system

Abstract

The system according to this embodiment aims to utilize data from all assets to discover cross-sectional insights and propose solutions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a discovery unit, and a proposal unit. The collection unit collects customer understanding data, behavioral logs, and statistical data. The analysis unit analyzes the data collected by the collection unit. The discovery unit discovers insights based on the data analyzed by the analysis unit. The proposal unit makes solution proposals based on the insights discovered by the discovery unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, each department conducts analysis within its own knowledge and limited information sources, and there is a problem that it is difficult to discover cross-cutting insights and propose solutions.

[0005] The system according to the embodiment aims to utilize data of all assets to discover cross-cutting insights and propose solutions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, and a proposal unit. The collection unit collects customer understanding data, behavioral logs, and statistical data. The analysis unit analyzes the data collected by the collection unit. The discovery unit discovers insights based on the data analyzed by the analysis unit. The proposal unit makes solution proposals based on the insights discovered by the discovery unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize data from all assets to discover cross-sectional insights and propose solutions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​platform system according to an embodiment of the present invention is a system that builds an AI platform that constantly inputs customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for all assets, thereby enabling cross-sectional insight discovery and solution proposals by grasping all new and old knowledge. This AI platform system allows all employees to have the same level of insight as excellent marketers, improving the productivity of analysis, strategy, and customer service. Furthermore, this AI platform system is a constantly evolving AI engine that cannot be imitated by competitors and will revolutionize the marketing research business. For example, the AI ​​platform system collects customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for all assets. This includes customer purchase history, website browsing history, and social media activity logs. For example, it collects data such as which products customers purchased, which pages they viewed, and which posts they "liked." Next, the AI ​​platform system inputs the collected data into the AI ​​platform and performs analysis. The AI ​​platform analyzes the collected data to understand customer behavior patterns and interests. For example, it can identify customers who are highly interested in a particular product and propose appropriate marketing strategies to those customers. Furthermore, the AI-powered system grasps all new and old knowledge, uncovering cross-functional insights. This allows for the integration of knowledge held independently by each department, leading to deeper insights. For example, it can analyze past marketing campaign successes and failures to inform future campaigns. Finally, the AI-powered system provides solutions. For instance, it offers specific suggestions on what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. This enables all employees to possess insights at the same level as top marketers, improving productivity in analysis, strategy, and customer service. This AI-powered system is a constantly evolving AI engine that competitors cannot easily replicate, transforming the marketing research business. For example, by learning new data daily and consistently providing the latest insights, the AI-powered system can differentiate itself from competitors.This allows the AI-based system to enable all employees to have the same level of insight as top marketers, improving productivity in analysis, strategy, and customer service.

[0029] The AI-based system according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, and a proposal unit. The collection unit collects customer understanding data, behavioral logs, and statistical data. The collection unit collects data such as customer purchase history, website browsing history, and social media activity logs. For example, the collection unit can collect data such as which products a customer purchased, which pages they viewed, and which posts they "liked." The collection unit can also acquire data from POS systems and online shopping platforms to collect customer purchase history. Furthermore, the collection unit can use web analytics tools to collect website browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand customer behavior patterns and interests. For example, the analysis unit can use machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. The analysis unit can also use natural language processing technology to analyze social media activity logs to understand customer interests. For example, the analysis unit can use text mining technology to analyze social media posts and identify topics of customer interest. The discovery unit discovers insights based on the data analyzed by the analysis unit. For example, the discovery unit can analyze past successful and unsuccessful marketing campaigns to discover insights that can be used for future campaigns. For example, the discovery unit can analyze past campaign data to identify success and failure factors. The discovery unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, the discovery unit can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that propose appropriate promotions for those customers. The proposal unit makes solutions based on the insights discovered by the discovery unit. For example, the proposal unit makes specific suggestions such as what kind of promotions should be conducted for specific customer segments and which channels should be used to approach them.For example, the proposal department can propose email marketing and social media advertising to specific customer segments based on customers' purchase history and website browsing history. The proposal department can also propose personalized promotions based on customers' interests. For instance, it can propose specific products or services based on topics of customer interest. As a result, the AI-based system according to this embodiment can improve the productivity of marketing research by collecting, analyzing, and analyzing customer understanding data, behavioral logs, and statistical data, discovering insights, and proposing solutions.

[0030] The data collection unit collects customer understanding data, behavioral logs, and statistical data. Specifically, it collects data such as customer purchase history, website browsing history, and social media activity logs. For example, it can collect data such as which products customers purchased, which pages they viewed, and which posts they "liked." The data collection unit collects customer purchase history by obtaining data from POS systems and online shopping platforms. POS systems collect in-store purchase data in real time, and online shopping platforms collect purchase data over the internet. Furthermore, the data collection unit collects website browsing history using web analytics tools. For social media activity logs, data is obtained via APIs to collect information such as which posts customers reacted to and what comments they left. This allows the data collection unit to collect a wide range of data from diverse data sources and gain a detailed understanding of customer behavior and interests. The collected data is stored on a cloud server and made accessible to the analytics and discovery units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the data collection department to understand customer behavior patterns and interests. Specifically, it uses machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. For example, it can use clustering algorithms to segment customers based on their purchase patterns and interests. This makes it possible to formulate optimal marketing strategies for specific customer groups. It also uses natural language processing technology to analyze social media activity logs to understand customer interests. For example, it can use text mining technology to analyze social media posts to identify what topics customers are interested in. Furthermore, sentiment analysis can be performed to extract positive and negative emotions from customer posts, allowing for an understanding of the customer's emotional state. Based on these analysis results, the analysis department can gain a detailed understanding of customer behavior patterns and interests, which can be used to formulate marketing strategies. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can use historical purchase data to forecast demand for specific products and services, optimizing inventory management and sales strategies. This allows the analysis unit to not only grasp the situation in real time but also to formulate long-term strategies, thereby improving the reliability and effectiveness of the entire system.

[0032] The Discovery Unit discovers insights based on data analyzed by the Analysis Unit. Specifically, it analyzes past marketing campaign successes and failures to discover insights that can be used for future campaigns. For example, it can analyze past campaign data to identify factors for success and failure. This allows it to predict what strategies will be effective in the next campaign and propose the optimal approach. The Discovery Unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, it can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that propose appropriate promotions for those customers. Furthermore, the Discovery Unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the Discovery Unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system. Based on these insights, the Discovery Unit can contribute to improving marketing strategies and discovering new business opportunities.

[0033] The Proposal Department provides solutions based on insights discovered by the Discovery Department. Specifically, it makes concrete suggestions such as what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. For example, based on a customer's purchase history or website browsing history, it can propose email marketing or social media advertising to a specific customer segment. The Proposal Department can also propose personalized promotions based on customer interests. For example, it can propose specific products or services based on topics of interest to the customer. This allows the Proposal Department to provide the most suitable promotions to customers and maximize marketing effectiveness. Furthermore, the Proposal Department can continuously monitor the effectiveness of its proposals and modify them as needed. For example, it can analyze the effectiveness of a proposed promotion and propose an alternative approach if the effect is low. In addition, the Proposal Department can effectively approach customers using multiple channels. For example, it can approach customers from multiple angles by using not only email marketing but also social media advertising and website banner advertising in combination. This allows the Proposal Department to provide the most suitable promotions to customers and maximize marketing effectiveness.

[0034] The data collection unit can collect data such as customer purchase history, website browsing history, and social media activity logs. For example, to collect customer purchase history, the data collection unit can obtain data from POS systems and online shopping platforms. For example, the data collection unit can collect data such as which products a customer purchased, the date and time of purchase, and the purchase amount. The data collection unit can also use web analytics tools to collect website browsing history. The data collection unit can also obtain data from social media platforms to collect social media activity logs. For example, the data collection unit can collect data such as user posts, the number of likes, and the number of followers. By collecting data such as customer purchase history, website browsing history, and social media activity logs, it is possible to understand customer behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from POS systems and online shopping platforms into a generating AI and have the generating AI perform data collection.

[0035] The analysis unit can analyze collected data to understand customer behavior patterns and interests. For example, the analysis unit can analyze collected data to identify customer behavior patterns. For example, the analysis unit can use machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. The analysis unit can also use natural language processing technology to analyze social media activity logs to understand customer interests. For example, the analysis unit can use text mining technology to analyze social media posts to identify topics of interest to customers. The analysis unit can also use data mining technology to understand customer behavior patterns and interests. For example, the analysis unit can use clustering algorithms to classify customers into different segments and identify behavior patterns for each segment. This allows the analysis unit to understand customer behavior patterns and interests by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the task of understanding customer behavior patterns and interests.

[0036] The discovery unit can analyze past successful and unsuccessful marketing campaigns to discover insights that can be used in future campaigns. For example, the discovery unit can analyze past campaign data to identify success and failure factors. For example, the discovery unit can analyze sales data and customer satisfaction data from past campaigns to identify commonalities in successful campaigns and the causes of unsuccessful campaigns. The discovery unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, the discovery unit can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that can propose appropriate promotions to those customers. Furthermore, the discovery unit can analyze past successful and unsuccessful marketing campaigns to identify best practices for future campaigns. For example, the discovery unit can analyze the methods used and channels employed in past campaigns to extract factors that contributed to successful campaigns. This allows for the discovery of insights that can be used in future campaigns by analyzing past successful and unsuccessful marketing campaigns. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input past campaign data into the generation AI and have the generation AI identify the factors for success and failure.

[0037] The proposal department can make specific suggestions regarding what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. For example, the proposal department can suggest email marketing or social media advertising to a specific customer segment based on the customer's purchase history and website browsing history. For instance, the proposal department can analyze a customer's purchase history and send promotional emails for related products to customers who show a high interest in a particular product. It can also analyze a customer's website browsing history and deliver social media advertisements to customers who frequently visit specific pages. Furthermore, the proposal department can suggest personalized promotions based on the customer's interests. For example, it can analyze a customer's social media activity log and suggest promotions for related products to customers who are interested in a particular topic. This enables the realization of an effective marketing strategy by making specific suggestions to a particular customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the customer's purchase history and website browsing history into a generating AI and have the generating AI execute promotion suggestions.

[0038] The proposal department can learn from new data daily and always provide the latest insights. For example, the proposal department can collect customer purchase history and website browsing history daily and provide insights based on the latest data. For example, the proposal department can update customer purchase history daily and understand the latest purchasing patterns. The proposal department can also collect website browsing history in real time and provide insights based on the latest browsing behavior. Furthermore, the proposal department can collect social media activity logs daily and understand the latest interests. For example, the proposal department can analyze the content of customers' social media posts daily and identify the latest topics of interest. In this way, by learning from new data daily, it can always provide the latest insights. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data collected daily into a generating AI and have the generating AI perform the provision of the latest insights.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past purchase history and prioritize collecting data on frequently purchased items. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit can analyze the user's past website browsing history and collect data during the time of day when visits are most frequent. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient collection method. For example, the data collection unit can analyze the user's past social media activity logs and select the most effective collection method. In this way, an efficient data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest and activity during data collection. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. For example, the data collection unit can analyze the user's current website browsing history and prioritize collecting data related to product categories of interest. The data collection unit can also filter highly relevant data based on the user's current activity. For example, the data collection unit can analyze the user's current social media activity log and prioritize collecting data related to topics of interest. Furthermore, the data collection unit can grasp the user's areas of interest and activity in real time and collect the most appropriate data. For example, the data collection unit can analyze the user's real-time website browsing history and aggregate data related to product categories of interest. This allows for the collection of highly relevant data by filtering the data based on the user's current areas of interest and activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's current areas of interest and activity into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's current geographical location information. The data collection unit can also collect region-specific promotional data based on the user's geographical location information. For example, the data collection unit can collect store information and event information in the area where the user is currently located. Furthermore, the data collection unit can collect highly relevant data by considering the user's travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of data related to frequently visited locations. This allows for the efficient collection of region-specific data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze the content of a user's social media posts and collect data related to topics of interest. The data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can analyze a user's "likes" and comment history and collect data related to topics of interest. Furthermore, the data collection unit can analyze a user's social media activity history and collect the most relevant data. For example, the data collection unit can analyze a user's follower count and the accounts they follow and collect relevant data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed statistical analysis on data with high business impact to provide concrete insights. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can provide a simple summary on data with low business impact. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. For instance, the analysis unit can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the analysis unit can apply a social network analysis algorithm to social media activity log data. For example, the analysis unit can apply a graph analysis algorithm to analyze a customer's social media activity log and identify the structure of the social network. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For instance, it can prioritize the analysis of the most recent purchase history data to identify the latest purchase patterns. The analysis unit can also analyze the most recent data while referring to past data. For example, it can analyze the latest browsing behavior while referring to past website browsing history data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. For example, it can prioritize the analysis of the most recent social media activity logs to identify the latest topics of interest. This allows for the prioritization of analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can evaluate the relevance between customer purchase history data and website browsing history data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, it can evaluate the relevance between social media activity logs and purchase history data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, it can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data based on customer behavior patterns. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The discovery unit can predict current insights by referring to past data when discovering insights. For example, the discovery unit can predict current insights by referring to successful past marketing campaigns. For example, the discovery unit can analyze past campaign data to identify success factors and apply them to the current campaign. The discovery unit can also predict current insights based on past customer behavior data. For example, the discovery unit can analyze past purchase history data to predict current purchase patterns. Furthermore, the discovery unit can also predict current insights by analyzing past market trends. For example, the discovery unit can analyze past market trend data to predict current market trends. This makes it easier to predict current insights by referring to past data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input past data into a generating AI and have the generating AI perform the prediction of current insights.

[0048] The discovery unit can apply different discovery methods to each data category when discovering insights. For example, the discovery unit can apply a purchase pattern discovery method to purchase history data. For instance, it can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The discovery unit can also apply a browsing behavior discovery method to website browsing history data. For example, it can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the discovery unit can apply a social network discovery method to social media activity log data. For example, it can apply a graph analysis algorithm to analyze a customer's social media activity logs and identify the structure of social networks. By applying different discovery methods to each data category, more accurate insights can be discovered. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input data categories into a generating AI and have the generating AI apply the appropriate discovery method.

[0049] The discovery unit can analyze changes in insights based on the data collection timing when an insight is discovered. For example, the discovery unit can analyze changes in insights based on the latest data. For example, the discovery unit can analyze changes in purchasing patterns based on the latest purchase history data. The discovery unit can also analyze changes in insights by referring to past data. For example, the discovery unit can analyze changes in browsing behavior by referring to past website browsing history data. Furthermore, the discovery unit can also analyze changes in insights depending on the data collection timing. For example, the discovery unit can analyze changes in topics of interest based on the latest social media activity logs. This allows for the understanding of the latest insights by analyzing changes in insights based on the data collection timing. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in insights.

[0050] The discovery unit can analyze insights by referring to relevant market data when discovering them. For example, the discovery unit can analyze insights based on relevant market data. For example, the discovery unit can analyze current market trends based on relevant market sales data. The discovery unit can also analyze insights by referring to relevant market trends. For example, the discovery unit can analyze current market trends based on relevant market trend data. Furthermore, the discovery unit can analyze insights based on relevant market competitor data. For example, the discovery unit can analyze competitor sales data and marketing strategies to grasp current market trends. This allows for the analysis of more accurate insights by referring to relevant market data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant market data into a generating AI and have the generating AI perform the insight analysis.

[0051] The proposal department can adjust the level of detail in its proposals based on the importance of the insights. For example, it can provide detailed proposals for high-importance insights. For example, it can propose detailed marketing strategies for insights with high business impact. It can also provide simplified proposals for low-importance insights. For example, it can provide a simple summary for insights with low business impact. Furthermore, the proposal department can prioritize proposals according to the importance of the insights. For example, it can prioritize proposals for high-importance insights and postpone those for low-importance insights. This allows for efficient proposals by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0052] The suggestion unit can apply different suggestion algorithms depending on the category of the insight when making suggestions. For example, for insights based on purchase history data, the suggestion unit can apply a purchase pattern suggestion algorithm. For instance, the suggestion unit can analyze a customer's purchase history, apply a clustering algorithm to identify purchase patterns, and then make suggestions based on the results. The suggestion unit can also apply a browsing behavior suggestion algorithm to insights based on website browsing history data. For example, the suggestion unit can analyze a customer's website browsing history, apply a sequence mining algorithm to identify browsing behavior, and then make suggestions based on the results. Furthermore, the suggestion unit can apply a social network suggestion algorithm to insights based on social media activity log data. For example, the suggestion unit can analyze a customer's social media activity logs, apply a graph analysis algorithm to identify the structure of social networks, and then make suggestions based on the results. By applying different suggestion algorithms depending on the category of the insight, more accurate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the insight categories into the generation AI and have the generation AI apply an appropriate proposal algorithm.

[0053] The suggestion unit can prioritize suggestions based on when the insights were collected. For example, the suggestion unit can prioritize suggestions based on the latest insights. For example, it can make suggestions based on the latest purchase patterns based on the latest purchase history data. It can also make suggestions based on the latest insights while referring to past insights. For example, it can make suggestions based on the latest browsing behavior while referring to past website browsing history data. Furthermore, the suggestion unit can adjust the priority of suggestions according to when the insights were collected. For example, it can make suggestions based on the latest topics of interest based on the latest social media activity logs. This makes it possible to make suggestions based on the latest insights by prioritizing suggestions based on when the insights were collected. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the timing of insight collection into a generating AI and have the generating AI determine the priority of suggestions.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the insights during the suggestion process. For example, the suggestion unit can prioritize suggesting highly relevant insights. For instance, it can evaluate the relevance between customer purchase history data and website browsing history data and prioritize suggesting highly relevant insights. The suggestion unit can also postpone suggesting less relevant insights. For example, it can evaluate the relevance between social media activity logs and purchase history data and postpone suggesting less relevant insights. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the insights. For example, it can prioritize suggesting highly relevant insights and postpone less relevant insights based on customer behavior patterns. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the insights. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the insights into a generating AI and have the generating AI adjust the order of suggestions.

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

[0056] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past purchase history and prioritize collecting data on frequently purchased items. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit can analyze the user's past website browsing history and collect data during the time of day when visits are most frequent. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient collection method. For example, the data collection unit can analyze the user's past social media activity logs and select the most effective collection method. In this way, an efficient data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed statistical analysis on data with high business impact to provide concrete insights. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can provide a simple summary on data with low business impact. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0058] The proposal department can adjust the level of detail in its proposals based on the importance of the insights. For example, it can provide detailed proposals for high-importance insights. For example, it can propose detailed marketing strategies for insights with high business impact. It can also provide simplified proposals for low-importance insights. For example, it can provide a simple summary for insights with low business impact. Furthermore, the proposal department can prioritize proposals according to the importance of the insights. For example, it can prioritize proposals for high-importance insights and postpone those for low-importance insights. This allows for efficient proposals by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0059] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's current geographical location information. The data collection unit can also collect region-specific promotional data based on the user's geographical location information. For example, the data collection unit can collect store information and event information in the area where the user is currently located. Furthermore, the data collection unit can collect highly relevant data by considering the user's travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of data related to frequently visited locations. This allows for the efficient collection of region-specific data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0060] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. For instance, the analysis unit can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the analysis unit can apply a social network analysis algorithm to social media activity log data. For example, the analysis unit can apply a graph analysis algorithm to analyze a customer's social media activity log and identify the structure of the social network. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0061] The discovery unit can analyze changes in insights based on the data collection timing when an insight is discovered. For example, the discovery unit can analyze changes in insights based on the latest data. For example, the discovery unit can analyze changes in purchasing patterns based on the latest purchase history data. The discovery unit can also analyze changes in insights by referring to past data. For example, the discovery unit can analyze changes in browsing behavior by referring to past website browsing history data. Furthermore, the discovery unit can also analyze changes in insights depending on the data collection timing. For example, the discovery unit can analyze changes in topics of interest based on the latest social media activity logs. This allows for the understanding of the latest insights by analyzing changes in insights based on the data collection timing. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in insights.

[0062] The suggestion unit can prioritize suggestions based on when the insights were collected. For example, the suggestion unit can prioritize suggestions based on the latest insights. For example, it can make suggestions based on the latest purchase patterns based on the latest purchase history data. It can also make suggestions based on the latest insights while referring to past insights. For example, it can make suggestions based on the latest browsing behavior while referring to past website browsing history data. Furthermore, the suggestion unit can adjust the priority of suggestions according to when the insights were collected. For example, it can make suggestions based on the latest topics of interest based on the latest social media activity logs. This makes it possible to make suggestions based on the latest insights by prioritizing suggestions based on when the insights were collected. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the timing of insight collection into a generating AI and have the generating AI determine the priority of suggestions.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects customer understanding data, behavioral logs, and statistical data. For example, it collects data such as customer purchase history, website browsing history, and social media activity logs. The data collection unit obtains data from POS systems and online shopping platforms and tracks the behavior of website visitors using web analytics tools. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. It also uses natural language processing technology to analyze social media activity logs to understand customer interests. It uses text mining technology to analyze social media posts to identify topics of interest to customers. Step 3: The Discovery Unit discovers insights based on the data analyzed by the Analysis Unit. For example, it analyzes past successful and unsuccessful marketing campaigns to discover insights that can be used for future campaigns. It also discovers insights that propose new marketing strategies based on customer behavior patterns and interests. Step 4: The proposal team proposes solutions based on the insights discovered by the discovery team. For example, they make specific suggestions such as what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. Based on the customer's purchase history and website browsing history, they propose email marketing or social media advertising to a particular customer segment. Based on the customer's interests, they propose specific products or services.

[0065] (Example of form 2) The AI ​​platform system according to an embodiment of the present invention is a system that builds an AI platform that constantly inputs customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for all assets, thereby enabling cross-sectional insight discovery and solution proposals by grasping all new and old knowledge. This AI platform system allows all employees to have the same level of insight as excellent marketers, improving the productivity of analysis, strategy, and customer service. Furthermore, this AI platform system is a constantly evolving AI engine that cannot be imitated by competitors and will revolutionize the marketing research business. For example, the AI ​​platform system collects customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for all assets. This includes customer purchase history, website browsing history, and social media activity logs. For example, it collects data such as which products customers purchased, which pages they viewed, and which posts they "liked." Next, the AI ​​platform system inputs the collected data into the AI ​​platform and performs analysis. The AI ​​platform analyzes the collected data to understand customer behavior patterns and interests. For example, it can identify customers who are highly interested in a particular product and propose appropriate marketing strategies to those customers. Furthermore, the AI-powered system grasps all new and old knowledge, uncovering cross-functional insights. This allows for the integration of knowledge held independently by each department, leading to deeper insights. For example, it can analyze past marketing campaign successes and failures to inform future campaigns. Finally, the AI-powered system provides solutions. For instance, it offers specific suggestions on what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. This enables all employees to possess insights at the same level as top marketers, improving productivity in analysis, strategy, and customer service. This AI-powered system is a constantly evolving AI engine that competitors cannot easily replicate, transforming the marketing research business. For example, by learning new data daily and consistently providing the latest insights, the AI-powered system can differentiate itself from competitors.This allows the AI-based system to enable all employees to have the same level of insight as top marketers, improving productivity in analysis, strategy, and customer service.

[0066] The AI-based system according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, and a proposal unit. The collection unit collects customer understanding data, behavioral logs, and statistical data. The collection unit collects data such as customer purchase history, website browsing history, and social media activity logs. For example, the collection unit can collect data such as which products a customer purchased, which pages they viewed, and which posts they "liked." The collection unit can also acquire data from POS systems and online shopping platforms to collect customer purchase history. Furthermore, the collection unit can use web analytics tools to collect website browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand customer behavior patterns and interests. For example, the analysis unit can use machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. The analysis unit can also use natural language processing technology to analyze social media activity logs to understand customer interests. For example, the analysis unit can use text mining technology to analyze social media posts and identify topics of customer interest. The discovery unit discovers insights based on the data analyzed by the analysis unit. For example, the discovery unit can analyze past successful and unsuccessful marketing campaigns to discover insights that can be used for future campaigns. For example, the discovery unit can analyze past campaign data to identify success and failure factors. The discovery unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, the discovery unit can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that propose appropriate promotions for those customers. The proposal unit makes solutions based on the insights discovered by the discovery unit. For example, the proposal unit makes specific suggestions such as what kind of promotions should be conducted for specific customer segments and which channels should be used to approach them.For example, the proposal department can propose email marketing and social media advertising to specific customer segments based on customers' purchase history and website browsing history. The proposal department can also propose personalized promotions based on customers' interests. For instance, it can propose specific products or services based on topics of customer interest. As a result, the AI-based system according to this embodiment can improve the productivity of marketing research by collecting, analyzing, and analyzing customer understanding data, behavioral logs, and statistical data, discovering insights, and proposing solutions.

[0067] The data collection unit collects customer understanding data, behavioral logs, and statistical data. Specifically, it collects data such as customer purchase history, website browsing history, and social media activity logs. For example, it can collect data such as which products customers purchased, which pages they viewed, and which posts they "liked." The data collection unit collects customer purchase history by obtaining data from POS systems and online shopping platforms. POS systems collect in-store purchase data in real time, and online shopping platforms collect purchase data over the internet. Furthermore, the data collection unit collects website browsing history using web analytics tools. For social media activity logs, data is obtained via APIs to collect information such as which posts customers reacted to and what comments they left. This allows the data collection unit to collect a wide range of data from diverse data sources and gain a detailed understanding of customer behavior and interests. The collected data is stored on a cloud server and made accessible to the analytics and discovery units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis department analyzes data collected by the data collection department to understand customer behavior patterns and interests. Specifically, it uses machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. For example, it can use clustering algorithms to segment customers based on their purchase patterns and interests. This makes it possible to formulate optimal marketing strategies for specific customer groups. It also uses natural language processing technology to analyze social media activity logs to understand customer interests. For example, it can use text mining technology to analyze social media posts to identify what topics customers are interested in. Furthermore, sentiment analysis can be performed to extract positive and negative emotions from customer posts, allowing for an understanding of the customer's emotional state. Based on these analysis results, the analysis department can gain a detailed understanding of customer behavior patterns and interests, which can be used to formulate marketing strategies. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can use historical purchase data to forecast demand for specific products and services, optimizing inventory management and sales strategies. This allows the analysis unit to not only grasp the situation in real time but also to formulate long-term strategies, thereby improving the reliability and effectiveness of the entire system.

[0069] The Discovery Unit discovers insights based on data analyzed by the Analysis Unit. Specifically, it analyzes past marketing campaign successes and failures to discover insights that can be used for future campaigns. For example, it can analyze past campaign data to identify factors for success and failure. This allows it to predict what strategies will be effective in the next campaign and propose the optimal approach. The Discovery Unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, it can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that propose appropriate promotions for those customers. Furthermore, the Discovery Unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the Discovery Unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system. Based on these insights, the Discovery Unit can contribute to improving marketing strategies and discovering new business opportunities.

[0070] The Proposal Department provides solutions based on insights discovered by the Discovery Department. Specifically, it makes concrete suggestions such as what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. For example, based on a customer's purchase history or website browsing history, it can propose email marketing or social media advertising to a specific customer segment. The Proposal Department can also propose personalized promotions based on customer interests. For example, it can propose specific products or services based on topics of interest to the customer. This allows the Proposal Department to provide the most suitable promotions to customers and maximize marketing effectiveness. Furthermore, the Proposal Department can continuously monitor the effectiveness of its proposals and modify them as needed. For example, it can analyze the effectiveness of a proposed promotion and propose an alternative approach if the effect is low. In addition, the Proposal Department can effectively approach customers using multiple channels. For example, it can approach customers from multiple angles by using not only email marketing but also social media advertising and website banner advertising in combination. This allows the Proposal Department to provide the most suitable promotions to customers and maximize marketing effectiveness.

[0071] The data collection unit can collect data such as customer purchase history, website browsing history, and social media activity logs. For example, to collect customer purchase history, the data collection unit can obtain data from POS systems and online shopping platforms. For example, the data collection unit can collect data such as which products a customer purchased, the date and time of purchase, and the purchase amount. The data collection unit can also use web analytics tools to collect website browsing history. The data collection unit can also obtain data from social media platforms to collect social media activity logs. For example, the data collection unit can collect data such as user posts, the number of likes, and the number of followers. By collecting data such as customer purchase history, website browsing history, and social media activity logs, it is possible to understand customer behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from POS systems and online shopping platforms into a generating AI and have the generating AI perform data collection.

[0072] The analysis unit can analyze collected data to understand customer behavior patterns and interests. For example, the analysis unit can analyze collected data to identify customer behavior patterns. For example, the analysis unit can use machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. The analysis unit can also use natural language processing technology to analyze social media activity logs to understand customer interests. For example, the analysis unit can use text mining technology to analyze social media posts to identify topics of interest to customers. The analysis unit can also use data mining technology to understand customer behavior patterns and interests. For example, the analysis unit can use clustering algorithms to classify customers into different segments and identify behavior patterns for each segment. This allows the analysis unit to understand customer behavior patterns and interests by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the task of understanding customer behavior patterns and interests.

[0073] The discovery unit can analyze past successful and unsuccessful marketing campaigns to discover insights that can be used in future campaigns. For example, the discovery unit can analyze past campaign data to identify success and failure factors. For example, the discovery unit can analyze sales data and customer satisfaction data from past campaigns to identify commonalities in successful campaigns and the causes of unsuccessful campaigns. The discovery unit can also discover insights that propose new marketing strategies based on customer behavior patterns and interests. For example, the discovery unit can analyze customer purchase history and website browsing history to identify customers with a high interest in specific products and discover insights that can propose appropriate promotions to those customers. Furthermore, the discovery unit can analyze past successful and unsuccessful marketing campaigns to identify best practices for future campaigns. For example, the discovery unit can analyze the methods used and channels employed in past campaigns to extract factors that contributed to successful campaigns. This allows for the discovery of insights that can be used in future campaigns by analyzing past successful and unsuccessful marketing campaigns. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input past campaign data into the generation AI and have the generation AI identify the factors for success and failure.

[0074] The proposal department can make specific suggestions regarding what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. For example, the proposal department can suggest email marketing or social media advertising to a specific customer segment based on the customer's purchase history and website browsing history. For instance, the proposal department can analyze a customer's purchase history and send promotional emails for related products to customers who show a high interest in a particular product. It can also analyze a customer's website browsing history and deliver social media advertisements to customers who frequently visit specific pages. Furthermore, the proposal department can suggest personalized promotions based on the customer's interests. For example, it can analyze a customer's social media activity log and suggest promotions for related products to customers who are interested in a particular topic. This enables the realization of an effective marketing strategy by making specific suggestions to a particular customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the customer's purchase history and website browsing history into a generating AI and have the generating AI execute promotion suggestions.

[0075] The proposal department can learn from new data daily and always provide the latest insights. For example, the proposal department can collect customer purchase history and website browsing history daily and provide insights based on the latest data. For example, the proposal department can update customer purchase history daily and understand the latest purchasing patterns. The proposal department can also collect website browsing history in real time and provide insights based on the latest browsing behavior. Furthermore, the proposal department can collect social media activity logs daily and understand the latest interests. For example, the proposal department can analyze the content of customers' social media posts daily and identify the latest topics of interest. In this way, by learning from new data daily, it can always provide the latest insights. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the data collected daily into a generating AI and have the generating AI perform the provision of the latest insights.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For instance, the data collection unit can capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is stressed. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, the data collection unit can record the user's voice and use voice analysis technology to determine if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and use an emotion estimation algorithm to determine if the user is in a hurry. This allows for efficient data collection by adjusting the timing of data collection based on the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0077] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past purchase history and prioritize collecting data on frequently purchased items. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit can analyze the user's past website browsing history and collect data during the time of day when visits are most frequent. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient collection method. For example, the data collection unit can analyze the user's past social media activity logs and select the most effective collection method. In this way, an efficient data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter data based on the user's current areas of interest and activity during data collection. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. For example, the data collection unit can analyze the user's current website browsing history and prioritize collecting data related to product categories of interest. The data collection unit can also filter highly relevant data based on the user's current activity. For example, the data collection unit can analyze the user's current social media activity log and prioritize collecting data related to topics of interest. Furthermore, the data collection unit can grasp the user's areas of interest and activity in real time and collect the most relevant data. For example, the data collection unit can analyze the user's real-time website browsing history and aggregate data related to product categories of interest. This allows for the collection of highly relevant data by filtering the data based on the user's current areas of interest and activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest and activity into a generating AI and have the generating AI perform data filtering.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For instance, it can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is stressed, and prioritize collecting high-priority data. Similarly, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and prioritize collecting data that can be collected quickly. This allows for efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's current geographical location information. The data collection unit can also collect region-specific promotional data based on the user's geographical location information. For example, the data collection unit can collect store information and event information in the area where the user is currently located. Furthermore, the data collection unit can collect highly relevant data by considering the user's travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of data related to frequently visited locations. This allows for the efficient collection of region-specific data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze the content of a user's social media posts and collect data related to topics of interest. The data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can analyze a user's "likes" and comment history and collect data related to topics of interest. Furthermore, the data collection unit can analyze a user's social media activity history and collect the most relevant data. For example, the data collection unit can analyze a user's follower count and the accounts they follow and collect relevant data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For instance, it can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is nervous, and provide the analysis results using simple graphs or charts. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and provide the analysis results using a detailed text report. Additionally, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and provide the analysis results using a concise summary report. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative 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 processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed statistical analysis on data with high business impact to provide concrete insights. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can provide a simple summary on data with low business impact. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. For instance, the analysis unit can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the analysis unit can apply a social network analysis algorithm to social media activity log data. For example, the analysis unit can apply a graph analysis algorithm to analyze a customer's social media activity log and identify the structure of the social network. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For instance, it can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is in a hurry, and provide a short summary report. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and provide a detailed text report. Additionally, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is excited, and provide the analysis results using visually stimulating graphs and charts. This allows for the provision of optimal analysis results for the user by adjusting the length of the analysis based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative 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 processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0086] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For instance, it can prioritize the analysis of the most recent purchase history data to identify the latest purchase patterns. The analysis unit can also analyze the most recent data while referring to past data. For example, it can analyze the latest browsing behavior while referring to past website browsing history data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. For example, it can prioritize the analysis of the most recent social media activity logs to identify the latest topics of interest. This allows for the prioritization of analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can evaluate the relevance between customer purchase history data and website browsing history data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, it can evaluate the relevance between social media activity logs and purchase history data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, it can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data based on customer behavior patterns. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The discovery unit can estimate the user's emotions and adjust how insights are displayed based on the estimated emotions. For example, if the user is tense, the discovery unit can provide a simple and highly visible display method. For instance, it can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is tense, and display insights using simple graphs or charts. Furthermore, if the user is relaxed, the discovery unit can provide a display method that includes detailed information. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and display insights using a detailed text report. Additionally, if the user is in a hurry, the discovery unit can provide a concise display method. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and display insights using a concise summary report. This allows for the provision of insights that are easy for the user to understand by adjusting how insights are displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the discovery unit may be performed using AI, or not using AI. For example, the discovery unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0089] The discovery unit can predict current insights by referring to past data when discovering insights. For example, the discovery unit can predict current insights by referring to successful past marketing campaigns. For example, the discovery unit can analyze past campaign data to identify success factors and apply them to the current campaign. The discovery unit can also predict current insights based on past customer behavior data. For example, the discovery unit can analyze past purchase history data to predict current purchase patterns. Furthermore, the discovery unit can also predict current insights by analyzing past market trends. For example, the discovery unit can analyze past market trend data to predict current market trends. This makes it easier to predict current insights by referring to past data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input past data into a generating AI and have the generating AI perform the prediction of current insights.

[0090] The discovery unit can apply different discovery methods to each data category when discovering insights. For example, the discovery unit can apply a purchase pattern discovery method to purchase history data. For instance, it can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The discovery unit can also apply a browsing behavior discovery method to website browsing history data. For example, it can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the discovery unit can apply a social network discovery method to social media activity log data. For example, it can apply a graph analysis algorithm to analyze a customer's social media activity logs and identify the structure of social networks. By applying different discovery methods to each data category, more accurate insights can be discovered. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input data categories into a generating AI and have the generating AI apply the appropriate discovery method.

[0091] The discovery unit can estimate the user's emotions and adjust the importance of insights based on the estimated emotions. For example, if the user is stressed, the discovery unit will prioritize displaying high-importance insights. For instance, it can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is stressed, and prioritize displaying high-importance insights. Furthermore, if the user is relaxed, the discovery unit can display detailed insights. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and display insights using a detailed text report. Additionally, if the user is in a hurry, the discovery unit can display concise insights. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and display insights using a concise summary report. This allows the system to prioritize insights that are important to the user by adjusting their importance based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the discovery unit may be performed using AI, or not using AI. For example, the discovery unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0092] The discovery unit can analyze changes in insights based on the data collection timing when an insight is discovered. For example, the discovery unit can analyze changes in insights based on the latest data. For example, the discovery unit can analyze changes in purchasing patterns based on the latest purchase history data. The discovery unit can also analyze changes in insights by referring to past data. For example, the discovery unit can analyze changes in browsing behavior by referring to past website browsing history data. Furthermore, the discovery unit can also analyze changes in insights depending on the data collection timing. For example, the discovery unit can analyze changes in topics of interest based on the latest social media activity logs. This allows for the understanding of the latest insights by analyzing changes in insights based on the data collection timing. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in insights.

[0093] The discovery unit can analyze insights by referring to relevant market data when discovering them. For example, the discovery unit can analyze insights based on relevant market data. For example, the discovery unit can analyze current market trends based on relevant market sales data. The discovery unit can also analyze insights by referring to relevant market trends. For example, the discovery unit can analyze current market trends based on relevant market trend data. Furthermore, the discovery unit can analyze insights based on relevant market competitor data. For example, the discovery unit can analyze competitor sales data and marketing strategies to grasp current market trends. This allows for the analysis of more accurate insights by referring to relevant market data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant market data into a generating AI and have the generating AI perform the insight analysis.

[0094] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easy-to-understand suggestions. For instance, it could capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is nervous, and then provide suggestions using simple graphs or charts. Furthermore, if the user is relaxed, the suggestion function can provide detailed suggestions. For example, it could record the user's voice, use voice analysis technology to determine if the user is relaxed, and then provide suggestions using a detailed text report. Additionally, if the user is in a hurry, the suggestion function can provide concise suggestions. For example, it could collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and then provide suggestions using a concise summary report. This allows the system to provide suggestions that are easy for the user to understand by adjusting the presentation based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The proposal department can adjust the level of detail in its proposals based on the importance of the insights. For example, it can provide detailed proposals for high-importance insights. For example, it can propose detailed marketing strategies for insights with high business impact. It can also provide simplified proposals for low-importance insights. For example, it can provide a simple summary for insights with low business impact. Furthermore, the proposal department can prioritize proposals according to the importance of the insights. For example, it can prioritize proposals for high-importance insights and postpone those for low-importance insights. This allows for efficient proposals by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0096] The suggestion unit can apply different suggestion algorithms depending on the category of the insight when making suggestions. For example, for insights based on purchase history data, the suggestion unit can apply a purchase pattern suggestion algorithm. For instance, the suggestion unit can analyze a customer's purchase history, apply a clustering algorithm to identify purchase patterns, and then make suggestions based on the results. The suggestion unit can also apply a browsing behavior suggestion algorithm to insights based on website browsing history data. For example, the suggestion unit can analyze a customer's website browsing history, apply a sequence mining algorithm to identify browsing behavior, and then make suggestions based on the results. Furthermore, the suggestion unit can apply a social network suggestion algorithm to insights based on social media activity log data. For example, the suggestion unit can analyze a customer's social media activity logs, apply a graph analysis algorithm to identify the structure of social networks, and then make suggestions based on the results. By applying different suggestion algorithms depending on the category of the insight, more accurate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the insight categories into the generation AI and have the generation AI apply an appropriate proposal algorithm.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For instance, it could capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is in a hurry, and provide a short summary report. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, it could record the user's voice, use voice analysis technology to determine if the user is relaxed, and provide a detailed text report. Additionally, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, it could collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is excited, and provide suggestions using visually stimulating graphs and charts. This allows the system to provide optimal suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0098] The suggestion unit can prioritize suggestions based on when the insights were collected. For example, the suggestion unit can prioritize suggestions based on the latest insights. For example, it can make suggestions based on the latest purchase patterns based on the latest purchase history data. It can also make suggestions based on the latest insights while referring to past insights. For example, it can make suggestions based on the latest browsing behavior while referring to past website browsing history data. Furthermore, the suggestion unit can adjust the priority of suggestions according to when the insights were collected. For example, it can make suggestions based on the latest topics of interest based on the latest social media activity logs. This makes it possible to make suggestions based on the latest insights by prioritizing suggestions based on when the insights were collected. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the timing of insight collection into a generating AI and have the generating AI determine the priority of suggestions.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the insights during the suggestion process. For example, the suggestion unit can prioritize suggesting highly relevant insights. For instance, it can evaluate the relevance between customer purchase history data and website browsing history data and prioritize suggesting highly relevant insights. The suggestion unit can also postpone suggesting less relevant insights. For example, it can evaluate the relevance between social media activity logs and purchase history data and postpone suggesting less relevant insights. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the insights. For example, it can prioritize suggesting highly relevant insights and postpone less relevant insights based on customer behavior patterns. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the insights. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the insights into a generating AI and have the generating AI adjust the order of suggestions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing high-priority data. For instance, it can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is stressed, and prioritize the analysis of high-priority data. Furthermore, if the user is relaxed, the analysis unit can prioritize the analysis of detailed data. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and prioritize the analysis of detailed data. Additionally, if the user is in a hurry, the analysis unit can prioritize the analysis of data that can be processed quickly. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and prioritize the analysis of data that can be processed quickly. This allows for efficient data analysis by prioritizing analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative 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 processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0102] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, the data collection unit can analyze the user's past purchase history and prioritize collecting data on frequently purchased items. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit can analyze the user's past website browsing history and collect data during the time of day when visits are most frequent. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient collection method. For example, the data collection unit can analyze the user's past social media activity logs and select the most effective collection method. In this way, an efficient data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed statistical analysis on data with high business impact to provide concrete insights. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can provide a simple summary on data with low business impact. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can prioritize the analysis of high-importance data and postpone the analysis of low-importance data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0104] The discovery unit can estimate the user's emotions and adjust how insights are displayed based on the estimated emotions. For example, if the user is tense, the discovery unit can provide a simple and highly visible display method. For instance, it can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is tense, and display insights using simple graphs or charts. Furthermore, if the user is relaxed, the discovery unit can provide a display method that includes detailed information. For example, it can record the user's voice, use voice analysis technology to determine if the user is relaxed, and display insights using a detailed text report. Additionally, if the user is in a hurry, the discovery unit can provide a concise display method. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and display insights using a concise summary report. This allows for the provision of insights that are easy for the user to understand by adjusting how insights are displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the discovery unit may be performed using AI, or not using AI. For example, the discovery unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0105] The proposal department can adjust the level of detail in its proposals based on the importance of the insights. For example, it can provide detailed proposals for high-importance insights. For example, it can propose detailed marketing strategies for insights with high business impact. It can also provide simplified proposals for low-importance insights. For example, it can provide a simple summary for insights with low business impact. Furthermore, the proposal department can prioritize proposals according to the importance of the insights. For example, it can prioritize proposals for high-importance insights and postpone those for low-importance insights. This allows for efficient proposals by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0106] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easy-to-understand suggestions. For instance, it could capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is nervous, and then provide suggestions using simple graphs or charts. Furthermore, if the user is relaxed, the suggestion function can provide detailed suggestions. For example, it could record the user's voice, use voice analysis technology to determine if the user is relaxed, and then provide suggestions using a detailed text report. Additionally, if the user is in a hurry, the suggestion function can provide concise suggestions. For example, it could collect the user's biometric data (heart rate and skin electrical activity) with sensors, use an emotion estimation algorithm to determine if the user is in a hurry, and then provide suggestions using a concise summary report. This allows the system to provide suggestions that are easy for the user to understand by adjusting the presentation based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0107] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's current geographical location information. The data collection unit can also collect region-specific promotional data based on the user's geographical location information. For example, the data collection unit can collect store information and event information in the area where the user is currently located. Furthermore, the data collection unit can collect highly relevant data by considering the user's travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of data related to frequently visited locations. This allows for the efficient collection of region-specific data by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0108] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. For instance, the analysis unit can apply a clustering algorithm to analyze a customer's purchase history and identify purchase patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit can apply a sequence mining algorithm to analyze a customer's website browsing history and identify browsing behavior. Furthermore, the analysis unit can apply a social network analysis algorithm to social media activity log data. For example, the analysis unit can apply a graph analysis algorithm to analyze a customer's social media activity log and identify the structure of the social network. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0109] The discovery unit can analyze changes in insights based on the data collection timing when an insight is discovered. For example, the discovery unit can analyze changes in insights based on the latest data. For example, the discovery unit can analyze changes in purchasing patterns based on the latest purchase history data. The discovery unit can also analyze changes in insights by referring to past data. For example, the discovery unit can analyze changes in browsing behavior by referring to past website browsing history data. Furthermore, the discovery unit can also analyze changes in insights depending on the data collection timing. For example, the discovery unit can analyze changes in topics of interest based on the latest social media activity logs. This allows for the understanding of the latest insights by analyzing changes in insights based on the data collection timing. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the data collection timing into a generating AI and have the generating AI perform the analysis of changes in insights.

[0110] The suggestion unit can prioritize suggestions based on when the insights were collected. For example, the suggestion unit can prioritize suggestions based on the latest insights. For example, it can make suggestions based on the latest purchase patterns based on the latest purchase history data. It can also make suggestions based on the latest insights while referring to past insights. For example, it can make suggestions based on the latest browsing behavior while referring to past website browsing history data. Furthermore, the suggestion unit can adjust the priority of suggestions according to when the insights were collected. For example, it can make suggestions based on the latest topics of interest based on the latest social media activity logs. This makes it possible to make suggestions based on the latest insights by prioritizing suggestions based on when the insights were collected. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the timing of insight collection into a generating AI and have the generating AI determine the priority of suggestions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects customer understanding data, behavioral logs, and statistical data. For example, it collects data such as customer purchase history, website browsing history, and social media activity logs. The data collection unit obtains data from POS systems and online shopping platforms and tracks the behavior of website visitors using web analytics tools. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses machine learning algorithms to analyze customer purchase history and website browsing history to identify customer behavior patterns. It also uses natural language processing technology to analyze social media activity logs to understand customer interests. It uses text mining technology to analyze social media posts to identify topics of interest to customers. Step 3: The Discovery Unit discovers insights based on the data analyzed by the Analysis Unit. For example, it analyzes past successful and unsuccessful marketing campaigns to discover insights that can be used for future campaigns. It also discovers insights that propose new marketing strategies based on customer behavior patterns and interests. Step 4: The proposal team proposes solutions based on the insights discovered by the discovery team. For example, they make specific suggestions such as what kind of promotions should be conducted for a particular customer segment and which channels should be used to approach them. Based on the customer's purchase history and website browsing history, they propose email marketing or social media advertising to a particular customer segment. Based on the customer's interests, they propose specific products or services.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer behavior logs using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand customer behavior patterns. The discovery unit is implemented in the specific processing unit 290 of the data processing unit 12 and discovers insights based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes concrete solution proposals based on the discovered insights. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer behavior logs using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand customer behavior patterns. The discovery unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and discovers insights based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes concrete solution proposals based on the discovered insights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer behavior logs using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand customer behavior patterns. The discovery unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and discovers insights based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes concrete solution proposals based on the discovered insights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, and proposal unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer behavior logs using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand customer behavior patterns. The discovery unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and discovers insights based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes concrete solution proposals based on the discovered insights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The data collection unit collects customer understanding data, behavioral logs, and statistical data. An analysis unit analyzes the data collected by the aforementioned collection unit, A discovery unit discovers insights based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that makes solution suggestions based on the insights discovered by the discovery unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as customer purchase history, website browsing history, and social media activity logs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we can understand customer behavior patterns and interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned detection unit is Analyze past marketing campaign successes and failures to discover insights that can be used for future campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide specific suggestions regarding what kind of promotions should be conducted for a particular customer segment and which channels should be used to reach them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We learn from new data every day and constantly provide the latest insights. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and activities. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned detection unit is It estimates the user's emotions and adjusts how insights are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned detection unit is When an insight is discovered, historical data is used to predict the current insight. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned detection unit is When discovering insights, apply different discovery methods to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned detection unit is It estimates user sentiment and adjusts the importance of insights based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned detection unit is When an insight is discovered, analyze how that insight changes based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned detection unit is When discovering insights, analyze those insights by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the insights. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the insight. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the insights were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the insights. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects customer understanding data, behavioral logs, and statistical data. An analysis unit analyzes the data collected by the aforementioned collection unit, A discovery unit discovers insights based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that makes solution suggestions based on the insights discovered by the discovery unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as customer purchase history, website browsing history, and social media activity logs. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, we can understand customer behavior patterns and interests. The system according to feature 1.

4. The aforementioned detection unit is Analyze past marketing campaign successes and failures to discover insights that can be used for future campaigns. The system according to feature 1.

5. The aforementioned proposal section is, We provide specific suggestions regarding what kind of promotions should be conducted for a particular customer segment and which channels should be used to reach them. The system according to feature 1.

6. The aforementioned proposal section is, We learn from new data every day and constantly provide the latest insights. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

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