JP2019053684A - Information processing device, information processing method, and program - Google Patents

Abstract

To solve the problem that it is conventionally difficult to propose improvement of merchandise.SOLUTION: An information processing device can propose improvement of merchandise. The information processing device includes: a learning information storage part for storing learning information obtained by learning two or more pieces of merchandise information having two or more attribute values and sales information of merchandise; an attribute value storage part for storing the two or more attribute values of the merchandise; a reception part or receiving object merchandise information having one or more attribute values of the merchandise; a candidate attribute value acquisition part for acquiring an additional candidate attribute value being an attribute value which the object merchandise information does not have and being an attribute value of the attribute value storage part; a sales information acquisition part for applying evaluation merchandise information having the one or more attribute values belonging to the object merchandise information and the additional candidate attribute value to the learning information to acquire second sales information; a determination part for determining whether the second sales information is excellent enough to satisfy a predetermined condition; and an improvement information output part for outputting improvement information including the additional candidate attribute value being the source to acquire the second sales information determined to be excellent enough to satisfy the predetermined condition.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an information processing apparatus and the like for making product improvement proposals.

  Conventionally, there has been a technique that can provide effective replacement conditions for the user, and the repair reception side can make a suggestion that the user is less likely to feel distrust (see, for example, Patent Document 1).

JP 2017-142734 A

  However, in the prior art, it has been difficult to propose improvement of products.

  The information processing apparatus according to the first aspect includes a learning information storage unit that stores learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to the sale of the product, A receiving unit that receives target product information having one or more attribute values, a sales information acquiring unit that applies the target product information received by the receiving unit to learning information, and acquires sales information; and sales information that outputs sales information And an output unit.

  With this configuration, the sales information of the product can be predicted.

  In addition, the information processing apparatus according to the second aspect of the invention includes an attribute value storage unit that stores two or more attribute values that can be adopted by the product, and an attribute value that does not have target product information. A candidate attribute value acquisition unit that acquires additional candidate attribute values that are attribute values of the attribute value storage unit, and the sales information acquisition unit replaces the target product information received by the reception unit with the target product The evaluation product information having one or more attribute values and additional candidate attribute values included in the information is applied to the learning information, the second sales information is acquired, and the second sales information satisfies a predetermined condition. Improvement information including an additional candidate attribute value from which a determination unit that determines whether or not the item is good and second sales information that the determination unit determines to be good enough to satisfy a predetermined condition Processing apparatus further comprising an improved information output unit for outputting A.

  With this configuration, it is possible to make an improvement proposal for adding an attribute value of a product that is predicted to be in a favorable sales situation.

  In addition, the information processing apparatus according to the third aspect of the invention determines that the predetermined condition is improved in terms of sales as a result of comparing the sales information and the second sales information with respect to the second invention. An information processing apparatus that further includes an improvement information output unit that outputs improvement information including additional candidate attribute values from which the second sales information that the determination unit has determined to be improved with respect to sales is possible. It is.

  With this configuration, it is possible to make an improvement proposal for adding an attribute value of a product whose sales situation is expected to improve.

  Further, in the information processing apparatus according to the fourth aspect of the invention, the candidate attribute value acquisition unit is an attribute value that the target product information does not have, and the attribute value of the attribute value storage unit is the second or third aspect of the invention. And the sales information acquisition unit applies evaluation product information having one or more attribute values of the target product information and two or more additional candidate attribute values to the learning information, This is an information processing apparatus that acquires second sales information.

  With this configuration, it is possible to make an improvement proposal for adding two or more attribute values of a product whose sales situation is expected to improve.

  The information processing apparatus according to the fifth aspect of the invention replaces the attribute value stored in the target product information with an attribute value storage unit that stores two or more attribute values that can be adopted by the product, as compared with the first invention. A candidate attribute value acquisition unit that acquires a change candidate attribute value that is an attribute value that can be adopted from the attribute value storage unit, and the sales information acquisition unit is a change candidate from one or more attribute values of the target product information Except for the attribute value replaced with the attribute value, the evaluation product information to which the change candidate attribute value is added is applied to the learning information, the second sales information is acquired, and the second sales information has a predetermined condition. It includes a determination unit that determines whether or not it is satisfactory as it is satisfied, and a change candidate attribute value that is a basis for acquiring the second sales information that the determination unit has determined to be satisfactory as a predetermined condition is satisfied. And an improvement information output unit for outputting the improvement information. Boric processing apparatus.

  With this configuration, it is possible to make an improvement proposal for a change in the attribute value of a product that is predicted to be in a favorable sales situation.

  In addition, the information processing apparatus according to the sixth aspect of the present invention determines that the predetermined condition is improved with respect to the sales as a result of comparing the sales information with the second sales information. And an improvement information output unit that outputs improvement information including change candidate attribute values from which the second sale information determined by the determination unit to be improved with respect to sales is obtained. Device.

  With this configuration, it is possible to make an improvement proposal for a change in the attribute value of a product whose sales situation is predicted to improve.

  Further, in the information processing device according to the seventh aspect of the present invention, in contrast to the fifth or sixth aspect, the candidate attribute value acquisition unit can employ attribute values that can be employed in place of two or more attribute values of the target product information. 2 or more change candidate attribute values are acquired from the attribute value storage unit, and the sales information acquisition unit excludes two or more attribute values replaced with the change candidate attribute value from one or more attribute values of the target product information This is an information processing apparatus that applies evaluation product information to which two or more change candidate attribute values are added to learning information and acquires second sales information.

  With this configuration, it is possible to make an improvement proposal for a change in two or more attribute values of a product whose sales situation is predicted to improve.

  Further, in the information processing device according to the eighth aspect of the invention, with respect to any one of the second to seventh aspects, the two or more attribute values of the product include price information for specifying the price of the product, and the additional candidate attribute A price information change unit that acquires price information to be changed in response to addition of a value or change to a change candidate attribute value, and the sales information acquisition unit replaces the price information before the change with price information This is an information processing apparatus that applies evaluation product information having changed price information acquired by a changing unit to learning information, and acquires second sales information.

  With such a configuration, it is possible to make a proposal for improving a product in consideration of price fluctuations.

  Further, in the information processing device according to the ninth aspect of the present invention, with respect to any one of the first to eighth aspects, the two or more attribute values of the product include a function identifier for identifying the function of the product or two or more of the product. The information processing apparatus includes function presence / absence information indicating the presence / absence of each function.

  With this configuration, it is possible to propose improvement in the function of the product.

  Further, in the information processing apparatus according to the tenth aspect of the present invention, with respect to any one of the first to ninth aspects, the sales information is information indicating the number of products sold or information indicating the total sales price of the products It is a processing device.

  With this configuration, it is possible to propose improvement of products that can increase the number of sales or the total sales.

  Further, in the information processing apparatus according to the eleventh aspect of the invention, with respect to any one of the first to tenth inventions, the accepting unit also accepts two or more pieces of product information. A learning unit that learns by an algorithm and acquires learning information is further provided, and the learning information in the learning information storage unit is an information processing apparatus that is learning information acquired by the learning unit.

  With this configuration, product information can be learned.

  According to the information processing apparatus of the present invention, it is possible to propose improvement of a product.

Conceptual diagram of information system A in the first embodiment Block diagram of the information system A The flowchart explaining the operation example of the information processing apparatus 1 Flow chart explaining the improvement information acquisition process The flowchart explaining the operation example of the terminal device 2 Figure showing the attribute information management table Figure showing the product price calculation table Diagram showing the product information group Figure showing the improvement information proposal screen Figure showing the same output example Overview of the computer system Block diagram of the computer system

  Hereinafter, embodiments of an information processing apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.

(Embodiment 1)
In this embodiment, learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to the sale of the product is stored, and the target product information having one or more attribute values of the product is stored. An information system including an information processing apparatus that receives input and outputs sales information will be described. That is, an information system including an information processing apparatus that performs prediction on sales will be described. In addition, you may think widely that a product is a sales object and includes a service.

  Also, in the present embodiment, an information system including an information processing apparatus that outputs one or more attribute values that improve sales status when added will be described. That is, an information system including an information processing apparatus that proposes improvement of sales by adding functions and the like will be described.

  In the present embodiment, an information system including an information processing apparatus that outputs one or two or more attribute values that improve sales status when changed will be described. That is, for example, an information system including an information processing apparatus that proposes improvement of sales by changing attribute values such as color and shape will be described.

  Also, in the present embodiment, an information system including an information processing apparatus that outputs one or more attribute values whose sales situation is improved in consideration of a price that fluctuates due to addition or change of attribute values will be described. That is, an information system including an information processing apparatus that proposes improvement of sales in consideration of a price that fluctuates due to addition or change of an attribute value will be described.

  Furthermore, in the present embodiment, an information system including an information processing apparatus having a function of automatically learning learning information will be described.

  FIG. 1 is a conceptual diagram of an information system A in the present embodiment. The information system A includes an information processing device 1 and one or more terminal devices 2. The information processing apparatus 1 is a so-called server apparatus here. The information processing apparatus 1 is, for example, a cloud server or an ASP server, but the type and installation location are not limited. The terminal device 2 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, and the type thereof is not limited.

  FIG. 2 is a block diagram of the information system A in the present embodiment.

  The information processing apparatus 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit 14. The storage unit 11 includes a learning information storage unit 111 and an attribute value storage unit 112. The processing unit 13 includes a learning unit 131, a candidate attribute value acquisition unit 132, a price information change unit 133, a sales information acquisition unit 134, and a determination unit 135. The output unit 14 includes a sales information output unit 141 and an improvement information output unit 142.

  The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit 26.

  Various types of information are stored in the storage unit 11 constituting the information processing apparatus 1. The various types of information are, for example, learning information described later and a set of attribute values described later. The various information is, for example, one or two or more price management information. The price management information is information for specifying an increased price corresponding to an attribute value to be added or changed. The price management information includes, for example, (function 1,1000 yen), (function 2,500 yen), ((color, yellow), 1000 yen), ((size, 65 inches), 10000 yen), and the like.

  The learning information storage unit 111 stores learning information. The learning information is, for example, information acquired by the learning unit 131 learning. Details of the learning unit 131 will be described later. The learning information is information obtained by learning two or more product information, for example. The learning information may be a set of two or more product information, for example.

  The product information includes two or more attribute values of the product and sales information. The attribute value is, for example, a function identifier for identifying the function of the product, function presence / absence information regarding the presence / absence of each function that the product can adopt, product attribute value (for example, color, shape, weight, type, etc.), presence / absence of the product attribute value Attribute value presence / absence information. It is preferable that the two or more attribute values of the product include price information specifying the price of the product. Sales information is information relating to the sale of a product. The sales information is, for example, information indicating the number of products sold, information indicating the total sales amount of the products, information indicating whether or not the products can be sold well.

  The learning information storage unit 111 may store two or more pieces of learning information in pairs with the product identifier. The product identifier is information for identifying a product. The product is, for example, a television, a personal computer, a digital camera, a car, clothes, a house, etc., and may be anything. The information for identifying the product may be information for identifying the type of the product.

  The attribute value storage unit 112 stores two or more attribute values that can be adopted by the product. Each of the two or more attribute values is, for example, one of a function identifier, function presence / absence information, product attribute value, and attribute value presence / absence information. Moreover, it is suitable that the two or more attribute values that can be adopted by the product are vectors. The two or more attribute values are, for example, (function presence / absence information indicating presence / absence of function 1, function presence / absence information indicating presence / absence of function 2,..., Function presence / absence information indicating presence / absence of function N). The function presence / absence information indicating the presence / absence of a function may be, for example, “1” (with a corresponding function) or “0” (without a corresponding function).

  The attribute value storage unit 112 stores two or more attribute values that can be adopted by the product for one or more attributes. The attribute value storage unit 112 may store many pairs of attribute names and attribute values. A set of attribute name and attribute value has, for example, a structure of (attribute name, attribute value), for example, (color, white), (color, yellow), (color, red), (shape, square) , (Shape, rectangle), (shape, circle), (component, liquid crystal), (component, organic EL), (size, 65 inches), (size, 38 inches), and the like.

  The receiving unit 12 receives target product information. The target product information is information regarding the product to be improved. The target product information has one or more attribute values of the product. The target product information is, for example, (1, 0, ..., 1). In the case where the target product information is, for example, vector data having (the presence / absence of function 1, the presence / absence of function 2,..., The presence / absence of function N), (1, 0,..., 1) This indicates that the product has function 1 and function N, but does not have function 2. Note that the target product information may include sales information.

  The reception unit 12 may receive two or more pieces of product information. Such two or more product information is information for learning. The product information for learning includes sales information.

  For example, the reception unit 12 may receive a learning instruction, an improvement proposal instruction, and a sales prediction instruction. The learning instruction is an instruction for learning product information. The learning instruction has two or more product information. The improvement proposal instruction is an instruction to output improvement information related to the improvement of the product. The improvement proposal instruction has target product information. The sales forecast instruction is an instruction for outputting a sales forecast. The sales prediction instruction has target product information.

  Here, the reception is reception of information transmitted from the terminal device 2 via, for example, a wired or wireless communication line. However, reception is a concept including, for example, reception of information input from an input device such as a keyboard, mouse, touch panel, reception of information read from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory. May be.

  The processing unit 13 performs various processes. The various processes are processes performed by the learning unit 131, the candidate attribute value acquisition unit 132, the price information change unit 133, the sales information acquisition unit 134, and the determination unit 135, for example.

  The learning unit 131 learns two or more pieces of product information using a machine learning algorithm, and acquires learning information. Here, the machine learning is, for example, SVR, deep learning, decision tree, random forest, SVM, or the like. However, any machine learning algorithm may be used. The learning information may be information for obtaining sales information when one or more attribute values of the product are applied. Note that the processing of the learning unit 131 is a known technique, and thus detailed description thereof is omitted.

  The candidate attribute value acquisition unit 132 acquires one or more additional candidate attribute values. The additional candidate attribute value is an attribute value that the received target product information does not have, and is an attribute value stored in the attribute value storage unit 112.

  The candidate attribute value acquisition unit 132 acquires one or more change candidate attribute values from the attribute value storage unit 112. The change candidate attribute value is an attribute value that can be adopted instead of the attribute value included in the target product information, and is an attribute value stored in the attribute value storage unit 112. Here, the change candidate attribute value may be a function identifier indicating a function that is no longer installed.

  The candidate attribute value acquisition unit 132 may acquire one or more additional candidate attribute values and one or more change candidate attribute values.

  The price information changing unit 133 acquires price information to be changed in response to the addition of the addition candidate attribute value or the change to the change candidate attribute value. For example, the price information changing unit 133 acquires price information corresponding to the additional candidate attribute value from the price management information in the storage unit 11 and acquires changed price information obtained by adding the price information. In addition, the price information changing unit 133 acquires, for example, price information corresponding to the attribute value before the change from the price management information in the storage unit 11, and price information corresponding to the change candidate attribute value in the price management of the storage unit 11. Obtained from the information, subtracts the price information corresponding to the attribute value before the change, adds the price information corresponding to the change candidate attribute value, and obtains the price information to be changed. In addition, the price information changing unit 133 adds, for example, one or more attribute values (additional candidate attribute value or change) finally included in the product to the basic price information indicating the basic price of the product stored in the storage unit 11. Price information corresponding to (including candidate attribute values) may be acquired from the price management information in the storage unit 11, and one or more pieces of price information acquired may be added to the basic price information to acquire changed price information. In addition, the acquisition algorithm of the price information changed according to addition of an addition candidate attribute value or a change to a change candidate attribute value is not ask | required.

  The sales information acquisition unit 134 applies the target product information received by the reception unit 12 to the learning information, and acquires the sales information. It can be said that the sales information here is information of sales prediction. For example, the sales information acquisition unit 134 applies the target product information to the learning information using a machine learning algorithm, and acquires the sales information. The machine learning is, for example, SVR, deep learning, decision tree, random forest, SVM, or the like. Moreover, it is suitable that the target product information accepted here does not include sales information.

  Further, the sales information acquisition unit 134 may select product information that is most similar to the target product information received by the reception unit 12 and may acquire the sales information included in the product information. Further, the sales information acquisition unit 134 selects two or more pieces of product information that are similar to the target product information received by the receiving unit 12 so as to satisfy a predetermined condition, and the two or more pieces of sales information included in the product information. The sales information may be acquired by statistically processing the two or more pieces of sales information (for example, calculating an average value, calculating a weighted average, majority vote, etc.).

  The sales information acquisition unit 134 may acquire the second sales information by applying evaluation product information having one or more attribute values and additional candidate attribute values of the target product information to the learning information. The second sales information may also be the same information as the sales information. The second sales information is, for example, information related to the number of sales, information related to the total sales, information related to whether or not the item can be sold well. The evaluation product information is product information used for a product improvement proposal.

  The process for applying the evaluation product information to the learning information is, for example, the machine learning process described above. The process of applying the evaluation product information to the learning information is, for example, determining the product information most similar to the evaluation product information from the learning information, and acquiring the sales information included in the product information as the second sales information. Processing is also acceptable. The process of applying the evaluation product information to the learning information is, for example, that two or more product information that is similar to the evaluation product information so as to satisfy a predetermined condition is determined from the learning information, and is included in the product information 2 The above sales information may be statistically processed to obtain second sales information. The statistical processing here is, for example, calculation of an average value, calculation of a weighted average, majority decision, and the like.

  The sales information acquisition unit 134 may acquire the second sales information by applying evaluation product information having one or more attribute values and two or more additional candidate attribute values included in the target product information to the learning information.

  The sales information acquisition unit 134 removes the attribute value replaced with the change candidate attribute value from one or more attribute values of the target product information, applies the evaluation product information to which the change candidate attribute value is added to the learning information, Sales information may be acquired.

  The sales information acquisition unit 134 learns evaluation product information to which two or more change candidate attribute values are added by excluding two or more attribute values replaced with the change candidate attribute value from one or more attribute values of the target product information. The second sales information may be acquired.

  In addition, the sales information acquisition unit 134 has one or more attribute values and one or more additional candidate attribute values that the target product information has, and replaces the one or more attribute values that the target product information has with the change candidate attribute values. The evaluation product information to which one or more change candidate attribute values are added may be applied to the learning information except for the one or more attribute values, and the second sales information may be acquired.

  The sales information acquisition unit 134 applies the evaluation product information including the changed price information acquired by the price information change unit 133 to the learning information instead of the price information before the change, and acquires the second sales information. Also good.

  The sales information acquisition unit 134 applies evaluation product information having one or more attribute values and additional candidate attribute values of the target product information to the learning information instead of the target product information received by the reception unit 12, and Sales information may be acquired.

  The sales information acquisition unit 134 applies the evaluation product information including the changed price information acquired by the price information change unit 133 to the learning information instead of the price information before the change, and acquires the second sales information. Also good.

  Note that the process of removing the attribute value may be, for example, setting information corresponding to the attribute value to “0”, “NULL”, or deleting the attribute value.

  The determination unit 135 determines whether the second sales information acquired by the sales information acquisition unit 134 is good enough to satisfy a predetermined condition. Here, being so good that the predetermined condition is satisfied is, for example, that the second sales information is equal to or larger than the threshold value or larger than the threshold value. For example, the sales volume is better than the threshold value or larger than the threshold value, or the total sales amount is larger than or equal to the threshold value or larger than the threshold value.

  The predetermined condition is that, for example, as a result of comparing the sales information with the second sales information, it can be determined that the sales are improved. That is, the determination unit 135 may compare the sales information output by the sales information output unit 141 with the second sales information to determine whether or not the sales are improved. This is, for example, “sales information <second sales information”. “Sales information <second sales information” is, for example, an increase in the number of sales or an increase in the total sales. Note that the sales information output by the sales information output unit 141 is the sales information passed to the determination unit 135 by the sales information output unit 141.

  For example, if “sales information <second sales information”, the determination unit 135 determines that the information has been improved. For example, if “second sales information−sales information> = threshold”, the determination unit 135 determines that the improvement has been made. In such a case, the second sales information and sales information are, for example, numerical values such as the number of sales and the total sales, information indicating ranks of three or more stages, and the like.

  Also, for example, when the sales information is information indicating “not selling well” or “not selling well” or “not selling”, and the second sales information is information indicating “selling well” or “selling” The determination unit 135 determines that the improvement has been made. In such a case, the second sales information and the sales information are, for example, one of binary information.

  The output unit 14 outputs various information. The various types of information are, for example, sales information and improvement information described later. Here, the output is usually transmission to an external device such as the terminal device 2. However, output is a concept that includes display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, delivery of processing results to other processing devices or other programs, etc. You may think that there is.

  The sales information output unit 141 outputs the sales information acquired by the sales information acquisition unit 134. The output here may be delivered to another process, transmitted, or the like.

  The improvement information output unit 142 outputs the improvement information. The improvement information is information regarding improvement of the product. The improvement information is information for proposing improvement of the product. The improvement information is information for proposing improvement of products for improving sales. The improvement information is, for example, information including an additional candidate attribute value from which the second sales information determined by the determination unit 135 to be improved with respect to sales is acquired. The improvement information is, for example, information including change candidate attribute values from which the second sales information that the determination unit 135 has determined to be improved with respect to sales is acquired. Further, the improvement information may be information including the addition candidate attribute value and the change candidate attribute value, for example. Furthermore, it is preferable that the improvement information includes, for example, an attribute value before change corresponding to the change candidate attribute value. The improvement information may be only the additional candidate attribute value. The improvement information is, for example, information indicating that the addition candidate attribute value should be added to the product.

  Various information is stored in the terminal storage unit 21 constituting the terminal device 2. The various types of information are, for example, user identifiers that identify users. The various information is information received by the terminal receiving unit 25, for example.

  The terminal reception unit 22 receives various instructions and information. Here, reception means reception of information input from an input device such as a keyboard, mouse, touch panel, reception of information transmitted via a wired or wireless communication line, recording on an optical disk, magnetic disk, semiconductor memory, or the like. It is a concept including reception of information read from a medium. The various instructions and information are, for example, target product information, improvement proposal instructions, two or more product information, and the like. The improvement proposal instruction is an instruction to propose improvement of a product, and includes target product information.

  Any means such as a touch panel, a keyboard, a mouse, or a menu screen may be used for inputting various instructions and information. The terminal reception unit 22 can be realized by a device driver for input means such as a touch panel and a keyboard, control software for a menu screen, and the like.

  The terminal processing unit 23 is, for example, a process that configures information received by the terminal receiving unit 25 into data to be displayed. The various types of processing are, for example, processing configured as an instruction to transmit an instruction received by the terminal reception unit 22 or the like.

  The terminal transmission unit 24 transmits various instructions and information to the information processing apparatus 1. The various instructions and information are, for example, instructions configured by the terminal processing unit 23, instructions and information received by the terminal receiving unit 22, and the like.

  The terminal receiving unit 25 receives various types of information from the information processing apparatus 1. The various types of information are, for example, sales information and improvement information.

  The terminal output unit 26 acquires various types of information. The various types of information are, for example, information received by the terminal receiving unit 22, information received by the terminal receiving unit 25, and information configured by the terminal processing unit 23. The various types of information are, for example, sales information and improvement information.

  Here, output refers to display on a display, projection using a projector, printing with a printer, sound output, transmission to an external device, storage in a recording medium, and output to other processing devices or other programs. It is a concept that includes delivery of processing results.

  The storage unit 11, the learning information storage unit 111, the attribute value storage unit 112, and the terminal storage unit 21 are preferably non-volatile recording media, but can also be realized by volatile recording media.

  The process in which information is stored in the storage unit 11 or the like is not limited. For example, information may be stored in the storage unit 11 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 11 or the like, Alternatively, information input via the input device may be stored in the storage unit 11 or the like.

  The processing unit 13, the learning unit 131, the candidate attribute value acquisition unit 132, the price information change unit 133, the sales information acquisition unit 134, the determination unit 135, and the terminal processing unit 23 can be usually realized by an MPU, a memory, or the like. The processing procedure of the processing unit 13 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 14, the sales information output unit 141, the improvement information output unit 142, and the terminal transmission unit 24 are realized by, for example, wireless or wired communication means, but may be realized by broadcasting means.

  The receiving unit 12 and the terminal receiving unit 25 are realized by, for example, a wireless or wired communication unit, but may be realized by a unit that receives a broadcast.

  The terminal output unit 26 may be considered as including or not including an output device such as a display or a speaker. The terminal output unit 26 may be implemented by output device driver software, or output device driver software and an output device.

  Next, the operation of the information system A will be described. First, an operation example of the information processing apparatus 1 will be described with reference to the flowchart of FIG.

  (Step S301) The receiving unit 12 determines whether a learning instruction has been received. When the learning instruction is accepted, the process goes to step S302, and when the learning instruction is not accepted, the process goes to step S304.

  (Step S302) The learning unit 131 acquires two or more pieces of product information included in the learning instruction received in step S301. Then, the learning unit 131 performs machine learning processing by pairing two or more attribute values included in two or more product information and sales information, and acquires learning information. Note that when two or more attribute values are applied to the learning information, sales information is acquired.

  (Step S303) The learning unit 131 accumulates the learning information acquired in step S302 in the learning information storage unit 111. The process returns to step S301. When the learning instruction includes a product identifier for identifying a product, the learning unit 131 may accumulate learning information in the learning information storage unit 111 in a pair with the product identifier.

  (Step S <b> 304) The receiving unit 12 determines whether an improvement proposal instruction has been received from the terminal device 2. If an improvement proposal instruction is received, the process goes to step S305. If no improvement proposal instruction is received, the process goes to step S307.

  (Step S305) The processing unit 13 acquires improvement information using the target product information included in the improvement proposal instruction. Such improvement information acquisition processing will be described with reference to the flowchart of FIG.

  (Step S306) The improvement information output unit 142 transmits the improvement information acquired in step S305 to the terminal device 2. The process returns to step S301.

  (Step S <b> 307) The receiving unit 12 determines whether a sales prediction instruction is received from the terminal device 2. If a sales prediction instruction is received, the process goes to step S308. If a sales prediction instruction is not received, the process returns to step S301.

  (Step S308) The sales information acquisition unit 134 acquires target product information included in the sales prediction instruction received in step S307.

  (Step S309) Sales information acquisition part 134 applies object product information acquired at Step S308 to learning information, and acquires sales information.

  (Step S310) The sales information output unit 141 transmits the sales information acquired in Step S309 to the terminal device 2. The process returns to step S301.

  In the flowchart of FIG. 3, the process ends when the power is turned off or the process is terminated.

  Next, the improvement information acquisition process of step S305 is demonstrated using the flowchart of FIG.

  (Step S401) The candidate attribute value acquisition unit 132 acquires target product information included in the improvement proposal instruction received in step S304.

  (Step S402) The candidate attribute value acquisition unit 132 acquires one or more additional candidate attribute values from the attribute value storage unit 112. The one or more additional candidate attribute values are attribute values that the target product information does not have, and are attribute values stored in the attribute value storage unit 112. Here, there may be a case where the additional candidate attribute value cannot be acquired.

  (Step S403) The candidate attribute value acquisition unit 132 acquires one or more change candidate attribute values from the attribute value storage unit 112. The one or more change candidate attribute values are attribute values that can be used instead of the attribute values included in the target product information, and are attribute values stored in the attribute value storage unit 112. Here, there may be a case where the change candidate attribute value cannot be acquired.

  (Step S404) Sales information acquisition part 134 substitutes 1 to counter i.

  (Step S405) The sales information acquisition unit 134 selects 1 selected from among 0 or 1 or more additional candidate attribute values acquired in Step S402 and 0 or 1 or more change candidate attribute values acquired in Step S403. It is determined whether or not the i-th combination of the above attribute values exists. If the i-th combination exists, the process goes to step S406. If the i-th combination does not exist, the process goes to step S414. The i-th combination of one or more attribute values is one or two or more attribute values. Note that processing for obtaining a combination of 1 to N pieces of information among N pieces (N is a natural number of 1 or more) pieces of information is a known technique.

  (Step S406) The sales information acquisition unit 134 acquires a set of one or more attribute values of the i-th combination. Each of the one or more attribute values is an addition candidate attribute value or a change candidate attribute value.

  (Step S407) The price information changing unit 133 acquires price information of the product using one or more attribute values of the i-th combination.

  (Step S408) The sales information acquisition unit 134 acquires evaluation product information using one or more attribute values of the i-th combination. In addition, it is suitable for evaluation merchandise information to have the price information acquired by step S407.

  (Step S409) The sales information acquisition unit 134 applies the evaluation product information acquired in step S408 to the learning information in the learning information storage unit 111, and acquires second sales information.

  (Step S410) The sales information acquisition unit 134 acquires the sales information corresponding to the improvement proposal instruction received in step S304. Note that the sales information acquisition unit 134 may acquire the sales information by applying the target product information acquired in step S401 to the learning information in the learning information storage unit 111. Further, the sales information acquisition unit 134 may acquire the sales information included in the improvement proposal instruction received in step S304.

  (Step S411) The determination unit 135 determines whether or not the second sales information acquired in step S409 satisfies a predetermined condition. If the predetermined condition is satisfied, the process goes to step S412. If the predetermined condition is not satisfied, the process goes to step S413. For example, the determination unit 135 compares the sales information acquired in step S410 with the second sales information acquired in step S409, and determines whether or not the sales are improved. If it is determined that it has been improved, the process goes to step S412. If it is determined that it has not been improved, the process goes to step S413.

  (Step S412) The determination unit 135 uses the information of the second sales information (for example, from a comparison result between the sales information and the second sales information) to acquire an improvement score indicating the degree of improvement. Then, the determination unit 135 accumulates the improvement score in association with information for identifying the i-th combination (for example, i, one or more attribute values of the combination).

  (Step S413) The sales information acquisition unit 134 increments the counter i by 1. The process returns to step S405.

  (Step S414) The processing unit 13 determines one or more combinations that are improved to satisfy a predetermined condition.

  (Step S415) The processing unit 13 configures improvement information using one or more combinations determined in step S414. Return to upper process.

  Next, an operation example of the terminal device 2 will be described using the flowchart of FIG.

  (Step S501) The terminal receiving unit 22 determines whether a learning instruction has been received. When the learning instruction is accepted, the process goes to step S502, and when the learning instruction is not accepted, the process goes to step S504.

  (Step S502) The terminal processing unit 23 configures a learning instruction to be transmitted. Note that the learning instruction to be transmitted includes two or more pieces of product information.

  (Step S503) The terminal transmission unit 24 transmits the learning instruction configured in Step S502 to the information processing apparatus 1. The process returns to step S501.

  (Step S504) The terminal reception unit 22 determines whether an improvement proposal instruction has been received. If an improvement proposal instruction is accepted, the process goes to step S505, and if no improvement proposal instruction is accepted, the process goes to step S510.

  (Step S505) The terminal processing unit 23 configures an improvement proposal instruction to be transmitted. Note that the improvement proposal instruction to be transmitted includes target product information.

  (Step S506) The terminal transmission unit 24 transmits the improvement proposal instruction configured in Step S505 to the information processing apparatus 1.

  (Step S507) The terminal reception unit 25 determines whether or not improvement information has been received from the information processing apparatus 1. When the improvement information is received, the process goes to step S508, and when the improvement information is not received, the process goes to step S507.

  (Step S508) The terminal processing unit 23 configures improvement information to be output.

  (Step S509) The terminal output unit 26 outputs the improvement information configured in step S508. The process returns to step S501.

  (Step S510) The terminal receiving unit 22 determines whether a sales prediction instruction has been received. If a sales forecast instruction is accepted, the process goes to step S511. If a sales forecast instruction is not accepted, the process returns to step S501.

  (Step S511) The terminal processing unit 23 constitutes a sales prediction instruction to be transmitted. The sales forecast instruction to be transmitted includes target product information.

  (Step S512) The terminal transmission unit 24 transmits the sales prediction instruction configured in Step S511 to the information processing apparatus 1.

  (Step S513) The terminal receiver 25 determines whether or not sales information has been received from the information processing apparatus 1. When sales information is received, it goes to step S514, and when sales information is not received, it goes to step S513.

  (Step S514) The terminal processing unit 23 constitutes sales information to be output.

  (Step S515) The terminal output unit 26 outputs the sales information configured in step S514. The process returns to step S501.

  In the flowchart of FIG. 5, the process ends when the power is turned off or the process ends.

  Hereinafter, a specific operation of the information system A in the present embodiment will be described. A conceptual diagram of the information system A is shown in FIG.

  Now, the attribute value management unit 112 shown in FIG. 6 is stored in the attribute value storage unit 112. The attribute information management table stores information on attribute values that can be taken by one or more products. The attribute information management table includes “product identifier”, “product name”, and “attribute value”. The “attribute value” includes “function identifier”, “product attribute value”, and “price information”. “Product identifier” is an ID for identifying a product. The product here is the type of the product. “Product name” is the name of the type of product, for example, “automobile”, “television”, and the like. “Function identifier” is information for identifying a function. “Function 11”, “Function 12”, etc. are names of functions and are simplified information. “Function 11” is, for example, a seat heater function, and “Function 12” is, for example, an automatic door function. The function may be optional. The “product attribute value” is an attribute value of a product that can be selected from two or more products. The “product attribute value” is, for example, a color, the number of doors, a shape, a size, or the like. Further, in FIG. 6, “element ID” is element identification information when product information is vectorized. For example, it has the function 11 and the function 12 of the product name “automobile”, does not have the function 1N, the color is “white”, the door is “4 doors”, and the price information is “2,500,000 yen”. A vector of certain product information is (1, 1,..., 0, 1, 0, 0, 1, 0,..., 2500,000). Here, the vehicle product information vector includes (function 11 presence / absence information, function 12 presence / absence information,..., Function 1N function presence / absence information, attribute value presence / absence information of color “white”, color “Black” attribute value presence / absence information, color “red” attribute value presence / absence information, door “4 door” attribute value presence / absence information, door “2 door” attribute value presence / absence information,... ) Is adopted. The product information vector may be product information or a part of product information.

  Further, the storage unit 11 is a commodity price calculation table for automobiles shown in FIG. FIG. 7 has “attribute value” and “increased price”. The “attribute value” is a function identifier of a function added to a product or a product attribute value that can be adopted by the product. “Increased price” is information indicating an increasing price when a paired attribute value is adopted.

  In this situation, it is assumed that the administrator of the information processing apparatus 1 has input a learning instruction including the product information group illustrated in FIG. The product information group has two or more product information. Here, the product information includes function presence / absence information for each function from function 11 to function 1N, product attribute values such as color and door, price information, and sales information. When the function presence / absence information is “1”, it indicates that the corresponding function is provided. When the function presence / absence information is “0”, it indicates that the corresponding function is not provided. The price information indicates a price (yen). Here, the sales information indicates the actual sales volume.

  Next, the terminal reception unit 22 receives a learning instruction. The terminal processing unit 23 constitutes a learning instruction to be transmitted. Note that the terminal processing unit 23 changes the product attribute values such as color and door to values of corresponding elements of the vector, for example. That is, the terminal processing unit 23 converts the color “white” into (..., 1, 0, 0,...) And the color “black” (..., 0, 1, 0,... )), And the color “black” is converted to (..., 0, 0, 1,...). That is, the terminal processing unit 23 reads “product identifier“ 1 ”, product information (0, 1,..., 1, 1, 0, 0, 1, 0, ..., 2165,000, 8000) ". Then, the terminal processing unit 23 has a learning instruction “product identifier“ 1 ”, product information (0, 1,..., 1, 1, 0, 0, 1, 0,..., 2165000, 8000) (1 , 0, ..., 1, 0, 1, 0, 0, 1, ..., 3250000, 5600) ... (1, 1, ..., 1, 0, 0, 1, 1, 0 ,..., 6543000, 7200). Next, the terminal transmission unit 24 transmits the configured learning instruction to the information processing apparatus 1.

  Next, the reception unit 12 of the information processing apparatus 1 receives a learning instruction. Next, the learning unit 131 includes two or more pieces of product information “(0, 1,..., 1, 1, 0, 0, 1, 0,..., 2165000, 8000) included in the received learning instruction ( 1,0, ..., 1,0,1,0,0,1, ..., 3250000,5600) ... (1,1, ..., 1,0,0,1,1, 0,..., 6543000, 7200). Next, the learning unit 131 performs machine learning processing by pairing two or more attribute values of two or more product information with sales information, and acquires learning information. Then, the learning unit 131 accumulates the acquired learning information in the learning information storage unit 111 in association with the product identifier “1” included in the received learning instruction.

  It is assumed that the learning information of the television is accumulated in the custom information storage unit 111 in association with the product identifier “2”, for example, by the same processing as described above.

  In this situation, the following three specific examples will be described. Specific example 1 is a case in which a function to be installed and / or a function not to be installed is determined. An attribute value whose mounting or adoption is unknown is determined, and a specification capable of predicting that the sales situation is better as the condition is satisfied. It is a case to propose. Specific example 2 is a case in which improvement is proposed for a product that has a sales record but is not satisfied with the sales. Specific example 3 is a process for performing sales prediction.

(Specific example 1)
Now, it is assumed that the user of the terminal device 2 inputs whether or not to install each function and selection of an attribute value as shown in FIG. 9 on the improvement information proposal screen shown in FIG. That is, it is assumed that the user selects “function 11” as the function to be installed, selects “function 13” as the function not to be installed, and other functions are unknown. In addition, as a condition, the user inputs that he / she desires a proposal for which the number of units sold is predicted to be “7500” or more (see 901). Then, it is assumed that the user presses the “Send” button 902.

  Next, the terminal reception unit 22 of the terminal device 2 receives an improvement proposal instruction. Next, the terminal processing unit 23 configures an improvement proposal instruction to be transmitted. Here, it is assumed that the terminal processing unit 23 detects input to the screen of FIG. 9 and acquires target product information (vector) having one or more attribute values of the product. Here, the vector is (function 11, function 12, function 13,..., Function 1N, white, black, red, 4 doors, 2 doors,...) = (1, −, 0,. ,-,-,-,-,-,-, ...). The vector element “-” indicates NULL. Further, the terminal processing unit 23 acquires the condition “sales information> = 7500”. Further, the terminal processing unit 23 configures an improvement proposal instruction having a product identifier “1”, target product information, and conditions included in the information on the screen of FIG. 9. Next, the terminal transmission unit 24 transmits the configured improvement proposal instruction to the information processing apparatus 1. The improvement proposal instruction includes an improvement proposal instruction “product identifier“ 1 ”, target product information (1, −, 0,..., −, −, −, −, −, −,...), Condition. “Sales information> = 7500”.

  Next, the reception unit 12 of the information processing device 1 receives an improvement proposal instruction from the terminal device 2. And the process part 13 acquires improvement information as follows using the object merchandise information which an improvement proposal instruction | indication has. That is, first, the candidate attribute value acquisition unit 132 includes target product information (1,-, 0, ...,-,-,-,-,-,-, ...) included in the received improvement proposal instruction. To get. Next, the candidate attribute value acquisition unit 132 acquires one or more additional candidate attribute values and one or more change candidate attribute values from the attribute value storage unit 112 from the attribute information management table of FIG. That is, the sales information acquisition unit 134 selects the element of NULL “−” from the target product information (1, −, 0,..., −, −, −, −, −, −,...). Get a function identifier and possible product attribute values. Next, the sales information acquisition unit 134 acquires a vector corresponding to the number of combinations described above, using each combination of the acquired function identifier and the product attribute value that can be taken and the value of the element that the target product information has. To do. Next, the price information changing unit 133 applies one or more attribute values of each combination to the product price calculation table (FIG. 7) corresponding to the product identifier “1”, and acquires price information corresponding to each vector. . And the sales information acquisition part 134 acquires evaluation merchandise information using the vector for the said number of combinations, and the price information corresponding to each vector.

  Next, the sales information acquisition unit 134 applies the evaluation product information for the number of the above combinations to the learning information in the learning information storage unit 111 in order, for example, a machine learning algorithm The second sales information is acquired for each combination in association with each combination.

  Next, the determination unit 135 determines whether or not the second sales information corresponding to each combination satisfies the condition “second sales information> = 7500”. For the combination that satisfies the condition, the determination unit 135 acquires the second sales information (predicted sales number) as the improvement score, and pairs the improvement score with a set of one or more attribute values of each combination. And temporarily stored in a buffer (not shown).

  Next, the processing unit 13 determines one or more combinations that are improved to satisfy a predetermined condition based on the improvement score in the buffer. Here, it is assumed that the processing unit 13 determines combinations with the top three improvement scores (predicted sales volume). Needless to say, the processing unit 13 may acquire all combinations satisfying “second sales information> = 7500”.

  Next, the processing unit 13 configures improvement information using the determined three combinations. Next, the sales information output unit 141 transmits the configured improvement information to the terminal device 2.

  Next, the terminal reception unit 25 of the terminal device 2 receives the improvement information from the information processing device 1. Next, the terminal processing unit 23 configures improvement information to be output. Then, the terminal output unit 26 outputs the configured improvement information. An example of such output is shown in FIG.

(Specific example 2)
The user now has target product information (function 12,..., Function 1N, color “black”, door “two doors”, price information “3250000 yen”, sales information “800”. ”) Is input to the terminal device 2. Then, the terminal reception unit 22 of the terminal device 2 receives the improvement proposal instruction.

  Next, the terminal processing unit 23 configures an improvement proposal instruction to be transmitted. Here, similarly to the above processing, the terminal processing unit 23 uses the input target product information, and uses the target product information (0, 1,..., 1, 0, 1, 0, 0) that is a vector. , 1,..., 3250000, 800). Next, the terminal processing unit 23 includes a product identifier “1” and target product information (0, 1,..., 1, 0, 1, 0, 0, 1,..., 3250000, 800). Configure improvement proposal instructions. Then, the terminal transmission unit 24 transmits the configured improvement proposal instruction to the information processing apparatus 1.

  Next, the reception unit 12 of the information processing device 1 receives an improvement proposal instruction from the terminal device 2. And the process part 13 acquires improvement information as follows using the object merchandise information which an improvement proposal instruction | indication has. That is, first, the candidate attribute value acquisition unit 132 includes target product information (0, 1,..., 1, 0, 1, 0, 0, 1,..., 3250000, included in the received improvement proposal instruction. 800). Next, the candidate attribute value acquisition unit 132 obtains one or more additional candidate attribute values and one or more change candidate attribute values by the processing described above using the target product information and the attribute information management table of FIG. Obtained from the attribute value storage unit 112. Note that the candidate attribute value acquisition unit 132 may also acquire two or more price information that may fluctuate as candidate attribute values. In this case, for example, the improvement proposal instruction has an allowable range of price information (for example, “2800000 or more”) and the like, and a plurality of price information that can be taken within the allowable range (for example, in units of 100,000 yen). , “20.8 million yen, 20.9 million yen, 3 million yen, 3.1 million yen, 3.2 million yen, 32.5 million yen” may be acquired as a candidate attribute value.

  Next, the sales information acquisition unit 134 acquires each combination of the acquired function identifier and the product attribute value that can be taken. Next, the price information changing unit 133 applies one or more attribute values of each combination to the product price calculation table (FIG. 7) corresponding to the product identifier “1”, and acquires price information corresponding to each vector. . And the sales information acquisition part 134 acquires evaluation merchandise information using the vector for the said number of combinations, and the price information corresponding to each vector. In addition, it is suitable for the sales information acquisition part 134 to acquire a combination including the fluctuation | variation of the price information mentioned above.

  Next, the sales information acquisition unit 134 applies the evaluation product information corresponding to the number of combinations described above to the learning information in the learning information storage unit 111, and associates each item with each combination using a machine learning algorithm, for example. Get second sales information.

  Next, the determination unit 135 acquires the sales information “800” included in the received improvement proposal instruction. Next, the determination unit 135 determines whether or not the second sales information corresponding to each combination satisfies the condition “second sales information> sales information (800)”. Then, for the combination that satisfies the condition, the determination unit 135 obtains an improvement score using the difference between the second sales information and the sales information (increased predicted sales volume) and the number of attribute values to be improved. To do. Here, it is preferable that the determination unit 135 increases the improvement score as “second sales information—sales information” increases. In addition, it is preferable to increase the improvement score as the number of attribute values to be improved is smaller. Next, the determination unit 135 makes a pair of one or more attribute values of each combination and the improvement score, and temporarily accumulates them in a buffer (not shown).

  Next, the processing unit 13 determines one or more combinations that are improved to satisfy a predetermined condition based on the improvement score in the buffer.

  Next, the processing unit 13 configures improvement information using one or more determined combinations. Next, the sales information output unit 141 transmits the configured improvement information to the terminal device 2.

  Next, the terminal reception unit 25 of the terminal device 2 receives the improvement information from the information processing device 1. Next, the terminal processing unit 23 configures improvement information to be output. Then, the terminal output unit 26 outputs the configured improvement information. In addition, the output aspect of improvement information is not ask | required.

(Specific example 3)
Now, the user of the terminal device 2 is the target product information of the vehicle of the determined specification (function 11,..., Function 1N, color “red”, door “2 door”, price information “5250,000 yen”). It is assumed that a sales prediction instruction including “” is input to the terminal device 2. Then, the terminal reception unit 22 of the terminal device 2 receives the sales prediction instruction.

  Next, the terminal processing unit 23 constitutes a sales forecast instruction to be transmitted. Here, similarly to the above processing, the terminal processing unit 23 uses the input target product information, and uses the target product information (1,0,..., 1, 0, 0, 1, 0) that is a vector. , 1,..., 5250,000). Next, the terminal processing unit 23 predicts the sales including the product identifier “1” and the target product information (1, 0,..., 1, 0, 0, 1, 0, 1,..., 5250,000). Configure instructions. Then, the terminal transmission unit 24 transmits the configured sales prediction instruction to the information processing apparatus 1.

  Next, the reception unit 12 of the information processing device 1 receives a sales prediction instruction from the terminal device 2. Next, the sales information acquisition unit 134 acquires target product information (1,0,..., 1, 0, 0, 1, 0, 1,..., 5250,000) included in the received sales prediction instruction. Suppose that Next, the sales information acquisition unit 134 applies the target product information to the learning information, and acquires sales information “7300” units. Next, the sales information output unit 141 transmits the acquired sales information “7300” to the terminal device 2.

  Next, the terminal receiving unit 25 of the terminal device 2 receives sales information from the information processing device 1. The terminal processing unit 23 constitutes sales information to be output. Here, for example, the sales information to be output was calculated as “7300” for the predicted sales volume based on the specifications and price received. Is. And the terminal output part 26 outputs the comprised sales information.

  As described above, according to the present embodiment, it is possible to make an improvement proposal for adding one or more attribute values of a product that is predicted to be in a favorable sales situation.

  Moreover, according to this Embodiment, the improvement proposal about addition of the 1 or more attribute value of goods with which a sales situation is predicted to improve can be performed.

  Moreover, according to this Embodiment, the improvement proposal about the change of the 1 or more attribute value of the goods estimated to become a favorable sales situation can be performed.

  Moreover, according to this Embodiment, the improvement proposal about the change of the 1 or more attribute value of the goods for which a sales situation is predicted to improve can be performed.

  In addition, according to the present embodiment, it is possible to make a product improvement proposal in consideration of price fluctuations.

  Furthermore, according to the present embodiment, sales information of products can be predicted.

  Note that the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. Note that the software that implements the information processing apparatus according to the present embodiment is the following program. That is, this program accesses a recording medium including a learning information storage unit that stores learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to the sale of the product. A receiving unit that receives target product information having one or more attribute values of a product, a sales information acquiring unit that applies the target product information received by the receiving unit to the learning information, and acquires sales information; , A program for functioning as a sales information output unit for outputting the sales information.

  In addition, the recording medium further includes an attribute value storage unit that stores two or more attribute values that can be adopted by the product, and the program is an attribute value that the computer does not have the target product information, It functions further as a candidate attribute value acquisition unit that acquires additional candidate attribute values that are attribute values of the attribute value storage unit, and the sales information acquisition unit replaces the target product information received by the reception unit with the target product information The evaluation merchandise information having one or more attribute values and the additional candidate attribute value included in is applied to the learning information, second sales information is acquired, and the computer is configured to determine the second sales information in advance. A determination unit that determines whether or not the condition is better as the condition is satisfied, and the second sales information that is determined to be better as the determination unit satisfies a predetermined condition. Additional candidates It is preferably a further programmed to function as improved information output unit that outputs the improvement information including sex value.

  The recording medium may further include an attribute value storage unit that stores two or more attribute values that can be adopted by the product, and the program can employ the computer in place of the attribute value that the target product information has. The change candidate attribute value that is an attribute value is further caused to function as a candidate attribute value acquisition unit that acquires the attribute value storage unit from the attribute value storage unit, and the sales information acquisition unit uses the change candidate attribute from one or more attribute values of the target product information The evaluation product information to which the change candidate attribute value is added is applied to the learning information except for the attribute value replaced with a value, second sales information is obtained, and the computer is preliminarily configured with the second sales information. A determination unit that determines whether or not a condition satisfying a predetermined condition is better, and a source for acquiring the second sales information that the determination part determines that the condition satisfies a predetermined condition is better. The It serial is a program to further function as improved information output unit that outputs the improvement information including additional candidate attribute values are preferred.

  FIG. 11 shows the external appearance of a computer that executes the program described in this specification to realize the information processing apparatus 1 and the like according to the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 11 is an overview diagram of the computer system 300, and FIG. 12 is a block diagram of the system 300.

  In FIG. 11, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

  In FIG. 12, in addition to the CD-ROM drive 3012, the computer 301 stores an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012, a ROM 3015 for storing programs such as a bootup program, and the MPU 3013. It includes a RAM 3016 for temporarily storing application program instructions and providing a temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card that provides connection to a LAN.

  A program that causes the computer system 300 to execute the functions of the information processing apparatus 1 and the like of the above-described embodiment may be stored in the CD-ROM 3101, inserted into the CD-ROM drive 3012, and further transferred to the hard disk 3017. . Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored in the hard disk 3017. The program is loaded into the RAM 3016 at the time of execution. The program may be loaded directly from the CD-ROM 3101 or the network.

  The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 301 to execute the functions of the information processing apparatus 1 according to the above-described embodiment. The program only needs to include an instruction portion that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 300 operates is well known and will not be described in detail.

  In the above program, in the step of transmitting information, the step of receiving information, etc., processing performed by hardware, for example, processing performed by a modem or an interface card in the transmission step (only performed by hardware) Processing) is not included.

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  Further, in each of the above embodiments, it goes without saying that two or more communication units existing in one apparatus may be physically realized by one medium.

  In each of the above embodiments, each process may be realized by centralized processing by a single device, or may be realized by distributed processing by a plurality of devices.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the information processing apparatus according to the present invention has an effect of being able to propose improvement of a product, and is useful as a server apparatus that proposes functional improvement.

DESCRIPTION OF SYMBOLS 1 Information processing apparatus 2 Terminal device 11 Storage part 12 Reception part 13 Processing part 14 Output part 21 Terminal storage part 22 Terminal reception part 23 Terminal processing part 24 Terminal transmission part 25 Terminal reception part 26 Terminal output part 111 Learning information storage part 111 Learning Information storage unit 112 Attribute value storage unit 131 Learning unit 132 Candidate attribute value acquisition unit 133 Price information change unit 134 Sales information acquisition unit 135 Judgment unit 141 Sales information output unit 142 Improvement information output unit

Claims (13)

A learning information storage unit that stores learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to the sale of the product;
A receiving unit that receives target product information having one or more attribute values of the product;
Applying the target product information received by the reception unit to the learning information, a sales information acquisition unit that acquires sales information;
An information processing apparatus comprising: a sales information output unit that outputs the sales information. An attribute value storage unit that stores two or more attribute values that can be adopted by the product;
A candidate attribute value acquisition unit that acquires an additional candidate attribute value that is an attribute value that the target product information does not have and is an attribute value of the attribute value storage unit;
The sales information acquisition unit
Instead of target product information received by the receiving unit, evaluation product information having one or more attribute values of the target product information and the additional candidate attribute value is applied to the learning information, and second sales information is Acquired,
A determination unit for determining whether the second sales information is good enough to satisfy a predetermined condition;
An improvement information output unit that outputs improvement information including the additional candidate attribute value from which the second sales information that has been determined to be satisfactory as the determination unit satisfies a predetermined condition is further included; The information processing apparatus according to claim 1. The predetermined condition is:
As a result of comparing the sales information and the second sales information, it can be determined that sales are improved,
The improvement information output part which outputs the improvement information containing the said addition candidate attribute value used as the origin which acquired the said 2nd sales information which the said judgment part judged that it was improved regarding sales is further provided. Information processing device. The candidate attribute value acquisition unit
Two or more additional candidate attribute values that are attribute values that the target product information does not have and that are attribute values of the attribute value storage unit;
The sales information acquisition unit
4. The second sale information is acquired by applying evaluation product information having one or more attribute values of the target product information and the two or more additional candidate attribute values to the learning information. 5. Information processing device. An attribute value storage unit that stores two or more attribute values that can be adopted by the product;
A candidate attribute value acquisition unit that acquires from the attribute value storage unit a change candidate attribute value that is an attribute value that can be adopted instead of the attribute value that the target product information has,
The sales information acquisition unit
The evaluation product information to which the change candidate attribute value is added is removed from one or more attribute values of the target product information, and the change candidate attribute value is added to the learning information. Get
A determination unit for determining whether the second sales information is good enough to satisfy a predetermined condition;
An improvement information output unit that outputs the improvement information including the change candidate attribute value from which the second sales information that is determined to be satisfactory as the determination unit satisfies a predetermined condition is further obtained The information processing apparatus according to claim 1. The predetermined condition is:
As a result of comparing the sales information and the second sales information, it can be determined that sales are improved,
The improvement information output part which outputs the improvement information containing the said change candidate attribute value used as the origin which acquired the said 2nd sales information which the said determination part judged that it was improved regarding sales is further provided. The information processing apparatus described. The candidate attribute value acquisition unit
Obtaining two or more change candidate attribute values that are attribute values that can be employed instead of the two or more attribute values of the target product information from the attribute value storage unit;
The sales information acquisition unit
The evaluation product information to which the two or more change candidate attribute values are added is excluded from the one or more attribute values included in the target product information, and the two or more change candidate attribute values are added to the learning information. The information processing apparatus according to claim 5, wherein the second sales information is acquired. The two or more attribute values of the product include price information specifying the price of the product,
A price information changing unit that acquires price information to be changed with respect to addition of the addition candidate attribute value or change to the change candidate attribute value;
The sales information acquisition unit
The second sale information is acquired by applying evaluation product information having changed price information acquired by the price information changing unit to the learning information instead of the price information before the change. The information processing apparatus according to any one of claims. The information according to any one of claims 1 to 8, wherein the two or more attribute values of the product include a function identifier for identifying a function of the product or function presence / absence information indicating presence / absence of each of the two or more functions of the product. Processing equipment. The sales information is
The information processing apparatus according to any one of claims 1 to 9, wherein the information processing apparatus is information indicating the number of products sold or information indicating a total sales amount of the products. The reception unit
Accept more than 2 product information,
A learning unit that learns the product information of two or more by a machine learning algorithm and obtains learning information;
The information processing apparatus according to any one of claims 1 to 10, wherein the learning information stored in the learning information storage unit is learning information acquired by the learning unit. A learning information storage unit, a reception unit, a sales information acquisition unit, and a sales information output unit that store learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to sales of the product An information processing method realized by
A receiving step in which the receiving unit receives target product information having one or more attribute values of the product;
The sales information acquisition unit applies the target product information received in the reception step to the learning information, and acquires sales information;
An information processing method, wherein the sales information output unit includes a sales information output step of outputting the sales information. A computer capable of accessing a recording medium having a learning information storage unit storing learning information obtained by learning two or more product information having two or more attribute values of the product and sales information related to the sale of the product; A receiving unit for receiving target product information having one or more attribute values;
Applying the target product information received by the reception unit to the learning information, a sales information acquisition unit that acquires sales information;
A program for functioning as a sales information output unit for outputting the sales information.