using an API via the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the market data using a machine learning algorithm via the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a business development direction based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit can provide advice based on the direction generated by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect public data on the Internet using web scraping technology via the control unit 46A of the robot 414. The collection unit can also acquire market data using an API via the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the market data using a machine learning algorithm via the specific processing unit 290 of the data processing device 12. The generation unit can generate a business development direction based on the analysis results via the specific processing unit 290 of the data processing device 12. The provision unit can provide advice based on the direction generated by the control unit 46A of the robot 414.

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

[0091] The collection unit can analyze the user's purchase history and select market data collection targets based on past purchase patterns. For example, the collection unit can identify product categories that the user has purchased in the past and prioritize collecting market data related to those categories. The collection unit can also analyze the user's purchase frequency and collect data related to frequently purchased products. Furthermore, the collection unit can take into account the time of the user's purchases and collect data related to specific seasons or events. In this way, by selecting market data collection targets based on the user's purchase history, more relevant data can be collected.

[0092] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the timing of the analysis can be delayed so that the user can receive the analysis results in a relaxed state. Also, if the user is relaxed, the timing of the analysis can be advanced to provide the analysis results quickly. Furthermore, if the user is in a hurry, the timing of the analysis can be set to immediate so that the analysis can be performed quickly. In this way, by adjusting the timing of the analysis according to the user's emotions, the analysis results can be provided at the optimal timing for the user.

[0093] The generation unit can customize the business development direction to be generated taking into account the user's business goals. For example, if the user aims to increase sales, the generation unit can propose a business development direction specialized for increasing sales. Also, if the user aims to expand market share, the generation unit can propose a business development direction specialized for expanding market share. Furthermore, if the user aims to enter a new market, the generation unit can propose a business development direction specialized for entering a new market. In this way, customizing the business development direction according to the user's business goals enables more effective business development.

[0094] The providing unit can estimate the user's emotions and adjust the format of advice based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice that focuses on the main points can be provided. In this way, by adjusting the format of advice according to the user's emotions, it is possible to provide the most suitable advice for the user.

[0095] The collection unit can analyze the user's social media activities and collect related market data. For example, the collection unit can analyze the content of the user's posts on social media and collect related market data. The collection unit can also analyze the activities of the user's followers and friends on social media and collect related market data. Furthermore, the collection unit can analyze the user's social media trends and collect related market data. In this way, highly relevant market data can be collected by analyzing the user's social media activities.

[0096] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide concise analysis results that focus on the main points. In this way, by adjusting the level of analysis detail according to the user's emotions, it is possible to provide the optimal analysis results for the user.

[0097] The generation unit can customize the business development direction to be generated by referring to the user's past business development history. For example, the generation unit can refer to the user's past success stories and propose similar business development directions. The generation unit can also analyze the user's past failure stories and propose business development directions that include areas for improvement. Furthermore, the generation unit can propose an optimal business development direction based on the user's past business development history. This allows for more effective business development by referring to the user's past business development history.

[0098] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, less important advice can be postponed and more important advice can be provided preferentially. Also, if the user is relaxed, all advice can be provided equally. Furthermore, if the user is in a hurry, the most important advice can be provided with the highest priority. In this way, by determining the priority of advice according to the user's emotions, important advice can be provided preferentially.

[0099] The collection unit can prioritize collection of highly relevant market data by taking into account the user's geographical location information. For example, the collection unit prioritizes collection of market data related to the user's current location. The collection unit can also collect highly relevant data based on the user's past location information. Furthermore, the collection unit can also collect highly relevant data by taking into account the user's future travel plans. In this way, highly relevant market data can be prioritized by taking into account the user's geographical location information.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, it can provide a short, to-the-point analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide the optimal analysis result for the user.

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

[0102] Step 1: The collection unit collects market data. Market data includes past sales data, consumer behavior data, competitor trends, and so on. The collection unit can use web scraping technology to collect publicly available data on the Internet. It can also use APIs to obtain market data from specific data sources. For example, it can run a script to automatically extract data from a specific website, or it can obtain the required information from a database through an API. Step 2: The analysis unit analyzes the market data collected by the collection unit. The analysis unit can analyze the market data using machine learning algorithms. For example, regression analysis can be used to predict sales, or clustering algorithms can be used to analyze consumer behavior patterns. Furthermore, deep learning techniques can be used to extract complex data patterns. For example, neural networks can be used to predict changes in consumer preferences. Step 3: The generation unit generates a business development direction based on the analysis results obtained by the analysis unit. Based on the analysis results, the generation unit can determine which market to enter and propose what products and services to offer. Furthermore, the generation unit can also generate what marketing strategy should be adopted. For example, it can analyze the trends of competitors in a specific market and propose the optimal marketing strategy based on that. Step 4: The delivery department provides advice based on the direction generated by the generation department. The delivery department can present specific action plans to the new business team. It can also update advice in real time in response to market fluctuations. For example, if a competitor launches a new product, the advice can be updated based on that information.

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

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

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

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

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

[0140] 7, 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.

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

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

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

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

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

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

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

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

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

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

[0174] [Explanation of symbols]

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

Claims

1. a collection unit that collects market data; an analysis unit that analyzes the market data collected by the collection unit; a generation unit that generates a business development direction based on the analysis result obtained by the analysis unit; a providing unit that provides advice based on the directionality generated by the generating unit. A system characterized by:

2. The collecting unit Use web scraping and APIs to gather market data 2. The system of claim 1.

3. The analysis unit Analyzing market data using machine learning and deep learning 2. The system of claim 1.

4. The generation unit Generate marketing strategies based on the analysis results 2. The system of claim 1.

5. The providing unit Provide the generated marketing strategy to the new business team 2. The system of claim 1.

6. The providing unit Real-time advice updates as the market fluctuates 2. The system of claim 1.

7. The collecting unit To estimate user sentiment and adjust timing of collecting market data based on the estimated user sentiment.

2. The system of claim 1.

8. The collecting unit Analyze past market data collection history and select the optimal collection method 2. The system of claim 1.

Images