System

Abstract

An object of a system according to an embodiment is to analyze a customer's preference or trend and propose a new menu or renew an existing menu.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit and determines the preference or trend of the customer. The proposal unit proposes a new menu or renews an existing menu on the basis of the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to accurately grasp customer preferences and trends and propose new menu items or revamp existing menu items.

[0005] The system according to the embodiment aims to analyze customer preferences and trends, and propose new menu items and revamp existing menu items. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit to determine customer preferences or trends. The proposal unit proposes new menu items or updates existing menu items based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer preferences and trends, and propose new menu items or revamp existing menu items. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu development support system according to an embodiment of the present invention collects customer data, analyzes it using a generation AI, and proposes new menu items or renews existing menu items. The menu development support system collects customer data, analyzes it using a generation AI, and determines customer preferences and trends to propose new menu items or renew existing menu items. For example, the menu development support system collects detailed data such as customer order history, ratings, and feedback. The menu development support system then analyzes the collected data using a generation AI to determine customer preferences and trends. For example, the generation AI determines whether a particular menu item is popular and what menu items are preferred each season. Based on the analysis results, the menu development support system then determines the timing for proposing new menu items or renewing existing menu items. For example, if the generation AI determines that a particular menu item is popular, it proposes a new menu item based on that menu item. Furthermore, if an existing menu item receives low ratings, it proposes a renewing of that menu item. This enables the menu development support system to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat and new customers. This enables the menu development support system to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat and new customers. For example, developing new menu items tailored to customer preferences can improve the satisfaction of repeat customers. Also, offering menu items that are in line with trends can help attract new customers. Furthermore, by entering the market in line with the growth of the restaurant industry due to the easing of COVID-19 restrictions, restaurants and restaurant operators can increase their market competitiveness through efficient menu development. For example, as the restaurant industry grows, quickly developing new menu items and updating existing menu items can help maintain competitiveness and respond quickly to customer needs.

[0029] A menu development support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data. The customer data includes, but is not limited to, order history, ratings, and feedback. For example, the collection unit acquires customer order history from a database. The collection unit can also collect customer ratings and feedback from surveys and review sites. The collection unit can also analyze customers' social media activities and collect related data. For example, the collection unit analyzes photos of meals shared by customers on social media and collects feedback. The analysis unit uses a generation AI to analyze the data collected by the collection unit and determine customer preferences and trends. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit determines customer preferences based on past order history and rating data. The analysis unit can also analyze time-series data and determine trends. The analysis unit can also use a generation AI to estimate customer sentiment and reflect this in the analysis results. For example, the analysis unit estimates customer emotions using facial expression analysis or voice analysis and reflects the estimation results in the analysis. The suggestion unit proposes new menu items or updates existing menu items based on the analysis results obtained by the analysis unit. The suggestions may be made using, for example, algorithmic suggestions or heuristic methods, but are not limited to these examples. For example, if the suggestion unit determines that a particular menu item is popular, it proposes a new menu item based on that menu item. Furthermore, if an existing menu item receives low ratings, the suggestion unit can also propose updates to that menu item. This enables the menu development support system according to the embodiment to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat customers and new customers. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may make suggestions using an AI model that inputs the analysis results obtained by the analysis unit and outputs suggestions for new menu items or updates to existing menu items.

[0030] The collection unit can collect at least one of the following data: customer order history, ratings, and feedback. The order history includes, but is not limited to, for example, the order date and time, order contents, and quantity. For example, the collection unit acquires the customer order history from a database. Ratings include, but are not limited to, for example, star ratings, comments, and feedback. For example, the collection unit collects customer ratings from surveys or review sites. Feedback includes, but is not limited to, for example, survey results and free comments. For example, the collection unit collects customer feedback from surveys or review sites. By collecting detailed customer data, more accurate analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may acquire the customer order history from a database, input it into the generation AI, and have the generation AI analyze the order history.

[0031] The analysis unit can analyze the collected data and determine customer preferences and trends. Determining preferences can be done, for example, using past order history and evaluation data, but is not limited to these examples. The analysis unit can determine customer preferences based on past order history, for example. Determining trends can be done, for example, using time-series data analysis and fashion analysis, but is not limited to these examples. The analysis unit can, for example, analyze time-series data to determine trends. The analysis unit can also analyze the collected data and determine customer preferences and trends using a generation AI. For example, the analysis unit can input past order history and evaluation data into the generation AI to determine customer preferences. The analysis unit can also input time-series data into the generation AI to determine trends. This enables the development of menus tailored to customer needs by determining customer preferences and trends. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI determine customer preferences and trends.

[0032] The suggestion unit can propose new menu items or revise existing menu items based on the analysis results. For example, algorithmic suggestions or heuristic methods can be used for the suggestions, but are not limited to these examples. For example, if the suggestion unit determines that a specific menu item is popular, it can propose a new menu item based on that menu item. Furthermore, if an existing menu item has a low rating, the suggestion unit can also suggest revamping the menu item. The suggestion unit can also use a generation AI to propose new menu items or revamp existing menu items based on the analysis results. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to propose new menu items or revamp existing menu items. This enables efficient menu development by proposing new menu items or revamping existing menu items based on the analysis results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to propose new menu items or revamp existing menu items.

[0033] If the suggestion unit determines that a specific menu item is popular, it can suggest a new menu item based on that menu item. Popularity can be determined, for example, using the number of orders, the high rating, etc., but is not limited to these examples. For example, if a specific menu item is ordered many times, the suggestion unit can suggest a new menu item based on that menu item. Furthermore, if a specific menu item is highly rated, the suggestion unit can also suggest a new menu item based on that menu item. The suggestion unit can also use a generation AI to determine that a specific menu item is popular and suggest a new menu item based on that menu item. For example, the suggestion unit can input order counts and rating data for a specific menu item into the generation AI and cause the generation AI to suggest a new menu item. This improves customer satisfaction by suggesting a new menu item based on a popular menu item. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input order counts and rating data for a specific menu item into the generation AI and cause the generation AI to suggest a new menu item.

[0034] The suggestion unit can suggest updating an existing menu item if it has a low rating. A low rating can be determined, for example, by a star rating below a certain level or a large amount of negative feedback, but is not limited to these examples. For example, the suggestion unit can suggest updating an existing menu item if it has a star rating below a certain level. The suggestion unit can also suggest updating an existing menu item if it has a large amount of negative feedback. The suggestion unit can also use a generation AI to suggest updating an existing menu item if it has a low rating. For example, the suggestion unit can input evaluation data for an existing menu item into the generation AI and cause the generation AI to update the menu. This improves the quality of the menu by updating a menu item with a low rating. Some or all of the above-described processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input evaluation data for an existing menu item into the generation AI and cause the generation AI to update the menu.

[0035] The collection unit can analyze the customer's past order history and select the optimal data collection method. Optimal data collection methods include, but are not limited to, questionnaires, sensor data, and behavior logs. For example, the collection unit can request detailed feedback on a specific menu item based on the customer's past frequent orders. The collection unit can also collect additional ratings and comments for menu items that the customer has previously rated highly. The collection unit can also specifically ask the customer what needs to be improved for menu items that the customer has previously rated poorly. In this way, the optimal data collection method can be selected by analyzing the customer's past order history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past order history into the generation AI and have the generation AI select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data based on the customer's current living situation and areas of interest. Examples of living situation include, but are not limited to, occupation, family structure, and lifestyle. For example, if the customer is health-conscious, the collection unit can prioritize collecting feedback on healthy menus. Furthermore, if the customer is interested in new dishes, the collection unit can also collect feedback on new menus. Furthermore, if the customer is allergic to a particular ingredient, the collection unit can exclude feedback on menus containing that ingredient. By filtering data based on the customer's living situation and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the customer's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the customer's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the customer prefers voice input, the collection unit can collect feedback via voice. Alternatively, if the customer prefers text input, the collection unit can collect feedback via text. Alternatively, if the customer provides feedback using images, the collection unit can collect data using image analysis. This improves the efficiency of data collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the customer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if a customer lives in a particular area, the collection unit can collect feedback on popular menu items in that area. Furthermore, if a customer is traveling, the collection unit can also collect feedback on meals eaten at the customer's travel destination. Furthermore, if a customer frequently visits a particular store, the collection unit can prioritize collecting feedback on that store. This allows highly relevant data to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0039] During data collection, the collection unit can analyze the customer's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can analyze photos of meals shared by customers on social media and collect feedback. The collection unit can also collect restaurant ratings left by customers on social media. The collection unit can also analyze customers' comments and reviews on social media and collect related data. This allows for efficient collection of related data by analyzing the customer's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the customer's social media data into the generation AI and cause the generation AI to collect related data.

[0040] When collecting data, the collection unit can customize the collection method by reflecting the customer's past feedback. Examples of feedback include, but are not limited to, questionnaire results and free comments. For example, if a customer has provided detailed feedback in the past, the collection unit can request similar detailed feedback. Furthermore, if a customer has provided brief feedback in the past, the collection unit can request brief feedback. Furthermore, if a customer has provided feedback on a specific menu item in the past, the collection unit can request additional feedback on that menu item. This allows the collection method to be optimized by reflecting the customer's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of importance include, but are not limited to, the frequency and impact of the data. For example, the analysis unit can perform a detailed analysis of important data and provide specific results. The analysis unit can also perform a concise analysis of less important data and provide an overview. The analysis unit can also determine analysis priorities and perform analysis efficiently based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Examples of categories include, but are not limited to, food categories and service categories. For example, the analysis unit can apply a sentiment analysis algorithm to customer evaluation data. The analysis unit can also apply a trend analysis algorithm to customer order history data. The analysis unit can also apply a text mining algorithm to customer feedback data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. Past analysis results include, but are not limited to, using a database or referencing historical data. For example, the analysis unit corrects the current analysis result based on the customer's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the customer's past analysis results. The analysis unit can also compare the customer's past analysis results and analyze trend changes. This allows the accuracy of the analysis to be improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also adjust the analysis schedule depending on the time of data submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Relevance includes, but is not limited to, for example, co-occurrence and correlation of data. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also postpone analysis of less related data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. Examples of technical terminology include, but are not limited to, questionnaire results, past learning history, etc. For example, if the customer has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results using simple, easy-to-understand language. The analysis unit can also adjust the way the analysis results are presented according to the customer's level of expertise. By adjusting the use of technical terminology in the analysis according to the customer's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the customer's level of expertise into the generation AI and have the generation AI use technical terminology.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the menu when making a suggestion. Examples of importance include, but are not limited to, the popularity and sales of the menu. For example, the suggestion unit can make detailed suggestions for important menu items. The suggestion unit can also make concise suggestions for less important menu items. The suggestion unit can also determine the priority of suggestions based on the importance of the menu items and make suggestions efficiently. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the menu items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the menu items to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the menu. Examples of categories include, but are not limited to, food categories and service categories. For example, the suggestion unit makes suggestions regarding sweetness and calories for dessert menu items. The suggestion unit can also make suggestions regarding nutritional balance and volume for main dishes. The suggestion unit can also make suggestions regarding alcohol content and calories for drink menu items. This allows for more accurate suggestions by applying different suggestion algorithms depending on the menu category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the menu category into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0049] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results for the customer. Past proposal results include, but are not limited to, using a database or referencing historical data. For example, the proposal unit corrects the current proposal based on the past proposal results for the customer. The proposal unit can also optimize the proposal algorithm by referring to the past proposal results for the customer. The proposal unit can also compare the past proposal results for the customer and make proposals taking into account changes in trends. In this way, the accuracy of the proposal can be improved by referring to the past proposal results for the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the past proposal results for the customer into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the menu. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. For example, the suggestion unit can prioritize the proposal for the most recent menu. The suggestion unit can also postpone the proposal for older menus. The suggestion unit can also adjust the proposal schedule depending on the time of submission of the menu. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the menu. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission of the menu to the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the menus when making suggestions. Relevance includes, but is not limited to, for example, co-occurrence and correlation between the menus. For example, the suggestion unit can prioritize suggestions for highly relevant menus. The suggestion unit can also postpone suggestions for less relevant menus. The suggestion unit can also optimize the order of suggestions based on the relevance of the menus. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the menus. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the relevance of the menus to a generation AI and cause the generation AI to adjust the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the customer's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past learning history. For example, if the customer has technical expertise, the suggestion unit can make the proposal using a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the suggestion unit can make the proposal using simple, easy-to-understand language. The suggestion unit can also adjust the way the proposal is expressed depending on the customer's level of expertise. By adjusting the use of technical terminology in the proposal depending on the customer's level of expertise, the proposal can be made more understandable. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's level of expertise into the generation AI and cause the generation AI to use technical terminology.

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

[0054] The analytics unit can analyze customers' past order history and determine menu trends related to specific seasons or events. For example, since cold drinks and light meals tend to be preferred in the summer, new menu items can be suggested based on this. It can also suggest menu items tailored to specific events such as Christmas or Valentine's Day. Furthermore, the analytics unit can predict seasonal trends based on past data and prepare menu items in advance. This makes it possible to develop menus tailored to the seasons and events.

[0055] The collection unit can analyze customers' social media activities and collect data based on the photos and comments of meals shared by customers. For example, it can analyze photos of meals posted by customers on Instagram and collect feedback on the menu. It can also collect restaurant ratings posted by customers on Twitter (registered trademark). It can also analyze customers' comments and reviews on social media and collect related data. This allows for the collection of more diverse data through customers' social media activities.

[0056] The collection unit can analyze regional trends and collect relevant data by taking into account customers' geographical location information. For example, if popular menu items differ between urban and rural areas, data specific to each region can be collected. It can also collect feedback on limited menu items only available in specific regions. Furthermore, it can collect traveler data and analyze the popularity of menu items in tourist destinations. This allows for an understanding of regional trends and more accurate menu development.

[0057] The proposal unit can improve the accuracy of proposals by referring to the results of past proposals made to customers. For example, if a previously proposed menu item received high ratings, a new proposal can be made based on that menu item. Also, if a previously proposed menu item received low ratings, the proposal can incorporate improvements based on that feedback. Furthermore, by analyzing past proposal results and understanding customer preferences and trends, more accurate proposals can be made. This makes it possible to improve the accuracy of proposals by utilizing past data.

[0058] The analysis unit can adjust the level of detail of the analysis according to the customer's level of expertise. For example, if the customer has expertise, it can provide detailed analysis results. On the other hand, if the customer does not have expertise, it can provide concise and easy-to-understand analysis results. Furthermore, it can also adjust the way in which the analysis results are presented according to the customer's level of expertise. In this way, by adjusting the level of detail of the analysis according to the customer's level of expertise, it is possible to provide analysis results that are easier to understand.

[0059] The collection unit can select the optimal collection means depending on the customer's input method. For example, if the customer prefers voice input, the feedback can be collected by voice. If the customer prefers text input, the feedback can also be collected by text. Furthermore, if the customer provides feedback using an image, the data can also be collected using image analysis. This improves the efficiency of data collection by selecting the optimal collection means depending on the customer's input method.

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

[0061] Step 1: The collection unit collects customer data. Customer data includes order history, ratings, feedback, etc. The collection unit retrieves customer order history from a database and collects ratings and feedback from surveys and review sites. It can also analyze customers' social media activity and collect related data. For example, it can analyze photos of meals shared by customers on social media and collect feedback. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and determine customer preferences and trends. The analysis is carried out using statistical analysis and machine learning algorithms. For example, customer preferences are determined based on past order history and evaluation data, and trends are determined by analyzing time-series data. In addition, customer emotions are estimated using facial expression analysis and voice analysis and reflected in the analysis results. Step 3: The suggestion unit proposes new menu items or updates existing menu items based on the analysis results obtained by the analysis unit. Suggestions are made using algorithms or heuristic methods. For example, if it determines that a particular menu item is popular, it proposes a new menu item based on that menu item, and if an existing menu item has low ratings, it proposes updating that menu item. The suggestion unit can also make suggestions using an AI model that takes the analysis results as input and outputs suggestions for new menu items or updates to existing menu items.

[0062] (Example 2) A menu development support system according to an embodiment of the present invention collects customer data, analyzes it using a generation AI, and proposes new menu items or renews existing menu items. The menu development support system collects customer data, analyzes it using a generation AI, and determines customer preferences and trends to propose new menu items or renew existing menu items. For example, the menu development support system collects detailed data such as customer order history, ratings, and feedback. The menu development support system then analyzes the collected data using a generation AI to determine customer preferences and trends. For example, the generation AI determines whether a particular menu item is popular and what menu items are preferred each season. Based on the analysis results, the menu development support system then determines the timing for proposing new menu items or renewing existing menu items. For example, if the generation AI determines that a particular menu item is popular, it proposes a new menu item based on that menu item. Furthermore, if an existing menu item receives low ratings, it proposes a renewing of that menu item. This enables the menu development support system to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat and new customers. This enables the menu development support system to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat and new customers. For example, developing new menu items tailored to customer preferences can improve the satisfaction of repeat customers. Also, offering menu items that are in line with trends can help attract new customers. Furthermore, by entering the market in line with the growth of the restaurant industry due to the easing of COVID-19 restrictions, restaurants and restaurant operators can increase their market competitiveness through efficient menu development. For example, as the restaurant industry grows, quickly developing new menu items and updating existing menu items can help maintain competitiveness and respond quickly to customer needs.

[0063] A menu development support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects customer data. The customer data includes, but is not limited to, order history, ratings, and feedback. For example, the collection unit acquires customer order history from a database. The collection unit can also collect customer ratings and feedback from surveys and review sites. The collection unit can also analyze customers' social media activities and collect related data. For example, the collection unit analyzes photos of meals shared by customers on social media and collects feedback. The analysis unit uses a generation AI to analyze the data collected by the collection unit and determine customer preferences and trends. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit determines customer preferences based on past order history and rating data. The analysis unit can also analyze time-series data and determine trends. The analysis unit can also use a generation AI to estimate customer sentiment and reflect this in the analysis results. For example, the analysis unit estimates customer emotions using facial expression analysis or voice analysis and reflects the estimation results in the analysis. The suggestion unit proposes new menu items or updates existing menu items based on the analysis results obtained by the analysis unit. The suggestions may be made using, for example, algorithmic suggestions or heuristic methods, but are not limited to these examples. For example, if the suggestion unit determines that a particular menu item is popular, it proposes a new menu item based on that menu item. Furthermore, if an existing menu item receives low ratings, the suggestion unit can also propose updates to that menu item. This enables the menu development support system according to the embodiment to develop menu items tailored to customer needs, effectively contributing to the acquisition of repeat customers and new customers. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may make suggestions using an AI model that inputs the analysis results obtained by the analysis unit and outputs suggestions for new menu items or updates to existing menu items.

[0064] The collection unit can collect at least one of the following data: customer order history, ratings, and feedback. The order history includes, but is not limited to, for example, the order date and time, order contents, and quantity. For example, the collection unit acquires the customer order history from a database. Ratings include, but are not limited to, for example, star ratings, comments, and feedback. For example, the collection unit collects customer ratings from surveys or review sites. Feedback includes, but is not limited to, for example, survey results and free comments. For example, the collection unit collects customer feedback from surveys or review sites. By collecting detailed customer data, more accurate analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may acquire the customer order history from a database, input it into the generation AI, and have the generation AI analyze the order history.

[0065] The analysis unit can analyze the collected data and determine customer preferences and trends. Determining preferences can be done, for example, using past order history and evaluation data, but is not limited to these examples. The analysis unit can determine customer preferences based on past order history, for example. Determining trends can be done, for example, using time-series data analysis and fashion analysis, but is not limited to these examples. The analysis unit can, for example, analyze time-series data to determine trends. The analysis unit can also analyze the collected data and determine customer preferences and trends using a generation AI. For example, the analysis unit can input past order history and evaluation data into the generation AI to determine customer preferences. The analysis unit can also input time-series data into the generation AI to determine trends. This enables the development of menus tailored to customer needs by determining customer preferences and trends. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI determine customer preferences and trends.

[0066] The suggestion unit can propose new menu items or revise existing menu items based on the analysis results. For example, algorithmic suggestions or heuristic methods can be used for the suggestions, but are not limited to these examples. For example, if the suggestion unit determines that a specific menu item is popular, it can propose a new menu item based on that menu item. Furthermore, if an existing menu item has a low rating, the suggestion unit can also suggest revamping the menu item. The suggestion unit can also use a generation AI to propose new menu items or revamp existing menu items based on the analysis results. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to propose new menu items or revamp existing menu items. This enables efficient menu development by proposing new menu items or revamping existing menu items based on the analysis results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to propose new menu items or revamp existing menu items.

[0067] If the suggestion unit determines that a specific menu item is popular, it can suggest a new menu item based on that menu item. Popularity can be determined, for example, using the number of orders, the high rating, etc., but is not limited to these examples. For example, if a specific menu item is ordered many times, the suggestion unit can suggest a new menu item based on that menu item. Furthermore, if a specific menu item is highly rated, the suggestion unit can also suggest a new menu item based on that menu item. The suggestion unit can also use a generation AI to determine that a specific menu item is popular and suggest a new menu item based on that menu item. For example, the suggestion unit can input order counts and rating data for a specific menu item into the generation AI and cause the generation AI to suggest a new menu item. This improves customer satisfaction by suggesting a new menu item based on a popular menu item. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input order counts and rating data for a specific menu item into the generation AI and cause the generation AI to suggest a new menu item.

[0068] The suggestion unit can suggest updating an existing menu item if it has a low rating. A low rating can be determined, for example, by a star rating below a certain level or a large amount of negative feedback, but is not limited to these examples. For example, the suggestion unit can suggest updating an existing menu item if it has a star rating below a certain level. The suggestion unit can also suggest updating an existing menu item if it has a large amount of negative feedback. The suggestion unit can also use a generation AI to suggest updating an existing menu item if it has a low rating. For example, the suggestion unit can input evaluation data for an existing menu item into the generation AI and cause the generation AI to update the menu. This improves the quality of the menu by updating a menu item with a low rating. Some or all of the above-described processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input evaluation data for an existing menu item into the generation AI and cause the generation AI to update the menu.

[0069] The collection unit can estimate a customer's emotions and adjust the timing of data collection based on the estimated customer emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if a customer is satisfied, the collection unit collects data immediately after the meal to collect positive feedback. If a customer is dissatisfied, the collection unit can collect data after a cooling-off period to obtain more accurate feedback. If a customer has neutral emotions, the collection unit can collect data in real time while the customer is eating to obtain an immediate response. This allows for more accurate feedback by adjusting the timing of data collection according to the customer's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] The collection unit can analyze the customer's past order history and select the optimal data collection method. Optimal data collection methods include, but are not limited to, questionnaires, sensor data, and behavior logs. For example, the collection unit can request detailed feedback on a specific menu item based on the customer's past frequent orders. The collection unit can also collect additional ratings and comments for menu items that the customer has previously rated highly. The collection unit can also specifically ask the customer what needs to be improved for menu items that the customer has previously rated poorly. In this way, the optimal data collection method can be selected by analyzing the customer's past order history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past order history into the generation AI and have the generation AI select the optimal data collection method.

[0071] When collecting data, the collection unit can filter the data based on the customer's current living situation and areas of interest. Examples of living situation include, but are not limited to, occupation, family structure, and lifestyle. For example, if the customer is health-conscious, the collection unit can prioritize collecting feedback on healthy menus. Furthermore, if the customer is interested in new dishes, the collection unit can also collect feedback on new menus. Furthermore, if the customer is allergic to a particular ingredient, the collection unit can exclude feedback on menus containing that ingredient. By filtering data based on the customer's living situation and areas of interest, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the customer's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0072] When collecting data, the collection unit can select the optimal collection means depending on the customer's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the customer prefers voice input, the collection unit can collect feedback via voice. Alternatively, if the customer prefers text input, the collection unit can collect feedback via text. Alternatively, if the customer provides feedback using images, the collection unit can collect data using image analysis. This improves the efficiency of data collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0073] The collection unit can estimate a customer's emotions and prioritize data to be collected based on the estimated customer emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if a customer is satisfied, the collection unit can prioritize collecting positive feedback. Also, if a customer is dissatisfied, the collection unit can prioritize collecting negative feedback. Also, if a customer has neutral emotions, the collection unit can collect overall feedback in a balanced manner. This allows important data to be prioritized by prioritizing data based on the customer's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the customer's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if a customer lives in a particular area, the collection unit can collect feedback on popular menu items in that area. Furthermore, if a customer is traveling, the collection unit can also collect feedback on meals eaten at the customer's travel destination. Furthermore, if a customer frequently visits a particular store, the collection unit can prioritize collecting feedback on that store. This allows highly relevant data to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0075] During data collection, the collection unit can analyze the customer's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit can analyze photos of meals shared by customers on social media and collect feedback. The collection unit can also collect restaurant ratings left by customers on social media. The collection unit can also analyze customers' comments and reviews on social media and collect related data. This allows for efficient collection of related data by analyzing the customer's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the customer's social media data into the generation AI and cause the generation AI to collect related data.

[0076] When collecting data, the collection unit can customize the collection method by reflecting the customer's past feedback. Examples of feedback include, but are not limited to, questionnaire results and free comments. For example, if a customer has provided detailed feedback in the past, the collection unit can request similar detailed feedback. Furthermore, if a customer has provided brief feedback in the past, the collection unit can request brief feedback. Furthermore, if a customer has provided feedback on a specific menu item in the past, the collection unit can request additional feedback on that menu item. This allows the collection method to be optimized by reflecting the customer's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI customize the collection method.

[0077] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the customer is satisfied, the analysis unit can display the analysis results by emphasizing positive feedback. Furthermore, if the customer is dissatisfied, the analysis unit can also analyze and display negative feedback in detail. Furthermore, if the customer has neutral emotions, the analysis unit can display the analysis results taking into account the overall balance. This allows for more appropriate analysis results by adjusting the way the analysis is presented based on the customer's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of importance include, but are not limited to, the frequency and impact of the data. For example, the analysis unit can perform a detailed analysis of important data and provide specific results. The analysis unit can also perform a concise analysis of less important data and provide an overview. The analysis unit can also determine analysis priorities and perform analysis efficiently based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Examples of categories include, but are not limited to, food categories and service categories. For example, the analysis unit can apply a sentiment analysis algorithm to customer evaluation data. The analysis unit can also apply a trend analysis algorithm to customer order history data. The analysis unit can also apply a text mining algorithm to customer feedback data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply different analysis algorithms.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. Past analysis results include, but are not limited to, using a database or referencing historical data. For example, the analysis unit corrects the current analysis result based on the customer's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the customer's past analysis results. The analysis unit can also compare the customer's past analysis results and analyze trend changes. This allows the accuracy of the analysis to be improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0081] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. Emotion estimation can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, the analysis unit can provide a concise analysis result if the customer is satisfied. For example, the analysis unit can provide a detailed analysis result if the customer is dissatisfied. For example, the analysis unit can provide a balanced analysis result if the customer has neutral emotions. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the customer's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also adjust the analysis schedule depending on the time of data submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Relevance includes, but is not limited to, for example, co-occurrence and correlation of data. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also postpone analysis of less related data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. Examples of technical terminology include, but are not limited to, questionnaire results, past learning history, etc. For example, if the customer has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results using simple, easy-to-understand language. The analysis unit can also adjust the way the analysis results are presented according to the customer's level of expertise. By adjusting the use of technical terminology in the analysis according to the customer's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the customer's level of expertise into the generation AI and have the generation AI use technical terminology.

[0085] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. Estimating emotions can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the customer is satisfied, the suggestion unit can suggest a new menu item using positive language. If the customer is dissatisfied, the suggestion unit can also suggest a new menu item by emphasizing areas for improvement. If the customer has neutral emotions, the suggestion unit can also suggest a new menu item using balanced language. This allows for more appropriate suggestions by adjusting the way the suggestion is presented based on the customer's emotions. Estimating emotions can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0086] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the menu when making a suggestion. Examples of importance include, but are not limited to, the popularity and sales of the menu. For example, the suggestion unit can make detailed suggestions for important menu items. The suggestion unit can also make concise suggestions for less important menu items. The suggestion unit can also determine the priority of suggestions based on the importance of the menu items and make suggestions efficiently. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the menu items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the importance of the menu items to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0087] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the menu. Examples of categories include, but are not limited to, food categories and service categories. For example, the suggestion unit makes suggestions regarding sweetness and calories for dessert menu items. The suggestion unit can also make suggestions regarding nutritional balance and volume for main dishes. The suggestion unit can also make suggestions regarding alcohol content and calories for drink menu items. This allows for more accurate suggestions by applying different suggestion algorithms depending on the menu category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the menu category into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0088] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results for the customer. Past proposal results include, but are not limited to, using a database or referencing historical data. For example, the proposal unit corrects the current proposal based on the past proposal results for the customer. The proposal unit can also optimize the proposal algorithm by referring to the past proposal results for the customer. The proposal unit can also compare the past proposal results for the customer and make proposals taking into account changes in trends. In this way, the accuracy of the proposal can be improved by referring to the past proposal results for the customer. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the past proposal results for the customer into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0089] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. Estimating emotions can be performed using, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, the suggestion unit can provide a concise suggestion if the customer is satisfied. For example, the suggestion unit can provide a detailed suggestion if the customer is dissatisfied. For example, the suggestion unit can provide a balanced suggestion if the customer has neutral emotions. This allows for adjusting the length of the suggestion based on the customer's emotions, resulting in a more appropriate suggestion. Estimating emotions can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0090] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the menu. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. For example, the suggestion unit can prioritize the proposal for the most recent menu. The suggestion unit can also postpone the proposal for older menus. The suggestion unit can also adjust the proposal schedule depending on the time of submission of the menu. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the menu. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time of submission of the menu to the generation AI and cause the generation AI to determine the priority of the proposals.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the menus when making suggestions. Relevance includes, but is not limited to, for example, co-occurrence and correlation between the menus. For example, the suggestion unit can prioritize suggestions for highly relevant menus. The suggestion unit can also postpone suggestions for less relevant menus. The suggestion unit can also optimize the order of suggestions based on the relevance of the menus. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the menus. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the relevance of the menus to a generation AI and cause the generation AI to adjust the order of suggestions.

[0092] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the customer's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past learning history. For example, if the customer has technical expertise, the suggestion unit can make the proposal using a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the suggestion unit can make the proposal using simple, easy-to-understand language. The suggestion unit can also adjust the way the proposal is expressed depending on the customer's level of expertise. By adjusting the use of technical terminology in the proposal depending on the customer's level of expertise, the proposal can be made more understandable. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's level of expertise into the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine customer preferences and trends. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests new menu items or updates existing menu items based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine customer preferences and trends. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests new menu items or updates existing menu items based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine customer preferences and trends. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests new menu items or updates existing menu items based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine customer preferences and trends. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes new menu items or updates existing menu items based on the analysis results.

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

[0094] The suggestion unit can estimate the customer's emotions and adjust the timing of suggestions based on the estimated customer emotions. For example, if the customer is satisfied, a new menu item can be suggested immediately after the meal. If the customer is dissatisfied, a cooling-off period can be given before a suggestion is made. Furthermore, if the customer has neutral emotions, a suggestion can be made in real time while the customer is eating. This allows for more effective suggestions to be made by adjusting the timing of suggestions according to the customer's emotions.

[0095] The analytics unit can analyze customers' past order history and determine menu trends related to specific seasons or events. For example, since cold drinks and light meals tend to be preferred in the summer, new menu items can be suggested based on this. It can also suggest menu items tailored to specific events such as Christmas or Valentine's Day. Furthermore, the analytics unit can predict seasonal trends based on past data and prepare menu items in advance. This makes it possible to develop menus tailored to the seasons and events.

[0096] The collection unit can analyze customers' social media activities and collect data based on the photos and comments of meals shared by customers. For example, it can analyze photos of meals posted by customers on Instagram and collect feedback on the menu. It can also collect restaurant ratings posted by customers on Twitter. It can also analyze customers' comments and reviews on social media and collect related data. This allows it to collect a wider variety of data through customers' social media activities.

[0097] The suggestion unit can estimate the customer's emotions and customize the content of the suggestion based on the estimated customer emotions. For example, if the customer is satisfied, it can suggest a new menu item using positive language. If the customer is dissatisfied, it can suggest a new menu item by emphasizing areas for improvement. Furthermore, if the customer has neutral emotions, it can suggest a new menu item using balanced language. This allows the system to make more appropriate suggestions by customizing the content of the suggestion based on the customer's emotions.

[0098] The analysis unit can estimate the customer's emotions and determine the priorities of analysis based on the estimated customer emotions. For example, if the customer is satisfied, it can prioritize analysis of positive feedback. Also, if the customer is dissatisfied, it can prioritize analysis of negative feedback. Furthermore, if the customer has neutral emotions, it can analyze the overall feedback in a balanced manner. In this way, by determining the priorities of analysis based on the customer's emotions, it is possible to prioritize analysis of important data.

[0099] The collection unit can analyze regional trends and collect relevant data by taking into account customers' geographical location information. For example, if popular menu items differ between urban and rural areas, data specific to each region can be collected. It can also collect feedback on limited menu items only available in specific regions. Furthermore, it can collect traveler data and analyze the popularity of menu items in tourist destinations. This allows for an understanding of regional trends and more accurate menu development.

[0100] The proposal unit can improve the accuracy of proposals by referring to the results of past proposals made to customers. For example, if a previously proposed menu item received high ratings, a new proposal can be made based on that menu item. Also, if a previously proposed menu item received low ratings, the proposal can incorporate improvements based on that feedback. Furthermore, by analyzing past proposal results and understanding customer preferences and trends, more accurate proposals can be made. This makes it possible to improve the accuracy of proposals by utilizing past data.

[0101] The analysis unit can adjust the level of detail of the analysis according to the customer's level of expertise. For example, if the customer has expertise, it can provide detailed analysis results. On the other hand, if the customer does not have expertise, it can provide concise and easy-to-understand analysis results. Furthermore, it can also adjust the way in which the analysis results are presented according to the customer's level of expertise. In this way, by adjusting the level of detail of the analysis according to the customer's level of expertise, it is possible to provide analysis results that are easier to understand.

[0102] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. For example, if the customer is satisfied, a concise suggestion can be made. If the customer is dissatisfied, a detailed suggestion can be made. Furthermore, if the customer has neutral emotions, a balanced suggestion can be made. As a result, by adjusting the length of the suggestion based on the customer's emotions, more appropriate suggestions can be made.

[0103] The collection unit can select the optimal collection means depending on the customer's input method. For example, if the customer prefers voice input, the feedback can be collected by voice. If the customer prefers text input, the feedback can also be collected by text. Furthermore, if the customer provides feedback using an image, the data can also be collected using image analysis. This improves the efficiency of data collection by selecting the optimal collection means depending on the customer's input method.

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

[0105] Step 1: The collection unit collects customer data. Customer data includes order history, ratings, feedback, etc. The collection unit retrieves customer order history from a database and collects ratings and feedback from surveys and review sites. It can also analyze customers' social media activity and collect related data. For example, it can analyze photos of meals shared by customers on social media and collect feedback. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and determine customer preferences and trends. The analysis is carried out using statistical analysis and machine learning algorithms. For example, customer preferences are determined based on past order history and evaluation data, and trends are determined by analyzing time-series data. In addition, customer emotions are estimated using facial expression analysis and voice analysis and reflected in the analysis results. Step 3: The suggestion unit proposes new menu items or updates existing menu items based on the analysis results obtained by the analysis unit. Suggestions are made using algorithms or heuristic methods. For example, if it determines that a particular menu item is popular, it proposes a new menu item based on that menu item, and if an existing menu item has low ratings, it proposes updating that menu item. The suggestion unit can also make suggestions using an AI model that takes the analysis results as input and outputs suggestions for new menu items or updates to existing menu items.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects customer data; an analysis unit that analyzes the data collected by the collection unit and determines customer preferences or trends; a proposal unit that proposes a new menu or updates an existing menu based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect at least one of the following data: customer order history, ratings, and feedback 2. The system of claim 1.

3. The analysis unit Analyze collected data to determine customer preferences and trends 2. The system of claim 1.

4. The proposal unit Propose new menu items or revamp existing menu items based on the analysis results 2. The system of claim 1.

5. The proposal unit If a particular menu item is determined to be popular, suggest new menu items based on that menu item.

2. The system of claim 1.

6. The proposal unit If the existing menu item has low ratings, propose a menu update.

2. The system of claim 1.

7. The collecting unit Estimate customer sentiment and adjust data collection timing based on estimated customer sentiment 2. The system of claim 1.

8. The collecting unit Analyze customer order history and choose the best data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filter it based on your customers' current life situations and interests.

2. The system of claim 1.

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