system

Abstract

We provide the system. [Solution] A means of collecting past supply history information, A means for predicting the required supply quantity based on collected supply history information, A means for automatically generating a supply schedule based on predicted supply requirements, A means of automatically carrying out supply procedures according to the generated supply schedule, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional supply process, stock shortages and supply leaks are likely to occur, and it depends on human labor, so there is a problem of lack of efficiency. Also, the prediction of the required supply amount depends on the experience of the person in charge, and there is a high risk of judgment errors. As a result, there are problems of overages and shortages, which hinder the efficiency of operations.

Means for Solving the Problems

[0005] This invention provides a system that predicts the required supply quantity based on past supply history information and automatically generates a supply schedule according to this prediction. Furthermore, by automatically carrying out supply procedures based on the generated supply schedule, it is possible to prevent inventory shortages and supply omissions. In addition, by issuing a warning when the required supply quantity falls below the predicted consumption quantity, it is possible to resolve the problem of surpluses and shortages. This streamlines the entire supply process and realizes accurate inventory management that does not rely on human judgment.

[0006] "Past supply history information" refers to data on the quantity, date, and frequency of each supply recorded by the system in the past.

[0007] "Means of collection" refers to system functions for obtaining past supply history information from databases or other data sources.

[0008] "Means for predicting supply requirements" refer to algorithms and functions that calculate the required quantity of future supplies based on collected historical information.

[0009] "Means for automatically generating supply schedules" refers to a function that automatically creates an optimal supply schedule using a computer program based on predicted supply requirements.

[0010] "Means of automatically performing supply procedures" refers to a function in which the system carries out the necessary supply procedures and arrangements according to the generated supply schedule without human intervention.

[0011] "A means of issuing warnings" refers to a function that sends notifications to users or system administrators to alert them when inventory shortages or supply delays are anticipated.

[0012] "Means for confirming supply completion" refers to the process by which the system verifies and records that the planned supply has been completed. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system for streamlining inventory management of supplies in stores, and more particularly to a form for predicting supply needs and automatically generating and implementing specific supply schedules.

[0035] First, the server retrieves historical supply information from the database. This historical information includes data such as the type, quantity, and frequency of replenishment of supplied items. Based on this data, the server uses AI models and algorithms to predict future supply needs. This prediction makes it possible to implement appropriate replenishment before inventory runs out.

[0036] Users can receive alerts when pre-set thresholds are exceeded. The server generates alerts as needed and notifies users via email or application. This alerting feature can reduce the risk of supply shortages.

[0037] Next, the server automatically generates an optimal supply schedule based on the predicted supply requirements. This schedule takes into account store demand and the availability of supplies, and users can check this schedule through their terminals.

[0038] The terminal automatically performs supply procedures according to the generated schedule. Specifically, it generates purchase orders for the necessary supplies and sends them to the warehouse management system. This allows the supply process to proceed smoothly without human intervention.

[0039] Finally, the user confirms and records on their terminal that the supply has been completed. The server receives this information, updates the database, and keeps the inventory information up to date. This ensures accurate inventory management at all times and enables efficient business operations.

[0040] As a concrete example, if a store consumes an average of 20 catalogs per day, the server calculates the required number for the following month based on past history, and the terminal automatically places an order for 200 catalogs at the appropriate time. The user can monitor this process and take action as needed. In this way, the system according to the present invention enables more efficient and accurate inventory management.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves historical supply information from the database. This information includes the type of goods supplied, the quantity supplied, the date and time, and the consumption rate. The retrieved data is used as foundational data for forecasting future demand.

[0044] Step 2:

[0045] The server preprocesses the data based on the acquired historical information. This preprocessing includes data cleaning and formatting. It imputes missing data and removes outliers to prepare the data for analysis.

[0046] Step 3:

[0047] The server uses pre-processed data to build a demand forecasting model and predict future supply needs. This model employs machine learning algorithms and statistical methods to forecast consumption on an hourly basis.

[0048] Step 4:

[0049] The server compares the predicted supply requirements with the current inventory levels and automatically generates a supply schedule. This schedule includes actual supply dates and quantities, and proposes an optimal replenishment plan.

[0050] Step 5:

[0051] The user reviews the supply schedule generated by the server via their terminal. They can also manually modify the schedule as needed. Once the user finalizes the schedule, the process proceeds.

[0052] Step 6:

[0053] The terminal automatically executes supply procedures according to the confirmed schedule. Specifically, it automatically generates purchase orders for the necessary supplies and sends them to the warehouse system.

[0054] Step 7:

[0055] Users monitor the progress of supply operations and confirm completion on their terminals. The confirmed completion information is recorded in the database by the server, and inventory information is updated to the latest state.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Traditional inventory management systems often fail to respond quickly to market demand fluctuations, frequently resulting in unnecessary excess inventory or stockouts. Furthermore, inventory replenishment is often done manually, leading to increased labor costs and human error. Therefore, there is a need for efficient and accurate inventory management.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring supply history information from a storage device, means for analyzing the acquired supply history information and predicting the required supply quantity using artificial intelligence, and means for automatically generating a supply schedule using optimization technology based on the predicted required supply quantity. This enables efficient inventory management that responds immediately to demand fluctuations, as well as reductions in labor costs and human errors through the automation of replenishment work.

[0061] "Supply history information" refers to data that includes details such as the type, quantity, date and time of supply, and frequency of replenishment of goods supplied in the past.

[0062] "Storage device" refers to a storage device or database used to store and manage supply history information.

[0063] "Artificial intelligence" refers to technology that learns complex patterns based on data and makes predictions and decisions.

[0064] "Supply requirement" refers to the quantity of a supply needed to meet the demand for it.

[0065] "Optimization techniques" refer to methods for calculating the most efficient solutions and schedules, taking resources and conditions into consideration.

[0066] A "supply schedule" is a schedule that indicates the specific dates, times, and frequency of planned replenishment and delivery of supplies.

[0067] "Electronic devices" refer to computers and terminals used for supply procedures and data communication.

[0068] "Supply procedures" refer to a series of operations for ordering, delivering, and replenishing supplies.

[0069] "Criteria" refers to the set values ​​or conditions that serve as the basis for decisions when monitoring supply requirements or issuing alerts.

[0070] "Monitoring" refers to the act of regularly checking the progress and status of supply procedures to ensure that they are functioning correctly.

[0071] "Completed" refers to a state where all planned supply procedures have been carried out and the necessary supplies have been properly replenished.

[0072] This invention is a system for streamlining inventory management of supplies in stores. The main elements of the system consist of a server, terminals, and users.

[0073] The server's primary role is to retrieve supply history information from the storage device. This data includes the type and quantity of supplies, as well as the date and time of supply. Next, the server uses artificial intelligence to predict the required supply quantity based on this history information. This process utilizes deep learning libraries such as TENSORFLOW® and PyTorch, enabling accurate prediction of future supply needs. Furthermore, based on the supply needs predicted through optimization techniques, the server automatically generates a supply schedule using Or-Tools and OptaPlanner. Based on the generated supply schedule, the server issues instructions to automatically execute the supply procedures via electronic devices.

[0074] The terminal is used to perform automated tasks according to supply procedures instructed by the server. Specifically, it utilizes Python scripts to generate purchase orders for necessary supplies and sends them to the warehouse management system via API. This improves efficiency and accuracy by eliminating human intervention.

[0075] Users are responsible for monitoring system operations and checking supply status. They can confirm supply completion on their devices, contributing to the stable operation of the system. Furthermore, they receive warnings when supply requirements fall below set levels, enabling prompt action as needed. Notifications are provided via email or mobile app push notifications to enhance the speed of response.

[0076] As a concrete example, the server predicts the supply requirements for a particular product using past consumption data. For instance, if a store consumes an average of 20 catalogs daily, the server predicts the quantity needed for the following month. Using a generative AI model, the terminal automatically places an order for 200 catalogs at the appropriate time. This process uses the prompt, "Describe a system that uses store inventory data to predict the next month's supply schedule and automatically places an order at the appropriate time." In this way, the system achieves increased efficiency and accuracy in inventory management.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server retrieves supply history information from the storage device. The input to this retrieval process is the supply history data stored in the storage device, which mainly includes the type, quantity, and frequency of replenishment of supplied items. The output is a dataset in a format that is easy for the AI ​​model to handle. This dataset is then formatted as input data to be supplied to the predictive model.

[0080] Step 2:

[0081] The server uses an AI model to predict supply requirements. The input for this step is a formatted dataset. The server runs the prediction model using libraries such as TensorFlow or PyTorch to calculate the future required quantity of each supply. This prediction is output, clearly indicating the future inventory levels that will be needed.

[0082] Step 3:

[0083] The server automatically generates a supply schedule based on predicted supply requirements. The input is the supply requirements predicted by an AI model. The server uses Or-Tools and OptaPlanner to optimize the schedule while considering the availability of supplies and store demand. The output of this process is a supply schedule that includes the optimal supply date, time, and quantity for each supply item.

[0084] Step 4:

[0085] The terminal automatically performs supply procedures according to the supply schedule received from the server. The input is a supply schedule, and the terminal generates a purchase order using a Python script based on it, which is then sent to the warehouse management system via API. The output of this step is the execution of supply procedures with the correct quantity and timing.

[0086] Step 5:

[0087] The user confirms delivery is complete, and the server updates the database. The input is a report from the terminal, indicating that the delivery was completed as scheduled. The server receives this information and updates the inventory information in the database. The output is that this update ensures that accurate inventory information is always maintained.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Modern retail stores require inventory management of a wide variety of products, but this process is still often reliant on manual labor. This can lead to inventory shortages and surpluses, resulting in decreased operational efficiency and lost sales opportunities. Furthermore, manual inventory management makes real-time situational awareness difficult, often requiring quick responses. In addition, there is a lack of support for store staff to perform necessary actions quickly and smoothly. For these reasons, an efficient and automated inventory management system is needed.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes a device for collecting past supply history information, a device for predicting the required supply quantity based on the collected supply history information, a device for automatically generating a supply schedule based on the predicted required supply quantity, a device for automatically performing supply procedures according to the generated supply schedule, a notification device for issuing a warning when the inventory level falls below a predetermined standard, and a user interface device for the user to monitor the supply status and make necessary adjustments. This enables automated supply management to efficiently manage in-store inventory and prevent inventory shortages in real time.

[0093] "Supply history information" refers to information including the type, quantity, and frequency of replenishment of goods supplied in the past.

[0094] "Supply requirement" refers to the amount of goods needed based on projected demand.

[0095] A "supply schedule" is a plan for replenishing supplies, generated based on predicted supply needs.

[0096] "Supply procedures" refer to the series of steps taken to actually order and replenish supplies according to the supply schedule.

[0097] An "device" is a device or its components designed to perform a specific function or role.

[0098] A "notification device" is a device that sends warnings or information to a user when a specific event or condition occurs.

[0099] A "user interface device" is a device that allows users to access a system, view information, and perform operations.

[0100] The system for carrying out this invention includes an algorithm for analyzing past supply history information and predicting future supply requirements, and a component for automatically generating supply schedules based on the predictions. A server acts as a central unit for performing these processes, ingesting supply history stored in a database and calculating supply requirements based on this. In data calculations, the server utilizes a generative AI model to predict supply requirements with high accuracy.

[0101] The user interface device is used to monitor inventory levels within the store and is designed for easy user access. Users can receive alerts and take necessary actions quickly based on them. When the notification device detects a drop in inventory levels, it sends a real-time warning to the user.

[0102] As a concrete example, in preparation for a special sale event at a store, the server uses a generative AI model to predict the required supply quantities. If a prompt message such as "Please tell me which products are expected to be in short supply at the store in preparation for next week's special sale event, and the recommended order quantities," is entered, the server will output the prediction results, helping the store manage its supply efficiently.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server retrieves historical supply information from the database. This information includes the type, quantity, and frequency of replenishment of supplied items. It receives supply history information as input and formats this data as output, preparing it for analysis.

[0106] Step 2:

[0107] The server uses the acquired supply history information to generate an AI model and predict the required supply quantity. In this process, the AI ​​model analyzes the input data to estimate future demand to prevent supply shortages or surpluses, and outputs the results. Specifically, the server invokes the AI ​​model and performs model inference.

[0108] Step 3:

[0109] The server automatically generates supply schedules based on predictions from an AI model. It uses the predicted supply requirements as input and creates a supply schedule as output. The server calculates this schedule and outputs an optimized supply schedule.

[0110] Step 4:

[0111] The terminal displays the supply schedule generated through the user interface to the user. It also allows the user to verify the supply as needed. It receives the supply schedule as input, visually represents it as output, and provides a means for the user to interact with it.

[0112] Step 5:

[0113] The notification device alerts the user when inventory levels fall below a certain threshold. The server monitors inventory information, receives data below the threshold as input, and outputs an alert. Specifically, a notification is generated and sent to the user via email or application.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention combines an emotion engine with a supply system to recognize user emotions and optimize the supply process. In this embodiment, the server, terminal, and emotion engine operate in coordination with each other.

[0116] First, the server retrieves historical supply information from a database and collects patterns of how the user has supplied supplies in the past. Next, the server uses AI to predict future supply needs based on this information. Based on this prediction, it automatically generates a supply schedule. The generated schedule is then presented to the user, and a process of requesting confirmation follows.

[0117] Furthermore, the emotion engine recognizes the user's current emotional state. Specifically, the device collects the user's emotional information, and the emotion engine analyzes this information. Based on this analysis, if the user is feeling stressed or anxious, the server adjusts the supply schedule accordingly. Possible adjustments include changing the supply priority based on urgency or altering the notification method.

[0118] For example, if the server detects that a user is frequently experiencing stress, it will flexibly adjust the timing of deliveries based on that information. Specifically, if the emotion engine detects a high stress level in a user, the server will pre-adjust the delivery schedule to ensure smooth delivery. The server can also provide the user with relaxation feedback as needed.

[0119] This configuration not only improves the efficiency of the supply system but also enhances the user experience. A supply process that reflects the user's emotional state reduces workload and optimizes supply.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server retrieves historical supply data from the database. This data includes the supply date, supply quantity, and consumed quantity, and the server uses this information to prepare for predicting future supply needs.

[0123] Step 2:

[0124] The server uses an AI model based on the acquired data to predict supply requirements. The algorithm takes into account predicted consumption patterns and seasonal fluctuations to calculate the required supply for the following month. Based on these calculations, a supply schedule is automatically generated.

[0125] Step 3:

[0126] The terminal displays the generated supply schedule to the user. The user can review this schedule and make adjustments as needed. For example, if there is an important event, it is possible to instruct the system to increase supply to coincide with that event.

[0127] Step 4:

[0128] The emotion engine collects user emotional data through the device. It analyzes the user's emotional state from their facial expressions, voice, and input content to determine if the user is experiencing stress.

[0129] Step 5:

[0130] The device sends the results of its emotion engine analysis to the server. Based on these results, the server adjusts the delivery schedule. For example, if the user is experiencing stress, the server will set the delivery timing with more buffer time and refrain from sending urgent notifications.

[0131] Step 6:

[0132] The terminal automatically executes supply procedures and places supply orders according to the generated schedule. This includes the process of generating supply purchase orders through the online system and sending them to the warehouse.

[0133] Step 7:

[0134] When a delivery is complete, the user confirms the completion information on their terminal and feeds this information back to the server. Based on this feedback, the server updates the inventory information in its database and uses it for the next forecasting model.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] In supply systems, it is necessary not only to improve supply efficiency but also to take into account the emotional state of users to simultaneously improve user experience and reduce workload. Conventional supply systems do not adjust supply in accordance with the emotional state of users, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide a more optimized supply process.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes means for collecting past supply history information, means for predicting future supply requirements based on the collected supply history information, means for automatically generating a supply schedule based on the predicted supply requirements, means for collecting user sentiment information, means for analyzing the collected sentiment information to recognize the user's sentiment state, and means for adjusting the supply schedule based on the recognized sentiment state. This enables not only efficient supply but also flexible supply adjustments that reflect the user's sentiment state.

[0140] "Supply history information" refers to a collection of data showing past supply records, including the date and time of supply, the quantity supplied, and related information.

[0141] "Supply requirement" refers to the amount of supply needed to ensure efficient supply during a specific period in the future.

[0142] "Supply schedule" refers to a plan that includes the specific date and time of supply and the corresponding supply quantity.

[0143] "Emotional information" refers to data that indicates the user's current emotional state, and includes information collected by sensors and other input devices.

[0144] "Emotional state" refers to the user's mental and psychological condition, such as stress, reassurance, or anxiety.

[0145] "Adjustment" refers to modifying or changing the content of a plan or procedure to suit a particular situation.

[0146] This invention aims to improve the efficiency of the supply process and enhance the user experience by combining past supply history information with the user's emotional state in the supply system. This system primarily operates using a server, terminals, and an emotion engine.

[0147] The server retrieves historical supply information from the database. This information includes the date, time, and quantity of each supply, as well as comparisons with predicted values, forming the basis for supply pattern analysis. Next, the server utilizes a generative AI model to predict future supply needs based on historical data. This AI model uses machine learning algorithms, and these predictions help in the automatic generation of supply schedules.

[0148] The device collects user emotional information through various sensors and cameras. For example, using facial recognition technology and voice analysis technology, this information is sent to an emotion engine. The emotion engine analyzes this information to evaluate whether the user is currently relaxed or stressed. The results of this analysis are provided to a server and used to adjust the supply schedule.

[0149] For example, if a user reports high stress levels, the server can revise the delivery schedule and reduce the frequency of notifications to alleviate stress. Furthermore, the server can, if necessary, provide the user with messages such as, "Try having some tea to relax."

[0150] Examples of prompts for a generative AI model are as follows:

[0151] "Based on last week's supply history, please propose a supply schedule for next week."

[0152] "How can we analyze the current emotional state of users and optimize the supply schedule?"

[0153] In this way, the server, terminal, and emotion engine work together to realize a flexible and efficient supply system that takes into account the user's emotional state.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server accesses the database to retrieve historical supply information. This information is retrieved using SQL queries, and the supply history data is input into the server. The retrieved data includes the date and time of supply, the quantity, and the results of a comparison with the forecast. This is output as a dataset for analysis.

[0157] Step 2:

[0158] The server uses a generative AI model to predict future supply requirements from supply history data. Historical supply pattern data is provided to the AI ​​model as input, and data calculations are performed. This allows the AI ​​model to output prediction results that take into account seasonal variations and changes due to events.

[0159] Step 3:

[0160] The server automatically generates a supply schedule based on the forecast results. In this process, the predicted supply requirements are used as input to create the supply schedule. The output supply schedule includes the supply times and quantities for specific dates.

[0161] Step 4:

[0162] The device collects emotional information from the user. Sensors and cameras are used to collect the user's facial expressions and voice tone. This input data is sent to the emotion engine and output as information indicating the user's emotional state.

[0163] Step 5:

[0164] The emotion engine analyzes the received emotional information to recognize the user's emotional state. This analysis evaluates emotional states such as stress and reassurance based on input facial expression and voice data. The results are then output as data.

[0165] Step 6:

[0166] The server receives the results from the emotion engine and adjusts the supply schedule based on them. If the user is experiencing stress, the notification frequency may be changed or the supply priority may be readjusted. This results in a supply plan optimized for the user.

[0167] Step 7:

[0168] Users can check the adjusted supply schedule through their terminal and input instructions for confirmation or modification as needed. This user feedback is also reflected in future supply plans, enabling a continuous improvement process.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] Traditional supply systems fail to adequately improve the user experience because they do not consider the emotional state of users when optimizing supply schedules. In particular, when users are experiencing stress or anxiety, the supply process may not be properly adjusted, potentially leading to further dissatisfaction. To address these challenges, there is a need for a flexible and effective supply system that reflects the emotional state of users.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for collecting past supply history information, means for predicting the required supply quantity based on the collected supply history information, means for automatically generating a supply schedule based on the predicted required supply quantity, means for recognizing the user's emotional state, means for adjusting the supply schedule based on the recognized emotional state, and means for providing feedback according to the emotional state. This enables a supply process adapted to the user's emotional state, improving the user experience and optimizing supply.

[0174] "Supply history information" refers to all data related to past supplies, including detailed information such as the date, time, quantity, location, and method of supply.

[0175] "Supply requirement" refers to a predicted value representing the amount of goods or services that will be needed within a specific period.

[0176] A "supply schedule" is a plan outlining the date, time, and order in which supplies will be delivered.

[0177] "Supply procedures" refer to the actions taken to carry out supply based on the supply schedule.

[0178] "Emotional state" refers to the psychological or emotional condition that a user is experiencing, including stress levels and relaxation levels.

[0179] "Feedback" refers to information and responses provided in accordance with the user's state, helping users understand their own psychological state and take appropriate action.

[0180] A "server" is a computing device that performs tasks such as collecting supply history information, automatically generating supply schedules, and analyzing emotional states on a network.

[0181] To realize this invention, the supply system consists of a server, a terminal, and an emotion engine. The server retrieves past supply history information from a database and analyzes patterns of how users have made purchases in the past. Based on this information, an artificial intelligence (AI) algorithm is used to predict future supply requirements. In this process, it is recommended to utilize machine learning frameworks such as TensorFlow.

[0182] The device functions as a tool for acquiring the user's emotional state in real time. It utilizes the smartphone's camera and microphone to collect user emotional data through facial recognition and voice analysis. For this purpose, it can use Google Cloud's Face Recognition API and Voice API.

[0183] The emotion engine analyzes the user's emotional state based on data collected from the device and sends the results to the server. The server uses these analysis results to adjust the supply schedule. For example, if the user indicates a high stress level, the server adjusts the timing of the supply to ensure it is delivered quickly. It can also send feedback to the device as needed and provide relaxation music playlists through voice assistants or other means.

[0184] For example, when a user orders lunch while busy at work, if the terminal detects the user's stress level, the server adjusts the order priority and takes measures to deliver the meal more quickly. In this case, the system functions efficiently by using a prompt to the generative AI model that says, "Design an algorithm that analyzes the user's emotional data to analyze their stress level and optimizes the meal delivery schedule."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server retrieves historical supply information from the database. By collecting data such as past supply dates, quantities, and destinations, it analyzes supply patterns and prepares basic data for predicting future supply needs.

[0188] Step 2:

[0189] The server uses artificial intelligence algorithms to predict future supply needs based on collected supply history information. By using machine learning frameworks such as TensorFlow, it creates a predictive model, analyzes the input data, and outputs the desired supply amount.

[0190] Step 3:

[0191] The device uses the smartphone's camera and microphone to capture data on the user's facial expressions and voice in order to collect user emotion information. Using Google Cloud's Face Recognition API and Voice API, it extracts emotion-related features and provides them as input data to be sent to the emotion engine.

[0192] Step 4:

[0193] The emotion engine analyzes emotional data sent from the device. This analysis uses facial features and voice tone to specifically evaluate the user's emotional state, such as stress level, and outputs the analysis results to the server.

[0194] Step 5:

[0195] The server receives the results of the emotional state analysis from the emotion engine and adjusts the supply schedule accordingly. For example, if the stress level is assessed as high, it may adjust the supply timing. This prepares the server to provide feedback to help the user reduce stress.

[0196] Step 6:

[0197] The server sends feedback to the device as needed. For example, it generates a prompt from a generative AI model instructing the device to play a music playlist for the user to relax, and outputs this prompt to the device. This helps the user receive the service comfortably.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention relates to a system for streamlining inventory management of supplies in stores, and more particularly to a form for predicting supply needs and automatically generating and implementing specific supply schedules.

[0215] First, the server retrieves historical supply information from the database. This historical information includes data such as the type, quantity, and frequency of replenishment of supplied items. Based on this data, the server uses AI models and algorithms to predict future supply needs. This prediction makes it possible to implement appropriate replenishment before inventory runs out.

[0216] Users can receive alerts when pre-set thresholds are exceeded. The server generates alerts as needed and notifies users via email or application. This alerting feature can reduce the risk of supply shortages.

[0217] Next, the server automatically generates an optimal supply schedule based on the predicted supply requirements. This schedule takes into account store demand and the availability of supplies, and users can check this schedule through their terminals.

[0218] The terminal automatically performs supply procedures according to the generated schedule. Specifically, it generates purchase orders for the necessary supplies and sends them to the warehouse management system. This allows the supply process to proceed smoothly without human intervention.

[0219] Finally, the user confirms and records on their terminal that the supply has been completed. The server receives this information, updates the database, and keeps the inventory information up to date. This ensures accurate inventory management at all times and enables efficient business operations.

[0220] As a concrete example, if a store consumes an average of 20 catalogs per day, the server calculates the required number for the following month based on past history, and the terminal automatically places an order for 200 catalogs at the appropriate time. The user can monitor this process and take action as needed. In this way, the system according to the present invention enables more efficient and accurate inventory management.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The server retrieves historical supply information from the database. This information includes the type of goods supplied, the quantity supplied, the date and time, and the consumption rate. The retrieved data is used as foundational data for forecasting future demand.

[0224] Step 2:

[0225] The server preprocesses the data based on the acquired historical information. This preprocessing includes data cleaning and formatting. It imputes missing data and removes outliers to prepare the data for analysis.

[0226] Step 3:

[0227] The server uses pre-processed data to build a demand forecasting model and predict future supply needs. This model employs machine learning algorithms and statistical methods to forecast consumption on an hourly basis.

[0228] Step 4:

[0229] The server compares the predicted supply requirements with the current inventory levels and automatically generates a supply schedule. This schedule includes actual supply dates and quantities, and proposes an optimal replenishment plan.

[0230] Step 5:

[0231] The user reviews the supply schedule generated by the server via their terminal. They can also manually modify the schedule as needed. Once the user finalizes the schedule, the process proceeds.

[0232] Step 6:

[0233] The terminal automatically executes supply procedures according to the confirmed schedule. Specifically, it automatically generates purchase orders for the necessary supplies and sends them to the warehouse system.

[0234] Step 7:

[0235] Users monitor the progress of supply operations and confirm completion on their terminals. The confirmed completion information is recorded in the database by the server, and inventory information is updated to the latest state.

[0236] (Example 1)

[0237] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0238] Traditional inventory management systems often fail to respond quickly to market demand fluctuations, frequently resulting in unnecessary excess inventory or stockouts. Furthermore, inventory replenishment is often done manually, leading to increased labor costs and human error. Therefore, there is a need for efficient and accurate inventory management.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes means for acquiring supply history information from a storage device, means for analyzing the acquired supply history information and predicting the required supply quantity using artificial intelligence, and means for automatically generating a supply schedule using optimization technology based on the predicted required supply quantity. This enables efficient inventory management that responds immediately to demand fluctuations, as well as reductions in labor costs and human errors through the automation of replenishment work.

[0241] "Supply history information" refers to data that includes details such as the type, quantity, date and time of supply, and frequency of replenishment of goods supplied in the past.

[0242] "Storage device" refers to a storage device or database used to store and manage supply history information.

[0243] "Artificial intelligence" refers to technology that learns complex patterns based on data and makes predictions and decisions.

[0244] "Supply requirement" refers to the quantity of a supply needed to meet the demand for it.

[0245] "Optimization techniques" refer to methods for calculating the most efficient solutions and schedules, taking resources and conditions into consideration.

[0246] A "supply schedule" is a schedule that indicates the specific dates, times, and frequency of planned replenishment and delivery of supplies.

[0247] "Electronic devices" refer to computers and terminals used for supply procedures and data communication.

[0248] "Supply procedures" refer to a series of operations for ordering, delivering, and replenishing supplies.

[0249] "Criteria" refers to the set values ​​or conditions that serve as the basis for decisions when monitoring supply requirements or issuing alerts.

[0250] "Monitoring" refers to the act of regularly checking the progress and status of supply procedures to ensure that they are functioning correctly.

[0251] "Completed" refers to a state where all planned supply procedures have been carried out and the necessary supplies have been properly replenished.

[0252] This invention is a system for streamlining inventory management of supplies in stores. The main elements of the system consist of a server, terminals, and users.

[0253] The server's primary role is to retrieve supply history information from the storage device. This data includes the type and quantity of supplies, as well as the date and time of supply. Next, the server uses artificial intelligence to predict the required supply quantity based on this history information. This process utilizes deep learning libraries such as TensorFlow and PyTorch, enabling accurate prediction of future supply needs. Furthermore, based on the supply needs predicted through optimization techniques, the server automatically generates a supply schedule using Or-Tools and OptaPlanner. Based on the generated supply schedule, the server issues instructions to automatically execute the supply procedures via electronic devices.

[0254] The terminal is used to perform automated tasks according to supply procedures instructed by the server. Specifically, it utilizes Python scripts to generate purchase orders for necessary supplies and sends them to the warehouse management system via API. This improves efficiency and accuracy by eliminating human intervention.

[0255] Users are responsible for monitoring system operations and checking supply status. They can confirm supply completion on their devices, contributing to the stable operation of the system. Furthermore, they receive warnings when supply requirements fall below set levels, enabling prompt action as needed. Notifications are provided via email or mobile app push notifications to enhance the speed of response.

[0256] As a concrete example, the server predicts the supply requirements for a particular product using past consumption data. For instance, if a store consumes an average of 20 catalogs daily, the server predicts the quantity needed for the following month. Using a generative AI model, the terminal automatically places an order for 200 catalogs at the appropriate time. This process uses the prompt, "Describe a system that uses store inventory data to predict the next month's supply schedule and automatically places an order at the appropriate time." In this way, the system achieves increased efficiency and accuracy in inventory management.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server retrieves supply history information from the storage device. The input to this retrieval process is the supply history data stored in the storage device, which mainly includes the type, quantity, and frequency of replenishment of supplied items. The output is a dataset in a format that is easy for the AI ​​model to handle. This dataset is then formatted as input data to be supplied to the predictive model.

[0260] Step 2:

[0261] The server uses an AI model to predict supply requirements. The input for this step is a formatted dataset. The server runs the prediction model using libraries such as TensorFlow or PyTorch to calculate the future required quantity of each supply. This prediction is output, clearly indicating the future inventory levels that will be needed.

[0262] Step 3:

[0263] The server automatically generates a supply schedule based on predicted supply requirements. The input is the supply requirements predicted by an AI model. The server uses Or-Tools and OptaPlanner to optimize the schedule while considering the availability of supplies and store demand. The output of this process is a supply schedule that includes the optimal supply date, time, and quantity for each supply item.

[0264] Step 4:

[0265] The terminal automatically performs supply procedures according to the supply schedule received from the server. The input is a supply schedule, and the terminal generates a purchase order using a Python script based on it, which is then sent to the warehouse management system via API. The output of this step is the execution of supply procedures with the correct quantity and timing.

[0266] Step 5:

[0267] The user confirms delivery is complete, and the server updates the database. The input is a report from the terminal, indicating that the delivery was completed as scheduled. The server receives this information and updates the inventory information in the database. The output is that this update ensures that accurate inventory information is always maintained.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] Modern retail stores require inventory management of a wide variety of products, but this process is still often reliant on manual labor. This can lead to inventory shortages and surpluses, resulting in decreased operational efficiency and lost sales opportunities. Furthermore, manual inventory management makes real-time situational awareness difficult, often requiring quick responses. In addition, there is a lack of support for store staff to perform necessary actions quickly and smoothly. For these reasons, an efficient and automated inventory management system is needed.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes a device for collecting past supply history information, a device for predicting the required supply quantity based on the collected supply history information, a device for automatically generating a supply schedule based on the predicted required supply quantity, a device for automatically performing supply procedures according to the generated supply schedule, a notification device for issuing a warning when the inventory level falls below a predetermined standard, and a user interface device for the user to monitor the supply status and make necessary adjustments. This enables automated supply management to efficiently manage in-store inventory and prevent inventory shortages in real time.

[0273] "Supply history information" refers to information including the type, quantity, and frequency of replenishment of goods supplied in the past.

[0274] "Supply requirement" refers to the amount of goods needed based on projected demand.

[0275] A "supply schedule" is a plan for replenishing supplies, generated based on predicted supply needs.

[0276] "Supply procedures" refer to the series of steps taken to actually order and replenish supplies according to the supply schedule.

[0277] A "device" is a device or its component designed to perform a specific function or role.

[0278] A "notification device" is a device for sending warnings or information to users when specific events or conditions occur.

[0279] A "user interface device" is a device for users to access the system, view information, and perform operations.

[0280] The system for implementing this invention includes an algorithm for analyzing past supply history information and predicting future supply requirements, and components for automatically generating a supply schedule based on the prediction. The server functions as a central unit for executing these processes, retrieves the supply history stored in the database, and calculates the supply requirements based on this. In data operations, the server utilizes a generated AI model to accurately predict the supply requirements.

[0281] The user interface device is used to monitor inventory levels in the store and is designed to be easily accessible to users. Users can receive alerts and take necessary actions promptly based on them. When the notification device detects a decrease in inventory levels, it sends a warning to the user in real time.

[0282] As a specific example, for a special sales event in a store, the server uses a generated AI model to predict the supply requirements. If the prompt sentence "Please tell me the products predicted to be out of stock in the store for next week's special sales event and the recommended order quantities." is input, the server outputs the prediction result to assist the store in efficiently managing supplies.

[0283] The flow of a specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The server retrieves past supply history information from the database. This information includes the type of supplies, quantity, and replenishment frequency. It receives the supply history information as input, formats these data as output, and prepares for analysis.

[0286] Step 2:

[0287] The server uses the generated AI model with the retrieved supply history information to predict the required supply quantity. In this process, the AI model analyzes the input data, estimates future demand to prevent supply shortages or surpluses, and outputs the results. Specifically, the server calls the AI model and executes the model's inference.

[0288] Step 3:

[0289] The server automatically generates a supply schedule based on the prediction results from the AI model. It uses the predicted required supply quantity as input and creates a supply schedule as output. The server calculates this schedule and outputs an optimized supply schedule.

[0290] Step 4:

[0291] The terminal displays the generated supply schedule to the user through the user interface. Also, it enables the user to perform supply confirmation operations if necessary. It receives the supply schedule as input, visually represents it as output, and provides means for the user to operate.

[0292] Step 5:

[0293] The notification device sends a warning to the user when the inventory level falls below the standard. The server monitors the inventory information, receives data below the standard as input, and outputs an alert. Specifically, a notification is generated and sent to the user through email or an application.

[0294] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0295] This invention combines an emotion engine with a supply system to recognize user emotions and optimize the supply process. In this embodiment, the server, terminal, and emotion engine operate in coordination with each other.

[0296] First, the server retrieves historical supply information from a database and collects patterns of how the user has supplied supplies in the past. Next, the server uses AI to predict future supply needs based on this information. Based on this prediction, it automatically generates a supply schedule. The generated schedule is then presented to the user, and a process of requesting confirmation follows.

[0297] Furthermore, the emotion engine recognizes the user's current emotional state. Specifically, the device collects the user's emotional information, and the emotion engine analyzes this information. Based on this analysis, if the user is feeling stressed or anxious, the server adjusts the supply schedule accordingly. Possible adjustments include changing the supply priority based on urgency or altering the notification method.

[0298] For example, if the server detects that a user is frequently experiencing stress, it will flexibly adjust the timing of deliveries based on that information. Specifically, if the emotion engine detects a high stress level in a user, the server will pre-adjust the delivery schedule to ensure smooth delivery. The server can also provide the user with relaxation feedback as needed.

[0299] This configuration not only improves the efficiency of the supply system but also enhances the user experience. A supply process that reflects the user's emotional state reduces workload and optimizes supply.

[0300] The process flow will be described below.

[0301] Step 1:

[0302] The server retrieves past supply history data from the database. The data retrieved includes the supply date, supply quantity, consumed quantity, etc., and preparations are made to predict the future required supply quantity based on this information.

[0303] Step 2:

[0304] The server utilizes an AI model to predict the required supply quantity based on the retrieved data. The algorithm takes into account the predicted consumption patterns and seasonal variations to calculate the required supply quantity for the next month. Based on this calculation result, a supply schedule is automatically generated.

[0305] Step 3:

[0306] The terminal displays the generated supply schedule to the user. The user can view this schedule and make fine-tuning as needed. For example, in the case of an important event, it is possible to instruct an increase in supply according to that event.

[0307] Step 4:

[0308] The emotion engine collects the user's emotion data through the terminal. The emotion state is analyzed from the user's expression, voice, input content, etc., to determine whether the user is feeling stressed.

[0309] Step 5:

[0310] The terminal sends the result analyzed by the emotion engine to the server. Based on this result, the server adjusts the supply schedule. For example, when the user is feeling stressed, the server takes measures such as setting the supply timing with a margin and withholding urgent notifications.

[0311] Step 6:

[0312] The terminal automatically executes supply procedures and places supply orders according to the generated schedule. This includes the process of generating supply purchase orders through the online system and sending them to the warehouse.

[0313] Step 7:

[0314] When a delivery is complete, the user confirms the completion information on their terminal and feeds this information back to the server. Based on this feedback, the server updates the inventory information in its database and uses it for the next forecasting model.

[0315] (Example 2)

[0316] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0317] In supply systems, it is necessary not only to improve supply efficiency but also to take into account the emotional state of users to simultaneously improve user experience and reduce workload. Conventional supply systems do not adjust supply in accordance with the emotional state of users, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide a more optimized supply process.

[0318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0319] In this invention, the server includes means for collecting past supply history information, means for predicting future supply requirements based on the collected supply history information, means for automatically generating a supply schedule based on the predicted supply requirements, means for collecting user sentiment information, means for analyzing the collected sentiment information to recognize the user's sentiment state, and means for adjusting the supply schedule based on the recognized sentiment state. This enables not only efficient supply but also flexible supply adjustments that reflect the user's sentiment state.

[0320] "Supply history information" refers to a collection of data showing past supply records, including the date and time of supply, the quantity supplied, and related information.

[0321] "Supply requirement" refers to the amount of supply needed to ensure efficient supply during a specific period in the future.

[0322] "Supply schedule" refers to a plan that includes the specific date and time of supply and the corresponding supply quantity.

[0323] "Emotional information" refers to data that indicates the user's current emotional state, and includes information collected by sensors and other input devices.

[0324] "Emotional state" refers to the user's mental and psychological condition, such as stress, reassurance, or anxiety.

[0325] "Adjustment" refers to modifying or changing the content of a plan or procedure to suit a particular situation.

[0326] This invention aims to improve the efficiency of the supply process and enhance the user experience by combining past supply history information with the user's emotional state in the supply system. This system primarily operates using a server, terminals, and an emotion engine.

[0327] The server retrieves historical supply information from the database. This information includes the date, time, and quantity of each supply, as well as comparisons with predicted values, forming the basis for supply pattern analysis. Next, the server utilizes a generative AI model to predict future supply needs based on historical data. This AI model uses machine learning algorithms, and these predictions help in the automatic generation of supply schedules.

[0328] The device collects user emotional information through various sensors and cameras. For example, using facial recognition technology and voice analysis technology, this information is sent to an emotion engine. The emotion engine analyzes this information to evaluate whether the user is currently relaxed or stressed. The results of this analysis are provided to a server and used to adjust the supply schedule.

[0329] For example, if a user reports high stress levels, the server can revise the delivery schedule and reduce the frequency of notifications to alleviate stress. Furthermore, the server can, if necessary, provide the user with messages such as, "Try having some tea to relax."

[0330] Examples of prompts for a generative AI model are as follows:

[0331] "Based on last week's supply history, please propose a supply schedule for next week."

[0332] "How can we analyze the current emotional state of users and optimize the supply schedule?"

[0333] In this way, the server, terminal, and emotion engine work together to realize a flexible and efficient supply system that takes into account the user's emotional state.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server accesses the database to retrieve historical supply information. This information is retrieved using SQL queries, and the supply history data is input into the server. The retrieved data includes the date and time of supply, the quantity, and the results of a comparison with the forecast. This is output as a dataset for analysis.

[0337] Step 2:

[0338] The server uses a generative AI model to predict future supply requirements from supply history data. Historical supply pattern data is provided to the AI ​​model as input, and data calculations are performed. This allows the AI ​​model to output prediction results that take into account seasonal variations and changes due to events.

[0339] Step 3:

[0340] The server automatically generates a supply schedule based on the forecast results. In this process, the predicted supply requirements are used as input to create the supply schedule. The output supply schedule includes the supply times and quantities for specific dates.

[0341] Step 4:

[0342] The device collects emotional information from the user. Sensors and cameras are used to collect the user's facial expressions and voice tone. This input data is sent to the emotion engine and output as information indicating the user's emotional state.

[0343] Step 5:

[0344] The emotion engine analyzes the received emotional information to recognize the user's emotional state. This analysis evaluates emotional states such as stress and reassurance based on input facial expression and voice data. The results are then output as data.

[0345] Step 6:

[0346] The server receives the results from the emotion engine and adjusts the supply schedule based on them. If the user is experiencing stress, the notification frequency may be changed or the supply priority may be readjusted. This results in a supply plan optimized for the user.

[0347] Step 7:

[0348] Users can check the adjusted supply schedule through their terminal and input instructions for confirmation or modification as needed. This user feedback is also reflected in future supply plans, enabling a continuous improvement process.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0351] Traditional supply systems fail to adequately improve the user experience because they do not consider the emotional state of users when optimizing supply schedules. In particular, when users are experiencing stress or anxiety, the supply process may not be properly adjusted, potentially leading to further dissatisfaction. To address these challenges, there is a need for a flexible and effective supply system that reflects the emotional state of users.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes means for collecting past supply history information, means for predicting the required supply quantity based on the collected supply history information, means for automatically generating a supply schedule based on the predicted required supply quantity, means for recognizing the user's emotional state, means for adjusting the supply schedule based on the recognized emotional state, and means for providing feedback according to the emotional state. This enables a supply process adapted to the user's emotional state, improving the user experience and optimizing supply.

[0354] "Supply history information" refers to all data related to past supplies, including detailed information such as the date, time, quantity, location, and method of supply.

[0355] "Supply requirement" refers to a predicted value representing the amount of goods or services that will be needed within a specific period.

[0356] A "supply schedule" is a plan outlining the date, time, and order in which supplies will be delivered.

[0357] "Supply procedures" refer to the actions taken to carry out supply based on the supply schedule.

[0358] "Emotional state" refers to the psychological or emotional condition that a user is experiencing, including stress levels and relaxation levels.

[0359] "Feedback" refers to information and responses provided in accordance with the user's state, helping users understand their own psychological state and take appropriate action.

[0360] A "server" is a computing device that performs tasks such as collecting supply history information, automatically generating supply schedules, and analyzing emotional states on a network.

[0361] To realize this invention, the supply system consists of a server, a terminal, and an emotion engine. The server retrieves past supply history information from a database and analyzes patterns of how users have made purchases in the past. Based on this information, an artificial intelligence (AI) algorithm is used to predict future supply requirements. In this process, it is recommended to utilize machine learning frameworks such as TensorFlow.

[0362] The device functions as a tool for acquiring the user's emotional state in real time. It utilizes the smartphone's camera and microphone to collect user emotional data through facial recognition and voice analysis. For this purpose, it can use Google Cloud's Face Recognition API and Voice API.

[0363] The emotion engine analyzes the user's emotional state based on data collected from the device and sends the results to the server. The server uses these analysis results to adjust the supply schedule. For example, if the user indicates a high stress level, the server adjusts the timing of the supply to ensure it is delivered quickly. It can also send feedback to the device as needed and provide relaxation music playlists through voice assistants or other means.

[0364] For example, when a user orders lunch while busy at work, if the terminal detects the user's stress level, the server adjusts the order priority and takes measures to deliver the meal more quickly. In this case, the system functions efficiently by using a prompt to the generative AI model that says, "Design an algorithm that analyzes the user's emotional data to analyze their stress level and optimizes the meal delivery schedule."

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server retrieves historical supply information from the database. By collecting data such as past supply dates, quantities, and destinations, it analyzes supply patterns and prepares basic data for predicting future supply needs.

[0368] Step 2:

[0369] The server uses artificial intelligence algorithms to predict future supply needs based on collected supply history information. By using machine learning frameworks such as TensorFlow, it creates a predictive model, analyzes the input data, and outputs the desired supply amount.

[0370] Step 3:

[0371] The device uses the smartphone's camera and microphone to capture data on the user's facial expressions and voice in order to collect user emotion information. Using Google Cloud's Face Recognition API and Voice API, it extracts emotion-related features and provides them as input data to be sent to the emotion engine.

[0372] Step 4:

[0373] The emotion engine analyzes emotional data sent from the device. This analysis uses facial features and voice tone to specifically evaluate the user's emotional state, such as stress level, and outputs the analysis results to the server.

[0374] Step 5:

[0375] The server receives the results of the emotional state analysis from the emotion engine and adjusts the supply schedule accordingly. For example, if the stress level is assessed as high, it may adjust the supply timing. This prepares the server to provide feedback to help the user reduce stress.

[0376] Step 6:

[0377] The server sends feedback to the device as needed. For example, it generates a prompt from a generative AI model instructing the device to play a music playlist for the user to relax, and outputs this prompt to the device. This helps the user receive the service comfortably.

[0378] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0379] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0384] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0386] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0388] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0389] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0390] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0391] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0393] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0394] This invention relates to a system for streamlining inventory management of supplies in stores, and more particularly to a form for predicting supply needs and automatically generating and implementing specific supply schedules.

[0395] First, the server retrieves historical supply information from the database. This historical information includes data such as the type, quantity, and frequency of replenishment of supplied items. Based on this data, the server uses AI models and algorithms to predict future supply needs. This prediction makes it possible to implement appropriate replenishment before inventory runs out.

[0396] Users can receive alerts when pre-set thresholds are exceeded. The server generates alerts as needed and notifies users via email or application. This alerting feature can reduce the risk of supply shortages.

[0397] Next, the server automatically generates an optimal supply schedule based on the predicted supply requirements. This schedule takes into account store demand and the availability of supplies, and users can check this schedule through their terminals.

[0398] The terminal automatically performs supply procedures according to the generated schedule. Specifically, it generates purchase orders for the necessary supplies and sends them to the warehouse management system. This allows the supply process to proceed smoothly without human intervention.

[0399] Finally, the user confirms and records on their terminal that the supply has been completed. The server receives this information, updates the database, and keeps the inventory information up to date. This ensures accurate inventory management at all times and enables efficient business operations.

[0400] As a concrete example, if a store consumes an average of 20 catalogs per day, the server calculates the required number for the following month based on past history, and the terminal automatically places an order for 200 catalogs at the appropriate time. The user can monitor this process and take action as needed. In this way, the system according to the present invention enables more efficient and accurate inventory management.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The server retrieves historical supply information from the database. This information includes the type of goods supplied, the quantity supplied, the date and time, and the consumption rate. The retrieved data is used as foundational data for forecasting future demand.

[0404] Step 2:

[0405] The server preprocesses the data based on the acquired historical information. This preprocessing includes data cleaning and formatting. It imputes missing data and removes outliers to prepare the data for analysis.

[0406] Step 3:

[0407] The server uses pre-processed data to build a demand forecasting model and predict future supply needs. This model employs machine learning algorithms and statistical methods to forecast consumption on an hourly basis.

[0408] Step 4:

[0409] The server compares the predicted supply requirements with the current inventory levels and automatically generates a supply schedule. This schedule includes actual supply dates and quantities, and proposes an optimal replenishment plan.

[0410] Step 5:

[0411] The user reviews the supply schedule generated by the server via their terminal. They can also manually modify the schedule as needed. Once the user finalizes the schedule, the process proceeds.

[0412] Step 6:

[0413] The terminal automatically executes supply procedures according to the confirmed schedule. Specifically, it automatically generates purchase orders for the necessary supplies and sends them to the warehouse system.

[0414] Step 7:

[0415] Users monitor the progress of supply operations and confirm completion on their terminals. The confirmed completion information is recorded in the database by the server, and inventory information is updated to the latest state.

[0416] (Example 1)

[0417] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0418] Traditional inventory management systems often fail to respond quickly to market demand fluctuations, frequently resulting in unnecessary excess inventory or stockouts. Furthermore, inventory replenishment is often done manually, leading to increased labor costs and human error. Therefore, there is a need for efficient and accurate inventory management.

[0419] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0420] In this invention, the server includes means for acquiring supply history information from a storage device, means for analyzing the acquired supply history information and predicting the required supply quantity using artificial intelligence, and means for automatically generating a supply schedule using optimization technology based on the predicted required supply quantity. This enables efficient inventory management that responds immediately to demand fluctuations, as well as reductions in labor costs and human errors through the automation of replenishment work.

[0421] "Supply history information" refers to data that includes details such as the type, quantity, date and time of supply, and frequency of replenishment of goods supplied in the past.

[0422] "Storage device" refers to a storage device or database used to store and manage supply history information.

[0423] "Artificial intelligence" refers to technology that learns complex patterns based on data and makes predictions and decisions.

[0424] "Supply requirement" refers to the quantity of a supply needed to meet the demand for it.

[0425] "Optimization techniques" refer to methods for calculating the most efficient solutions and schedules, taking resources and conditions into consideration.

[0426] A "supply schedule" is a schedule that indicates the specific dates, times, and frequency of planned replenishment and delivery of supplies.

[0427] "Electronic devices" refer to computers and terminals used for supply procedures and data communication.

[0428] "Supply procedures" refer to a series of operations for ordering, delivering, and replenishing supplies.

[0429] "Criteria" refers to the set values ​​or conditions that serve as the basis for decisions when monitoring supply requirements or issuing alerts.

[0430] "Monitoring" refers to the act of regularly checking the progress and status of supply procedures to ensure that they are functioning correctly.

[0431] "Completed" refers to a state where all planned supply procedures have been carried out and the necessary supplies have been properly replenished.

[0432] This invention is a system for streamlining inventory management of supplies in stores. The main elements of the system consist of a server, terminals, and users.

[0433] The server's primary role is to retrieve supply history information from the storage device. This data includes the type and quantity of supplies, as well as the date and time of supply. Next, the server uses artificial intelligence to predict the required supply quantity based on this history information. This process utilizes deep learning libraries such as TensorFlow and PyTorch, enabling accurate prediction of future supply needs. Furthermore, based on the supply needs predicted through optimization techniques, the server automatically generates a supply schedule using Or-Tools and OptaPlanner. Based on the generated supply schedule, the server issues instructions to automatically execute the supply procedures via electronic devices.

[0434] The terminal is used to perform automated tasks according to supply procedures instructed by the server. Specifically, it utilizes Python scripts to generate purchase orders for necessary supplies and sends them to the warehouse management system via API. This improves efficiency and accuracy by eliminating human intervention.

[0435] Users are responsible for monitoring system operations and checking supply status. They can confirm supply completion on their devices, contributing to the stable operation of the system. Furthermore, they receive warnings when supply requirements fall below set levels, enabling prompt action as needed. Notifications are provided via email or mobile app push notifications to enhance the speed of response.

[0436] As a concrete example, the server predicts the supply requirements for a particular product using past consumption data. For instance, if a store consumes an average of 20 catalogs daily, the server predicts the quantity needed for the following month. Using a generative AI model, the terminal automatically places an order for 200 catalogs at the appropriate time. This process uses the prompt, "Describe a system that uses store inventory data to predict the next month's supply schedule and automatically places an order at the appropriate time." In this way, the system achieves increased efficiency and accuracy in inventory management.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The server retrieves supply history information from the storage device. The input to this retrieval process is the supply history data stored in the storage device, which mainly includes the type, quantity, and frequency of replenishment of supplied items. The output is a dataset in a format that is easy for the AI ​​model to handle. This dataset is then formatted as input data to be supplied to the predictive model.

[0440] Step 2:

[0441] The server uses an AI model to predict supply requirements. The input for this step is a formatted dataset. The server runs the prediction model using libraries such as TensorFlow or PyTorch to calculate the future required quantity of each supply. This prediction is output, clearly indicating the future inventory levels that will be needed.

[0442] Step 3:

[0443] The server automatically generates a supply schedule based on predicted supply requirements. The input is the supply requirements predicted by an AI model. The server uses Or-Tools and OptaPlanner to optimize the schedule while considering the availability of supplies and store demand. The output of this process is a supply schedule that includes the optimal supply date, time, and quantity for each supply item.

[0444] Step 4:

[0445] The terminal automatically performs supply procedures according to the supply schedule received from the server. The input is a supply schedule, and the terminal generates a purchase order using a Python script based on it, which is then sent to the warehouse management system via API. The output of this step is the execution of supply procedures with the correct quantity and timing.

[0446] Step 5:

[0447] The user confirms delivery is complete, and the server updates the database. The input is a report from the terminal, indicating that the delivery was completed as scheduled. The server receives this information and updates the inventory information in the database. The output is that this update ensures that accurate inventory information is always maintained.

[0448] (Application Example 1)

[0449] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0450] Modern retail stores require inventory management of a wide variety of products, but this process is still often reliant on manual labor. This can lead to inventory shortages and surpluses, resulting in decreased operational efficiency and lost sales opportunities. Furthermore, manual inventory management makes real-time situational awareness difficult, often requiring quick responses. In addition, there is a lack of support for store staff to perform necessary actions quickly and smoothly. For these reasons, an efficient and automated inventory management system is needed.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0452] In this invention, the server includes a device for collecting past supply history information, a device for predicting the required supply quantity based on the collected supply history information, a device for automatically generating a supply schedule based on the predicted required supply quantity, a device for automatically performing supply procedures according to the generated supply schedule, a notification device for issuing a warning when the inventory level falls below a predetermined standard, and a user interface device for the user to monitor the supply status and make necessary adjustments. This enables automated supply management to efficiently manage in-store inventory and prevent inventory shortages in real time.

[0453] "Supply history information" refers to information including the type, quantity, and frequency of replenishment of goods supplied in the past.

[0454] "Supply requirement" refers to the amount of goods needed based on projected demand.

[0455] A "supply schedule" is a plan for replenishing supplies, generated based on predicted supply needs.

[0456] "Supply procedures" refer to the series of steps taken to actually order and replenish supplies according to the supply schedule.

[0457] An "device" is a device or its components designed to perform a specific function or role.

[0458] A "notification device" is a device that sends warnings or information to a user when a specific event or condition occurs.

[0459] A "user interface device" is a device that allows users to access a system, view information, and perform operations.

[0460] The system for carrying out this invention includes an algorithm for analyzing past supply history information and predicting future supply requirements, and a component for automatically generating supply schedules based on the predictions. A server acts as a central unit for performing these processes, ingesting supply history stored in a database and calculating supply requirements based on this. In data calculations, the server utilizes a generative AI model to predict supply requirements with high accuracy.

[0461] The user interface device is used to monitor inventory levels within the store and is designed for easy user access. Users can receive alerts and take necessary actions quickly based on them. When the notification device detects a drop in inventory levels, it sends a real-time warning to the user.

[0462] As a concrete example, in preparation for a special sale event at a store, the server uses a generative AI model to predict the required supply quantities. If a prompt message such as "Please tell me which products are expected to be in short supply at the store in preparation for next week's special sale event, and the recommended order quantities," is entered, the server will output the prediction results, helping the store manage its supply efficiently.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The server retrieves historical supply information from the database. This information includes the type, quantity, and frequency of replenishment of supplied items. It receives supply history information as input and formats this data as output, preparing it for analysis.

[0466] Step 2:

[0467] The server uses the acquired supply history information to generate an AI model and predict the required supply quantity. In this process, the AI ​​model analyzes the input data to estimate future demand to prevent supply shortages or surpluses, and outputs the results. Specifically, the server invokes the AI ​​model and performs model inference.

[0468] Step 3:

[0469] The server automatically generates supply schedules based on predictions from an AI model. It uses the predicted supply requirements as input and creates a supply schedule as output. The server calculates this schedule and outputs an optimized supply schedule.

[0470] Step 4:

[0471] The terminal displays the supply schedule generated through the user interface to the user. It also allows the user to verify the supply as needed. It receives the supply schedule as input, visually represents it as output, and provides a means for the user to interact with it.

[0472] Step 5:

[0473] The notification device alerts the user when inventory levels fall below a certain threshold. The server monitors inventory information, receives data below the threshold as input, and outputs an alert. Specifically, a notification is generated and sent to the user via email or application.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention combines an emotion engine with a supply system to recognize user emotions and optimize the supply process. In this embodiment, the server, terminal, and emotion engine operate in coordination with each other.

[0476] First, the server retrieves historical supply information from a database and collects patterns of how the user has supplied supplies in the past. Next, the server uses AI to predict future supply needs based on this information. Based on this prediction, it automatically generates a supply schedule. The generated schedule is then presented to the user, and a process of requesting confirmation follows.

[0477] Furthermore, the emotion engine recognizes the user's current emotional state. Specifically, the device collects the user's emotional information, and the emotion engine analyzes this information. Based on this analysis, if the user is feeling stressed or anxious, the server adjusts the supply schedule accordingly. Possible adjustments include changing the supply priority based on urgency or altering the notification method.

[0478] For example, if the server detects that a user is frequently experiencing stress, it will flexibly adjust the timing of deliveries based on that information. Specifically, if the emotion engine detects a high stress level in a user, the server will pre-adjust the delivery schedule to ensure smooth delivery. The server can also provide the user with relaxation feedback as needed.

[0479] This configuration not only improves the efficiency of the supply system but also enhances the user experience. A supply process that reflects the user's emotional state reduces workload and optimizes supply.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The server retrieves historical supply data from the database. This data includes the supply date, supply quantity, and consumed quantity, and the server uses this information to prepare for predicting future supply needs.

[0483] Step 2:

[0484] The server uses an AI model based on the acquired data to predict supply requirements. The algorithm takes into account predicted consumption patterns and seasonal fluctuations to calculate the required supply for the following month. Based on these calculations, a supply schedule is automatically generated.

[0485] Step 3:

[0486] The terminal displays the generated supply schedule to the user. The user can review this schedule and make adjustments as needed. For example, if there is an important event, it is possible to instruct the system to increase supply to coincide with that event.

[0487] Step 4:

[0488] The emotion engine collects user emotional data through the device. It analyzes the user's emotional state from their facial expressions, voice, and input content to determine if the user is experiencing stress.

[0489] Step 5:

[0490] The device sends the results of its emotion engine analysis to the server. Based on these results, the server adjusts the delivery schedule. For example, if the user is experiencing stress, the server will set the delivery timing with more buffer time and refrain from sending urgent notifications.

[0491] Step 6:

[0492] The terminal automatically executes supply procedures and places supply orders according to the generated schedule. This includes the process of generating supply purchase orders through the online system and sending them to the warehouse.

[0493] Step 7:

[0494] When a delivery is complete, the user confirms the completion information on their terminal and feeds this information back to the server. Based on this feedback, the server updates the inventory information in its database and uses it for the next forecasting model.

[0495] (Example 2)

[0496] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0497] In supply systems, it is necessary not only to improve supply efficiency but also to take into account the emotional state of users to simultaneously improve user experience and reduce workload. Conventional supply systems do not adjust supply in accordance with the emotional state of users, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide a more optimized supply process.

[0498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0499] In this invention, the server includes means for collecting past supply history information, means for predicting future supply requirements based on the collected supply history information, means for automatically generating a supply schedule based on the predicted supply requirements, means for collecting user sentiment information, means for analyzing the collected sentiment information to recognize the user's sentiment state, and means for adjusting the supply schedule based on the recognized sentiment state. This enables not only efficient supply but also flexible supply adjustments that reflect the user's sentiment state.

[0500] "Supply history information" refers to a collection of data showing past supply records, including the date and time of supply, the quantity supplied, and related information.

[0501] "Supply requirement" refers to the amount of supply needed to ensure efficient supply during a specific period in the future.

[0502] "Supply schedule" refers to a plan that includes the specific date and time of supply and the corresponding supply quantity.

[0503] "Emotional information" refers to data that indicates the user's current emotional state, and includes information collected by sensors and other input devices.

[0504] "Emotional state" refers to the user's mental and psychological condition, such as stress, reassurance, or anxiety.

[0505] "Adjustment" refers to modifying or changing the content of a plan or procedure to suit a particular situation.

[0506] This invention aims to improve the efficiency of the supply process and enhance the user experience by combining past supply history information with the user's emotional state in the supply system. This system primarily operates using a server, terminals, and an emotion engine.

[0507] The server retrieves historical supply information from the database. This information includes the date, time, and quantity of each supply, as well as comparisons with predicted values, forming the basis for supply pattern analysis. Next, the server utilizes a generative AI model to predict future supply needs based on historical data. This AI model uses machine learning algorithms, and these predictions help in the automatic generation of supply schedules.

[0508] The device collects user emotional information through various sensors and cameras. For example, using facial recognition technology and voice analysis technology, this information is sent to an emotion engine. The emotion engine analyzes this information to evaluate whether the user is currently relaxed or stressed. The results of this analysis are provided to a server and used to adjust the supply schedule.

[0509] For example, if a user reports high stress levels, the server can revise the delivery schedule and reduce the frequency of notifications to alleviate stress. Furthermore, the server can, if necessary, provide the user with messages such as, "Try having some tea to relax."

[0510] Examples of prompts for a generative AI model are as follows:

[0511] "Based on last week's supply history, please propose a supply schedule for next week."

[0512] "How can we analyze the current emotional state of users and optimize the supply schedule?"

[0513] In this way, the server, terminal, and emotion engine work together to realize a flexible and efficient supply system that takes into account the user's emotional state.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server accesses the database to retrieve historical supply information. This information is retrieved using SQL queries, and the supply history data is input into the server. The retrieved data includes the date and time of supply, the quantity, and the results of a comparison with the forecast. This is output as a dataset for analysis.

[0517] Step 2:

[0518] The server uses a generative AI model to predict future supply requirements from supply history data. Historical supply pattern data is provided to the AI ​​model as input, and data calculations are performed. This allows the AI ​​model to output prediction results that take into account seasonal variations and changes due to events.

[0519] Step 3:

[0520] The server automatically generates a supply schedule based on the forecast results. In this process, the predicted supply requirements are used as input to create the supply schedule. The output supply schedule includes the supply times and quantities for specific dates.

[0521] Step 4:

[0522] The device collects emotional information from the user. Sensors and cameras are used to collect the user's facial expressions and voice tone. This input data is sent to the emotion engine and output as information indicating the user's emotional state.

[0523] Step 5:

[0524] The emotion engine analyzes the received emotional information to recognize the user's emotional state. This analysis evaluates emotional states such as stress and reassurance based on input facial expression and voice data. The results are then output as data.

[0525] Step 6:

[0526] The server receives the results from the emotion engine and adjusts the supply schedule based on them. If the user is experiencing stress, the notification frequency may be changed or the supply priority may be readjusted. This results in a supply plan optimized for the user.

[0527] Step 7:

[0528] Users can check the adjusted supply schedule through their terminal and input instructions for confirmation or modification as needed. This user feedback is also reflected in future supply plans, enabling a continuous improvement process.

[0529] (Application Example 2)

[0530] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] Traditional supply systems fail to adequately improve the user experience because they do not consider the emotional state of users when optimizing supply schedules. In particular, when users are experiencing stress or anxiety, the supply process may not be properly adjusted, potentially leading to further dissatisfaction. To address these challenges, there is a need for a flexible and effective supply system that reflects the emotional state of users.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] In this invention, the server includes means for collecting past supply history information, means for predicting the required supply quantity based on the collected supply history information, means for automatically generating a supply schedule based on the predicted required supply quantity, means for recognizing the user's emotional state, means for adjusting the supply schedule based on the recognized emotional state, and means for providing feedback according to the emotional state. This enables a supply process adapted to the user's emotional state, improving the user experience and optimizing supply.

[0534] "Supply history information" refers to all data related to past supplies, including detailed information such as the date, time, quantity, location, and method of supply.

[0535] "Supply requirement" refers to a predicted value representing the amount of goods or services that will be needed within a specific period.

[0536] A "supply schedule" is a plan outlining the date, time, and order in which supplies will be delivered.

[0537] "Supply procedures" refer to the actions taken to carry out supply based on the supply schedule.

[0538] "Emotional state" refers to the psychological or emotional condition that a user is experiencing, including stress levels and relaxation levels.

[0539] "Feedback" refers to information and responses provided in accordance with the user's state, helping users understand their own psychological state and take appropriate action.

[0540] A "server" is a computing device that performs tasks such as collecting supply history information, automatically generating supply schedules, and analyzing emotional states on a network.

[0541] To realize this invention, the supply system consists of a server, a terminal, and an emotion engine. The server retrieves past supply history information from a database and analyzes patterns of how users have made purchases in the past. Based on this information, an artificial intelligence (AI) algorithm is used to predict future supply requirements. In this process, it is recommended to utilize machine learning frameworks such as TensorFlow.

[0542] The device functions as a tool for acquiring the user's emotional state in real time. It utilizes the smartphone's camera and microphone to collect user emotional data through facial recognition and voice analysis. For this purpose, it can use Google Cloud's Face Recognition API and Voice API.

[0543] The emotion engine analyzes the user's emotional state based on data collected from the device and sends the results to the server. The server uses these analysis results to adjust the supply schedule. For example, if the user indicates a high stress level, the server adjusts the timing of the supply to ensure it is delivered quickly. It can also send feedback to the device as needed and provide relaxation music playlists through voice assistants or other means.

[0544] For example, when a user orders lunch while busy at work, if the terminal detects the user's stress level, the server adjusts the order priority and takes measures to deliver the meal more quickly. In this case, the system functions efficiently by using a prompt to the generative AI model that says, "Design an algorithm that analyzes the user's emotional data to analyze their stress level and optimizes the meal delivery schedule."

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The server retrieves historical supply information from the database. By collecting data such as past supply dates, quantities, and destinations, it analyzes supply patterns and prepares basic data for predicting future supply needs.

[0548] Step 2:

[0549] The server uses artificial intelligence algorithms to predict future supply needs based on collected supply history information. By using machine learning frameworks such as TensorFlow, it creates a predictive model, analyzes the input data, and outputs the desired supply amount.

[0550] Step 3:

[0551] The device uses the smartphone's camera and microphone to capture data on the user's facial expressions and voice in order to collect user emotion information. Using Google Cloud's Face Recognition API and Voice API, it extracts emotion-related features and provides them as input data to be sent to the emotion engine.

[0552] Step 4:

[0553] The emotion engine analyzes emotional data sent from the device. This analysis uses facial features and voice tone to specifically evaluate the user's emotional state, such as stress level, and outputs the analysis results to the server.

[0554] Step 5:

[0555] The server receives the results of the emotional state analysis from the emotion engine and adjusts the supply schedule accordingly. For example, if the stress level is assessed as high, it may adjust the supply timing. This prepares the server to provide feedback to help the user reduce stress.

[0556] Step 6:

[0557] The server sends feedback to the device as needed. For example, it generates a prompt from a generative AI model instructing the device to play a music playlist for the user to relax, and outputs this prompt to the device. This helps the user receive the service comfortably.

[0558] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0559] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0560] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0561] [Fourth Embodiment]

[0562] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0563] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0564] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0565] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0566] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0567] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0568] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0569] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0570] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0571] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0572] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0573] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0574] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0575] This invention relates to a system for streamlining inventory management of supplies in stores, and more particularly to a form for predicting supply needs and automatically generating and implementing specific supply schedules.

[0576] First, the server retrieves historical supply information from the database. This historical information includes data such as the type, quantity, and frequency of replenishment of supplied items. Based on this data, the server uses AI models and algorithms to predict future supply needs. This prediction makes it possible to implement appropriate replenishment before inventory runs out.

[0577] Users can receive alerts when pre-set thresholds are exceeded. The server generates alerts as needed and notifies users via email or application. This alerting feature can reduce the risk of supply shortages.

[0578] Next, the server automatically generates an optimal supply schedule based on the predicted supply requirements. This schedule takes into account store demand and the availability of supplies, and users can check this schedule through their terminals.

[0579] The terminal automatically performs supply procedures according to the generated schedule. Specifically, it generates purchase orders for the necessary supplies and sends them to the warehouse management system. This allows the supply process to proceed smoothly without human intervention.

[0580] Finally, the user confirms and records on their terminal that the supply has been completed. The server receives this information, updates the database, and keeps the inventory information up to date. This ensures accurate inventory management at all times and enables efficient business operations.

[0581] As a concrete example, if a store consumes an average of 20 catalogs per day, the server calculates the required number for the following month based on past history, and the terminal automatically places an order for 200 catalogs at the appropriate time. The user can monitor this process and take action as needed. In this way, the system according to the present invention enables more efficient and accurate inventory management.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The server retrieves historical supply information from the database. This information includes the type of goods supplied, the quantity supplied, the date and time, and the consumption rate. The retrieved data is used as foundational data for forecasting future demand.

[0585] Step 2:

[0586] The server preprocesses the data based on the acquired historical information. This preprocessing includes data cleaning and formatting. It imputes missing data and removes outliers to prepare the data for analysis.

[0587] Step 3:

[0588] The server uses pre-processed data to build a demand forecasting model and predict future supply needs. This model employs machine learning algorithms and statistical methods to forecast consumption on an hourly basis.

[0589] Step 4:

[0590] The server compares the predicted supply requirements with the current inventory levels and automatically generates a supply schedule. This schedule includes actual supply dates and quantities, and proposes an optimal replenishment plan.

[0591] Step 5:

[0592] The user reviews the supply schedule generated by the server via their terminal. They can also manually modify the schedule as needed. Once the user finalizes the schedule, the process proceeds.

[0593] Step 6:

[0594] The terminal automatically executes supply procedures according to the confirmed schedule. Specifically, it automatically generates purchase orders for the necessary supplies and sends them to the warehouse system.

[0595] Step 7:

[0596] Users monitor the progress of supply operations and confirm completion on their terminals. The confirmed completion information is recorded in the database by the server, and inventory information is updated to the latest state.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] Traditional inventory management systems often fail to respond quickly to market demand fluctuations, frequently resulting in unnecessary excess inventory or stockouts. Furthermore, inventory replenishment is often done manually, leading to increased labor costs and human error. Therefore, there is a need for efficient and accurate inventory management.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes means for acquiring supply history information from a storage device, means for analyzing the acquired supply history information and predicting the required supply quantity using artificial intelligence, and means for automatically generating a supply schedule using optimization technology based on the predicted required supply quantity. This enables efficient inventory management that responds immediately to demand fluctuations, as well as reductions in labor costs and human errors through the automation of replenishment work.

[0602] "Supply history information" refers to data that includes details such as the type, quantity, date and time of supply, and frequency of replenishment of goods supplied in the past.

[0603] "Storage device" refers to a storage device or database used to store and manage supply history information.

[0604] "Artificial intelligence" refers to technology that learns complex patterns based on data and makes predictions and decisions.

[0605] "Supply requirement" refers to the quantity of a supply needed to meet the demand for it.

[0606] "Optimization techniques" refer to methods for calculating the most efficient solutions and schedules, taking resources and conditions into consideration.

[0607] A "supply schedule" is a schedule that indicates the specific dates, times, and frequency of planned replenishment and delivery of supplies.

[0608] "Electronic devices" refer to computers and terminals used for supply procedures and data communication.

[0609] "Supply procedures" refer to a series of operations for ordering, delivering, and replenishing supplies.

[0610] "Criteria" refers to the set values ​​or conditions that serve as the basis for decisions when monitoring supply requirements or issuing alerts.

[0611] "Monitoring" refers to the act of regularly checking the progress and status of supply procedures to ensure that they are functioning correctly.

[0612] "Completed" refers to a state where all planned supply procedures have been carried out and the necessary supplies have been properly replenished.

[0613] This invention is a system for streamlining inventory management of supplies in stores. The main elements of the system consist of a server, terminals, and users.

[0614] The server's primary role is to retrieve supply history information from the storage device. This data includes the type and quantity of supplies, as well as the date and time of supply. Next, the server uses artificial intelligence to predict the required supply quantity based on this history information. This process utilizes deep learning libraries such as TensorFlow and PyTorch, enabling accurate prediction of future supply needs. Furthermore, based on the supply needs predicted through optimization techniques, the server automatically generates a supply schedule using Or-Tools and OptaPlanner. Based on the generated supply schedule, the server issues instructions to automatically execute the supply procedures via electronic devices.

[0615] The terminal is used to perform automated tasks according to supply procedures instructed by the server. Specifically, it utilizes Python scripts to generate purchase orders for necessary supplies and sends them to the warehouse management system via API. This improves efficiency and accuracy by eliminating human intervention.

[0616] Users are responsible for monitoring system operations and checking supply status. They can confirm supply completion on their devices, contributing to the stable operation of the system. Furthermore, they receive warnings when supply requirements fall below set levels, enabling prompt action as needed. Notifications are provided via email or mobile app push notifications to enhance the speed of response.

[0617] As a concrete example, the server predicts the supply requirements for a particular product using past consumption data. For instance, if a store consumes an average of 20 catalogs daily, the server predicts the quantity needed for the following month. Using a generative AI model, the terminal automatically places an order for 200 catalogs at the appropriate time. This process uses the prompt, "Describe a system that uses store inventory data to predict the next month's supply schedule and automatically places an order at the appropriate time." In this way, the system achieves increased efficiency and accuracy in inventory management.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The server retrieves supply history information from the storage device. The input to this retrieval process is the supply history data stored in the storage device, which mainly includes the type, quantity, and frequency of replenishment of supplied items. The output is a dataset in a format that is easy for the AI ​​model to handle. This dataset is then formatted as input data to be supplied to the predictive model.

[0621] Step 2:

[0622] The server uses an AI model to predict supply requirements. The input for this step is a formatted dataset. The server runs the prediction model using libraries such as TensorFlow or PyTorch to calculate the future required quantity of each supply. This prediction is output, clearly indicating the future inventory levels that will be needed.

[0623] Step 3:

[0624] The server automatically generates a supply schedule based on predicted supply requirements. The input is the supply requirements predicted by an AI model. The server uses Or-Tools and OptaPlanner to optimize the schedule while considering the availability of supplies and store demand. The output of this process is a supply schedule that includes the optimal supply date, time, and quantity for each supply item.

[0625] Step 4:

[0626] The terminal automatically performs supply procedures according to the supply schedule received from the server. The input is a supply schedule, and the terminal generates a purchase order using a Python script based on it, which is then sent to the warehouse management system via API. The output of this step is the execution of supply procedures with the correct quantity and timing.

[0627] Step 5:

[0628] The user confirms delivery is complete, and the server updates the database. The input is a report from the terminal, indicating that the delivery was completed as scheduled. The server receives this information and updates the inventory information in the database. The output is that this update ensures that accurate inventory information is always maintained.

[0629] (Application Example 1)

[0630] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] Modern retail stores require inventory management of a wide variety of products, but this process is still often reliant on manual labor. This can lead to inventory shortages and surpluses, resulting in decreased operational efficiency and lost sales opportunities. Furthermore, manual inventory management makes real-time situational awareness difficult, often requiring quick responses. In addition, there is a lack of support for store staff to perform necessary actions quickly and smoothly. For these reasons, an efficient and automated inventory management system is needed.

[0632] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0633] In this invention, the server includes a device for collecting past supply history information, a device for predicting the required supply quantity based on the collected supply history information, a device for automatically generating a supply schedule based on the predicted required supply quantity, a device for automatically performing supply procedures according to the generated supply schedule, a notification device for issuing a warning when the inventory level falls below a predetermined standard, and a user interface device for the user to monitor the supply status and make necessary adjustments. This enables automated supply management to efficiently manage in-store inventory and prevent inventory shortages in real time.

[0634] "Supply history information" refers to information including the type, quantity, and frequency of replenishment of goods supplied in the past.

[0635] "Supply requirement" refers to the amount of goods needed based on projected demand.

[0636] A "supply schedule" is a plan for replenishing supplies, generated based on predicted supply needs.

[0637] "Supply procedures" refer to the series of steps taken to actually order and replenish supplies according to the supply schedule.

[0638] An "device" is a device or its components designed to perform a specific function or role.

[0639] A "notification device" is a device that sends warnings or information to a user when a specific event or condition occurs.

[0640] A "user interface device" is a device that allows users to access a system, view information, and perform operations.

[0641] The system for carrying out this invention includes an algorithm for analyzing past supply history information and predicting future supply requirements, and a component for automatically generating supply schedules based on the predictions. A server acts as a central unit for performing these processes, ingesting supply history stored in a database and calculating supply requirements based on this. In data calculations, the server utilizes a generative AI model to predict supply requirements with high accuracy.

[0642] The user interface device is used to monitor inventory levels within the store and is designed for easy user access. Users can receive alerts and take necessary actions quickly based on them. When the notification device detects a drop in inventory levels, it sends a real-time warning to the user.

[0643] As a concrete example, in preparation for a special sale event at a store, the server uses a generative AI model to predict the required supply quantities. If a prompt message such as "Please tell me which products are expected to be in short supply at the store in preparation for next week's special sale event, and the recommended order quantities," is entered, the server will output the prediction results, helping the store manage its supply efficiently.

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The server retrieves historical supply information from the database. This information includes the type, quantity, and frequency of replenishment of supplied items. It receives supply history information as input and formats this data as output, preparing it for analysis.

[0647] Step 2:

[0648] The server uses the acquired supply history information to generate an AI model and predict the required supply quantity. In this process, the AI ​​model analyzes the input data to estimate future demand to prevent supply shortages or surpluses, and outputs the results. Specifically, the server invokes the AI ​​model and performs model inference.

[0649] Step 3:

[0650] The server automatically generates supply schedules based on predictions from an AI model. It uses the predicted supply requirements as input and creates a supply schedule as output. The server calculates this schedule and outputs an optimized supply schedule.

[0651] Step 4:

[0652] The terminal displays the supply schedule generated through the user interface to the user. It also allows the user to verify the supply as needed. It receives the supply schedule as input, visually represents it as output, and provides a means for the user to interact with it.

[0653] Step 5:

[0654] The notification device alerts the user when inventory levels fall below a certain threshold. The server monitors inventory information, receives data below the threshold as input, and outputs an alert. Specifically, a notification is generated and sent to the user via email or application.

[0655] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0656] This invention combines an emotion engine with a supply system to recognize user emotions and optimize the supply process. In this embodiment, the server, terminal, and emotion engine operate in coordination with each other.

[0657] First, the server retrieves historical supply information from a database and collects patterns of how the user has supplied supplies in the past. Next, the server uses AI to predict future supply needs based on this information. Based on this prediction, it automatically generates a supply schedule. The generated schedule is then presented to the user, and a process of requesting confirmation follows.

[0658] Furthermore, the emotion engine recognizes the user's current emotional state. Specifically, the device collects the user's emotional information, and the emotion engine analyzes this information. Based on this analysis, if the user is feeling stressed or anxious, the server adjusts the supply schedule accordingly. Possible adjustments include changing the supply priority based on urgency or altering the notification method.

[0659] For example, if the server detects that a user is frequently experiencing stress, it will flexibly adjust the timing of deliveries based on that information. Specifically, if the emotion engine detects a high stress level in a user, the server will pre-adjust the delivery schedule to ensure smooth delivery. The server can also provide the user with relaxation feedback as needed.

[0660] This configuration not only improves the efficiency of the supply system but also enhances the user experience. A supply process that reflects the user's emotional state reduces workload and optimizes supply.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The server retrieves historical supply data from the database. This data includes the supply date, supply quantity, and consumed quantity, and the server uses this information to prepare for predicting future supply needs.

[0664] Step 2:

[0665] The server uses an AI model based on the acquired data to predict supply requirements. The algorithm takes into account predicted consumption patterns and seasonal fluctuations to calculate the required supply for the following month. Based on these calculations, a supply schedule is automatically generated.

[0666] Step 3:

[0667] The terminal displays the generated supply schedule to the user. The user can review this schedule and make adjustments as needed. For example, if there is an important event, it is possible to instruct the system to increase supply to coincide with that event.

[0668] Step 4:

[0669] The emotion engine collects user emotional data through the device. It analyzes the user's emotional state from their facial expressions, voice, and input content to determine if the user is experiencing stress.

[0670] Step 5:

[0671] The device sends the results of its emotion engine analysis to the server. Based on these results, the server adjusts the delivery schedule. For example, if the user is experiencing stress, the server will set the delivery timing with more buffer time and refrain from sending urgent notifications.

[0672] Step 6:

[0673] The terminal automatically executes supply procedures and places supply orders according to the generated schedule. This includes the process of generating supply purchase orders through the online system and sending them to the warehouse.

[0674] Step 7:

[0675] When a delivery is complete, the user confirms the completion information on their terminal and feeds this information back to the server. Based on this feedback, the server updates the inventory information in its database and uses it for the next forecasting model.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] In supply systems, it is necessary not only to improve supply efficiency but also to take into account the emotional state of users to simultaneously improve user experience and reduce workload. Conventional supply systems do not adjust supply in accordance with the emotional state of users, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide a more optimized supply process.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes means for collecting past supply history information, means for predicting future supply requirements based on the collected supply history information, means for automatically generating a supply schedule based on the predicted supply requirements, means for collecting user sentiment information, means for analyzing the collected sentiment information to recognize the user's sentiment state, and means for adjusting the supply schedule based on the recognized sentiment state. This enables not only efficient supply but also flexible supply adjustments that reflect the user's sentiment state.

[0681] "Supply history information" refers to a collection of data showing past supply records, including the date and time of supply, the quantity supplied, and related information.

[0682] "Supply requirement" refers to the amount of supply needed to ensure efficient supply during a specific period in the future.

[0683] "Supply schedule" refers to a plan that includes the specific date and time of supply and the corresponding supply quantity.

[0684] "Emotional information" refers to data that indicates the user's current emotional state, and includes information collected by sensors and other input devices.

[0685] "Emotional state" refers to the user's mental and psychological condition, such as stress, reassurance, or anxiety.

[0686] "Adjustment" refers to modifying or changing the content of a plan or procedure to suit a particular situation.

[0687] This invention aims to improve the efficiency of the supply process and enhance the user experience by combining past supply history information with the user's emotional state in the supply system. This system primarily operates using a server, terminals, and an emotion engine.

[0688] The server retrieves historical supply information from the database. This information includes the date, time, and quantity of each supply, as well as comparisons with predicted values, forming the basis for supply pattern analysis. Next, the server utilizes a generative AI model to predict future supply needs based on historical data. This AI model uses machine learning algorithms, and these predictions help in the automatic generation of supply schedules.

[0689] The device collects user emotional information through various sensors and cameras. For example, using facial recognition technology and voice analysis technology, this information is sent to an emotion engine. The emotion engine analyzes this information to evaluate whether the user is currently relaxed or stressed. The results of this analysis are provided to a server and used to adjust the supply schedule.

[0690] For example, if a user reports high stress levels, the server can revise the delivery schedule and reduce the frequency of notifications to alleviate stress. Furthermore, the server can, if necessary, provide the user with messages such as, "Try having some tea to relax."

[0691] Examples of prompts for a generative AI model are as follows:

[0692] "Based on last week's supply history, please propose a supply schedule for next week."

[0693] "How can we analyze the current emotional state of users and optimize the supply schedule?"

[0694] In this way, the server, terminal, and emotion engine work together to realize a flexible and efficient supply system that takes into account the user's emotional state.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The server accesses the database to retrieve historical supply information. This information is retrieved using SQL queries, and the supply history data is input into the server. The retrieved data includes the date and time of supply, the quantity, and the results of a comparison with the forecast. This is output as a dataset for analysis.

[0698] Step 2:

[0699] The server uses a generative AI model to predict future supply requirements from supply history data. Historical supply pattern data is provided to the AI ​​model as input, and data calculations are performed. This allows the AI ​​model to output prediction results that take into account seasonal variations and changes due to events.

[0700] Step 3:

[0701] The server automatically generates a supply schedule based on the forecast results. In this process, the predicted supply requirements are used as input to create the supply schedule. The output supply schedule includes the supply times and quantities for specific dates.

[0702] Step 4:

[0703] The device collects emotional information from the user. Sensors and cameras are used to collect the user's facial expressions and voice tone. This input data is sent to the emotion engine and output as information indicating the user's emotional state.

[0704] Step 5:

[0705] The emotion engine analyzes the received emotional information to recognize the user's emotional state. This analysis evaluates emotional states such as stress and reassurance based on input facial expression and voice data. The results are then output as data.

[0706] Step 6:

[0707] The server receives the results from the emotion engine and adjusts the supply schedule based on them. If the user is experiencing stress, the notification frequency may be changed or the supply priority may be readjusted. This results in a supply plan optimized for the user.

[0708] Step 7:

[0709] Users can check the adjusted supply schedule through their terminal and input instructions for confirmation or modification as needed. This user feedback is also reflected in future supply plans, enabling a continuous improvement process.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Traditional supply systems fail to adequately improve the user experience because they do not consider the emotional state of users when optimizing supply schedules. In particular, when users are experiencing stress or anxiety, the supply process may not be properly adjusted, potentially leading to further dissatisfaction. To address these challenges, there is a need for a flexible and effective supply system that reflects the emotional state of users.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes means for collecting past supply history information, means for predicting the required supply quantity based on the collected supply history information, means for automatically generating a supply schedule based on the predicted required supply quantity, means for recognizing the user's emotional state, means for adjusting the supply schedule based on the recognized emotional state, and means for providing feedback according to the emotional state. This enables a supply process adapted to the user's emotional state, improving the user experience and optimizing supply.

[0715] "Supply history information" refers to all data related to past supplies, including detailed information such as the date, time, quantity, location, and method of supply.

[0716] "Supply requirement" refers to a predicted value representing the amount of goods or services that will be needed within a specific period.

[0717] A "supply schedule" is a plan outlining the date, time, and order in which supplies will be delivered.

[0718] "Supply procedures" refer to the actions taken to carry out supply based on the supply schedule.

[0719] "Emotional state" refers to the psychological or emotional condition that a user is experiencing, including stress levels and relaxation levels.

[0720] "Feedback" refers to information and responses provided in accordance with the user's state, helping users understand their own psychological state and take appropriate action.

[0721] A "server" is a computing device that performs tasks such as collecting supply history information, automatically generating supply schedules, and analyzing emotional states on a network.

[0722] To realize this invention, the supply system consists of a server, a terminal, and an emotion engine. The server retrieves past supply history information from a database and analyzes patterns of how users have made purchases in the past. Based on this information, an artificial intelligence (AI) algorithm is used to predict future supply requirements. In this process, it is recommended to utilize machine learning frameworks such as TensorFlow.

[0723] The device functions as a tool for acquiring the user's emotional state in real time. It utilizes the smartphone's camera and microphone to collect user emotional data through facial recognition and voice analysis. For this purpose, it can use Google Cloud's Face Recognition API and Voice API.

[0724] The emotion engine analyzes the user's emotional state based on data collected from the device and sends the results to the server. The server uses these analysis results to adjust the supply schedule. For example, if the user indicates a high stress level, the server adjusts the timing of the supply to ensure it is delivered quickly. It can also send feedback to the device as needed and provide relaxation music playlists through voice assistants or other means.

[0725] For example, when a user orders lunch while busy at work, if the terminal detects the user's stress level, the server adjusts the order priority and takes measures to deliver the meal more quickly. In this case, the system functions efficiently by using a prompt to the generative AI model that says, "Design an algorithm that analyzes the user's emotional data to analyze their stress level and optimizes the meal delivery schedule."

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The server retrieves historical supply information from the database. By collecting data such as past supply dates, quantities, and destinations, it analyzes supply patterns and prepares basic data for predicting future supply needs.

[0729] Step 2:

[0730] The server uses artificial intelligence algorithms to predict future supply needs based on collected supply history information. By using machine learning frameworks such as TensorFlow, it creates a predictive model, analyzes the input data, and outputs the desired supply amount.

[0731] Step 3:

[0732] The device uses the smartphone's camera and microphone to capture data on the user's facial expressions and voice in order to collect user emotion information. Using Google Cloud's Face Recognition API and Voice API, it extracts emotion-related features and provides them as input data to be sent to the emotion engine.

[0733] Step 4:

[0734] The emotion engine analyzes emotional data sent from the device. This analysis uses facial features and voice tone to specifically evaluate the user's emotional state, such as stress level, and outputs the analysis results to the server.

[0735] Step 5:

[0736] The server receives the results of the emotional state analysis from the emotion engine and adjusts the supply schedule accordingly. For example, if the stress level is assessed as high, it may adjust the supply timing. This prepares the server to provide feedback to help the user reduce stress.

[0737] Step 6:

[0738] The server sends feedback to the device as needed. For example, it generates a prompt from a generative AI model instructing the device to play a music playlist for the user to relax, and outputs this prompt to the device. This helps the user receive the service comfortably.

[0739] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0740] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0742] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0743] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0744] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0745] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0746] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0747] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0748] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0749] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0750] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0751] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0752] 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.

[0753] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0754] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0755] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0756] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0757] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0758] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0759] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0760] The following is further disclosed regarding the embodiments described above.

[0761] (Claim 1)

[0762] A means of collecting past supply history information,

[0763] A means for predicting the required supply quantity based on collected supply history information,

[0764] A means for automatically generating a supply schedule based on predicted supply requirements,

[0765] A means of automatically carrying out supply procedures according to the generated supply schedule,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising means for issuing a warning when the supply requirement falls below the predicted consumption.

[0769] (Claim 3)

[0770] The system according to claim 1, comprising means for confirming the completion of supply based on an automatically generated supply schedule.

[0771] "Example 1"

[0772] (Claim 1)

[0773] Means for obtaining supply history information from a storage device,

[0774] A means for analyzing acquired supply history information and predicting supply requirements using artificial intelligence,

[0775] A means for automatically generating a supply schedule using optimization technology based on predicted supply requirements,

[0776] A means of automatically executing the supply procedure via an electronic device according to the generated supply schedule,

[0777] A means of recording the completion of supply and updating the storage device,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for issuing a warning when the required supply falls below a set standard.

[0781] (Claim 3)

[0782] The system according to claim 1, comprising means for monitoring and confirming the completion of supply procedures carried out based on an automatically generated supply schedule.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] A device for collecting past supply history information,

[0786] A device that predicts the required supply quantity based on collected supply history information,

[0787] A device that automatically generates a supply schedule based on predicted supply requirements,

[0788] A device that automatically performs supply procedures according to the generated supply schedule,

[0789] A notification device that issues a warning when the inventory level falls below a predetermined standard,

[0790] It includes a user interface device that allows the user to monitor the supply status and make necessary adjustments.

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, comprising a device that confirms the completion of supply based on an automatically generated supply schedule and updates inventory information.

[0794] (Claim 3)

[0795] The system according to claim 1, further comprising an operating device for prompting the user to place an additional order when the required supply falls below the predicted consumption.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] A means of collecting past supply history information,

[0799] A means for predicting future supply requirements based on collected supply history information,

[0800] A means for automatically generating a supply schedule based on predicted supply requirements,

[0801] Means for collecting user sentiment information,

[0802] A means of analyzing collected emotional information to recognize the emotional state of the user,

[0803] Means for adjusting supply schedules based on recognized emotional states,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for issuing a warning when the supply requirement falls below the predicted consumption.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for confirming the completion of supply based on an automatically generated supply schedule.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A means of collecting past supply history information,

[0812] A means for predicting the required supply quantity based on collected supply history information,

[0813] A means for automatically generating a supply schedule based on predicted supply requirements,

[0814] A means of automatically carrying out supply procedures according to the generated supply schedule,

[0815] Means for recognizing the emotional state of the user,

[0816] A means of adjusting the supply schedule based on the recognized emotional state,

[0817] A means of providing feedback according to emotional state,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, further comprising means for issuing a warning when the supply requirement falls below the predicted consumption.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for confirming the completion of supply based on an automatically generated supply schedule. [Explanation of Symbols]

[0823] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting past supply history information, A means for predicting the required supply quantity based on collected supply history information, A means for automatically generating a supply schedule based on predicted supply requirements, A means of automatically carrying out supply procedures according to the generated supply schedule, A system that includes this.

2. The system according to claim 1, further comprising means for issuing a warning when the supply requirement falls below the predicted consumption.

3. The system according to claim 1, comprising means for confirming the completion of supply based on an automatically generated supply schedule.

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