system

Abstract

The system according to this embodiment aims to analyze customer behavior data and provide appropriate suggestions to staff. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a communication unit. The collection unit collects customer behavior data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The communication unit transmits the content proposed by the proposal unit to staff through at least one terminal, such as a POS system or earphones.

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 in 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, customer behavior data has not been fully utilized effectively to make appropriate proposals to staff, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer behavior data and make appropriate proposals to staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a communication unit. The data collection unit collects customer behavior data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The communication unit transmits the content proposed by the proposal unit to staff through at least one terminal, such as a POS system or earphones. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer behavior data and make appropriate suggestions to staff. [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, etc. 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 including 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 behavioral analysis suggestion system according to an embodiment of the present invention is a system that collects, analyzes, makes suggestions to, and communicates customer behavior data in stores, hotels, etc. First, the AI ​​collects customer behavior data through the store's surveillance cameras and payment system data. At this time, anonymous data such as where and what was purchased, the season and weather at the time, facial expression, and companions are accumulated. Next, the AI ​​analyzes the accumulated data and suggests recommended menus and services for the customer's next visit. These suggestions are communicated to staff via POS systems or terminals such as earphones. This improves hospitality with each visit, enabling even new staff to provide a high level of service to regular customers. For example, if a customer tends to prefer ordering a specific menu item during a particular season, the behavioral analysis suggestion system will accumulate that data. Next, the AI ​​analyzes the accumulated data. Based on the collected data, the AI ​​analyzes the customer's behavior patterns and suggests recommended menus and services for their next visit. For example, if a customer has previously preferred to order a specific menu item, that menu item can be suggested for their next visit. These suggestions are communicated to staff via POS systems or terminals such as earphones. This allows staff to make appropriate suggestions to customers. For example, even new staff members can provide a high level of service to regular customers based on menus and services suggested by the AI. This system improves hospitality with each visit. Customers receive suggestions tailored to their preferences and needs, leading to increased satisfaction. Furthermore, staff can perform their duties more efficiently by responding based on AI suggestions. For example, even new staff members can provide a high level of service to regular customers based on menus and services suggested by the AI. This allows the behavioral analysis and suggestion system to automatically collect, analyze, suggest, and communicate customer behavior data to staff.

[0029] The behavioral analysis and proposal system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a transmission unit. The collection unit collects customer behavioral data. Customer behavioral data includes, but is not limited to, purchase history, time spent in a location, and travel routes. The collection unit records customer behavior using, for example, surveillance cameras. The collection unit can also collect customer purchase history using payment system data. Furthermore, the collection unit can track customer travel routes using sensors. For example, the collection unit records customer behavior in real time using cameras installed in the store. The payment system data includes information on products purchased by the customer, and purchase history is collected based on this. Sensors track customer travel routes and record which areas the customer stayed in. The analysis unit analyzes the data collected by the collection unit. Analysis includes, but is not limited to, behavioral pattern analysis and purchase history analysis. For example, the analysis unit analyzes customer behavioral patterns to find tendencies to take specific actions at specific times of day. The analysis unit can also analyze purchase history to identify products and services that customers prefer. Furthermore, the analysis unit can analyze travel routes and identify which areas customers are interested in. For example, the analysis unit can find tendencies for customers to take specific actions at specific times of day based on their behavioral patterns. Purchase history analysis is based on information about products customers have purchased in the past. Travel route analysis is based on which areas customers have stayed in. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, suggesting recommended menu items or services for the next visit. For example, the suggestion unit may suggest menu items that customers have previously ordered on their next visit. The suggestion unit can also suggest new menu items or services based on customer behavioral patterns. Furthermore, the suggestion unit can suggest services related to specific areas based on the customer's travel route. For example, the suggestion unit may suggest menu items that customers have previously ordered on their next visit. Suggestions based on behavioral patterns are based on the tendency for customers to take specific actions at specific times of day. Suggestions based on travel routes are based on which areas customers are interested in.The communication unit conveys the content proposed by the proposal unit to staff via a terminal such as a POS system or earphones. Communication includes, but is not limited to, displaying the proposal content to staff via a POS system or conveying the proposal content verbally via earphones. For example, the communication unit displays the proposal content to staff via a POS system. The communication unit can also convey the proposal content verbally via earphones. Furthermore, the communication unit can notify staff of the proposal content via a mobile device. For example, the communication unit displays the proposal content to staff via a POS system. Communication via earphones allows staff to check the proposal content without using their hands. Notification via a mobile device allows staff to check the proposal content even while on the move. This enables the behavioral analysis proposal system according to the embodiment to automatically collect, analyze, propose, and communicate customer behavioral data to staff. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or not using AI. For example, the communication unit can perform communication using an AI model that takes the content proposed by the proposal unit as input and outputs the content to be conveyed to staff.

[0030] The data collection unit can collect customer behavior data through surveillance cameras or payment system data. For example, the data collection unit can record customer behavior using surveillance cameras. For example, the data collection unit can record customer behavior in real time using cameras installed in the store. The data collection unit can also collect customer purchase history using payment system data. For example, the data collection unit can collect purchase history based on information about the products purchased by the customer. Furthermore, the data collection unit can track customer movement paths using sensors. For example, the data collection unit can track customer movement paths and record which areas they stayed in. This allows for the acquisition of detailed data by collecting customer behavior data through surveillance cameras and payment system data. 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 video data acquired from surveillance cameras into a generating AI and have the generating AI extract behavior data from the video data.

[0031] The analysis unit can analyze customer behavior patterns based on collected data. For example, the analysis unit can analyze customer behavior patterns to identify tendencies to take specific actions at specific times of day. The analysis unit can also analyze purchase history to identify products and services that customers prefer. For example, the analysis unit can do this based on information about products that customers have purchased in the past. Furthermore, the analysis unit can analyze travel routes to identify which areas customers are interested in. For example, the analysis unit can do this based on which areas customers have stayed in. By analyzing customer behavior patterns, it becomes possible to make appropriate suggestions for their next visit. 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 behavioral data into a generating AI and have the generating AI perform the behavioral pattern analysis.

[0032] The suggestion department can suggest recommended menu items and services for the customer's next visit based on the analysis results. For example, the suggestion department can suggest menu items that the customer has previously ordered on their next visit. The suggestion department can also suggest new menu items and services based on the customer's behavior patterns. For example, the suggestion department can suggest items based on the customer's tendency to take specific actions at specific times of day. Furthermore, the suggestion department can suggest services related to specific areas based on the customer's travel route. For example, the suggestion department can suggest items based on which areas the customer is interested in. By making suggestions based on the analysis results, customer satisfaction is improved. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the analysis results into a generating AI and have the generating AI generate the suggestion content.

[0033] The communication unit can convey the proposed content to staff through terminals such as POS systems or earphones. For example, the communication unit can display the proposed content to staff through a POS system. The communication unit can also convey the proposed content by voice through earphones. Furthermore, the communication unit can notify staff of the proposed content via mobile devices. This allows staff to take appropriate action by communicating the proposed content. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can perform communication using an AI model that takes the content proposed by the proposal unit as input and outputs the content to be conveyed to staff.

[0034] The data collection unit can analyze past customer behavior data and select the optimal collection method. For example, the data collection unit can collect data during times when customers frequently visited in the past to understand their behavior patterns. The data collection unit can also focus on collecting data on specific days of the week if customers tend to visit on those days. Furthermore, if customers prefer specific menu items during certain seasons, the data collection unit can collect data accordingly. This allows the optimal collection method to be selected by analyzing past behavior data. 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 past behavior data into a generating AI and have the generating AI select the optimal collection method.

[0035] The data collection unit can filter behavioral data based on the customer's current activities and areas of interest. For example, if the customer is eating, the data collection unit will prioritize collecting data related to the meal. The data collection unit can also collect data based on the customer's purchase history and areas of interest if the customer is shopping. Furthermore, if the customer is attending an event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering the data based on the customer's activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the customer's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location when collecting behavioral data. For example, if the customer is in a specific area, the data collection unit can prioritize the collection of data related to that area. The data collection unit can also collect data related to the customer's travel route if the customer is on the move. Furthermore, if the customer is in a specific store, the data collection unit can collect data related to the store's products and services. This allows for the priority collection of highly relevant data by considering the customer's geographical location. 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 customer's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0037] The data collection unit can analyze customers' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can identify menus and services of interest based on information shared by customers on social media. The data collection unit can also collect relevant data based on information about accounts that customers follow on social media. Furthermore, the data collection unit can collect behavioral data based on information about locations where customers have checked in on social media. This allows for the collection of data based on customer interests and preferences by analyzing 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 customer social media activity data into a generating AI and have the generating AI collect relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on important behavioral data. The analysis unit can also perform a basic analysis on general behavioral data. Furthermore, the analysis unit can perform condition-specific analysis on behavioral data under specific conditions. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the behavioral 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 importance of the behavioral data into a generating AI and have the generating AI adjust the level of detail of the analysis based on the importance.

[0039] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a purchase behavior analysis algorithm to purchase data. The analysis unit can also apply a movement pattern analysis algorithm to movement data. Furthermore, the analysis unit can apply a social network analysis algorithm to social media data. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis becomes possible. 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 categories of behavioral data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0040] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit can also prioritize the analysis of behavioral data during specific seasons or events. Furthermore, the analysis unit can prioritize the analysis of current behavioral data while referring to past behavioral data. This allows for the prioritization of analysis based on the timing of behavioral data collection, thereby ensuring that the most recent data is analyzed first. 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 timing of behavioral data collection into a generating AI and have the generating AI determine the priority of analysis based on the collection timing.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit may prioritize the analysis of behavioral data with high relevance. The analysis unit may also analyze behavioral data with moderate relevance next. For example, the analysis unit may also analyze behavioral data with moderate relevance next. Furthermore, the analysis unit may also analyze behavioral data with low relevance last. For example, the analysis unit may also analyze behavioral data with low relevance last. By adjusting the order of analysis based on the relevance of the behavioral data, highly relevant data can be prioritized. 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 behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0042] The proposal department can adjust the level of detail of its proposals based on the importance of the customer's behavioral patterns. For example, it can provide detailed proposals for important behavioral patterns. It can also provide basic proposals for general behavioral patterns. Furthermore, it can provide condition-specific proposals for behavioral patterns under specific conditions. By adjusting the level of detail of proposals based on the importance of the customer's behavioral patterns, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of behavioral patterns into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0043] The suggestion unit can apply different suggestion algorithms depending on the customer's behavior pattern category when making a suggestion. For example, the suggestion unit can apply a purchase suggestion algorithm to a purchase behavior pattern. The suggestion unit can also apply a movement suggestion algorithm to a movement behavior pattern. Furthermore, the suggestion unit can apply a social suggestion algorithm to a social media behavior pattern. By applying different suggestion algorithms depending on the customer's behavior pattern category, 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 suggestion unit can input the behavior pattern category into a generating AI and have the generating AI apply a suggestion algorithm according to the category.

[0044] The proposal department can determine the priority of proposals based on when customer behavior patterns are collected. For example, the proposal department can make proposals based on the latest behavior patterns. The proposal department can also make proposals based on behavior patterns during specific seasons or events. Furthermore, the proposal department can make proposals based on current behavior patterns while referring to past behavior patterns. This makes it possible to make proposals based on the latest behavior patterns by determining the priority of proposals based on when customer behavior patterns are collected. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the timing of behavior pattern collection into a generating AI and have the generating AI determine the priority of proposals based on the collection timing.

[0045] The proposal unit can adjust the order of proposals based on the relevance of customer behavior patterns. For example, the proposal unit can make proposals based on highly relevant behavior patterns. For example, the proposal unit can make proposals based on moderately relevant behavior patterns. For example, the proposal unit can make proposals based on moderately relevant behavior patterns. Furthermore, the proposal unit can make proposals based on lowly relevant behavior patterns. For example, the proposal unit can make proposals based on lowly relevant behavior patterns. By adjusting the order of proposals based on the relevance of customer behavior patterns, it becomes possible to make highly relevant proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of behavior patterns into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0046] The communication unit can select the optimal communication method by referring to the customer's past behavioral data during communication. For example, the communication unit can select the optimal method based on the communication method the customer has preferred in the past. The communication unit can also select an alternative method based on the communication method the customer has avoided in the past. Furthermore, the communication unit can analyze the customer's past behavioral data to select the most effective communication method. In this way, the optimal communication method can be selected by referring to the customer's past behavioral data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's past behavioral data into a generating AI and have the generating AI select the optimal communication method.

[0047] The communication unit can select the optimal communication method by considering the customer's current activity status when communicating. For example, if the customer is eating, the communication unit can provide quiet voice guidance. The communication unit can also provide visual guidance if the customer is shopping. Furthermore, if the customer is participating in an event, the communication unit can provide guidance via a short message. This allows the system to select the optimal communication method by considering the customer's current activity status. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's current activity status into a generating AI and have the generating AI select the optimal communication method based on the activity status.

[0048] The communication unit can select the optimal communication method by considering the customer's geographical location information during communication. For example, if the customer is in a specific area, the communication unit will prioritize communicating information related to that area. The communication unit can also communicate information related to the customer's travel route if the customer is on the move. Furthermore, if the customer is in a specific store, the communication unit can communicate information related to the store's products and services. This allows the communication unit to select the optimal communication method by considering the customer's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's geographical location information into a generating AI and have the generating AI select the optimal communication method based on the geographical location information.

[0049] The communication unit can select the optimal communication method by considering the customer's device information during communication. For example, if the customer is using a smartphone, the communication unit can provide information tailored to the screen size. For example, if the customer is using a tablet, the communication unit can provide information optimized for a larger screen. For example, if the customer is using a tablet, the communication unit can provide information optimized for a larger screen. Furthermore, if the customer is using a smartwatch, the communication unit can provide concise and highly visible information. For example, if the customer is using a smartwatch, the communication unit can provide concise and highly visible information. This allows the optimal communication method to be selected by considering the customer's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's device information into a generating AI and have the generating AI select the optimal communication method based on the device information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The behavioral analysis and recommendation system can also be equipped with the ability to check the inventory status of specific products in real time based on customer purchase history and notify staff when inventory is low. For example, if a product that a customer frequently purchases is about to run out of stock, the system will automatically notify staff and prompt them to replenish it quickly. It can also monitor the inventory status of specific seasonal or limited-edition products and send alerts to staff as needed. Furthermore, it can collect reviews and ratings of products that customers have purchased in the past and incorporate them into future recommendations. This improves the customer purchasing experience and increases the efficiency of inventory management.

[0052] The data collection unit can collect data on customers' health status when gathering behavioral data. For example, if a customer is using a wearable device, it can acquire data such as heart rate, steps taken, and sleep patterns from that device. If a customer is using a health app, it can also collect meal records and exercise history from that app. Furthermore, if a customer has set specific health goals, it can collect behavioral data based on those goals and provide health management suggestions. This enables the provision of services tailored to each customer's health condition.

[0053] The analytics unit can consider customers' social media activity when analyzing their behavioral patterns. For example, it can analyze posts and comments shared by customers on social media to identify their interests. It can also find relevant behavioral patterns based on information about accounts customers follow and groups they participate in. Furthermore, it can analyze information about places and events customers check in to on social media and reflect this in their behavioral patterns. This allows for an integrated analysis of customers' online and offline activities, enabling more accurate recommendations.

[0054] The suggestion department can consider past customer feedback when proposing recommended menus and services for the customer's next visit based on the analysis results. For example, it can adjust the suggestions based on the customer's evaluations and comments on services and menus previously provided. Furthermore, if a customer expresses dissatisfaction with a particular service or menu, that information can be reflected in alternative suggestions. Additionally, if a customer highly rates a particular service or menu, that information can be used to strengthen suggestions for the next visit. This, in turn, can improve customer satisfaction.

[0055] The communication department can adjust the method of conveying proposed ideas to staff according to their skill level and experience. For example, it can provide detailed procedures and explanations to new staff and concise instructions to experienced staff. It can also convey proposals that leverage specific skills to staff members with particular abilities. Furthermore, it can communicate proposals at the optimal time, taking into account staff shift schedules and workloads. This enables staff to perform their tasks efficiently.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects customer behavior data. This data includes purchase history, time spent in the store, and travel routes. The collection unit records customer behavior using surveillance cameras, collects purchase history using payment system data, and tracks travel routes using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis includes behavioral pattern analysis, purchase history analysis, and travel route analysis. The analysis unit analyzes customer behavioral patterns to identify tendencies to take specific actions at specific times of day, analyzes purchase history to identify products and services that customers prefer, and analyzes travel routes to identify areas that customers are interested in. Step 3: The proposal department makes suggestions based on the analysis results obtained by the analysis department. These suggestions include recommendations for menu items and services for the customer's next visit. The proposal department suggests menu items that the customer has previously ordered, proposes new menu items and services based on the customer's behavior patterns, and suggests services related to specific areas based on their travel route. Step 4: The communication department conveys the proposed content from the proposal department to the staff via terminals such as POS systems or earphones. This communication may include displaying the proposed content to staff via the POS system or conveying the content verbally via earphones. The communication department may also notify staff of the proposed content via mobile devices.

[0058] (Example of form 2) The behavioral analysis suggestion system according to an embodiment of the present invention is a system that collects, analyzes, makes suggestions to, and communicates customer behavior data in stores, hotels, etc. First, the AI ​​collects customer behavior data through the store's surveillance cameras and payment system data. At this time, anonymous data such as where and what was purchased, the season and weather at the time, facial expression, and companions are accumulated. Next, the AI ​​analyzes the accumulated data and suggests recommended menus and services for the customer's next visit. These suggestions are communicated to staff via POS systems or terminals such as earphones. This improves hospitality with each visit, enabling even new staff to provide a high level of service to regular customers. For example, if a customer tends to prefer ordering a specific menu item during a particular season, the behavioral analysis suggestion system will accumulate that data. Next, the AI ​​analyzes the accumulated data. Based on the collected data, the AI ​​analyzes the customer's behavior patterns and suggests recommended menus and services for their next visit. For example, if a customer has previously preferred to order a specific menu item, that menu item can be suggested for their next visit. These suggestions are communicated to staff via POS systems or terminals such as earphones. This allows staff to make appropriate suggestions to customers. For example, even new staff members can provide a high level of service to regular customers based on menus and services suggested by the AI. This system improves hospitality with each visit. Customers receive suggestions tailored to their preferences and needs, leading to increased satisfaction. Furthermore, staff can perform their duties more efficiently by responding based on AI suggestions. For example, even new staff members can provide a high level of service to regular customers based on menus and services suggested by the AI. This allows the behavioral analysis and suggestion system to automatically collect, analyze, suggest, and communicate customer behavior data to staff.

[0059] The behavioral analysis and proposal system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a transmission unit. The collection unit collects customer behavioral data. Customer behavioral data includes, but is not limited to, purchase history, time spent in a location, and travel routes. The collection unit records customer behavior using, for example, surveillance cameras. The collection unit can also collect customer purchase history using payment system data. Furthermore, the collection unit can track customer travel routes using sensors. For example, the collection unit records customer behavior in real time using cameras installed in the store. The payment system data includes information on products purchased by the customer, and purchase history is collected based on this. Sensors track customer travel routes and record which areas the customer stayed in. The analysis unit analyzes the data collected by the collection unit. Analysis includes, but is not limited to, behavioral pattern analysis and purchase history analysis. For example, the analysis unit analyzes customer behavioral patterns to find tendencies to take specific actions at specific times of day. The analysis unit can also analyze purchase history to identify products and services that customers prefer. Furthermore, the analysis unit can analyze travel routes and identify which areas customers are interested in. For example, the analysis unit can find tendencies for customers to take specific actions at specific times of day based on their behavioral patterns. Purchase history analysis is based on information about products customers have purchased in the past. Travel route analysis is based on which areas customers have stayed in. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, suggesting recommended menu items or services for the next visit. For example, the suggestion unit may suggest menu items that customers have previously ordered on their next visit. The suggestion unit can also suggest new menu items or services based on customer behavioral patterns. Furthermore, the suggestion unit can suggest services related to specific areas based on the customer's travel route. For example, the suggestion unit may suggest menu items that customers have previously ordered on their next visit. Suggestions based on behavioral patterns are based on the tendency for customers to take specific actions at specific times of day. Suggestions based on travel routes are based on which areas customers are interested in.The communication unit conveys the content proposed by the proposal unit to staff via a terminal such as a POS system or earphones. Communication includes, but is not limited to, displaying the proposal content to staff via a POS system or conveying the proposal content verbally via earphones. For example, the communication unit displays the proposal content to staff via a POS system. The communication unit can also convey the proposal content verbally via earphones. Furthermore, the communication unit can notify staff of the proposal content via a mobile device. For example, the communication unit displays the proposal content to staff via a POS system. Communication via earphones allows staff to check the proposal content without using their hands. Notification via a mobile device allows staff to check the proposal content even while on the move. This enables the behavioral analysis proposal system according to the embodiment to automatically collect, analyze, propose, and communicate customer behavioral data to staff. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or not using AI. For example, the communication unit can perform communication using an AI model that takes the content proposed by the proposal unit as input and outputs the content to be conveyed to staff.

[0060] The data collection unit can collect customer behavior data through surveillance cameras or payment system data. For example, the data collection unit can record customer behavior using surveillance cameras. For example, the data collection unit can record customer behavior in real time using cameras installed in the store. The data collection unit can also collect customer purchase history using payment system data. For example, the data collection unit can collect purchase history based on information about the products purchased by the customer. Furthermore, the data collection unit can track customer movement paths using sensors. For example, the data collection unit can track customer movement paths and record which areas they stayed in. This allows for the acquisition of detailed data by collecting customer behavior data through surveillance cameras and payment system data. 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 video data acquired from surveillance cameras into a generating AI and have the generating AI extract behavior data from the video data.

[0061] The analysis unit can analyze customer behavior patterns based on collected data. For example, the analysis unit can analyze customer behavior patterns to identify tendencies to take specific actions at specific times of day. The analysis unit can also analyze purchase history to identify products and services that customers prefer. For example, the analysis unit can do this based on information about products that customers have purchased in the past. Furthermore, the analysis unit can analyze travel routes to identify which areas customers are interested in. For example, the analysis unit can do this based on which areas customers have stayed in. By analyzing customer behavior patterns, it becomes possible to make appropriate suggestions for their next visit. 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 behavioral data into a generating AI and have the generating AI perform the behavioral pattern analysis.

[0062] The suggestion department can suggest recommended menu items and services for the customer's next visit based on the analysis results. For example, the suggestion department can suggest menu items that the customer has previously ordered on their next visit. The suggestion department can also suggest new menu items and services based on the customer's behavior patterns. For example, the suggestion department can suggest items based on the customer's tendency to take specific actions at specific times of day. Furthermore, the suggestion department can suggest services related to specific areas based on the customer's travel route. For example, the suggestion department can suggest items based on which areas the customer is interested in. By making suggestions based on the analysis results, customer satisfaction is improved. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the analysis results into a generating AI and have the generating AI generate the suggestion content.

[0063] The communication unit can convey the proposed content to staff through terminals such as POS systems or earphones. For example, the communication unit can display the proposed content to staff through a POS system. The communication unit can also convey the proposed content by voice through earphones. Furthermore, the communication unit can notify staff of the proposed content via mobile devices. This allows staff to take appropriate action by communicating the proposed content. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can perform communication using an AI model that takes the content proposed by the proposal unit as input and outputs the content to be conveyed to staff.

[0064] The data collection unit can estimate the customer's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit can collect behavioral data more frequently to obtain detailed data. The data collection unit can also reduce the amount of behavioral data collected if the customer is stressed to reduce the burden on the customer. Furthermore, if the customer is excited, the data collection unit can focus on specific behaviors to collect data and identify menus or services of interest. This allows for more appropriate data collection by adjusting the timing of collection according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer emotion data into a generating AI and have the generating AI adjust the timing of data collection based on emotion.

[0065] The data collection unit can analyze past customer behavior data and select the optimal collection method. For example, the data collection unit can collect data during times when customers frequently visited in the past to understand their behavior patterns. The data collection unit can also focus on collecting data on specific days of the week if customers tend to visit on those days. Furthermore, if customers prefer specific menu items during certain seasons, the data collection unit can collect data accordingly. This allows the optimal collection method to be selected by analyzing past behavior data. 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 past behavior data into a generating AI and have the generating AI select the optimal collection method.

[0066] The data collection unit can filter behavioral data based on the customer's current activities and areas of interest. For example, if the customer is eating, the data collection unit will prioritize collecting data related to the meal. The data collection unit can also collect data based on the customer's purchase history and areas of interest if the customer is shopping. Furthermore, if the customer is attending an event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering the data based on the customer's activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the customer's current activities and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the customer's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the customer is relaxed, the data collection unit may prioritize collecting detailed behavioral data. For example, if the customer is relaxed, the data collection unit may prioritize collecting detailed behavioral data. For example, if the customer is stressed, the data collection unit may prioritize collecting only basic behavioral data. For example, if the customer is excited, the data collection unit may prioritize collecting data in areas of particular interest to the customer. For example, if the customer is excited, the data collection unit may prioritize collecting data in areas of particular interest to the customer. This allows for the priority collection of important data by prioritizing data based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 customer emotion data into a generating AI, which can then perform the task of prioritizing the data based on those emotions.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location when collecting behavioral data. For example, if the customer is in a specific area, the data collection unit can prioritize the collection of data related to that area. The data collection unit can also collect data related to the customer's travel route if the customer is on the move. Furthermore, if the customer is in a specific store, the data collection unit can collect data related to the store's products and services. This allows for the priority collection of highly relevant data by considering the customer's geographical location. 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 customer's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0069] The data collection unit can analyze customers' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can identify menus and services of interest based on information shared by customers on social media. The data collection unit can also collect relevant data based on information about accounts that customers follow on social media. Furthermore, the data collection unit can collect behavioral data based on information about locations where customers have checked in on social media. This allows for the collection of data based on customer interests and preferences by analyzing 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 customer social media activity data into a generating AI and have the generating AI collect relevant data.

[0070] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise analysis results if the customer is stressed. For example, if the customer is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis based on the customer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 customer emotion data into the generating AI and have the generating AI adjust the way the emotion-based analysis is expressed.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on important behavioral data. The analysis unit can also perform a basic analysis on general behavioral data. Furthermore, the analysis unit can perform condition-specific analysis on behavioral data under specific conditions. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the behavioral 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 importance of the behavioral data into a generating AI and have the generating AI adjust the level of detail of the analysis based on the importance.

[0072] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a purchase behavior analysis algorithm to purchase data. The analysis unit can also apply a movement pattern analysis algorithm to movement data. Furthermore, the analysis unit can apply a social network analysis algorithm to social media data. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis becomes possible. 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 categories of behavioral data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0073] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis and provide a longer report. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis and provide a longer report. For example, if the customer is stressed, the analysis unit can perform a concise analysis and provide a shorter report. For example, if the customer is excited, the analysis unit can perform a visually appealing analysis and provide a report of appropriate length. For example, if the customer is excited, the analysis unit can perform a visually appealing analysis and provide a report of appropriate length. In this way, by adjusting the length of the analysis based on the customer's emotions, the optimal analysis results can be provided to the customer. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 customer emotion data into a generating AI and have the generating AI adjust the length of the emotion-based analysis.

[0074] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit can also prioritize the analysis of behavioral data during specific seasons or events. Furthermore, the analysis unit can prioritize the analysis of current behavioral data while referring to past behavioral data. This allows for the prioritization of analysis based on the timing of behavioral data collection, thereby ensuring that the most recent data is analyzed first. 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 timing of behavioral data collection into a generating AI and have the generating AI determine the priority of analysis based on the collection timing.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit may prioritize the analysis of behavioral data with high relevance. The analysis unit may also analyze behavioral data with moderate relevance next. For example, the analysis unit may also analyze behavioral data with moderate relevance next. Furthermore, the analysis unit may also analyze behavioral data with low relevance last. For example, the analysis unit may also analyze behavioral data with low relevance last. By adjusting the order of analysis based on the relevance of the behavioral data, highly relevant data can be prioritized. 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 behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0076] The suggestion unit can estimate the customer's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the customer is relaxed, the suggestion unit can offer detailed suggestions. For example, if the customer is relaxed, the suggestion unit can offer detailed suggestions. For example, if the customer is stressed, the suggestion unit can offer concise suggestions. For example, if the customer is excited, the suggestion unit can offer visually appealing suggestions. For example, if the customer is excited, the suggestion unit can offer visually appealing suggestions. By adjusting the way it presents its suggestions based on the customer's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 department can input customer emotional data into a generating AI and have the AI ​​adjust the way proposals are expressed based on those emotional data.

[0077] The proposal department can adjust the level of detail of its proposals based on the importance of the customer's behavioral patterns. For example, it can provide detailed proposals for important behavioral patterns. It can also provide basic proposals for general behavioral patterns. Furthermore, it can provide condition-specific proposals for behavioral patterns under specific conditions. By adjusting the level of detail of proposals based on the importance of the customer's behavioral patterns, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of behavioral patterns into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0078] The suggestion unit can apply different suggestion algorithms depending on the customer's behavior pattern category when making a suggestion. For example, the suggestion unit can apply a purchase suggestion algorithm to a purchase behavior pattern. The suggestion unit can also apply a movement suggestion algorithm to a movement behavior pattern. Furthermore, the suggestion unit can apply a social suggestion algorithm to a social media behavior pattern. By applying different suggestion algorithms depending on the customer's behavior pattern category, 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 suggestion unit can input the behavior pattern category into a generating AI and have the generating AI apply a suggestion algorithm according to the category.

[0079] The suggestion function can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is relaxed, the suggestion function can provide a detailed suggestion and a longer explanation. For example, if the customer is relaxed, the suggestion function can provide a detailed suggestion and a longer explanation. For example, if the customer is stressed, the suggestion function can provide a concise suggestion and a shorter explanation. For example, if the customer is excited, the suggestion function can provide a visually appealing suggestion and a moderately lengthy explanation. For example, if the customer is excited, the suggestion function can provide a visually appealing suggestion and a moderately lengthy explanation. This allows for optimal suggestions for the customer by adjusting the length of the suggestion based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input customer emotion data into a generating AI and have the generating AI adjust the length of the proposal based on that emotion.

[0080] The proposal department can determine the priority of proposals based on when customer behavior patterns are collected. For example, the proposal department can make proposals based on the latest behavior patterns. The proposal department can also make proposals based on behavior patterns during specific seasons or events. Furthermore, the proposal department can make proposals based on current behavior patterns while referring to past behavior patterns. This makes it possible to make proposals based on the latest behavior patterns by determining the priority of proposals based on when customer behavior patterns are collected. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the timing of behavior pattern collection into a generating AI and have the generating AI determine the priority of proposals based on the collection timing.

[0081] The proposal unit can adjust the order of proposals based on the relevance of customer behavior patterns. For example, the proposal unit can make proposals based on highly relevant behavior patterns. For example, the proposal unit can make proposals based on moderately relevant behavior patterns. For example, the proposal unit can make proposals based on moderately relevant behavior patterns. Furthermore, the proposal unit can make proposals based on lowly relevant behavior patterns. For example, the proposal unit can make proposals based on lowly relevant behavior patterns. By adjusting the order of proposals based on the relevance of customer behavior patterns, it becomes possible to make highly relevant proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of behavior patterns into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0082] The communication unit can estimate the customer's emotions and adjust the method of communication based on the estimated emotions. For example, if the customer is relaxed, the communication unit can convey detailed information. For example, if the customer is relaxed, the communication unit can convey detailed information. The communication unit can also convey concise information if the customer is stressed. For example, if the customer is excited, the communication unit can convey visually appealing information. This allows for more appropriate information delivery by adjusting the method of communication based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input customer emotion data into a generating AI and have the AI ​​adjust the communication method based on those emotions.

[0083] The communication unit can select the optimal communication method by referring to the customer's past behavioral data during communication. For example, the communication unit can select the optimal method based on the communication method the customer has preferred in the past. The communication unit can also select an alternative method based on the communication method the customer has avoided in the past. Furthermore, the communication unit can analyze the customer's past behavioral data to select the most effective communication method. In this way, the optimal communication method can be selected by referring to the customer's past behavioral data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's past behavioral data into a generating AI and have the generating AI select the optimal communication method.

[0084] The communication unit can select the optimal communication method by considering the customer's current activity status when communicating. For example, if the customer is eating, the communication unit can provide quiet voice guidance. The communication unit can also provide visual guidance if the customer is shopping. Furthermore, if the customer is participating in an event, the communication unit can provide guidance via a short message. This allows the system to select the optimal communication method by considering the customer's current activity status. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's current activity status into a generating AI and have the generating AI select the optimal communication method based on the activity status.

[0085] The communication unit can estimate the customer's emotions and determine the priority of communication based on the estimated emotions. For example, if the customer is relaxed, the communication unit may prioritize the delivery of detailed information. For example, if the customer is relaxed, the communication unit may prioritize the delivery of detailed information. For example, if the customer is stressed, the communication unit may prioritize the delivery of only important information. For example, if the customer is excited, the communication unit may prioritize the delivery of only important information. For example, if the customer is excited, the communication unit may prioritize the delivery of interesting information. In this way, by determining the priority of communication based on the customer's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input customer emotional data into a generating AI, which can then perform the task of determining the priority of communications based on those emotional states.

[0086] The communication unit can select the optimal communication method by considering the customer's geographical location information during communication. For example, if the customer is in a specific area, the communication unit will prioritize communicating information related to that area. The communication unit can also communicate information related to the customer's travel route if the customer is on the move. Furthermore, if the customer is in a specific store, the communication unit can communicate information related to the store's products and services. This allows the communication unit to select the optimal communication method by considering the customer's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's geographical location information into a generating AI and have the generating AI select the optimal communication method based on the geographical location information.

[0087] The communication unit can select the optimal communication method by considering the customer's device information during communication. For example, if the customer is using a smartphone, the communication unit can provide information tailored to the screen size. For example, if the customer is using a tablet, the communication unit can provide information optimized for a larger screen. For example, if the customer is using a tablet, the communication unit can provide information optimized for a larger screen. Furthermore, if the customer is using a smartwatch, the communication unit can provide concise and highly visible information. For example, if the customer is using a smartwatch, the communication unit can provide concise and highly visible information. This allows the optimal communication method to be selected by considering the customer's device information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the customer's device information into a generating AI and have the generating AI select the optimal communication method based on the device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and communication unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect customer behavior data using the camera 42 and sensors of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The communication unit is implemented, for example, by the control unit 46A of the smart device 14, and communicates the content of the proposal to the staff. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and communication unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect customer behavior data using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The communication unit is implemented, for example, in the control unit 46A of the smart glasses 214, and communicates the content of the proposal to the staff. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and communication unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect customer behavior data using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The communication unit is implemented, for example, by the control unit 46A of the headset terminal 314, and communicates the content of the proposal to the staff. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and communication unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect customer behavior data using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The communication unit is implemented, for example, by the control unit 46A of the robot 414, and communicates the content of the proposal to the staff.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The behavioral analysis and recommendation system can also be equipped with the ability to check the inventory status of specific products in real time based on customer purchase history and notify staff when inventory is low. For example, if a product that a customer frequently purchases is about to run out of stock, the system will automatically notify staff and prompt them to replenish it quickly. It can also monitor the inventory status of specific seasonal or limited-edition products and send alerts to staff as needed. Furthermore, it can collect reviews and ratings of products that customers have purchased in the past and incorporate them into future recommendations. This improves the customer purchasing experience and increases the efficiency of inventory management.

[0090] The data collection unit can collect data on customers' health status when gathering behavioral data. For example, if a customer is using a wearable device, it can acquire data such as heart rate, steps taken, and sleep patterns from that device. If a customer is using a health app, it can also collect meal records and exercise history from that app. Furthermore, if a customer has set specific health goals, it can collect behavioral data based on those goals and provide health management suggestions. This enables the provision of services tailored to each customer's health condition.

[0091] The analytics unit can consider customers' social media activity when analyzing their behavioral patterns. For example, it can analyze posts and comments shared by customers on social media to identify their interests. It can also find relevant behavioral patterns based on information about accounts customers follow and groups they participate in. Furthermore, it can analyze information about places and events customers check in to on social media and reflect this in their behavioral patterns. This allows for an integrated analysis of customers' online and offline activities, enabling more accurate recommendations.

[0092] The suggestion department can consider past customer feedback when proposing recommended menus and services for the customer's next visit based on the analysis results. For example, it can adjust the suggestions based on the customer's evaluations and comments on services and menus previously provided. Furthermore, if a customer expresses dissatisfaction with a particular service or menu, that information can be reflected in alternative suggestions. Additionally, if a customer highly rates a particular service or menu, that information can be used to strengthen suggestions for the next visit. This, in turn, can improve customer satisfaction.

[0093] The communication department can adjust the method of conveying proposed ideas to staff according to their skill level and experience. For example, it can provide detailed procedures and explanations to new staff and concise instructions to experienced staff. It can also convey proposals that leverage specific skills to staff members with particular abilities. Furthermore, it can communicate proposals at the optimal time, taking into account staff shift schedules and workloads. This enables staff to perform their tasks efficiently.

[0094] The data collection unit can estimate customer emotions and adjust the method of collecting behavioral data based on those estimated emotions. For example, if a customer is relaxed, detailed behavioral data can be collected to gain a deeper understanding of their preferences and interests. If a customer is stressed, the data collected can be minimized to reduce the customer's burden. Furthermore, if a customer is excited, data can be collected focusing on specific behaviors to identify menus or services of interest. This enables flexible data collection tailored to the customer's emotions.

[0095] The analysis unit can estimate customer emotions and prioritize analysis based on those emotions. For example, if a customer is relaxed, detailed analysis can be prioritized to gain a deeper understanding of their behavioral patterns. If a customer is stressed, basic analysis can be prioritized to reduce their burden. Furthermore, if a customer is excited, analysis in areas of specific interest can be prioritized to enhance relevant suggestions. This enables effective analysis based on customer emotions.

[0096] The proposal department can estimate the customer's emotions and adjust the timing of proposals based on those estimates. For example, if the customer is relaxed, the proposal can be made proactively and detailed. If the customer is stressed, the proposal can be made more subtly and concisely. Furthermore, if the customer is excited, the proposal can be made visually appealing and offer engaging content. This enables the provision of optimal proposals tailored to the customer's emotions.

[0097] The communication unit can estimate the customer's emotions and prioritize the content of the communication based on those emotions. For example, if the customer is relaxed, detailed information can be prioritized to deepen their understanding. If the customer is stressed, only essential information can be prioritized to reduce their burden. Furthermore, if the customer is excited, information that piques their interest can be prioritized to increase their engagement. This enables effective information delivery based on the customer's emotions.

[0098] The proposal department can estimate the customer's emotions and adjust the content of the proposal based on those estimates. For example, if the customer is relaxed, it can provide a detailed proposal with content that will pique their interest. If the customer is stressed, it can provide a concise proposal to reduce their burden. Furthermore, if the customer is excited, it can provide a visually appealing proposal to increase their interest. This makes it possible to provide the optimal proposal tailored to the customer's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit collects customer behavior data. This data includes purchase history, time spent in the store, and travel routes. The collection unit records customer behavior using surveillance cameras, collects purchase history using payment system data, and tracks travel routes using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis includes behavioral pattern analysis, purchase history analysis, and travel route analysis. The analysis unit analyzes customer behavioral patterns to identify tendencies to take specific actions at specific times of day, analyzes purchase history to identify products and services that customers prefer, and analyzes travel routes to identify areas that customers are interested in. Step 3: The proposal department makes suggestions based on the analysis results obtained by the analysis department. These suggestions include recommendations for menu items and services for the customer's next visit. The proposal department suggests menu items that the customer has previously ordered, proposes new menu items and services based on the customer's behavior patterns, and suggests services related to specific areas based on their travel route. Step 4: The communication department conveys the proposed content from the proposal department to the staff via terminals such as POS systems or earphones. This communication may include displaying the proposed content to staff via the POS system or conveying the content verbally via earphones. The communication department may also notify staff of the proposed content via mobile devices.

[0101] 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.

[0102] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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. 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.

[0119] 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.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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. 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.

[0135] 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.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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. 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.

[0152] 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.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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. A data collection unit that collects customer behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, A communication unit that conveys the content proposed by the proposal unit to the staff through at least one terminal, either a POS system or an earphone, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect customer behavior data through surveillance cameras or payment system data. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we analyze customer behavior patterns. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we will suggest recommended menu items and services for your next visit. The system according to feature 1.

5. The aforementioned transmission unit is The proposed content will be communicated to staff via a POS system or a terminal such as an earphone. The system according to feature 1.

6. The aforementioned collection unit is We estimate customer emotions and adjust the timing of behavioral data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past customer behavior data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the customer's current activities and areas of interest. The system according to feature 1.

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