System

Abstract

An object of a system according to an embodiment is to analyze past data and feedback, analyze a trend, and propose an optimal event.SOLUTION: A system includes a data analysis part, a trend analysis part, a proposal part, an adjustment part, and a support part. A data-analysis component analyzes the historical AI and the feedback using the generated datum. The trend analysis unit analyzes a trend based on the data analyzed by the data analysis unit. The proposal unit proposes an event on the basis of the trend analyzed by the trend analysis unit. The adjustment unit performs adjustment and improvement in real time. The support unit provides flexible support through interaction with the customer.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 effectively utilize past data and feedback when planning an event, analyze trends, and make optimal proposals.

[0005] The system according to the embodiment aims to analyze past data and feedback, analyze trends, and propose optimal events. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a trend analysis unit, a proposal unit, an adjustment unit, and a support unit. The data analysis unit analyzes past data and feedback using a generative AI. The trend analysis unit analyzes trends based on the data analyzed by the data analysis unit. The proposal unit proposes events based on the trends analyzed by the trend analysis unit. The adjustment unit makes adjustments and improvements in real time. The support unit provides flexible support through dialogue with customers. [Effects of the Invention]

[0007] The system according to the embodiment can analyze past data and feedback, analyze trends, and propose optimal events. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The intelligent event planner according to an embodiment of the present invention is a system that utilizes generative AI to reduce the effort required for event planning and deliver high-quality events. This system analyzes past data and feedback to propose events that take into account customer requests, budgets, and participant interests. Furthermore, it makes real-time adjustments and improvements, providing flexible support through dialogue with customers. This allows the intelligent event planner to significantly reduce the effort required for event planning and deliver high-quality events.

[0029] An intelligent event planner according to an embodiment includes a data analysis unit, a trend analysis unit, a proposal unit, an adjustment unit, and a support unit. The data analysis unit analyzes past data and feedback using a generation AI. For example, it analyzes data such as the number of participants, satisfaction levels, and popular programs from past events. The generation AI analyzes data using a text generation AI (e.g., LLM) or a multimodal generation AI. The trend analysis unit analyzes trends based on the data analyzed by the data analysis unit. For example, it identifies the latest trends and participant preferences based on the data analyzed by the generation AI. The proposal unit makes event proposals based on the trends analyzed by the trend analysis unit. For example, it proposes an optimal event plan taking into account customer requests, budget, and participant interests. The adjustment unit makes adjustments and improvements in real time. For example, it collects participant reactions and feedback during the event and proposes program changes or additions. The support unit provides flexible support through dialogue with customers. For example, if a customer makes a new request during the event, the proposal unit adjusts the plan accordingly. This allows intelligent event planners to use generative AI to reduce the effort of event planning and deliver high-quality events.

[0030] The trend analysis unit can identify the success factors of an event based on the data analyzed by the generation AI and propose specific measures to strengthen those factors. For example, the trend analysis unit uses the generation AI to analyze past event data and identify success factors. For example, it extracts success factors based on data such as the number of participants, satisfaction, and repeat rate. The generation AI then proposes specific measures to strengthen the success factors. For example, if the success factor is "participant interaction," the trend analysis unit proposes increasing the number of interactive programs. The trend analysis unit also concretizes the event plan based on the measures proposed by the generation AI. For example, if the success factor is "music selection," the trend analysis unit proposes a playlist to select music that suits the participants' preferences. In this way, the success factors of an event can be identified and strengthened, thereby improving the event's success rate.

[0031] The proposal department can use the generative AI to analyze event data from different industries and apply success stories from those industries to event planning. For example, the proposal department uses the generative AI to collect and analyze event data from different industries. For example, it analyzes data from sporting events and music festivals and extracts success stories. The proposal department also applies success stories from other industries to event planning. For example, it could incorporate interactive elements from sporting events into music events. The proposal department could also propose new event plans based on success stories from other industries analyzed by the generative AI. For example, it could plan a hybrid event that combines success factors from different industries. This allows new ideas to be incorporated by applying success stories from other industries to event planning.

[0032] The coordination unit can predict the progress of the event based on data collected by the generation AI in real time and make suggestions to avoid problems in advance. The coordination unit, for example, uses the generation AI to analyze data collected in real time and predict the progress of the event. For example, it predicts the possibility of problems occurring based on the movements of participants and the progress of the program. The coordination unit also makes suggestions to avoid problems predicted by the generation AI. For example, it proposes deploying additional staff in areas where congestion is expected. The coordination unit also predicts the progress of the event based on data collected by the generation AI in real time and proposes specific measures to avoid problems in advance. For example, it proposes changing the order of the program to avoid congestion. In this way, by predicting the progress of the event and avoiding problems in advance, it is possible to run the event smoothly.

[0033] The support department can use the generation AI to analyze the interaction history with the customer and provide customized support that reflects the customer's requests and feedback. For example, the support department uses the generation AI to collect and analyze the interaction history with the customer. For example, it analyzes past email and chat history to extract the customer's requests and feedback. The support department also uses the generation AI to provide customized support that reflects the customer's requests and feedback. For example, it makes specific proposals to solve problems that the customer has pointed out in the past. The support department also provides customized support based on the customer's requests and feedback analyzed by the generation AI. For example, it proposes programs and services that the customer desires. In this way, the support department can provide customized support that reflects the customer's requests and feedback, thereby improving customer satisfaction.

[0034] The data analysis unit uses generative AI to analyze event data from different industries and apply success stories from those industries to event planning. For example, the data analysis unit uses generative AI to collect and analyze event data from different industries. For example, it analyzes data from sporting events and music festivals and extracts success stories. The data analysis unit also applies success stories from other industries to event planning. For example, it could incorporate interactive elements from sporting events into music events. The data analysis unit also proposes new event plans based on success stories from other industries analyzed by generative AI. For example, it could plan a hybrid event that combines success factors from different industries. This allows new ideas to be incorporated by applying success stories from other industries to event planning.

[0035] The trend analysis unit can analyze the impact of external factors such as season and weather on the success of an event based on the data analyzed by the generation AI, and suggest the optimal time to hold the event. For example, the trend analysis unit uses the generation AI to analyze past event data and seasonal and weather data to analyze the impact of external factors on the success of the event. For example, it analyzes the impact of weather on the number of event participants. The trend analysis unit also takes into account external factors such as season and weather to suggest the optimal time to hold the event. For example, it selects a season with stable weather or a time with many participants. The trend analysis unit also adjusts the event schedule based on the optimal time proposed by the generation AI. For example, it plans an outdoor event during a time when the weather is stable. In this way, the success rate of the event is improved by suggesting the optimal time to hold the event while taking into account external factors such as season and weather.

[0036] The coordination unit can use the generation AI to analyze participants' behavioral patterns based on data collected in real time and instantly optimize the program. The coordination unit, for example, uses the generation AI to analyze data collected in real time to understand participants' behavioral patterns. For example, it analyzes participants' movement routes and length of stay. The coordination unit also optimizes the program based on participants' behavioral patterns. For example, it extends the duration of popular programs or changes the order of programs to avoid congestion. The coordination unit also instantly proposes program optimization based on data collected in real time by the generation AI. For example, it adds new activities based on participants' interests. In this way, analyzing participants' behavioral patterns and instantly optimizing the program ensures the event progresses smoothly.

[0037] The support department can use the generation AI to introduce success stories of other customers based on the dialogue history with the customer and make suggestions for reference. For example, the support department uses the generation AI to analyze the dialogue history with the customer and extract success stories of other customers. For example, it identifies programs and services that have been successful in past events. The support department also makes suggestions to the customer for reference based on the success stories of other customers. For example, it introduces success stories of customers with similar requests and makes suggestions to incorporate elements of those cases. The support department also makes specific suggestions based on the success stories of other customers analyzed by the generation AI. For example, it makes suggestions to incorporate the programs and services of those success stories into a new event. In this way, by introducing success stories of other customers and making suggestions for reference, the quality of the customer's event planning is improved.

[0038] The support department can propose multiple countermeasures when new requests arise during the progress of an event based on the dialogue with the customer using the generation AI. For example, the support department uses the generation AI to analyze the dialogue history with the customer and propose multiple countermeasures when new requests arise during the progress of an event. For example, if a customer wants to add a new program, the support department proposes multiple options. The support department also proposes specific countermeasures for new requests based on the data analyzed by the generation AI. For example, the support department evaluates the feasibility and cost of the program desired by the customer and proposes the optimal option. The support department also uses the generation AI to propose multiple countermeasures for new requests based on the dialogue with the customer, allowing the customer to choose. For example, the support department presents multiple program or service options and allows the customer to select the most appropriate one. This enables flexible responses by proposing multiple countermeasures when new requests arise during the progress of an event.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The intelligent event planner can also be equipped with a health management unit that monitors the health status of participants. For example, if a participant becomes ill during an event, the health management unit can detect the abnormality in real time and issue an alert for prompt action. The health management unit can also collect participants' health data and adjust the event program. For example, it can suggest avoiding activities that require excessive exercise. Furthermore, the health management unit can suggest appropriate break times and timing for hydration based on participants' health status. This allows the event to proceed smoothly while protecting the health of participants.

[0041] The intelligent event planner can further estimate participants' interests and suggest activities during the event based on the estimated interests. For example, if a participant is interested in sports, sports-related activities can be suggested. Also, if a participant is interested in music, music-related activities can be suggested. Furthermore, the intelligent event planner can optimize the placement of booths and exhibits during the event based on participants' interests. For example, if a participant is interested in art, art-related booths can be placed in prominent locations. This allows the planner to provide activities and exhibits that match participants' interests and improve event satisfaction.

[0042] The intelligent event planner can also analyze participant movement patterns and suggest routes to avoid congestion. For example, if participants are congested in a specific area, it can restrict access to that area and guide them to other areas. It can also deploy additional guide staff in areas where congestion is expected based on participant movement patterns. It can also analyze participant movement patterns and suggest specific routes to avoid congestion. For example, it can suggest routes that avoid areas that are expected to be congested at certain times of the day. This allows participants to move more smoothly, avoiding congestion and ensuring the smooth progress of the event.

[0043] The intelligent event planner can further include a feedback collection unit that can collect participant feedback in real time and reflect it immediately as the event progresses. For example, if participants are dissatisfied with a particular program, that feedback can be collected immediately and the program content can be adjusted. New activities can also be added as the event progresses based on participant feedback. Furthermore, the feedback collection unit can make specific suggestions to optimize the progress of the event based on participant feedback. For example, it can make suggestions to add programs that participants desire. This allows participant feedback to be reflected in real time and improves event satisfaction.

[0044] The intelligent event planner can further estimate the interests of participants and optimize the placement of booths and exhibits during the event based on the estimated interests. For example, if participants are interested in technology, technology-related booths can be placed in prominent locations. Also, if participants are interested in art, art-related exhibits can be placed in prominent locations. Furthermore, specific suggestions can be made to optimize the placement of booths and exhibits based on participants' interests. For example, a suggestion can be made to place multiple booths that participants are interested in close together. This allows for the placement of booths and exhibits according to participants' interests, thereby improving event satisfaction.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The data analysis department uses generation AI to analyze past data and feedback. For example, it analyzes data such as the number of participants and satisfaction levels of past events, and popular programs. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 2: The trend analysis unit analyzes trends based on the data analyzed by the data analysis unit. For example, it identifies the latest trends and participant preferences based on the data analyzed by the generation AI. Step 3: The proposal department makes event proposals based on the trends analyzed by the trend analysis department. For example, they propose optimal event plans taking into account the customer's requests, budget, and participants' interests. Step 4: The Coordination Department makes real-time adjustments and improvements, for example, by collecting participant reactions and feedback during the event and proposing changes or additions to the program. Step 5: The support department provides flexible support through dialogue with the customer. For example, if the customer makes new requests during the event, the plan is adjusted accordingly.

[0047] (Example 2) The intelligent event planner according to an embodiment of the present invention is a system that utilizes generative AI to reduce the effort required for event planning and deliver high-quality events. This system analyzes past data and feedback to propose events that take into account customer requests, budgets, and participant interests. Furthermore, it makes real-time adjustments and improvements, providing flexible support through dialogue with customers. This allows the intelligent event planner to significantly reduce the effort required for event planning and deliver high-quality events.

[0048] An intelligent event planner according to an embodiment includes a data analysis unit, a trend analysis unit, a proposal unit, an adjustment unit, and a support unit. The data analysis unit analyzes past data and feedback using a generation AI. For example, it analyzes data such as the number of participants, satisfaction levels, and popular programs from past events. The generation AI analyzes data using a text generation AI (e.g., LLM) or a multimodal generation AI. The trend analysis unit analyzes trends based on the data analyzed by the data analysis unit. For example, it identifies the latest trends and participant preferences based on the data analyzed by the generation AI. The proposal unit makes event proposals based on the trends analyzed by the trend analysis unit. For example, it proposes an optimal event plan taking into account customer requests, budget, and participant interests. The adjustment unit makes adjustments and improvements in real time. For example, it collects participant reactions and feedback during the event and proposes program changes or additions. The support unit provides flexible support through dialogue with customers. For example, if a customer makes a new request during the event, the proposal unit adjusts the plan accordingly. This allows intelligent event planners to use generative AI to reduce the effort of event planning and deliver high-quality events.

[0049] The data analysis unit can analyze changes in participants' emotions from past event data and identify programs that were performed when emotions peaked. For example, the data analysis unit collects past event data and analyzes changes in participants' emotions. For example, it identifies times when emotions peaked based on participants' social media posts and survey results, and extracts programs that were performed at those times. The data analysis unit also uses generative AI to analyze participants' facial expressions and voice data during events to understand changes in emotions over time. For example, it collects emotional data in real time using cameras and microphones and identifies times when emotions peaked. Furthermore, based on the results of the emotion analysis, the data analysis unit identifies programs that elicited the most positive emotions at past events and incorporates those elements into new event planning. For example, if a specific activity or performance produced an emotional peak, it can strengthen that element. This improves participant satisfaction by identifying programs that were performed when emotions peaked.

[0050] The trend analysis unit can identify the success factors of an event based on the data analyzed by the generation AI and propose specific measures to strengthen those factors. For example, the trend analysis unit uses the generation AI to analyze past event data and identify success factors. For example, it extracts success factors based on data such as the number of participants, satisfaction, and repeat rate. The generation AI then proposes specific measures to strengthen the success factors. For example, if the success factor is "participant interaction," the trend analysis unit proposes increasing the number of interactive programs. The trend analysis unit also concretizes the event plan based on the measures proposed by the generation AI. For example, if the success factor is "music selection," the trend analysis unit proposes a playlist to select music that suits the participants' preferences. In this way, the success factors of an event can be identified and strengthened, thereby improving the event's success rate.

[0051] The proposal department can use the generative AI to analyze event data from different industries and apply success stories from those industries to event planning. For example, the proposal department uses the generative AI to collect and analyze event data from different industries. For example, it analyzes data from sporting events and music festivals and extracts success stories. The proposal department also applies success stories from other industries to event planning. For example, it could incorporate interactive elements from sporting events into music events. The proposal department could also propose new event plans based on success stories from other industries analyzed by the generative AI. For example, it could plan a hybrid event that combines success factors from different industries. This allows new ideas to be incorporated by applying success stories from other industries to event planning.

[0052] The coordination unit can predict the progress of the event based on data collected by the generation AI in real time and make suggestions to avoid problems in advance. The coordination unit, for example, uses the generation AI to analyze data collected in real time and predict the progress of the event. For example, it predicts the possibility of problems occurring based on the movements of participants and the progress of the program. The coordination unit also makes suggestions to avoid problems predicted by the generation AI. For example, it proposes deploying additional staff in areas where congestion is expected. The coordination unit also predicts the progress of the event based on data collected by the generation AI in real time and proposes specific measures to avoid problems in advance. For example, it proposes changing the order of the program to avoid congestion. In this way, by predicting the progress of the event and avoiding problems in advance, it is possible to run the event smoothly.

[0053] The support department can use the generation AI to analyze the interaction history with the customer and provide customized support that reflects the customer's requests and feedback. For example, the support department uses the generation AI to collect and analyze the interaction history with the customer. For example, it analyzes past email and chat history to extract the customer's requests and feedback. The support department also uses the generation AI to provide customized support that reflects the customer's requests and feedback. For example, it makes specific proposals to solve problems that the customer has pointed out in the past. The support department also provides customized support based on the customer's requests and feedback analyzed by the generation AI. For example, it proposes programs and services that the customer desires. In this way, the support department can provide customized support that reflects the customer's requests and feedback, thereby improving customer satisfaction.

[0054] The support department can use the emotion estimation function to analyze emotions during a conversation with a customer and suggest communication methods to elicit positive emotions. The support department, for example, uses the emotion estimation function to analyze emotions during a conversation with a customer. For example, it analyzes the customer's facial expressions and voice to understand changes in emotions. The support department also suggests communication methods to elicit positive emotions based on the emotion data analyzed by the generative AI. For example, it suggests words and attitudes that make the customer feel happy and at ease. The support department also uses the emotion estimation function to analyze emotions during a conversation with a customer and suggest specific communication methods to elicit positive emotions. For example, if the customer is feeling anxious, it suggests responses that will give the customer a sense of security. In this way, customer satisfaction is improved by eliciting positive emotions during conversations with customers.

[0055] The data analysis unit uses generative AI to analyze event data from different industries and apply success stories from those industries to event planning. For example, the data analysis unit uses generative AI to collect and analyze event data from different industries. For example, it analyzes data from sporting events and music festivals and extracts success stories. The data analysis unit also applies success stories from other industries to event planning. For example, it could incorporate interactive elements from sporting events into music events. The data analysis unit also proposes new event plans based on success stories from other industries analyzed by generative AI. For example, it could plan a hybrid event that combines success factors from different industries. This allows new ideas to be incorporated by applying success stories from other industries to event planning.

[0056] The trend analysis unit can analyze the impact of external factors such as season and weather on the success of an event based on the data analyzed by the generation AI, and suggest the optimal time to hold the event. For example, the trend analysis unit uses the generation AI to analyze past event data and seasonal and weather data to analyze the impact of external factors on the success of the event. For example, it analyzes the impact of weather on the number of event participants. The trend analysis unit also takes into account external factors such as season and weather to suggest the optimal time to hold the event. For example, it selects a season with stable weather or a time with many participants. The trend analysis unit also adjusts the event schedule based on the optimal time proposed by the generation AI. For example, it plans an outdoor event during a time when the weather is stable. In this way, the success rate of the event is improved by suggesting the optimal time to hold the event while taking into account external factors such as season and weather.

[0057] The proposal unit can use the emotion estimation function to analyze event data from different cultural spheres and propose event plans that are appropriate for the cultural background. The proposal unit, for example, uses the emotion estimation function to analyze event data from different cultural spheres. For example, it identifies success factors appropriate for the cultural background based on participant emotion data. The proposal unit also proposes new event plans based on the success factors appropriate for the cultural background analyzed by the generation AI. For example, it incorporates activities and performances that are popular in a particular cultural sphere. The proposal unit also uses the emotion estimation function to analyze event data from different cultural spheres and concretizes event plans that are appropriate for the cultural background. For example, it incorporates music and food that are rooted in a particular culture. In this way, by proposing event plans that are appropriate for the cultural background, participant satisfaction is improved.

[0058] The coordination unit can use the generation AI to analyze participants' behavioral patterns based on data collected in real time and instantly optimize the program. The coordination unit, for example, uses the generation AI to analyze data collected in real time to understand participants' behavioral patterns. For example, it analyzes participants' movement routes and length of stay. The coordination unit also optimizes the program based on participants' behavioral patterns. For example, it extends the duration of popular programs or changes the order of programs to avoid congestion. The coordination unit also instantly proposes program optimization based on data collected in real time by the generation AI. For example, it adds new activities based on participants' interests. In this way, analyzing participants' behavioral patterns and instantly optimizing the program ensures the event progresses smoothly.

[0059] The adjustment unit can use the emotion estimation function to analyze participants' emotions in real time and propose immediate responses to alleviate negative emotions. The adjustment unit, for example, uses the emotion estimation function to analyze participants' emotions in real time. For example, it may use a camera or microphone to analyze participants' facial expressions and voices to detect negative emotions. The adjustment unit also proposes immediate responses to alleviate negative emotions based on the emotion data analyzed by the generative AI. For example, if a participant is feeling dissatisfied, it may provide additional activities or services. The adjustment unit also uses the emotion estimation function to analyze participants' emotions in real time and propose specific measures to alleviate negative emotions. For example, if a participant is bored, it may add interactive elements. In this way, by analyzing participants' emotions in real time and proposing immediate responses to alleviate negative emotions, participant satisfaction is improved.

[0060] The support department can use the generation AI to introduce success stories of other customers based on the dialogue history with the customer and make suggestions for reference. For example, the support department uses the generation AI to analyze the dialogue history with the customer and extract success stories of other customers. For example, it identifies programs and services that have been successful in past events. The support department also makes suggestions to the customer for reference based on the success stories of other customers. For example, it introduces success stories of customers with similar requests and makes suggestions to incorporate elements of those cases. The support department also makes specific suggestions based on the success stories of other customers analyzed by the generation AI. For example, it makes suggestions to incorporate the programs and services of those success stories into a new event. In this way, by introducing success stories of other customers and making suggestions for reference, the quality of the customer's event planning is improved.

[0061] The support department can propose multiple countermeasures when new requests arise during the progress of an event based on the dialogue with the customer using the generation AI. For example, the support department uses the generation AI to analyze the dialogue history with the customer and propose multiple countermeasures when new requests arise during the progress of an event. For example, if a customer wants to add a new program, the support department proposes multiple options. The support department also proposes specific countermeasures for new requests based on the data analyzed by the generation AI. For example, the support department evaluates the feasibility and cost of the program desired by the customer and proposes the optimal option. The support department also uses the generation AI to propose multiple countermeasures for new requests based on the dialogue with the customer, allowing the customer to choose. For example, the support department presents multiple program or service options and allows the customer to select the most appropriate one. This enables flexible responses by proposing multiple countermeasures when new requests arise during the progress of an event.

[0062] The support department can use the emotion estimation function to analyze emotions during a conversation with a customer and propose countermeasures to reduce negative emotions. For example, the support department uses the emotion estimation function to analyze emotions during a conversation with a customer and propose countermeasures to reduce negative emotions. For example, if a customer is feeling dissatisfied, the support department identifies the cause and proposes a solution. The support department also proposes specific countermeasures to reduce negative emotions based on the emotion data analyzed by the generative AI. For example, if a customer is feeling anxious, the support department proposes actions to give the customer a sense of security. The support department also uses the emotion estimation function to analyze emotions during a conversation with a customer and proposes specific countermeasures to reduce negative emotions. For example, if a customer is feeling anger, the support department proposes specific actions to resolve the cause. In this way, by proposing countermeasures to reduce negative emotions during a conversation with a customer, customer satisfaction is improved.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The intelligent event planner can also be equipped with a health management unit that monitors the health status of participants. For example, if a participant becomes ill during an event, the health management unit can detect the abnormality in real time and issue an alert for prompt action. The health management unit can also collect participants' health data and adjust the event program. For example, it can suggest avoiding activities that require excessive exercise. Furthermore, the health management unit can suggest appropriate break times and timing for hydration based on participants' health status. This allows the event to proceed smoothly while protecting the health of participants.

[0065] The intelligent event planner can further estimate participants' emotions and adjust the music and lighting during the event based on the estimated emotions. For example, if participants are relaxed, calm music and soft lighting can be provided to maintain a relaxed atmosphere. If participants are excited, upbeat music and bright lighting can be used to further increase their excitement. Furthermore, if participants are feeling anxious, the emotion estimation function can also be used to provide reassuring music and lighting to reduce their anxiety. This can provide an environment that suits participants' emotions and improve event satisfaction.

[0066] The intelligent event planner can further estimate participants' interests and suggest activities during the event based on the estimated interests. For example, if a participant is interested in sports, sports-related activities can be suggested. Also, if a participant is interested in music, music-related activities can be suggested. Furthermore, the intelligent event planner can optimize the placement of booths and exhibits during the event based on participants' interests. For example, if a participant is interested in art, art-related booths can be placed in prominent locations. This allows the planner to provide activities and exhibits that match participants' interests and improve event satisfaction.

[0067] The intelligent event planner can further estimate participants' emotions and adjust the content of speeches and presentations during the event based on the estimated emotions. For example, if participants are excited, an energetic speech or presentation can be provided to maintain their excitement. If participants are relaxed, a calm speech or presentation can be provided to maintain the relaxed atmosphere. Furthermore, if participants are feeling anxious, the emotion estimation function can be used to provide a reassuring speech or presentation to alleviate their anxiety. This allows speeches and presentations to be provided that are appropriate for participants' emotions, improving event satisfaction.

[0068] The intelligent event planner can also analyze participant movement patterns and suggest routes to avoid congestion. For example, if participants are congested in a specific area, it can restrict access to that area and guide them to other areas. It can also deploy additional guide staff in areas where congestion is expected based on participant movement patterns. It can also analyze participant movement patterns and suggest specific routes to avoid congestion. For example, it can suggest routes that avoid areas that are expected to be congested at certain times of the day. This allows participants to move more smoothly, avoiding congestion and ensuring the smooth progress of the event.

[0069] The intelligent event planner can further estimate participants' emotions and adjust break times and refreshment during the event based on the estimated emotions. For example, if participants are tired, break times can be extended and refreshments can be provided. Also, if participants are feeling refreshed, breaks can be provided at appropriate times to maintain that state. Furthermore, if participants are feeling stressed, the emotion estimation function can be used to provide a relaxing environment to reduce that stress. This allows for break times and refreshments to be provided according to participants' emotions, improving event satisfaction.

[0070] The intelligent event planner can further include a feedback collection unit that can collect participant feedback in real time and reflect it immediately as the event progresses. For example, if participants are dissatisfied with a particular program, that feedback can be collected immediately and the program content can be adjusted. New activities can also be added as the event progresses based on participant feedback. Furthermore, the feedback collection unit can make specific suggestions to optimize the progress of the event based on participant feedback. For example, it can make suggestions to add programs that participants desire. This allows participant feedback to be reflected in real time and improves event satisfaction.

[0071] The intelligent event planner can further estimate participants' emotions and adjust the sequence of activities during the event based on the estimated emotions. For example, if participants are excited, energetic activities can be provided in succession to maintain their excitement. If participants are relaxed, calm activities can be provided in succession to maintain the relaxed atmosphere. Furthermore, if participants are tired, the emotion estimation function can also be used to provide relaxing activities to reduce their fatigue. This makes it possible to provide an activity sequence that corresponds to participants' emotions and improve event satisfaction.

[0072] The intelligent event planner can further estimate the interests of participants and optimize the placement of booths and exhibits during the event based on the estimated interests. For example, if participants are interested in technology, technology-related booths can be placed in prominent locations. Also, if participants are interested in art, art-related exhibits can be placed in prominent locations. Furthermore, specific suggestions can be made to optimize the placement of booths and exhibits based on participants' interests. For example, a suggestion can be made to place multiple booths that participants are interested in close together. This allows for the placement of booths and exhibits according to participants' interests, thereby improving event satisfaction.

[0073] The intelligent event planner can further estimate participants' emotions and provide surprise elements during the event based on the estimated emotions. For example, if participants are excited, a surprise guest can appear to further increase their excitement. If participants are relaxed, a surprise element that helps them relax can be provided to maintain that relaxed atmosphere. Furthermore, if participants are feeling anxious, the emotion estimation function can also be used to provide a surprise element that gives a sense of security to alleviate their anxiety. This allows for the provision of surprise elements according to participants' emotions and improves event satisfaction.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The data analysis department uses generation AI to analyze past data and feedback. For example, it analyzes data such as the number of participants and satisfaction levels of past events, and popular programs. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 2: The trend analysis unit analyzes trends based on the data analyzed by the data analysis unit. For example, it identifies the latest trends and participant preferences based on the data analyzed by the generation AI. Step 3: The proposal department makes event proposals based on the trends analyzed by the trend analysis department. For example, they propose optimal event plans taking into account the customer's requests, budget, and participants' interests. Step 4: The Coordination Department makes real-time adjustments and improvements, for example, by collecting participant reactions and feedback during the event and proposing changes or additions to the program. Step 5: The support department provides flexible support through dialogue with the customer. For example, if the customer makes new requests during the event, the plan is adjusted accordingly.

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] 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.

[0091] 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.

[0092] 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 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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 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] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 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 data analysis unit that uses generative AI to analyze past data and feedback; a trend analysis unit that analyzes trends based on the data analyzed by the data analysis unit; a suggestion unit that suggests an event based on the trend analyzed by the trend analysis unit; The adjustment department makes adjustments and improvements in real time, A support department that provides flexible support through dialogue with customers. A system characterized by:

2. The data analysis unit Analyzing changes in participants' emotions from past event data and identifying programs that were held at peak emotional times 2. The system of claim 1.

3. The trend analysis unit Based on the data analyzed by the generative AI, the factors that contributed to the success of the event are identified and specific measures to strengthen those factors are proposed.

2. The system of claim 1.

4. The proposal unit Using the generative AI, we will analyze event data from different industries and apply successful examples from other industries to event planning.

2. The system of claim 1.

5. The adjustment unit Based on the data collected in real time, the generative AI predicts the progress of events and makes suggestions to prevent problems in advance.

2. The system of claim 1.

6. The support portion is The generative AI is used to analyze the dialogue history with the customer and provide customized support that reflects the customer's requests and feedback.

2. The system of claim 1.

7. The support portion is Analyze emotions during conversations with said customers and propose communication methods to elicit positive emotions 2. The system of claim 1.

8. The adjustment unit Analyze participants' emotions in real time and suggest immediate actions to reduce negative emotions 2. The system of claim 1.

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