JP7631064B2 - Information processing device, information processing method, and program - Google Patents

Description

Embodiments of the present invention relate to an information processing device, an information processing method, and a program.

Conventionally, for example, in stores such as supermarkets, mass retailers, and convenience stores, future sales (e.g., for today) are predicted based on past sales records. In addition, machine learning is performed using past sales records to create machine learning models (trained models), and the created machine learning models are used to predict sales, etc.

By the way, there are multiple learning methods for machine learning, and in order to determine which method is more appropriate, multiple machine learning models are actually created and the learning results are checked. Even if the same learning method is used, the learning results may vary depending on the parameters used, so in this case, it is preferable to actually create and check multiple machine learning models. However, depending on the learning method and parameters, it may take a considerable amount of time to obtain the learning results. In such cases, it may take a considerable amount of time to select one machine learning model from the multiple machine learning models.

The problem that the present invention aims to solve is to provide an information processing device, an information processing method, and a program that can select a machine learning model without spending a lot of time.

An information processing device of an embodiment includes a learning means for creating a plurality of machine learning models having different learning methods or parameter conditions related to creation, an evaluation means for evaluating the accuracy of each of the plurality of machine learning models based on output results for each machine learning model obtained by inputting evaluation data into each of the machine learning models for which the machine learning has been completed after a first predetermined time has elapsed from the start of the machine learning, a model selection means for selecting one of the machine learning models based on the evaluation result of the evaluation means, and a learning termination means for terminating the machine learning for a machine learning model for which the machine learning has not been completed even after a second predetermined time longer than the first predetermined time has elapsed from the start of the machine learning .

FIG. 1 is a diagram illustrating an information processing system according to an embodiment. FIG. 2 is an explanatory diagram showing the mechanism of machine learning. FIG. 3 is a block diagram showing a hardware configuration of the information processing device. FIG. 4 is a memory map showing the configuration of the sales management section. FIG. 5 is a functional block diagram showing the functional configuration of the information processing device. FIG. 6 is a flowchart showing the flow of a control process related to the selection of a machine learning model in an information processing device. FIG. 7 is a flowchart showing the flow of a control process related to the output of prediction data in an information processing device.

The following describes the embodiments with reference to the drawings. In the embodiments, a headquarters server (cloud server) installed at the headquarters, for example, which selects a machine learning model to be used for sales forecasting in a store such as a supermarket, mass retailer, or convenience store, is described as an example of an information processing device. Note that the present invention should not be interpreted in a limited manner based on the embodiments.

Machine learning is a computer algorithm that automatically improves by learning from experience. It is a well-known technique that uses large amounts of training data to learn patterns and uses the learning results to predict (infer) unknown data. For example, it can analyze a person's preferences based on large amounts of data on product purchasing history, and provide them with information that suits them based on information that they are likely to be interested in. It can also be used for OCR, which learns from large amounts of character data and infers characters written by people.

When performing machine learning, multiple machine learning models with multiple patterns are usually used, and each machine learning model has a different learning method and/or input parameter conditions. This is because the accuracy of each machine learning model differs depending on the learning method and type of parameters used, so it is possible to select a machine learning model with high accuracy for the input data.

The headquarters server of the embodiment uses the most accurate machine learning model among the machine learning models created by machine learning to make sales forecasts for stores and products. Specifically, past sales data associated with sales performance is input and the most accurate machine learning model is selected. The headquarters server of the embodiment also uses the machine learning model to infer future sales data.

FIG. 1 is a diagram showing an information processing system according to an embodiment. As shown in FIG. 1, the information processing system 10 has a headquarters server 1, a store server 3, a POS (Point of Sales) terminal 5, etc. The headquarters server 1 and the store server 3 are connected to each other so that they can communicate with each other via a network N, such as the Internet or a dedicated line. One or more store servers 3 are connected to the headquarters server 1 via the network N. The store server 3 and the POS terminal 5 are connected to each other so that they can communicate with each other within the store T, via a communication line L, such as a LAN (Local Area Network). One or more POS terminals 5 are connected to the store server 3 via the communication line L.

The POS terminal 5 executes product registration and payment processes for products sold at store T. Product registration includes processing to read a symbol such as a barcode attached to a product, and to read and store product information for that product (product name, product price, etc.) based on the product code that identifies the product contained in the symbol. Payment processing includes processing to make payment for registered products. Specifically, it includes processing to calculate the total price of registered products, calculate the amount of change if there is any, and issue a receipt on which payment information such as product information, total price, and change amount is printed.

The POS terminal 5 also performs a process of sending sales information (including product information and payment information) related to the paid items to the store server 3 sequentially or at a specified timing.

The store server 3 receives the sales information sent by the POS terminals 5. The store server 3 aggregates the sales information received from each POS terminal 5 and stores it cumulatively in the sales information section 31. The store server 3 also periodically (for example, once a day) transmits the sales information stored in the sales information section 31 to the headquarters server 1.

The headquarters server 1 stores the sales information received from each store server 3. The headquarters server 1 cumulatively stores the sales information received from each store server 3. The headquarters server 1 also classifies the received sales information into various units and stores it as information by type. For example, the headquarters server 1 stores the sales information by store T. For example, the headquarters server 1 also stores the sales information by product. For example, the headquarters server 1 also stores the sales information by period (by year, month, week, day, time period, day of the week, season, etc.). The headquarters server 1 also stores the sales information in various other units.

The headquarters server 1 periodically performs machine learning to predict future sales based on past sales information. FIG. 2 is an explanatory diagram showing the mechanism of machine learning in the headquarters server 1. As shown in FIG. 2, the headquarters server 1 creates (generates) multiple machine learning models using past data. In the embodiment, three types of machine learning models, model 1, model 2, and model 3, are created. Each machine learning model has a different learning method and/or parameter conditions related to the creation of the machine learning model. Specifically, in the creation of each machine learning model, a set of different parameters including information on the weather, temperature, and implemented measures and the sales information of that day is accumulated every day. The headquarters server 1 performs machine learning from the accumulated daily sales information and parameter information set to create each machine learning model that can output a predetermined prediction result (sales forecast) showing a trend for each machine learning model. Then, the headquarters server 1 compares the prediction results (hereinafter also referred to as learning results) obtained by inputting evaluation data such as sales performance information related to the past sales information to each of the created machine learning models, and selects one machine learning model whose learning result is closest to the sales performance information (i.e., has high accuracy). The evaluation data is a portion of the sales information that was not entered when the machine learning model was created. Most of the sales information is entered when the machine learning model is created, and a portion of the sales information is entered into the evaluation data created as the evaluation data.

Next, the headquarters server 1 inputs past sales data related to the data for which sales forecasting is desired into the selected machine learning model, thereby predicting future sales (for example, today's sales).

Furthermore, when a first predetermined time has elapsed since starting machine learning for multiple machine learning models, the headquarters server 1 evaluates the learning results for the machine learning models for which machine learning has been completed at that time, compares them with sales performance information, and selects one machine learning model. Furthermore, after a certain period of time has elapsed, the headquarters server 1 again evaluates the machine learning models for which machine learning has been completed at that time, compares the sales performance information with the accuracy of the one machine learning model previously selected, and selects another one machine learning model with high accuracy.

In addition, for a machine learning model for which machine learning has not ended even after a second specified time (e.g., 24 hours from the start of machine learning) that is longer than the first specified time has elapsed after the first specified time has elapsed, the headquarters server 1 forcibly ends machine learning for that machine learning model.

For example, when store T closes (a specified time), the headquarters server 1 inputs past sales information and performs machine learning on the machine learning model. If there is a machine learning model for which machine learning has been completed at 5 a.m. the following day, when a first specified time has passed since the specified time, for example, that machine learning model is extracted. The learning results of the machine learning models for which machine learning has been completed are then evaluated. Then, based on the evaluation of each machine learning model, the machine learning model with the highest evaluation (accuracy) is selected.

Next, the headquarters server 1 inputs sales information for the same day of the week last week, for example, into the selected machine learning model, performs inference, and predicts today's sales.

Furthermore, when a certain period of time has elapsed after the first predetermined time has elapsed, if there is a new machine learning model for which machine learning has been completed, the headquarters server 1 extracts that machine learning model. Then, evaluation data is input into the machine learning model for which machine learning has been completed, and the output results obtained are evaluated. Then, the accuracy is compared with that of the previously selected machine learning model, and one machine learning model with higher accuracy is selected. If the previously selected machine learning model is selected again, inference is not performed again, but if a later selected machine learning model is selected, inference is redone using that machine learning model.

In addition, the headquarters server 1 forcibly terminates machine learning for a machine learning model for which machine learning has not terminated even after the first specified time has elapsed and then the second specified time has elapsed.

From here, the hardware of the headquarters server 1 will be described. FIG. 3 is a block diagram showing the hardware configuration of the headquarters server 1. As shown in FIG. 3, the headquarters server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a memory unit 14, and the like. The CPU 11 is the main controller. The ROM 12 stores various programs. The RAM 13 expands programs and various data. The memory unit 14 stores various programs. The CPU 11, the ROM 12, the RAM 13, and the memory unit 14 are connected to each other via a bus 15. The CPU 11, the ROM 12, and the RAM 13 constitute the control unit 100. That is, the control unit 100 executes the control processing of the headquarters server 1, which will be described later, by the CPU 11 operating according to the control programs stored in the ROM 12 and the memory unit 14 and expanded in the RAM 13.

The RAM 33 includes a sales management unit 131, a learning end information unit 132, and an end flag unit 133. The sales management unit 131 cumulatively stores sales information for each store T received from each store server 3. The sales management unit 131 will be described later with reference to FIG. 4. The learning end information unit 132 stores model identification information that identifies (identifies) a machine learning model for which machine learning has ended, and information indicating the learning result of the machine learning model. The end flag unit 133 stores the state of the end flag. If there is a machine learning model for which machine learning has not yet ended, the end flag unit 133 stores the end flag "0". If machine learning has ended for all machine learning models, the end flag unit 133 stores the end flag "1".

The memory unit 14 is composed of a non-volatile memory such as a hard disk drive (HDD) or a flash memory that retains stored information even when the power is turned off, and has a control program unit 141 that stores a control program for controlling the headquarters server 1, a model storage unit 142, a parameter unit 143, and a time storage unit 144. The model storage unit 142 stores a machine learning model that periodically performs machine learning. In the embodiment, as an example, three types of machine learning models, model 1, model 2, and model 3, are stored. The parameter unit 143 stores parameters that are input during machine learning of the machine learning model by model, day, and store. The parameters are, for example, data on daily customer demographics for each store T, information on daily events in the vicinity of each store T, date, day of the week, daily weather, etc. The time storage unit 144 stores information on the time when sales information is input, the first specified time, the second specified time, and a fixed time.

The control unit 100 is also connected to the operation unit 17 and display unit 18 via the bus 15 and the controller 16. The controller 16 receives instructions from the control unit 100 and controls the operation unit 17 and display unit 18. However, for the sake of convenience, the control performed by the controller 16 will be described as being performed by the control unit 100. The operation unit 17 is, for example, a keyboard or touch panel, and is operated by the operator of the headquarters server 1. The display unit 18 is, for example, a liquid crystal display, and displays information to the operator of the headquarters server 1.

The control unit 100 is also connected to the communication unit 19 via the bus 15. The communication unit 19 is connected to each store server 3 via the network N. The control unit 100 is also connected to a timer 20. The timer 20 keeps track of the current time and measures a first predetermined time, a second predetermined time, and a fixed time.

From here, the sales management unit 131 will be described. The sales management unit 131 aggregates and cumulatively stores sales information for each store T received from each store server 3. FIG. 4 is a memory map showing the configuration of the sales management unit 131. As shown in FIG. 4, the sales management unit 131 includes a store sales management unit 1311, a product sales management unit 1312, a total sales management unit 1313, and the like. The store sales management unit 1311 stores, for example, past sales information for each store T, for example, by year. Similarly, the store sales management unit 1311 stores sales information for each store T, for example, by month, week, day, etc. The product sales management unit 1312 stores sales information for each product (or by product classification), for example, by month, week, day, etc. The total sales management unit 1313 stores sales information received from each store server 3, for example, by month, week, day, etc. In addition, the sales management unit 131 stores sales information summarized from other perspectives, for example, by month, week, day, etc.

From here, the functional configuration of the headquarters server 1 will be explained. Figure 5 is a functional block diagram showing the functional configuration of the headquarters server 1. As shown in Figure 5, the control unit 100 of the headquarters server 1 functions as a learning means 101, an evaluation means 102, a model selection means 103, a learning termination means 104, and an inference means 105 by following the control programs stored in the ROM 12 and the control program unit 141 of the memory unit 14.

The learning means 101 has a function of creating multiple machine learning models with different learning methods or parameter conditions related to creation. Sales information is a large amount of data, such as a year's worth of data, that is accumulated by associating daily sales performance data with parameters related to that day.

The evaluation means 102 has a function of evaluating the accuracy of each of the machine learning models based on the output result for each machine learning model obtained by inputting evaluation data into each of the machine learning models for which machine learning has been completed after a first predetermined time has elapsed from the start of machine learning. Specifically, when the model identification information of the machine learning model for which machine learning by the learning means 101 has been completed is stored in the learning completion storage unit 132 at the time when the first predetermined time measured by the timer 20 has elapsed from the start of machine learning, the evaluation means 102 inputs evaluation data into the machine learning model, and evaluates the accuracy of each of the machine learning models based on the obtained learning result (output result) for each machine learning model and the performance data of the evaluation data for the learning result. When there are multiple machine learning models for which machine learning has been completed, the evaluation means 102 evaluates the accuracy of each of the machine learning models based on the obtained learning result for each machine learning model and the performance data for the learning result.

The model selection means 103 has a function of selecting one machine learning model that has been most highly evaluated by the evaluation means 102. Specifically, the model selection means 103 selects one machine learning model that has been most highly evaluated by the evaluation means 102. More specifically, the model selection means 103 selects one machine learning model that has been evaluated by the evaluation means 102 as having the highest accuracy. If only one machine learning model has been evaluated, the model selection means 103 selects that machine learning model. If multiple machine learning models have been evaluated, the model selection means 103 selects the machine learning model that has been most highly evaluated.

The learning termination means 104 has a function of terminating machine learning for a machine learning model for which machine learning has not been completed even after a second predetermined time longer than the first predetermined time has elapsed. Specifically, the learning termination means 104 forcibly terminates machine learning for a machine learning model for which machine learning has not been completed at the time when the timer 20 measures the second predetermined time longer than the first predetermined time.

The inference means 105 has a function of outputting prediction data such as sales information using one of the machine learning models selected by the model selection means 103. Specifically, the inference means 105 inputs the conditions (parameters) to be predicted into one of the machine learning models selected by the model selection means 103, and acquires the output result of the sales forecast output by the machine learning model as prediction data. For example, the inference means 105 inputs parameters such as the day of the week, time of day, and weather conditions of the day for which sales are to be predicted into the machine learning model, and acquires the output result of the future sales forecast inferred from these parameters as prediction data.

From here, the control of the headquarters server 1 will be explained. Figure 6 is a flowchart showing the flow of the control process related to the selection of a machine learning model in the headquarters server 1. Figure 7 is a flowchart showing the flow of the control process related to the output of prediction data in the headquarters server 1.

As shown in FIG. 6, the control unit 100 of the headquarters server 1 judges whether it is a predetermined time based on the timing of the timer 20 (S11). The predetermined time is, for example, a set time after the store T closes, and is stored in the time memory unit 144. If it is judged based on the timing of the timer 20 that it is the predetermined time (Yes in S11), the control unit 100 inputs sales information stored in the sales management unit 131 as past data for outputting forecast data (S12).

For example, if it is desired to predict today's sales for a certain store T, past sales information for that store T is retrieved and input from the store sales management unit 1311. Also, if it is desired to predict sales for a certain product, sales information for that product is retrieved and input from the product sales management unit 1312. Also, if it is desired to predict sales for all stores T combined, sales information is retrieved and input from the total sales management unit 1313. In either case, as much sales information as possible (for example, one year's worth) is input, since the more data there is, the more accurate the machine learning model's output will be.

Next, the learning means 101 starts machine learning for each of the machine learning models (in this embodiment, model 1, model 2, and model 3) (S13). The parameters for model 1, model 2, and model 3 are different from each other. Then, the control unit 100 returns to S11. For the machine learning models for which machine learning has been completed, the control unit 100 stores model identification information that identifies the machine learning model and the output of the machine learning learning results by the machine learning model in the learning completion information unit 132.

If it is determined that the time has not yet reached the predetermined time (No in S11), the control unit 100 determines whether the first predetermined time has elapsed based on the timer 20's counting of the predetermined time and the first predetermined time stored in the time memory unit 144 (S21). The first predetermined time is a predetermined time from the time the machine learning model starts machine learning in S13, and is, for example, the time until a predetermined time in the early morning of the store T (for example, 5:00 AM). If it is determined that the first predetermined time has elapsed (Yes in S21), the control unit 100 determines whether there is a machine learning model that has finished machine learning at the time when the first predetermined time has elapsed since the start of machine learning (S22). Specifically, the control unit 100 determines whether there is a machine learning model that has finished machine learning based on whether model-specific information is stored in the learning end information unit 132.

When it is determined that there is a machine learning model that has completed machine learning (Yes in S22), the control unit 100 determines whether all machine learning models have completed machine learning (S23). In the case of the embodiment, three types of machine learning models, model 1, model 2, and model 3, are performing machine learning, so if model identification information that identifies all (three types) of machine learning models is stored in the learning end information unit 132, the control unit 100 determines that all machine learning models have completed machine learning. If model identification information that identifies all (three types) of machine learning models is not stored in the learning end information unit 132, the control unit 100 determines that all machine learning models have not completed machine learning (i.e., there is a machine learning model that is still in the middle of machine learning).

When it is determined that all machine learning models have finished machine learning (Yes in S23), the control unit 100 stores a flag indicating that all machine learning models have finished machine learning in the end flag unit 133 (S24). For example, when it is determined that all machine learning models have finished machine learning, a flag "1" indicating that all machine learning models have finished machine learning is stored. Note that when it is determined that all machine learning models have not finished machine learning, a flag "0" indicating that all machine learning models have not finished machine learning is stored.

After the process of S24 or in S23, if it is determined that not all machine learning models have completed machine learning (No in S23), the control unit 100 then inputs evaluation data into the machine learning models for which machine learning has been completed and stored in the learning completion information unit 132 to extract the learning results (S25). The evaluation means 102 then compares the extracted learning results for each machine learning model with performance data for the learning results to evaluate the accuracy of each machine learning model (S26). The performance data is an answer (performance data) actually measured for the input past data, and in the case of the embodiment, is, for example, performance data related to sales information for the past year. The closer the learning results are to the performance data, the higher the accuracy of the machine learning model is evaluated.

Next, the control unit 100 compares the multiple evaluated machine learning models to determine which machine learning model has the highest evaluation (accuracy) (S27). The model selection means 103 then selects the single machine learning model with the highest evaluation (S28).

Next, the control unit 100 judges whether the end flag stored in the end flag unit 133 is "1" (S29). If the end flag stored in the end flag unit 133 is not "1" (i.e., "0") (No in S29), the control unit 100 then judges whether a certain time stored in the end time unit 144 has elapsed since the control unit 100 selected one machine learning model (S30). The certain time is the time before the second predetermined time is clocked. The control unit 100 waits until the certain time has elapsed (No in S30), and if it is judged that the certain time has elapsed (Yes in S30), it judges whether the second predetermined time stored in the end time unit 144 has elapsed from the time of the timer 20's predetermined time (S31). The second predetermined time is a time longer than the first predetermined time, for example, 24 hours. For a machine learning model for which machine learning has not ended even after 24 hours have elapsed from the time of the timer 20's predetermined time, it will be the next day, so there is little point in continuing machine learning any further. The second predetermined time is thus set to a time when it becomes less meaningful to continue machine learning from the time the timer 20 reaches the predetermined time. If it is determined that the second predetermined time has not elapsed from the time the timer 20 reaches the predetermined time (No in S31), the control unit 100 returns to S22 and determines whether the machine learning model that is still continuing machine learning has ended machine learning.

On the other hand, if it is determined that the second predetermined time has elapsed since the timer 20 reached the predetermined time (Yes in S31), the learning termination means 104 forcibly terminates the machine learning in the machine learning model that is continuing the machine learning (S32). Then, the control unit 100 terminates the process. Also, if the termination flag stored in the termination flag unit 133 is "1" (Yes in S29), the control unit 100 terminates the process. Note that, if it is determined in S22 that there is no machine learning model that has completed the machine learning (No in S22), the control unit 100 performs the determinations of S30 and S31. That is, the control unit 100 waits for a certain period of time to elapse again and determines whether there is a machine learning model that has completed the machine learning. That is, the control unit 100 determines whether there is a machine learning model that has completed the machine learning every time a certain period of time elapses.

Note that if one machine learning model is selected in S28, and then after determining No in S31, it is determined again in S22 that there is a new machine learning model for which machine learning has been completed (Yes in S22), the control unit 100 evaluates the machine learning model in S26, and in S27 compares the evaluation of the machine learning model with the evaluation of the machine learning model previously selected in S28. The control unit 100 then selects the machine learning model with the highest evaluation in S28.

From here, we will explain the control of inference using one machine learning model selected in S28 and outputting predicted data. FIG. 7 is a flowchart showing the flow of control processing related to the output of predicted data in the headquarters server 1. As shown in FIG. 7, the control unit 100 judges whether one machine learning model has been selected in the processing of S28 (S41). It waits until one machine learning model has been selected (No in S41), and if it is determined that one machine learning model has been selected (Yes in S41), the control unit 100 inputs parameter information, which is past data corresponding to the day for which prediction is desired, to the selected machine learning model (S42). The parameters input in S42 are, for example, parameters for the previous day. Also, for example, parameters for the same day of the week in the previous week. Also, for example, parameters for the same day of the previous month. Also, for example, parameters for the same day of the previous year.

Next, the inference means 105 causes the machine learning model to make an inference based on the input past data (S43). The control unit 100 then outputs future prediction data as the result of the inference made by the machine learning model (S44). This prediction data is, for example, today's sales forecast information. The control unit 100 then ends the process.

As described above, the headquarters server 1 of the embodiment includes a learning means 101 that creates multiple machine learning models with different learning methods or parameter conditions related to creation, an evaluation means 102 that evaluates the accuracy of each machine learning model based on the output results for each machine learning model obtained by inputting evaluation data into each of the multiple machine learning models for which machine learning has been completed after a first predetermined time has elapsed since the start of machine learning, and a model selection means 103 that selects one machine learning model based on the evaluation result of the evaluation means 102.

The headquarters server 1 evaluates the output of the machine learning models that have completed machine learning after the first predetermined time has elapsed, and selects the single machine learning model with the highest evaluation. This makes it possible to select a single machine learning model without spending a lot of time.

Although an embodiment of the present invention has been described above, this embodiment is presented as an example and is not intended to limit the scope of the invention. This new embodiment can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims.

For example, in the embodiment, sales information for store T has been described as an example of input past data. However, the input past data is not limited to this, and any type of past data may be used as long as it corresponds to the output to be obtained.

In addition, in the embodiment, the headquarters server 1 is described as a cloud server, but the headquarters server 1 may be a server other than a cloud server.

In addition, in the embodiment, the headquarters server 1 has been described as an example of an information processing device, but when implementing a machine learning function in one store, for example, the store server 3 installed in store T may be an information processing device.

The programs executed by the headquarters server 1 in this embodiment are provided in the form of installable or executable files recorded on a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, or a digital versatile disk (DVD).

The program executed by the headquarters server 1 of the embodiment may be stored on a computer connected to a network such as the Internet and provided by downloading it via the network. The program executed by the headquarters server 1 of the embodiment may be provided or distributed via a network such as the Internet.

In addition, the programs executed by the headquarters server 1 in the embodiment may be configured to be provided by being pre-installed in a ROM or the like.

REFERENCE SIGNS LIST 1 Headquarters server 3 Store server 5 POS terminal 10 Information processing system 11 CPU
12 ROM
13 RAM
14 Memory section 17 Operation section 18 Display section 19 Communication section 20 Timer 31 Sales information section 33 RAM
REFERENCE SIGNS LIST 100 Control unit 101 Learning means 102 Evaluation means 103 Model selection means 104 Learning end means 105 Inference means 131 Sales management unit 132 Learning end information unit 133 End flag unit 142 Model storage unit 143 Parameter unit 144 Time storage unit 1311 Store sales management unit 1312 Product sales management unit 1313 Total sales management unit

JP 2017-228086 A

Claims (5)

A learning means for creating a plurality of machine learning models having different learning methods or parameter conditions related to the creation;
an evaluation means for evaluating accuracy of each of the machine learning models based on an output result for each of the machine learning models obtained by inputting evaluation data into each of the machine learning models for which the machine learning has been completed after a first predetermined time has elapsed since the start of machine learning; and
A model selection means for selecting one of the machine learning models based on the evaluation result of the evaluation means;
A learning termination means for terminating the machine learning for the machine learning model in which the machine learning has not been terminated even after a second predetermined time that is longer than the first predetermined time has elapsed since the start of the machine learning;
An information processing device comprising: the evaluation means evaluates accuracy of each of the machine learning models based on output results for each of the machine learning models obtained by inputting the evaluation data into the machine learning models for which machine learning by the learning means has been completed, and performance data for the output results, within a certain time period before the second predetermined time has elapsed since the machine learning models were evaluated;
The model selection means selects the most evaluated machine learning model from the evaluated machine learning models and the selected machine learning model.
The information processing device according to claim 1 . an inference means for outputting prediction data inferred based on input past data using the machine learning model selected by the model selection means;
The information processing device according to claim 1 , further comprising: A learning process for creating a plurality of machine learning models having different learning methods or parameter conditions related to the creation;
an evaluation step of evaluating accuracy of each of the machine learning models based on an output result for each of the machine learning models obtained by inputting evaluation data into each of the machine learning models for which the machine learning has been completed after a first predetermined time has elapsed since the start of machine learning;
a model selection step of selecting one of the machine learning models based on the evaluation result of the evaluation step;
a learning termination step of terminating the machine learning for the machine learning model in which the machine learning has not been terminated even after a second predetermined time that is longer than the first predetermined time has elapsed since the start of the machine learning;
An information processing method comprising: A computer as an information processing device
A learning means for creating a plurality of machine learning models having different learning methods or parameter conditions related to the creation;
an evaluation means for evaluating accuracy of each of the machine learning models based on an output result for each of the machine learning models obtained by inputting evaluation data into each of the machine learning models for which the machine learning has been completed after a first predetermined time has elapsed since the start of machine learning;
A model selection means for selecting one of the machine learning models based on the evaluation result of the evaluation means;
A learning termination means for terminating the machine learning for the machine learning model in which the machine learning has not been terminated even after a second predetermined time that is longer than the first predetermined time has elapsed since the start of the machine learning;
A program to make it function as such.

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