system

Abstract

The system according to the embodiment aims to predict future demand and propose appropriate staffing arrangements. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects past event data and shift data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts future demand based on the analysis results obtained by the analysis unit. The proposal unit proposes staffing arrangements based on the demand predicted by the prediction unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately predict future demand and allocate personnel appropriately when planning events and shifts.

[0005] The system according to the embodiment aims to predict future demand and propose appropriate staffing arrangements. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects past event data and shift data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts future demand based on the analysis results obtained by the analysis unit. The proposal unit proposes staffing arrangements based on the demand predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict future demand and propose appropriate staffing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A staffing support system according to an embodiment of the present invention predicts store and local expectations when deciding event and shift plans, supporting appropriate staffing. This staffing support system collects past event data and shift data and uses AI to analyze the data to predict demand for future events and shifts and propose appropriate staffing. For example, a collection unit is configured to collect past event data and shift data. The collection unit may collect, for example, store sales data and shift work hours data. This allows past trends to be understood. Next, an analysis unit is configured to analyze the collected data. The analysis unit uses AI to perform analysis based on the data collected by the collection unit. For example, the analysis unit may analyze trends in the number of attendees and sales for past events to predict the number of attendees and sales for the next event. Furthermore, the analysis unit may analyze past shift data to predict demand for the next shift. Furthermore, a prediction unit is configured to predict future demand based on the analysis results. The prediction unit predicts demand for future events and shifts based on the analysis results obtained by the analysis unit. For example, the system predicts the required number of staff and their allocation based on the predicted number of attendees for the event. Finally, a proposal unit is configured to propose appropriate staffing allocation based on the prediction results. The proposal unit proposes appropriate staffing allocation based on the demand predicted by the prediction unit. For example, the proposal unit proposes an appropriate shift combination according to the predicted shift demand. This allows events and shifts to be planned efficiently, and optimal staffing allocation according to store and local expectations is realized. This allows the staffing allocation support system to efficiently plan events and shifts, and realize optimal staffing allocation according to store and local expectations.

[0029] A staffing support system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects past event data and shift data. The collection unit collects, for example, store sales data and shift work hour data. For example, the collection unit collects store sales data and records sales periods and sales items. The collection unit also collects shift work hour data and can clarify the units of work hours and recording methods. The analysis unit uses AI to perform analysis based on the data collected by the collection unit. For example, the analysis unit analyzes the number of participants and sales trends of past events to predict the number of participants and sales of the next event. For example, the analysis unit counts the number of participants of past events and identifies the range of participants. The analysis unit can also analyze sales increase / decrease patterns and seasonality to grasp sales trends. The prediction unit predicts demand for future events and shifts based on the analysis results obtained by the analysis unit. For example, the prediction unit predicts the required number of staff and their allocation based on the predicted number of participants of the event. For example, the prediction unit sets the required number of staff and staff allocation standards based on the number of participants in an event. The prediction unit can also predict shift demand and clarify shift time periods and the roles of staff members. The proposal unit proposes appropriate staff allocation based on the demand predicted by the prediction unit. The proposal unit proposes appropriate shift combinations, for example, according to the predicted shift demand. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand. The proposal unit can also optimize shift combinations to achieve efficient staff allocation. As a result, the staff allocation support system according to the embodiment can efficiently plan events and shifts and achieve optimal staff allocation according to the expectations of the store and the area.

[0030] The collection unit can collect store sales data or shift work hour data. For example, the collection unit collects store sales data and records sales periods and sales items. For example, the collection unit can collect monthly sales data and grasp increases or decreases in sales. The collection unit can also collect shift work hour data and clarify the units of work hours and recording methods. For example, the collection unit can record shift work hours daily and grasp fluctuations in work hours. This makes it possible to grasp past trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sales data into AI and analyze increases or decreases in sales.

[0031] The analysis unit can analyze trends in the number of participants and sales of past events and predict the number of participants and sales of the next event. The analysis unit, for example, counts the number of participants of past events and identifies the range of participants. For example, the analysis unit can record the number of participants of an event on a daily basis and grasp the increase or decrease in the number of participants. The analysis unit can also analyze patterns of increase or decrease in sales and seasonality to grasp the sales trend. For example, the analysis unit can analyze monthly sales data and identify patterns of increase or decrease in sales. This makes it possible to predict the number of participants and sales of the next event. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input participant number data into AI to analyze the increase or decrease in the number of participants.

[0032] The analysis unit can analyze past shift data and predict demand for the next shift. The analysis unit, for example, analyzes past shift data and predicts shift demand. For example, the analysis unit can record shift work hour data on a daily basis and grasp fluctuations in work hours. The analysis unit can also predict shift demand and clarify shift time periods and the roles of staff members. For example, the analysis unit can predict shift demand on a monthly basis and adjust shift time periods and the roles of staff members. This makes it possible to predict demand for the next shift. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input shift data into AI and predict shift demand.

[0033] The prediction unit can predict the number of staff members required or their allocation based on the predicted number of participants in the event. The prediction unit, for example, predicts the number of staff members required and their allocation based on the predicted number of participants in the event. For example, the prediction unit sets standards for the number of staff members required and their allocation based on the number of participants in the event. The prediction unit can also predict shift demand and clarify shift time periods and the roles of staff members. For example, the prediction unit can predict shift demand on a monthly basis and adjust shift time periods and the roles of staff members. This makes it possible to predict the number of staff members required and their allocation. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input participant number data into AI and predict the number of staff members required and their allocation.

[0034] The proposal unit can propose appropriate shift combinations according to the predicted shift demand. The proposal unit proposes appropriate shift combinations according to, for example, the predicted shift demand. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand. The proposal unit can also optimize shift combinations to achieve efficient personnel allocation. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand to propose optimal shift combinations. In this way, appropriate shift combinations can be proposed. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input shift data into AI to propose optimal shift combinations.

[0035] The collection unit can analyze past data collection history and select a collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. For example, the collection unit optimizes the timing of data collection based on the past data collection history. The collection unit can also analyze the past data collection history and select a method for improving the accuracy of data collection. For example, the collection unit selects a method for improving the accuracy of data collection based on the past data collection history. This makes it possible to select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and select the optimal collection method.

[0036] The collection unit can filter data based on the current situation of the store or a specific event when collecting data. The collection unit filters the data to be collected based on, for example, the current sales situation of the store. For example, the collection unit filters the data to be collected based on sales data of the store. Furthermore, when a specific event is being held, the collection unit can preferentially collect data related to the event. For example, the collection unit filters the data to be collected based on event data. Furthermore, the collection unit can adjust the timing of data collection based on the congestion situation of the store. For example, the collection unit adjusts the timing of data collection based on the congestion situation of the store. This makes it possible to filter data based on the current situation of the store or a specific event. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input store sales data into AI to filter the data to be collected.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit prioritizes collecting data on nearby competing stores based on the geographical location information of the store. For example, the collection unit collects sales data on nearby competing stores based on the geographical location information of the store. The collection unit can also collect local event information by taking into account the geographical location information of the store. For example, the collection unit collects local event information based on the geographical location information of the store. The collection unit can also prioritize collecting local demographic data based on the geographical location information of the store. For example, the collection unit collects local demographic data based on the geographical location information of the store. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the store. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the store into AI and prioritize collecting highly relevant data.

[0038] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit collects and analyzes posts about the store on social media. For example, the collection unit collects posts about the store on social media to understand the store's reputation. The collection unit can also collect and analyze posts about an event on social media. For example, the collection unit collects posts about the event on social media to understand the reaction to the event. The collection unit can also collect and analyze user feedback on social media. For example, the collection unit collects user feedback on social media to understand user opinions. This makes it possible to analyze social media activity and collect related data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis based on the importance of the data. Furthermore, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis based on the importance of the data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources based on the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI and adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a sales forecasting algorithm to sales data. For example, the analysis unit applies a sales forecasting algorithm based on the sales data. The analysis unit can also apply a shift optimization algorithm to shift data. For example, the analysis unit applies a shift optimization algorithm based on the shift data. The analysis unit can also apply an event participant number prediction algorithm to event data. For example, the analysis unit applies an event participant number prediction algorithm based on the event data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes past data and identifies trends based on the time when the data was collected. The analysis unit can also optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit optimally allocates analysis resources based on the time when the data was collected. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI to determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data based on the relevance of the data. The analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the data into AI and adjust the order of analysis.

[0043] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data when making a prediction. The prediction unit, for example, makes a prediction by taking into account the interrelationship between sales data and shift data. For example, the prediction unit makes a prediction based on the interrelationship between sales data and shift data. The prediction unit can also make a prediction by taking into account the interrelationship between event data and participant count data. For example, the prediction unit makes a prediction based on the interrelationship between event data and participant count data. The prediction unit can also improve the accuracy of the prediction based on the interrelationship between data. For example, the prediction unit improves the accuracy of the prediction based on the interrelationship between data. This makes it possible to improve the accuracy of the prediction by taking into account the interrelationship between data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the interrelationship between data into AI to improve the accuracy of the prediction.

[0044] The prediction unit can make predictions taking into account attribute information of the data submitter when making predictions. The prediction unit can make predictions taking into account, for example, the age and gender of the data submitter. For example, the prediction unit can make predictions based on the age and gender of the data submitter. The prediction unit can also make predictions taking into account the occupation and job title of the data submitter. For example, the prediction unit can make predictions based on the occupation and job title of the data submitter. The prediction unit can also make predictions taking into account the past behavioral history of the data submitter. For example, the prediction unit can make predictions based on the past behavioral history of the data submitter. This allows predictions to be made taking into account the attribute information of the data submitter. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input attribute information of the data submitter into AI and make predictions.

[0045] The prediction unit can make predictions taking into account the geographical distribution of data when making predictions. The prediction unit, for example, makes predictions based on data from geographically close stores. For example, the prediction unit makes predictions based on data from geographically close stores. The prediction unit can also make predictions by comparing data from different geographical regions. For example, the prediction unit makes predictions based on data from different geographical regions. The prediction unit can also improve the accuracy of the predictions by taking the geographical distribution into account. For example, the prediction unit improves the accuracy of the predictions based on the geographical distribution. This makes it possible to make predictions taking the geographical distribution of data into account. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input geographical distribution data into AI and make predictions.

[0046] The prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. The prediction unit, for example, improves the prediction algorithm based on related literature. For example, the prediction unit improves the prediction algorithm based on related literature. The prediction unit can also improve the accuracy of the prediction by referring to data from related literature. For example, the prediction unit improves the accuracy of the prediction based on data from related literature. The prediction unit can also improve the reliability of the prediction by utilizing knowledge from related literature. For example, the prediction unit improves the reliability of the prediction based on knowledge from related literature. This makes it possible to improve the accuracy of the prediction by referring to related literature. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data from related literature into AI to improve the accuracy of the prediction.

[0047] When making a proposal, the proposal unit can analyze past proposal history and select an optimal proposal method. The proposal unit, for example, selects the most effective proposal method from the past proposal history. For example, the proposal unit selects the most effective proposal method based on the past proposal history. The proposal unit can also optimize the timing of the proposal based on the past proposal history. For example, the proposal unit optimizes the timing of the proposal based on the past proposal history. The proposal unit can also analyze the past proposal history and select a method for improving the accuracy of the proposal. For example, the proposal unit selects a method for improving the accuracy of the proposal based on the past proposal history. In this way, the past proposal history can be analyzed and the optimal proposal method can be selected. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the past proposal history into AI and select the optimal proposal method.

[0048] The proposal unit can customize the proposal means based on the current situation when making a proposal. The proposal unit customizes the proposal means based on, for example, the current sales situation. For example, the proposal unit customizes the proposal means based on the current sales situation. The proposal unit can also customize the proposal means based on the current shift situation. For example, the proposal unit customizes the proposal means based on the current shift situation. The proposal unit can also customize the proposal means based on the current event situation. For example, the proposal unit customizes the proposal means based on the current event situation. This makes it possible to customize the proposal means based on the current situation. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input current situation data into AI and customize the proposal means.

[0049] The suggestion unit can select the optimal suggestion method by taking into consideration the geographical location information when making a suggestion. For example, the suggestion unit makes a suggestion taking into consideration the status of nearby competing stores based on the geographical location information of the store. For example, the suggestion unit makes a suggestion taking into consideration the status of nearby competing stores based on the geographical location information of the store. The suggestion unit can also make a suggestion based on local event information by taking into consideration the geographical location information of the store. For example, the suggestion unit can make a suggestion based on local event information based on the geographical location information of the store. The suggestion unit can also make a suggestion taking into consideration local demographic data based on the geographical location information of the store. For example, the suggestion unit can make a suggestion taking into consideration local demographic data based on the geographical location information of the store. This makes it possible to select the optimal suggestion method by taking into consideration the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical location information into AI and select the optimal suggestion method.

[0050] When making a proposal, the suggestion unit can analyze social media activity and propose a means of suggestion. For example, the suggestion unit analyzes posts about the store on social media and determines a means of suggestion. For example, the suggestion unit analyzes posts about the store on social media and determines a means of suggestion based on the store's reputation. The suggestion unit can also analyze posts about an event on social media and determine a means of suggestion. For example, the suggestion unit analyzes posts about the event on social media and determines a means of suggestion based on the response to the event. The suggestion unit can also analyze user feedback on social media and determine a means of suggestion. For example, the suggestion unit analyzes user feedback on social media and determines a means of suggestion based on the user's opinion. In this way, it is possible to analyze social media activity and propose a means of suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input social media data into AI and determine a means of suggestion.

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

[0052] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit can prioritize collecting data on nearby competing stores based on the geographical location information of the store. The collection unit can also collect local event information by taking into account the geographical location information of the store. Furthermore, the collection unit can prioritize collecting local demographic data based on the geographical location information of the store. This allows highly relevant data to be prioritized by taking into account the geographical location information of the store.

[0053] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships between data when making predictions. For example, the prediction unit can make predictions by taking into account the interrelationships between sales data and shift data. The prediction unit can also make predictions by taking into account the interrelationships between event data and participant count data. Furthermore, the prediction unit can improve the accuracy of the prediction based on the interrelationships between data. This makes it possible to improve the accuracy of the prediction by taking into account the interrelationships between data.

[0054] When making a suggestion, the suggestion unit can analyze social media activity and suggest a means of suggestion. For example, the suggestion unit can analyze posts about a store on social media and determine a means of suggestion. The suggestion unit can also analyze posts about an event on social media and determine a means of suggestion. Furthermore, the suggestion unit can analyze user feedback on social media and determine a means of suggestion. In this way, it is possible to analyze social media activity and suggest a means of suggestion.

[0055] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a sales forecasting algorithm to sales data. The analysis unit can also apply a shift optimization algorithm to shift data. Furthermore, the analysis unit can apply an event attendee number forecasting algorithm to event data. This makes it possible to apply different analysis algorithms depending on the category of data.

[0056] When making a proposal, the proposal unit can analyze past proposal history and select the optimal proposal method. For example, the proposal unit can select the most effective proposal method from the past proposal history. The proposal unit can also optimize the timing of the proposal based on the past proposal history. Furthermore, the proposal unit can analyze the past proposal history and select a method for improving the accuracy of the proposal. In this way, the optimal proposal method can be selected by analyzing the past proposal history.

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

[0058] Step 1: The collection unit collects past event data and shift data. For example, it collects store sales data and shift work hours data, and clarifies the sales period, sales items, working hour units, and recording method. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. For example, it analyzes the number of attendees and sales trends of past events to predict the number of attendees and sales of the next event. It also analyzes patterns of increase and decrease in sales and seasonality to understand sales trends. Step 3: The forecasting unit predicts future events and shift demand based on the analysis results obtained by the analysis unit. For example, it predicts the number of staff required and their deployment based on the predicted number of participants in an event, and clarifies shift times and the roles of staff members. Step 4: The proposal unit proposes appropriate staffing arrangements based on the demand predicted by the forecast unit. For example, it proposes appropriate shift combinations according to the predicted shift demand, and adjusts the shift times and the roles of personnel.

[0059] (Example 2) A staffing support system according to an embodiment of the present invention predicts store and local expectations when deciding event and shift plans, supporting appropriate staffing. This staffing support system collects past event data and shift data and uses AI to analyze the data to predict demand for future events and shifts and propose appropriate staffing. For example, a collection unit is configured to collect past event data and shift data. The collection unit may collect, for example, store sales data and shift work hours data. This allows past trends to be understood. Next, an analysis unit is configured to analyze the collected data. The analysis unit uses AI to perform analysis based on the data collected by the collection unit. For example, the analysis unit may analyze trends in the number of attendees and sales for past events to predict the number of attendees and sales for the next event. Furthermore, the analysis unit may analyze past shift data to predict demand for the next shift. Furthermore, a prediction unit is configured to predict future demand based on the analysis results. The prediction unit predicts demand for future events and shifts based on the analysis results obtained by the analysis unit. For example, the system predicts the required number of staff and their allocation based on the predicted number of attendees for the event. Finally, a proposal unit is configured to propose appropriate staffing allocation based on the prediction results. The proposal unit proposes appropriate staffing allocation based on the demand predicted by the prediction unit. For example, the proposal unit proposes an appropriate shift combination according to the predicted shift demand. This allows events and shifts to be planned efficiently, and optimal staffing allocation according to store and local expectations is realized. This allows the staffing allocation support system to efficiently plan events and shifts, and realize optimal staffing allocation according to store and local expectations.

[0060] A staffing support system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects past event data and shift data. The collection unit collects, for example, store sales data and shift work hour data. For example, the collection unit collects store sales data and records sales periods and sales items. The collection unit also collects shift work hour data and can clarify the units of work hours and recording methods. The analysis unit uses AI to perform analysis based on the data collected by the collection unit. For example, the analysis unit analyzes the number of participants and sales trends of past events to predict the number of participants and sales of the next event. For example, the analysis unit counts the number of participants of past events and identifies the range of participants. The analysis unit can also analyze sales increase / decrease patterns and seasonality to grasp sales trends. The prediction unit predicts demand for future events and shifts based on the analysis results obtained by the analysis unit. For example, the prediction unit predicts the required number of staff and their allocation based on the predicted number of participants of the event. For example, the prediction unit sets the required number of staff and staff allocation standards based on the number of participants in an event. The prediction unit can also predict shift demand and clarify shift time periods and the roles of staff members. The proposal unit proposes appropriate staff allocation based on the demand predicted by the prediction unit. The proposal unit proposes appropriate shift combinations, for example, according to the predicted shift demand. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand. The proposal unit can also optimize shift combinations to achieve efficient staff allocation. As a result, the staff allocation support system according to the embodiment can efficiently plan events and shifts and achieve optimal staff allocation according to the expectations of the store and the area.

[0061] The collection unit can collect store sales data or shift work hour data. For example, the collection unit collects store sales data and records sales periods and sales items. For example, the collection unit can collect monthly sales data and grasp increases or decreases in sales. The collection unit can also collect shift work hour data and clarify the units of work hours and recording methods. For example, the collection unit can record shift work hours daily and grasp fluctuations in work hours. This makes it possible to grasp past trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input sales data into AI and analyze increases or decreases in sales.

[0062] The analysis unit can analyze trends in the number of participants and sales of past events and predict the number of participants and sales of the next event. The analysis unit, for example, counts the number of participants of past events and identifies the range of participants. For example, the analysis unit can record the number of participants of an event on a daily basis and grasp the increase or decrease in the number of participants. The analysis unit can also analyze patterns of increase or decrease in sales and seasonality to grasp the sales trend. For example, the analysis unit can analyze monthly sales data and identify patterns of increase or decrease in sales. This makes it possible to predict the number of participants and sales of the next event. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input participant number data into AI to analyze the increase or decrease in the number of participants.

[0063] The analysis unit can analyze past shift data and predict demand for the next shift. The analysis unit, for example, analyzes past shift data and predicts shift demand. For example, the analysis unit can record shift work hour data on a daily basis and grasp fluctuations in work hours. The analysis unit can also predict shift demand and clarify shift time periods and the roles of staff members. For example, the analysis unit can predict shift demand on a monthly basis and adjust shift time periods and the roles of staff members. This makes it possible to predict demand for the next shift. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input shift data into AI and predict shift demand.

[0064] The prediction unit can predict the number of staff members required or their allocation based on the predicted number of participants in the event. The prediction unit, for example, predicts the number of staff members required and their allocation based on the predicted number of participants in the event. For example, the prediction unit sets standards for the number of staff members required and their allocation based on the number of participants in the event. The prediction unit can also predict shift demand and clarify shift time periods and the roles of staff members. For example, the prediction unit can predict shift demand on a monthly basis and adjust shift time periods and the roles of staff members. This makes it possible to predict the number of staff members required and their allocation. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input participant number data into AI and predict the number of staff members required and their allocation.

[0065] The proposal unit can propose appropriate shift combinations according to the predicted shift demand. The proposal unit proposes appropriate shift combinations according to, for example, the predicted shift demand. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand. The proposal unit can also optimize shift combinations to achieve efficient personnel allocation. For example, the proposal unit adjusts shift time periods and the roles of staff members based on the shift demand to propose optimal shift combinations. In this way, appropriate shift combinations can be proposed. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input shift data into AI to propose optimal shift combinations.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, the collection unit adjusts the frequency of data collection based on the user's emotion data. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, the collection unit adjusts the frequency of data collection based on the user's emotion data. Furthermore, if the user is busy, the collection unit can adjust the timing of data collection to match the user's schedule. For example, the collection unit adjusts the timing of data collection based on the user's emotion data. This makes it possible to adjust the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI ​​and adjust the timing of data collection.

[0067] The collection unit can analyze past data collection history and select a collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. For example, the collection unit optimizes the timing of data collection based on the past data collection history. The collection unit can also analyze the past data collection history and select a method for improving the accuracy of data collection. For example, the collection unit selects a method for improving the accuracy of data collection based on the past data collection history. This makes it possible to select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and select the optimal collection method.

[0068] The collection unit can filter data based on the current situation of the store or a specific event when collecting data. The collection unit filters the data to be collected based on, for example, the current sales situation of the store. For example, the collection unit filters the data to be collected based on sales data of the store. Furthermore, when a specific event is being held, the collection unit can preferentially collect data related to the event. For example, the collection unit filters the data to be collected based on event data. Furthermore, the collection unit can adjust the timing of data collection based on the congestion situation of the store. For example, the collection unit adjusts the timing of data collection based on the congestion situation of the store. This makes it possible to filter data based on the current situation of the store or a specific event. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input store sales data into AI to filter the data to be collected.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, the collection unit determines the priority of data to be collected based on the user's emotion data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, the collection unit determines the priority of data to be collected based on the user's emotion data. Furthermore, when the user is busy, the collection unit can reduce the amount of data to be collected and collect only important data. For example, the collection unit determines the priority of data to be collected based on the user's emotion data. In this way, the priority of data to be collected can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI ​​and determine the priority of the data to be collected.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit prioritizes collecting data on nearby competing stores based on the geographical location information of the store. For example, the collection unit collects sales data on nearby competing stores based on the geographical location information of the store. The collection unit can also collect local event information by taking into account the geographical location information of the store. For example, the collection unit collects local event information based on the geographical location information of the store. The collection unit can also prioritize collecting local demographic data based on the geographical location information of the store. For example, the collection unit collects local demographic data based on the geographical location information of the store. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the store. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the store into AI and prioritize collecting highly relevant data.

[0071] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit collects and analyzes posts about the store on social media. For example, the collection unit collects posts about the store on social media to understand the store's reputation. The collection unit can also collect and analyze posts about an event on social media. For example, the collection unit collects posts about the event on social media to understand the reaction to the event. The collection unit can also collect and analyze user feedback on social media. For example, the collection unit collects user feedback on social media to understand user opinions. This makes it possible to analyze social media activity and collect related data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI to collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit provides a simple, highly visible analysis result based on the user's emotion data. Furthermore, the analysis unit can provide a detailed analysis result if the user is relaxed. For example, the analysis unit provides a detailed analysis result based on the user's emotion data. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, the analysis unit provides a summary analysis result based on the user's emotion data. This allows the way the analysis is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the AI ​​and adjust how the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis based on the importance of the data. Furthermore, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis based on the importance of the data. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit optimally allocates analysis resources based on the importance of the data. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI and adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a sales forecasting algorithm to sales data. For example, the analysis unit applies a sales forecasting algorithm based on the sales data. The analysis unit can also apply a shift optimization algorithm to shift data. For example, the analysis unit applies a shift optimization algorithm based on the shift data. The analysis unit can also apply an event participant number prediction algorithm to event data. For example, the analysis unit applies an event participant number prediction algorithm based on the event data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and apply different analysis algorithms.

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

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes past data and identifies trends based on the time when the data was collected. The analysis unit can also optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit optimally allocates analysis resources based on the time when the data was collected. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI to determine the priority of analysis.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data based on the relevance of the data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data based on the relevance of the data. The analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the data into AI and adjust the order of analysis.

[0078] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated user emotions. For example, the prediction unit provides a detailed prediction when the user is relaxed. For example, the prediction unit provides a detailed prediction based on the user's emotion data. Furthermore, the prediction unit can provide a concise prediction when the user is in a hurry. For example, the prediction unit provides a concise prediction based on the user's emotion data. Furthermore, the prediction unit can provide a prediction with a visually stimulating effect when the user is excited. For example, the prediction unit provides a prediction with a visually stimulating effect based on the user's emotion data. This allows the prediction criteria to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit may be performed using an AI, for example, or without an AI. For example, the prediction unit can input the user's emotion data into an AI and adjust the prediction criteria.

[0079] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data when making a prediction. The prediction unit, for example, makes a prediction by taking into account the interrelationship between sales data and shift data. For example, the prediction unit makes a prediction based on the interrelationship between sales data and shift data. The prediction unit can also make a prediction by taking into account the interrelationship between event data and participant count data. For example, the prediction unit makes a prediction based on the interrelationship between event data and participant count data. The prediction unit can also improve the accuracy of the prediction based on the interrelationship between data. For example, the prediction unit improves the accuracy of the prediction based on the interrelationship between data. This makes it possible to improve the accuracy of the prediction by taking into account the interrelationship between data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the interrelationship between data into AI to improve the accuracy of the prediction.

[0080] The prediction unit can make predictions taking into account attribute information of the data submitter when making predictions. The prediction unit can make predictions taking into account, for example, the age and gender of the data submitter. For example, the prediction unit can make predictions based on the age and gender of the data submitter. The prediction unit can also make predictions taking into account the occupation and job title of the data submitter. For example, the prediction unit can make predictions based on the occupation and job title of the data submitter. The prediction unit can also make predictions taking into account the past behavioral history of the data submitter. For example, the prediction unit can make predictions based on the past behavioral history of the data submitter. This allows predictions to be made taking into account the attribute information of the data submitter. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input attribute information of the data submitter into AI and make predictions.

[0081] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated user emotions. For example, if the user is nervous, the prediction unit displays important prediction results first. For example, the prediction unit displays important prediction results first based on the user's emotion data. Furthermore, if the user is relaxed, the prediction unit can display detailed prediction results in an orderly manner. For example, the prediction unit displays detailed prediction results in an orderly manner based on the user's emotion data. Furthermore, if the user is in a hurry, the prediction unit can display prediction results that emphasize the main points first. For example, the prediction unit displays prediction results that emphasize the main points first based on the user's emotion data. This makes it possible to adjust the order in which prediction results are displayed depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input user emotional data into the AI ​​and adjust the order in which prediction results are displayed.

[0082] The prediction unit can make predictions taking into account the geographical distribution of data when making predictions. The prediction unit, for example, makes predictions based on data from geographically close stores. For example, the prediction unit makes predictions based on data from geographically close stores. The prediction unit can also make predictions by comparing data from different geographical regions. For example, the prediction unit makes predictions based on data from different geographical regions. The prediction unit can also improve the accuracy of the predictions by taking the geographical distribution into account. For example, the prediction unit improves the accuracy of the predictions based on the geographical distribution. This makes it possible to make predictions taking the geographical distribution of data into account. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input geographical distribution data into AI and make predictions.

[0083] The prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. The prediction unit, for example, improves the prediction algorithm based on related literature. For example, the prediction unit improves the prediction algorithm based on related literature. The prediction unit can also improve the accuracy of the prediction by referring to data from related literature. For example, the prediction unit improves the accuracy of the prediction based on data from related literature. The prediction unit can also improve the reliability of the prediction by utilizing knowledge from related literature. For example, the prediction unit improves the reliability of the prediction based on knowledge from related literature. This makes it possible to improve the accuracy of the prediction by referring to related literature. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data from related literature into AI to improve the accuracy of the prediction.

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

[0085] When making a proposal, the proposal unit can analyze past proposal history and select an optimal proposal method. The proposal unit, for example, selects the most effective proposal method from the past proposal history. For example, the proposal unit selects the most effective proposal method based on the past proposal history. The proposal unit can also optimize the timing of the proposal based on the past proposal history. For example, the proposal unit optimizes the timing of the proposal based on the past proposal history. The proposal unit can also analyze the past proposal history and select a method for improving the accuracy of the proposal. For example, the proposal unit selects a method for improving the accuracy of the proposal based on the past proposal history. In this way, the past proposal history can be analyzed and the optimal proposal method can be selected. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the past proposal history into AI and select the optimal proposal method.

[0086] The proposal unit can customize the proposal means based on the current situation when making a proposal. The proposal unit customizes the proposal means based on, for example, the current sales situation. For example, the proposal unit customizes the proposal means based on the current sales situation. The proposal unit can also customize the proposal means based on the current shift situation. For example, the proposal unit customizes the proposal means based on the current shift situation. The proposal unit can also customize the proposal means based on the current event situation. For example, the proposal unit customizes the proposal means based on the current event situation. This makes it possible to customize the proposal means based on the current situation. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input current situation data into AI and customize the proposal means.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes suggestions with higher importance. For example, the suggestion unit prioritizes suggestions with higher importance based on the user's emotion data. Furthermore, when the user is relaxed, the suggestion unit can prioritize detailed suggestions. For example, the suggestion unit prioritizes detailed suggestions based on the user's emotion data. Furthermore, when the user is busy, the suggestion unit prioritizes concise and to-the-point suggestions. For example, the suggestion unit prioritizes concise and to-the-point suggestions based on the user's emotion data. This allows the priority of suggestions to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and determine the priority of suggestions.

[0088] The suggestion unit can select the optimal suggestion method by taking into consideration the geographical location information when making a suggestion. For example, the suggestion unit makes a suggestion taking into consideration the status of nearby competing stores based on the geographical location information of the store. For example, the suggestion unit makes a suggestion taking into consideration the status of nearby competing stores based on the geographical location information of the store. The suggestion unit can also make a suggestion based on local event information by taking into consideration the geographical location information of the store. For example, the suggestion unit can make a suggestion based on local event information based on the geographical location information of the store. The suggestion unit can also make a suggestion taking into consideration local demographic data based on the geographical location information of the store. For example, the suggestion unit can make a suggestion taking into consideration local demographic data based on the geographical location information of the store. This makes it possible to select the optimal suggestion method by taking into consideration the geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical location information into AI and select the optimal suggestion method.

[0089] When making a proposal, the suggestion unit can analyze social media activity and propose a means of suggestion. For example, the suggestion unit analyzes posts about the store on social media and determines a means of suggestion. For example, the suggestion unit analyzes posts about the store on social media and determines a means of suggestion based on the store's reputation. The suggestion unit can also analyze posts about an event on social media and determine a means of suggestion. For example, the suggestion unit analyzes posts about the event on social media and determines a means of suggestion based on the response to the event. The suggestion unit can also analyze user feedback on social media and determine a means of suggestion. For example, the suggestion unit analyzes user feedback on social media and determines a means of suggestion based on the user's opinion. In this way, it is possible to analyze social media activity and propose a means of suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input social media data into AI and determine a means of suggestion. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects store sales data and shift work hour data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs analysis using AI based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future events and shift demand based on the analysis results. The proposal unit is realized, for example, by the control unit 46A of the smart device 14 and proposes appropriate staffing arrangements based on the predicted demand. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and proposal unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects store sales data and shift work hour data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs analysis using AI based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future events and shift demand based on the analysis results. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and proposes appropriate staffing arrangements based on the predicted demand. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and proposal unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 and collects store sales data and shift work hour data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs analysis using AI based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future events and shift demand based on the analysis results. The proposal unit is realized, for example, by the control unit 46A of the headset terminal 314 and proposes appropriate staffing arrangements based on the predicted demand. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects store sales data and shift work hour data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs analysis using AI based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future events and shift demand based on the analysis results. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes appropriate staffing arrangements based on the predicted demand.

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

[0091] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. This makes it possible to customize the content of the suggestions according to the user's emotions.

[0092] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit can prioritize collecting data on nearby competing stores based on the geographical location information of the store. The collection unit can also collect local event information by taking into account the geographical location information of the store. Furthermore, the collection unit can prioritize collecting local demographic data based on the geographical location information of the store. This allows highly relevant data to be prioritized by taking into account the geographical location information of the store.

[0093] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary of the analysis result. This makes it possible to adjust the way the analysis is presented depending on the user's emotions.

[0094] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships between data when making predictions. For example, the prediction unit can make predictions by taking into account the interrelationships between sales data and shift data. The prediction unit can also make predictions by taking into account the interrelationships between event data and participant count data. Furthermore, the prediction unit can improve the accuracy of the prediction based on the interrelationships between data. This makes it possible to improve the accuracy of the prediction by taking into account the interrelationships between data.

[0095] When making a suggestion, the suggestion unit can analyze social media activity and suggest a means of suggestion. For example, the suggestion unit can analyze posts about a store on social media and determine a means of suggestion. The suggestion unit can also analyze posts about an event on social media and determine a means of suggestion. Furthermore, the suggestion unit can analyze user feedback on social media and determine a means of suggestion. In this way, it is possible to analyze social media activity and suggest a means of suggestion.

[0096] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is busy, the collection unit can adjust the timing of data collection to match the user's schedule. This makes it possible to adjust the timing of data collection according to the user's emotions.

[0097] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a sales forecasting algorithm to sales data. The analysis unit can also apply a shift optimization algorithm to shift data. Furthermore, the analysis unit can apply an event attendee number forecasting algorithm to event data. This makes it possible to apply different analysis algorithms depending on the category of data.

[0098] The prediction unit can estimate the user's emotion and adjust the prediction standard based on the estimated user's emotion. For example, if the user is relaxed, the prediction unit can provide a detailed prediction. If the user is in a hurry, the prediction unit can provide a concise prediction. Furthermore, if the user is excited, the prediction unit can provide a prediction with a visually stimulating effect. In this way, the prediction standard can be adjusted according to the user's emotion.

[0099] When making a proposal, the proposal unit can analyze past proposal history and select the optimal proposal method. For example, the proposal unit can select the most effective proposal method from the past proposal history. The proposal unit can also optimize the timing of the proposal based on the past proposal history. Furthermore, the proposal unit can analyze the past proposal history and select a method for improving the accuracy of the proposal. In this way, the optimal proposal method can be selected by analyzing the past proposal history.

[0100] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated user's emotions. For example, if the user is nervous, the prediction unit can display important prediction results first. If the user is relaxed, the prediction unit can display detailed prediction results in an orderly manner. If the user is in a hurry, the prediction unit can display key prediction results first. In this way, the order in which prediction results are displayed can be adjusted according to the user's emotions.

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

[0102] Step 1: The collection unit collects past event data and shift data. For example, it collects store sales data and shift work hours data, and clarifies the sales period, sales items, working hour units, and recording method. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. For example, it analyzes the number of attendees and sales trends of past events to predict the number of attendees and sales of the next event. It also analyzes patterns of increase and decrease in sales and seasonality to understand sales trends. Step 3: The forecasting unit predicts future events and shift demand based on the analysis results obtained by the analysis unit. For example, it predicts the number of staff required and their deployment based on the predicted number of participants in an event, and clarifies shift times and the roles of staff members. Step 4: The proposal unit proposes appropriate staffing arrangements based on the demand predicted by the forecast unit. For example, it proposes appropriate shift combinations according to the predicted shift demand, and adjusts the shift times and the roles of personnel.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0174] [Explanation of symbols]

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

Claims

1. a collection unit that collects past event data and shift data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts future demand based on the analysis results obtained by the analysis unit; a proposal unit that proposes staffing arrangements based on the demand predicted by the prediction unit; Equipped with A system characterized by:

2. The collecting unit Collect store sales data or shift hours data 2. The system of claim 1.

3. The analysis unit Analyze past event attendance and sales trends to predict future event attendance and sales.

2. The system of claim 1.

4. The analysis unit Analyze past shift data and predict demand for the next shift 2. The system of claim 1.

5. The prediction unit Estimate the number or staffing required based on the expected number of attendees at the event 2. The system of claim 1.

6. The proposal unit Propose appropriate shift combinations based on predicted shift demand 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past data collection history and select collection methods 2. The system of claim 1.

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