JP6866705B2 - Information processing equipment and programs - Google Patents

Description

The present invention relates to an information processing creation device and a program.

Conventionally, the development of an advertisement creation device for creating an advertisement such as POP (Point of purchase advertising) has been promoted for the purpose of enhancing the sales promotion effect. As the advertisement creation device, for example, a printed matter creation system that creates one POP based on the contents such as a layout selected by the user, a recipe example, a logo, a comment input by the user, and a selling price has been proposed ( For example, see Patent Document 1).

Japanese Unexamined Patent Publication No. 2005-128677

However, in the above-mentioned prior art, the user needs to select the above-mentioned various information every time the POP is created, and each time, the desired information is selected from the options including the ones that the user does not like. You need to find out and select this. Therefore, it takes time and is complicated every time a POP is created.

Therefore, an object of the present invention is to provide an information processing device and a program capable of obtaining an advertisement closer to a user's preference in a short time when repeatedly obtaining an advertisement.

To solve the above problems, an information processing apparatus of the first aspect of the present invention includes: a generating means for generating a predetermined number of advertisement candidates, each of said predetermined number of advertisement candidates created by the creating means A display control means for executing switching display of an advertisement candidate targeting the predetermined number of advertisement candidates to the display means in response to a first user operation so as to be individually displayed on the display means, and the predetermined number. With a selection means for selecting the displayed advertisement candidate as a user's desired advertisement candidate in response to a second user operation in a state where any of the advertisement candidates in the above is displayed on the display means. , A learning means for deriving a learning value indicating a user's preference based on the time until the user's desired advertisement candidate is selected by the selection means when the user's desired advertisement candidate is selected by the selection means. The creating means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means, and the learning means selects the advertisement candidate desired by the user by the selection means. If there is an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the advertisement candidate desired by the user, the advertisement candidate is common to the advertisement candidate desired by the user. It is characterized in that if the advertisement component is included, the amount of change in the learning value is larger than that in the case where the common advertisement component is not included .
In addition, the information processing apparatus according to the second aspect of the present invention includes an extraction means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements, and the predetermined number of advertisements extracted by the extraction means. A display control means for executing switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means in response to the first user operation so that each of the candidates is individually displayed on the display means. , The displayed advertisement candidate is selected as the user's desired advertisement candidate in response to the second user operation in the state where any of the predetermined number of advertisement candidates is displayed on the display means. A learning value indicating a user's preference is obtained based on the selection means to be selected and the time until the user's desired advertisement candidate is selected by the selection means when the user's desired advertisement candidate is selected by the selection means. The extraction means includes a learning means to be derived, the extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means, and the learning means is desired by the user by the selection means. If there is an advertisement candidate whose display time on the display means is longer than a predetermined value in an advertisement candidate different from the advertisement candidate desired by the user before the advertisement candidate is selected, the advertisement desired by the user is selected as the advertisement candidate. If an advertisement component common to the candidate is included, the amount of change in the learning value is made larger than in the case where the common advertisement component is not included.
Further, in the program of the first aspect according to the present invention, the computer is individually displayed on the display means by the creating means for creating a predetermined number of advertising candidates and the predetermined number of advertising candidates created by the creating means. Any of the display control means for executing the switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means in response to the first user operation, and the predetermined number of advertisement candidates. A selection means for selecting the displayed advertisement candidate as a user-desired advertisement candidate in response to a second user operation in a state where the advertisement candidate is displayed on the display means, the user desired by the selection means. When the advertisement candidate of the above is selected, it is made to function as a learning means for deriving a learning value indicating a user's preference based on the time until the advertisement candidate desired by the user is selected by the selection means, and the creating means. Creates the predetermined number of advertisement candidates based on the learning value derived by the learning means, and the learning means uses the selection means until the user's desired advertisement candidate is selected by the user. When there is an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the desired advertisement candidate, the advertisement candidate includes an advertisement component common to the user's desired advertisement candidate. For example, the amount of change in the learning value is made larger than in the case where the common advertising component is not included.
In addition, the program of the second aspect according to the present invention is an extraction means for extracting a predetermined number of advertisement candidates from a plurality of created advertisements, and the predetermined number of advertisements extracted by the extraction means. A display control means for executing switching display of an advertisement candidate targeting a predetermined number of advertisement candidates to the display means in response to a first user operation so that each of the candidates is individually displayed on the display means. In response to a second user operation in a state where any of the predetermined number of advertisement candidates is displayed on the display means, the displayed advertisement candidate is selected as the user's desired advertisement candidate. When the user's desired advertisement candidate is selected by the selection means and the selection means, a learning value indicating the user's preference is derived based on the time until the user's desired advertisement candidate is selected by the selection means. The extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means, and the learning means uses the selection means to extract the advertisement candidates desired by the user. If there is an advertisement candidate whose display time on the display means is longer than a predetermined value in an advertisement candidate different from the advertisement candidate desired by the user before is selected, the advertisement candidate is regarded as the advertisement candidate desired by the user. It is characterized in that if a common advertisement component is included, the amount of change in the learning value is larger than in the case where the common advertisement component is not included.

According to the present invention, it is possible to provide an information processing device and a program capable of obtaining an advertisement closer to a user's preference in a short time when repeatedly obtaining an advertisement.

It is a schematic block diagram which shows the information processing system of this embodiment. It is a block diagram which shows the functional structure of a server. It is a figure which shows an example of the layout data table stored in the storage part. It is a figure which shows an example of the eye-catching data table stored in the storage part. It is a figure which shows an example of the learning value data table which is stored in a storage part, and is updated every time the POP data creation process is performed. It is a block diagram which shows the functional structure of a mobile terminal. It is a flowchart which shows an example of the POP data creation process performed by a server. It is a figure which shows an example of the input screen before information input. It is a figure which shows an example of the input screen after information input. It is a figure which shows an example of a preview screen. It is a figure which shows the example of the 25 kinds of POP candidate data to be created. It is a flowchart which shows an example of the 1st acquisition process performed by a server. It is a flowchart which shows an example of the 2nd acquisition process performed by a server. It is a figure which shows the total of the morpheme characteristic values obtained from the information input by a user. It is a flowchart which shows an example of the learning process performed by a server. It is a figure which shows the POP data component included in 3 kinds of POP candidate data.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. However, although the embodiments described below are provided with various technically preferable limitations for carrying out the present invention, the scope of the invention is not limited to the following embodiments and illustrated examples.

<< Configuration of information processing system 1 >>
First, the configuration of the present embodiment will be described with reference to FIG. FIG. 1 is a schematic configuration diagram of the information processing system 1 of the present embodiment.

The information processing system 1 of the present embodiment is a system that creates POP data as an advertisement based on the input information, learns the user's preference by repeating the creation of the POP data, and responds to the user's preference. It is a system that can create POP data.

As shown in FIG. 1, the information processing system 1 prints a server (information processing device) 4, mobile terminals 2a and 2b such as smartphones and tablet terminals which are user terminals, and PCs (personal computers) 3a and 3b. A device 11 is provided, and each device is communicated and connected via a communication network N. Hereinafter, mobile terminals 2a and 2b and PC3a and 3b may be collectively referred to as user terminals. It is assumed that the mobile terminals 2a, 2b and PC3a, 3b are connected to the server 4 by communication, but the present invention is not limited to this, and a plurality of user terminals may be provided according to the number of users who receive the service provided by the server 4. It exists and they make a communication connection with the server 4. Further, the server 4 may be connected to a POS (Point Of Sales) terminal or the like as a user terminal by communication.

The server 4 creates POP data as an advertisement for the purpose of sales promotion, and manages the created POP data. Further, the server 4 distributes an input screen 5 for inputting information for creating POP data, a preview screen 6 of the created POP candidate data (advertisement candidate), and the like to the user terminal in response to a request from the user terminal. To do.

User terminals such as mobile terminals 2a and 2b receive and display various display information transmitted from the server 4, receive operation input of the user, and transmit the operation information to the server 4.

The printing device 11 is an electrophotographic type, inkjet type, or other home or business printer, and prints POP data or the like on paper in response to input printing instructions to create a POP or the like as an advertisement. To do.

The communication network N is assumed to be composed of the Internet, but may include a LAN (Local Area Network), a WAN (Wide Area Network), etc., and may include a telephone line, a dedicated line, a mobile communication network, and a communication satellite network. , CATV (Cable Television) line and the like may be included.

Further, the information processing system 1 may be configured to be able to execute other application programs used by various user terminals. The application program includes, for example, sales management business that aggregates and analyzes daily and monthly sales of stores, accounting management business that performs tax payment processing such as property tax and personal business tax, and employee salary calculation. Payroll management work that handles the processing of, employment management work that manages personnel shifts at stores, etc. and employee attendance management, and document management work that creates and manages notification documents to related businesses and employees. The information processing system 1 may function as a management support system by making it feasible.

<< Configuration of server 4 >>
Next, the internal configuration of the server 4 will be described. FIG. 2 shows the internal configuration of the server 4. Further, FIGS. 3 to 5 show an example of the layout data table 451, the eye-catching data table 452, and the learning value data table 453, respectively.

As shown in FIG. 2, the server 4 includes a CPU (Central Processing Unit) 41, an operation input unit 42, a RAM (Random Access Memory) 43, a server display unit 44, a storage unit (storage means) 45, and the like. A communication unit 46 and a timekeeping unit 47 are provided, and each unit is connected via a bus 48.

The CPU 41 reads out the system program stored in the storage unit 45, expands it into the work area of the RAM 43, and controls each unit according to the system program. Further, the CPU 41 reads the processing program stored in the storage unit 45 and expands it in the work area, and processes on the server 4 side such as POP data creation processing, first acquisition processing, second acquisition processing, and learning processing, which will be described later. It executes various processes such as, and functions as a creation means, a display control means, a selection means, and a learning means.

The operation input unit 42 includes a keyboard including a cursor key, a character, a number input key, various function keys, and the like, receives a user's press input of each key, and outputs the operation information to the CPU 41. Further, the operation input unit 42 may include a pointing device such as a mouse, receive a position input, and output it to the CPU 41 as operation information.

The RAM 43 is a volatile memory. Further, the RAM 43 has a work area for storing various programs to be executed and data related to these various programs.

The server display unit 44 is composed of an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube), or the like, and displays a screen according to a display control signal from the CPU 41.

The storage unit 45 is composed of an HDD (Hard Disk Drive) or the like having a magnetic recording medium. The storage unit 45 stores system programs and processing programs executed by the CPU 41, application programs such as Web server programs, and data necessary for executing these programs. The processing program includes a program for executing various processes such as a POP data creation process, a first acquisition process, a second acquisition process, and a learning process, which will be described later, on the server 4 side.
These programs are stored in the storage unit 45 in the form of a computer-readable program code. The CPU 41 sequentially executes operations according to the program code.

In addition, the storage unit 45 stores various data used for POP data creation processing and the like in advance. That is, the storage unit 45 stores, for example, POP data components (advertising components) such as the layout of POP data, the eye catch included in the POP data, the typeface, the font color, the background, the frame / texture, and the illustration. In addition, the storage unit 45 stores user information and the like including the type of business and business type of the store or company to which the user manages or belongs.

Further, the storage unit 45 contains a layout data table 451 (see FIG. 3), an eye-catching data table 452 (see FIG. 4), and learning value data used in the first acquisition process, the second acquisition process, and the learning process, which will be described later. Table 453 (see FIG. 5) is stored. Further, the storage unit 45 stores similar data tables (not shown) for POP data components other than layout and eye-catching, that is, typefaces, font colors, backgrounds, frames / textures, and illustrations.

The layout data table 451 is a table in which layout types L1 to L20 and index values set for each item according to each layout type are stored in association with each other. Examples of the item include, but are not limited to, “vertical / horizontal”, “photo size”, “number of photographs”, “eye catch size”, “number of illustrations”, and the like. In the "vertical / horizontal" item, an index value of "0" is set when the layout type is vertical, and an index value of "1" is set when the layout type is horizontal. Further, in the items of "photograph size" and "eye catch size", the closer the index value is to "1", the larger the size of the image data and the eye catch is with respect to the entire POP data to be created. ..

The eye-catching data table 452 is a table in which the eye-catching types i1 to i20 and the index values set for each item according to each eye-catching type are stored in association with each other. Examples of the item include, but are not limited to, "vertical / horizontal", "Japanese food", "Western food", "cafe", "Chinese food", and the like. In the "vertical / horizontal" item, an index value of "0" is set when the eye-catching type is vertical, and an index value of "1" is set when the eye-catching type is horizontal. In addition, in the items of "Japanese food", "Western food", "Cafe" and "Chinese food", if it is suitable for those items, it is "1", and it is suitable for those items. If not, an index value of "0" is set.

The learning value data table 453 is a table in which a user who receives a service provided by the server 4 and a learning value indicating a user's preference for a POP data component calculated and updated for each user are stored in association with each other. Further, the learning value is calculated and updated for each item preset for each POP data component. The items concerned include, for example, "vertical / horizontal", "photo size", "eye catch size" as layout items, and "Japanese food", "Western food", "cafe", and "eye catch size" as eye catch items. Examples of typefaces such as "for China" include, but are not limited to, "typeface A", "typeface B", and "typeface C". Such a learning value data table 453 is updated every time the POP data creation process is executed. In the "vertical / horizontal" item, the closer the learning value is to "1", the more the user prefers the horizontal layout. Further, in the items of "photo size" and "eye catch size", the closer the learning value is to "1", the more the user prefers image data and eye catch, which are larger in size than the entire POP data to be created. Is shown.

In addition, the storage unit 45 stores characteristic values set in advance for each word. Further, the characteristic value is set for each item and is used when acquiring the total morpheme characteristic value in the second acquisition process described later. The items overlap with some of the items included in the above-mentioned learning value data table 453, for example, "vertical / horizontal", "for Japanese food", "for Western food", "for cafe", "for Chinese food", etc. Can be mentioned. In the "vertical / horizontal" item, a characteristic value of "0" is set when the word is suitable for vertical arrangement, and a characteristic value of "1" is set when the word is suitable for horizontal arrangement. There is. In addition, in the items of "Japanese food", "Western food", "Cafe" and "Chinese food", if the word is suitable for those items, "1", for those items If it is not suitable, a characteristic value of "0" is set. Further, among the words, the characteristic values of the part of speech other than the noun, adverb, verb and adjective, and the symbol are all set to "0". For example, for the word "Yuzu", the characteristic value of "vertical / horizontal" is "1", the characteristic value of "for Japanese food" is "1", and the characteristic value of "for Western food" is "0". The characteristic value for "for cafes" is set to "1", and the characteristic value for "for Chinese food" is set to "0". Further, for example, for the word of the particle "no", the characteristic value is set to "0" in each item.

Further, the storage unit 45 stores various data input by the user in the POP data creation process or the like. That is, for example, the storage unit 45 stores the image data input on the input screen 5, the creation history of the POP data created in the past, and the like.

The communication unit 46 is composed of a modem, a TA (Terminal Adapter), a router, a network card, and the like. The communication unit 46 communicates with an external device such as a user terminal on the communication network N to transmit / receive data.

The timekeeping unit 47 has a built-in timekeeping circuit, measures the current time and date, and outputs the current time information.

<< Configuration of mobile terminal 2a >>
Next, the internal configuration of the user terminal will be described using the mobile terminal 2a as an example. FIG. 6 shows the internal configuration of the mobile terminal 2a.

As shown in FIG. 6, the mobile terminal 2a includes a CPU 21, an operation input unit 22, a RAM 23, a terminal display unit (display means) 24, a storage unit 25, a communication unit 26, and a timekeeping unit 27. Each part is connected via a bus 28.
The mobile terminals 2b and PC3a, 3b are also configured in substantially the same manner.

The CPU 21, the operation input unit 22, the RAM 23, the terminal display unit 24, the storage unit 25, the communication unit 26, and the timekeeping unit 27 are the CPU 41 of the server 4, the operation input unit 42, the RAM 43, the server display unit 44, and the storage unit 45, respectively. The configuration is substantially the same as that of the communication unit 46 and the timekeeping unit 47, and different parts will be mainly described.

The CPU 21 reads out the system program stored in the storage unit 25, expands it into the work area of the RAM 23, and controls each unit according to the system program. Further, the CPU 21 reads the processing program stored in the storage unit 25 and develops it in the work area, and performs POP data creation processing, first acquisition processing, second acquisition processing, learning processing, etc., which will be described later, on the mobile terminal 2a side. Execute various processes including processing.

The operation input unit 22 includes a push button switch, a touch sensor provided on the terminal display unit 24, and the like, receives a user's input operation, converts the operation content into an electric signal, and outputs the operation content to the CPU 21. ..

The terminal display unit 24 is composed of an LCD, a CRT, or the like, and displays a screen according to a display control signal from the CPU 21. Further, the touch sensor is provided on the display screen of the terminal display unit 24, and functions as a touch panel type operation display means.

The storage unit 25 is composed of an HDD or the like having a magnetic recording medium. The storage unit 25 stores system programs and processing programs executed by the CPU 21, application programs such as a Web browser, and data necessary for executing these programs. The processing program includes a program for executing various processes such as a POP data creation process, a first acquisition process, a second acquisition process, and a learning process, which will be described later, on the mobile terminal 2a side.

The communication unit 26 communicates with an external device such as a server 4 on the communication network N.

<< Operation of server 4 >>
(POP data creation process)
The POP data creation process performed by the server 4 of the present embodiment will be described with reference to FIGS. 7 to 11 and the like. FIG. 7 is a flowchart showing an example of the POP data creation process. FIG. 8 is a diagram showing an input screen 5 displayed on the terminal display unit 24 before inputting information. FIG. 9 is a diagram showing an input screen 5 after inputting information displayed on the terminal display unit 24. FIG. 10 is a diagram showing an example of the preview screen 6 displayed on the terminal display unit 24. FIG. 11 is a diagram showing an example of 25 types of POP candidate data to be created.

The server 4 is configured to perform a POP data creation process that creates POP data that introduces a target product or service based on text or image data input by the user via a user terminal.

Specifically, as shown in FIG. 7, the CPU 41 of the server 4 displays the input screen 5 shown in FIG. 8 on the terminal display unit 24 when the user terminal requests the execution of the program for creating POP data. (Step S101). The input screen 5 is a screen for inputting information necessary for creating POP data. The necessary information is the name (product name) of the target product to be introduced in the created POP data, the price of the target product, the catch phrase of the target product, and the image data of the target product. Therefore, the input screen 5 is provided with a product name input field 51, a price input field 52, a catch phrase input field 53, and an image data input field 54 as input fields for inputting each of the information. For example, as shown in FIG. 9, "Yuzu Shio Ramen" is in the product name input field 51, "700" is in the price input field 52, and "Yuzu flavor is refreshing ramen" in the catch copy input field 53. , The image data of ramen is input to the image data input field 54.

Further, on the input screen 5, in addition to the input fields 51 to 54, a product addition button 55 selected by the user when adding the product to be introduced to the POP data, and a user when creating the POP data after inputting the information. The create button 56 selected by the user, the cancel button 57 selected by the user when the display of the input screen 5 is finished, and information that can be used as a reference for the catch phrase input (for example, a catch phrase sample or a trend word) are displayed. A reference button 58 or the like that is selected by the user at that time is provided.

When the product addition button 55 is selected by the user on the input screen 5, a product name input field 51, a price input field 52, a catch phrase input field 53, and an image data input field 54 are added to the input screen 5 one by one. To. By inputting information into each of the added input fields 51 to 54 by the user, the CPU 41 can create POP data including the introduction of the two types of products. Therefore, when the product addition button 55 is selected a plurality of times and information is input to each of the input fields 51 to 54, the CPU 41 can create POP data including the introduction of a plurality of types of products.

It is sufficient that at least one of the product name and the image data of the target product is input for one product in each input field 51 to 54 of the input screen 5, but the more information is input, the more the information is created. In order to improve the accuracy of POP data, it is preferable that information is input to all of the input fields 51 to 54.

Next, as shown in FIG. 7, the CPU 41 determines whether or not the input is completed (step S102). Specifically, for example, in the CPU 41, the product name is input by the user at least in the product name input field 51, or the image data is input in the image data input field 54, and then the create button 56 is selected. In this case, it is determined that the input is completed.

If it is determined that the input is not completed (step S102; NO), the CPU 41 performs the process of step S102 again. When it is determined that the input is completed (step S102; YES), the CPU 41 selects and acquires five types of layouts to be used for creating the POP data based on the information input in the image data input field 54. Perform processing (step S103). The details of the first acquisition process will be described later.

Next, the CPU 41 uses POP data components other than the layout (eye catch, typeface, font color, background, etc.) to be used for creating POP data based on the information input in the product name input field 51 and the catch copy input field 53. A second acquisition process is performed in which five types of frames / textures (frames / textures, illustrations) are selected and acquired (step S104). The details of the second acquisition process will be described later.

Next, the CPU 41 creates 25 types of POP candidate data based on the acquired POP data components (step S105). That is, the CPU 41 creates five types of designs by combining the five types of eye catches, typefaces, font colors, backgrounds, frames / textures, and illustrations acquired in step S104. Then, the CPU 41 creates 25 types of POP candidate data by combining the five types of layouts acquired in step S103, the created five types of designs, and the information input in each input field 51 to 54. The 25 types of POP candidate data to be created include the information input in each of the input fields 51 to 54 in common.

Next, the CPU 41 causes the terminal display unit 24 to display the created 25 types of POP candidate data (step S106). Specifically, as shown in FIG. 10, the CPU 41 causes the terminal display unit 24 to display the preview screen 6 on which one of the created plurality of types of POP candidate data is displayed.

Further, on the preview screen 6, the CPU 41 sequentially displays other POP candidate data of the 25 types of POP candidate data created in step S105 by performing a swipe operation by the user.
For example, a case where the POP candidate data a1 to a5, b1 to b5, c1 to c5, d1 to d5, and e1 to e5 shown in FIG. 11 are generated as 25 types of POP candidate data will be described. In FIG. 11, the POP candidate data arranged in the horizontal direction have a common layout, and the POP candidate data arranged in the vertical direction have a common design. By inputting a horizontal or vertical swipe operation by the user, the POP candidate data displayed on the terminal display unit 24 is arranged adjacent to the horizontal or vertical direction in the array shown in FIG. POP candidate data is displayed in sequence. Further, in each POP candidate data array shown in FIG. 11, the POP candidate data is configured to be loop-displayed in both the horizontal direction and the vertical direction. For example, when the POP candidate data a1 is displayed on the terminal display unit 24 and a left swipe operation is input, the POP candidate data a5 is displayed on the terminal display unit 24 and an upward swipe operation is input. Then, the POP candidate data e1 is displayed on the terminal display unit 24. Further, the POP candidate data including the POP data component having a higher evaluation value, which will be described later, is arranged as the one arranged in the upper left in FIG. 11 is arranged. Therefore, it is preferable that the POP candidate data that is first displayed on the terminal display unit 24 in the process of step S106 is the POP candidate data a1. As a result, the user can find POP candidate data close to his / her taste in a shorter time.

Next, as shown in FIG. 7, the CPU 41 determines whether or not a selection operation for selecting desired POP candidate data has been input by the user (step S107). Specifically, for example, the CPU 41 performs the POP when the selection button 61 is selected by the user while the user-desired POP candidate data among the 25 types of POP candidate data is displayed on the terminal display unit 24. It is determined that the selection operation for selecting the candidate data has been input.

If it is determined that the selection operation is not input (step S107; NO), the CPU 41 determines whether or not the cancel operation for ending the display of the preview screen 6 has been input by the user (step S108). Specifically, for example, the CPU 41 determines that the cancel operation has been input when the cancel button 62 is selected by the user.

When it is determined that the cancel operation has been input (step S108; YES), the CPU 41 ends the display of the preview screen 6 and performs the process of step S101 again. If it is determined that the cancel operation is not input (step S108; NO), the CPU 41 again performs the process of step S107.

Further, when it is determined that the selection operation has been input (step S107; YES), the CPU 41 selects one POP candidate data from the plurality of types of POP candidate data created in step S105 based on the selection operation. (Step S109). Further, at this time, the CPU 41 measures the time from when 25 kinds of POP candidate data are displayed on the terminal display unit 24 in the process of step S106 until the user's desired POP candidate data is selected in the process of step S109. Count by. Further, the CPU 41 displays that each POP candidate data is displayed on the terminal display unit 24 between the time when 25 types of POP candidate data are displayed on the terminal display unit 24 and the time when the user-desired POP candidate data is selected. Each time is counted by the timekeeping unit 47.

Next, the CPU 41 is a learning value indicating the user's preference for the POP data component based on the time from when 25 types of POP candidate data are displayed on the terminal display unit 24 until the user's desired POP candidate data is selected. Is performed (step S110). The details of the learning process will be described later.
As described above, the POP data creation process is performed.

After the completion of the POP data creation process, the CPU 41 may store the created POP candidate data as a completed version in the storage unit 45, or when a predetermined operation is performed by the user, the edit screen of the POP data. (Not shown) may be displayed on the terminal display unit 24. As a result, each POP data component included in the POP data can be further changed according to the user's desire, and the changed POP data closer to the user's preference can be stored in the storage unit 45 as a completed version. .. Further, when the user terminal requests the execution of printing of the completed POP data, the CPU 41 outputs the POP data to the printing device 11 via the communication unit 46 and causes the printing device 11 to print the POP data. May be.

In the process of step S105, 25 types of POP candidate data are created from 5 types of layouts and 5 types of designs, but the present invention is not limited to this. For example, the number of layouts and designs used to create the POP candidate data may be any, and the combination thereof may be any. Further, for example, the number of POP candidate data to be created may be 24 or less, or 26 or more.

(First acquisition process)
Next, the first acquisition process performed by the server 4 of the present embodiment will be described with reference to FIG. 12 and the like. FIG. 12 is a flowchart showing an example of the first acquisition process.

As shown in FIG. 12, first, the CPU 41 acquires the number n of image data input by the user in the image data input field 54 of the input screen 5 (step S201). For example, when the image data is not input to the image data input field 54 on the input screen 5, the number of image data n = 0, and when the image data is input to the image data input field 54, the number of image data n = 1. Further, for example, when the product addition button 55 is selected once and the image data is input to the two image data input fields 54, the number of image data n = 2.

Next, the CPU 41 refers to the layout data table 451 shown in FIG. 3 and extracts a layout among the layout types L1 to L20 whose "number of photographs" is n or more of the image data acquired in step S201 (step). S202). For example, when the number of image data n = 1 acquired in step S201, layout types L1, L2, L3, and the like are extracted.

Next, as shown in FIG. 12, the CPU 41 determines whether or not the number of extracted layouts is 5 or more (step S203). If it is determined that the number of extracted layouts is not 5 or more (step S203; NO), the CPU 41 also extracts layouts whose "number of photographs" is less than the number of image data n (step S204), and in the next step S205. Perform processing. Therefore, when the layout extracted in step S202 is less than 5, all of the layout types L1 to L20 are extracted.

On the other hand, when it is determined that the number of extracted layouts is 5 or more (step S203; YES), the CPU 41 calculates an evaluation value for each of the extracted layouts (step S205). Specifically, for example, the CPU 41 describes the following based on the index value (see FIG. 3) set for each item according to the extracted layout and the learning value associated with each user (see FIG. 5). The evaluation value is calculated according to the formula (1).
Equation (1): Evaluation value = "vertical / horizontal" index value of the extracted layout x "vertical / horizontal" learning value of the user's layout + "photo size" index value of the extracted layout x user's Learning value of "photo size" + index value of "eye catch size" of extracted layout x learning value of user's "eye catch size"

Items such as "the number of photographs" and "the number of illustrations" included in the layout are not included in the equation (1) because they are not stored in the learning value data table 453 as shown in FIG.

For example, when the layout extracted in step S202 or S204 is the layout type L1 and the user code of the user is "000001", the evaluation value of the layout type L1 is as follows.
Evaluation value of layout type L1 = 1 × 0.3 + 1.0 × 0.4 + 0.7 × 0.5 = 1.05

Further, for example, when the layout extracted in step S202 or S204 is the layout type L2 and the user code of the user is "000001", the evaluation value of the layout type L2 is as follows.
Evaluation value of layout type L2 = 1 × 0.3 + 0.4 × 0.4 + 0.6 × 0.5 = 0.76

Next, as shown in FIG. 12, the CPU 41 acquires the layout from the storage unit 45 based on the evaluation value (step S206). Specifically, for example, the CPU 41 ranks the layout types extracted in step S202 or S204 in descending order of the evaluation values calculated in step S205. Then, the CPU 41 acquires from the storage unit 45 two layout types having the highest rank, two layout types having an intermediate rank, and one layout type having the lowest rank. For example, when all 20 layout types are extracted, layout types having evaluation values of 1st, 2nd, 10th, 11th, and 20th are acquired.
As described above, the first acquisition process is performed.

In the process of step S205, the evaluation value is calculated based only on the items of "vertical / horizontal", "photograph size", and "eye catch size", but the present invention is not limited to this. For example, any combination may be used as long as it is common between the index value item associated with each layout and the learning value item associated with each user.

Further, in the process of step S206, it is assumed that two highest layout types, two intermediate layout types, and one lowest layout type are acquired, but the present invention is not limited to this, and the ranking is not limited to this. As long as a high layout type is included, the layout type of any rank may be acquired in any quantity.

(Second acquisition process)
Next, the second profit processing performed by the server 4 of the present embodiment will be described with reference to FIGS. 13, 14 and the like. FIG. 13 is a flowchart showing an example of the second acquisition process. FIG. 14 is a diagram showing an example of the total morpheme characteristic values obtained based on the information input by the user in the product name input field 51 and the catch phrase input field 53.

First, as shown in FIG. 13, the CPU 41 morphologically analyzes the product name input in the product name input field 51 and the catch phrase input in the catch phrase input field 53, and extracts the morpheme contained in those texts. (Step S301). For example, as shown in FIG. 9, when "Yuzu salt ramen" is input in the product name input field 51, the CPU 41 morphologically analyzes this and morphological elements of "Yuzu", "salt" and "ramen". Is extracted. Further, as shown in FIG. 9, when "a refreshing ramen with a scent of yuzu" is input in the catch phrase input field 53, the CPU 41 morphologically analyzes this and "yuzu" and "no". , "Flavor", "ga", "fragrance", "refreshing", "shi", "ta", "ramen", "desu" and "."

If neither the product name nor the catch copy is input in the product name input field 51 and the catch copy input field 53, and the image data of the target product is input in the image data input field 54, the CPU 41 will perform the CPU 41. , Image analysis may be performed on the input image data, and the CPU 41 may specify the name (word) of the product indicated by the image data. For example, as shown in FIG. 9, when the image data of "ramen" is input to the image data input field 54, the CPU 41 analyzes the image and analyzes the image, and the name of the product indicated by the image data is "ramen". It may be used to specify the word "."

Next, as shown in FIG. 13, the CPU 41 acquires the total morpheme characteristic value from the morpheme extracted by the morphological analysis (step S302). Specifically, for example, the CPU 41 enters "Yuzu", "salt" and "ramen" extracted from the product name of "Yuzu salt ramen" input in the product name input field 51, and the catch phrase input field 53. "Yuzu", "no", "flavor", "ga", "fragrance", "refresh", "shi", extracted from the catch phrase of "Yuzu flavor is a refreshing ramen." As shown in FIG. 14, characteristic values set in advance for each word are acquired from the storage unit 45 for each of the morphological elements of “ta”, “ramen”, “desu”, and “.”. As described above, the storage unit 45 stores the characteristic values set in advance for each item in association with the words.
The CPU 41 totals the characteristic values set for each word for each item, and obtains the total morpheme characteristic value for each item. For the "vertical / horizontal" item, the total morpheme characteristic value is calculated by dividing the total value of the characteristic values by the total number of nouns, adverbs, verbs, and adjectives. For example, since the total value of the characteristic values of the "vertical / horizontal" item is "6" and the total number of nouns, adverbs, verbs and adjectives is "9", the morpheme characteristic value of the "vertical / horizontal" item The total is 6/9 = 0.667.

Next, as shown in FIG. 13, the CPU 41 selects one unacquired POP data component from the POP data components of the eye catch, the typeface, the font color, the background, the frame / texture, and the illustration (step). S303). Hereinafter, the case where the eye catch is selected from each POP data component will be described.

Next, the CPU 41 performs the total morpheme characteristic value acquired in step S302 (see FIG. 14), the index value set for each item for eye catch (see FIG. 4), and the learning value associated with each user (see FIG. 4). Based on (see FIG. 5), the evaluation value is calculated according to the following equation (2) (step S304). Here, the evaluation values are calculated for all the eye-catching species stored in the storage unit 45.
Equation (2): Evaluation value = Eye catch "vertical / horizontal" index value x "vertical / horizontal" morpheme characteristic value total + eye catch "Japanese food" index value x "Japanese food" morpheme characteristic value Total x user's "Japanese food" learning value + eye-catching "Western food" index value x "Western food" morpheme characteristic value total x user's "Western food" learning value + eye-catching "for cafe" Index value of "for cafe" x total morpheme characteristic value of "for cafe" x learning value of "for cafe" of user + index value of "for Chinese" of eye catch x total of morpheme characteristic value of "for Chinese" x user "for Chinese" Learning value

For example, when the user code of the user is "000001", the evaluation value of the eye-catching type i1 is as follows.
Evaluation value of eye-catching species i1 = 0 × 0.667 + 1 × 6 × 0.2 + 1 × 2 × 0.9 + 1 × 5 × 0.7 + 0 × 2 × 0.5 = 6.5

Further, for example, when the user code of the user is "000001", the evaluation value of the eye-catching type i2 is as follows.
Evaluation value of eye-catching species i2 = 0 × 0.667 + 1 × 6 × 0.2 + 0 × 2 × 0.9 + 1 × 5 × 0.7 + 1 × 2 × 0.5 = 5.7

Next, as shown in FIG. 13, the CPU 41 acquires an eye catch from the storage unit 45 based on the evaluation value (step S305). Specifically, for example, the CPU 41 ranks the eye-catching species in descending order of the evaluation value calculated in step S304. Then, the CPU 41 acquires from the storage unit 45 two eye-catching species having the highest rank, two eye-catching species having an intermediate rank, and one eye-catching species having the lowest rank. Since 20 kinds of eye-catching species are stored in the storage unit 45, the 1st, 2nd, 10th, 11th, and 20th-ranked eye-catching species are acquired.

Next, the CPU 41 determines whether or not there is an unacquired POP data component among the POP data components of the eye catch, the typeface, the font color, the background, the frame / texture, and the illustration (step S306). If it is determined that there is an unacquired POP data component (step S306; YES), the CPU 41 performs the process of step S303 again. On the other hand, when it is determined that there is no unacquired POP data component (step S306; NO), the CPU 41 ends the second acquisition process.
The second acquisition process is performed as described above.

In step S302, the total morpheme characteristic value is calculated based on the information input in the product name input field 51 and the catch phrase input field 53, but the present invention is not limited to this. For example, the CPU 41 may further calculate the total morpheme characteristic value based on the user's industry / business type stored as user information in the storage unit 45, the current season / event specified by the timekeeping unit 47, and the like. .. In this case, the storage unit 45 stores the characteristic values for each item in advance in association with the type of business / business type and the season / event. The characteristic value may be set to "1", and the characteristic value of "1" may be added when calculating the total morpheme characteristic value. For example, for the season / event of "Christmas", the characteristic value of the item "for Western food" is set to "1", and the characteristic value of "1" is added when calculating the total morpheme characteristic value. Is also good.

Further, in the process of step S304, the evaluation value is calculated based only on the items of "vertical / horizontal", "for Japanese food", "for Western food", "for cafe" and "for Chinese food". It is not limited to. For example, if the item of the total morpheme characteristic value, the item of the index value associated with each eye catch, and the item of the learning value associated with each user are common, any combination can be used. There may be.

Further, in the process of step S305, it is assumed that two highest-level POP data components, two intermediate-level POP data components, and one lowest-level POP data component are acquired, but the present invention is limited to this. As long as the POP data component having a high rank is included, the POP data component having any rank may be acquired in any quantity.

(Learning process)
Next, the learning process performed by the server 4 of the present embodiment will be described with reference to FIGS. 15 and 16. FIG. 15 is a flowchart showing an example of the learning process. FIG. 16 is a diagram showing POP data components included in each of the three types of POP candidate data.

First, as shown in FIG. 15, the CPU 41 performs the process of step S109 after 25 kinds of POP candidate data are displayed on the terminal display unit 24 in the process of step S106 in the POP data creation process counted by the timekeeping unit 47. Acquires the time until the user's desired POP candidate data is selected in step S401 (step S401).

Next, the CPU 41 determines whether or not the time acquired in step S401 is less than 10 seconds (step S402).

If it is determined that the time is less than 10 seconds (step S402; YES), the CPU 41 sets the coefficient used when calculating the learning value in the process of step S410 described later to “3” (step S403). As a result, when the POP candidate data is selected in a short time of less than 10 seconds, the weighting corresponding to the selection of the POP candidate data for "3" times is performed.

If it is determined that the time is not less than 10 seconds, that is, the time acquired in step S401 is 10 seconds or more (step S402; NO), the CPU 41 determines whether the time acquired in step S401 is less than 20 seconds. Is determined (step S404).

If it is determined that the time is less than 20 seconds (step S404; YES), the CPU 41 sets the coefficient used when calculating the learning value in the process of step S410 described later to “2” (step S405). As a result, when the POP candidate data is selected in 10 seconds or more and less than 20 seconds, the weighting corresponding to the selection of the POP candidate data for "2" times is performed.

If it is determined that the time is not less than 20 seconds, that is, the time acquired in step S401 is 20 seconds or more (step S404; NO), the CPU 41 is used when calculating the learning value in the process of step S410 described later. The coefficient is set to "1" (step S406). As a result, when the POP candidate data is selected over a long time of 20 seconds or more, the weighting corresponding to the selection of the POP candidate data for "1" times is performed.

After the process of step S405 or S406, the CPU 41 determines whether or not there is POP candidate data whose display time displayed on the terminal display unit 24 is equal to or longer than a predetermined value among the 25 types of POP candidate data (step S407). ..

When it is determined that there is no POP candidate data whose display time is equal to or longer than a predetermined value (step S407; NO), the CPU 41 omits the processes of steps S408 and S409 described later and performs the process of step S410.

When it is determined that there is POP candidate data whose display time is equal to or longer than a predetermined value (step S407; YES), the CPU 41 shares the POP candidate data desired by the user selected in the process of step S109 among them. It is determined whether or not there is a POP data component (step S408). Specifically, among the 25 types of POP candidate data shown in FIG. 11, the POP candidate data a2 is selected in the process of step S109, and the POP candidate data c3 has a display time of a predetermined value or longer. In the case of the two types of e5, the POP candidate data a2, c3, and e5 each include the POP data component shown in FIG. 16, for example. In this case, the POP candidate data a2, c3, and e5 all include "typeface C" as the "typeface" and "background 5" as the "background". Therefore, it is determined that the POP candidate data c3 and e5 have a POP data component common to the user-desired POP candidate data a2.

If it is determined that there is no common POP data component (step S408; NO), the CPU 41 omits the process of step S409 described later and performs the process of step S410.

When it is determined that there is a common POP data component (step S408; YES), the CPU 41 calculates a learning value for the common POP data component determined to be present in the process of step S410 described later. The coefficient used in this case is set to "3" (step S409).

After the process of step S403 or S409, the CPU 41 calculates the learning value based on the selection result in the process of step S109 of the POP data creation process (step S410). Specifically, the CPU 41 has the learning value for each item (see FIG. 5) calculated for each user calculated at the time of the previous POP data creation, and the number of times the POP data has been created in the past including this time (POP creation). The learning value is calculated according to the following equation (3) based on the number of times), the selection result in step S109, and the coefficients set in steps S403, S405, S406, and S409. The calculation of the learning value by the following formula (3) is executed for each item included in the learning value data table 453 shown in FIG.
Equation (3): Learning value = (learning value of the user up to the previous time × number of POP creations + index value of POP data component included in the selected POP candidate data × coefficient) / (number of POP creations + coefficient)

For example, when the number of POP creations is 20, the eye catch included in the POP candidate data selected in the process of step S109 is the eye catch type i1, the user code of the user is "000001", and the coefficient is "3". The learning values for "for Japanese food" and "for Chinese food" in eye catch are as follows.
Learning value for "Japanese food" = (0.2 × 20 + 1 × 3) / (20 + 3) = 0.304
Learning value for "Chinese food" = (0.5 x 20 + 0 x 3) / (20 + 3) = 0.435

Next, as shown in FIG. 15, the CPU 41 updates the learning value stored in the learning value data table 453 to the learning value calculated in step S410 (step S411).
The learning process is performed as described above.

Although the determination is made based on 10 seconds in step S402 and 20 seconds in step S404, the determination is not limited to this, and the reference may be set to any value. Further, the standard may be changed by the input of the user.

Further, in steps S403, S405, and S406, the coefficients are set to "3", "2", and "1", respectively, but the present invention is not limited to this, and the coefficients are set to any value. May be. Further, the value of the coefficient set in steps S403, S405, and S406 may be changed by the input of the user.

Further, in steps S407 and S408, when there is POP candidate data whose display time is equal to or longer than a predetermined value, whether or not the POP data components common to the selected user-desired POP candidate data are included in them. However, it is not limited to this. For example, among the 25 types of POP candidate data displayed on the terminal display unit 24, a predetermined number of POP candidate data are specified in order from the one having the longest display time, and among them, the selected POP candidate desired by the user is specified. It may be used to determine whether or not a POP data component common to the data is included.

Further, when the coefficient is set to "2" or "1" in the processing of steps S405 and S406, the processing of steps S407 to S409 is performed, but the present invention is not limited to this. For example, even when the coefficient is set to "3" in the process of step S403, the process of steps S407 to S409 may be performed.
Further, the processing of steps S407 to S409 may not be performed.

Further, in step S409, the coefficient is set to "3" for the common POP data component, but the present invention is not limited to this, and the coefficient may be set to any value. Further, the value of the coefficient set in step S409 may be changed by the input of the user.

<< Technical effect of this embodiment >>
According to the present embodiment, the server 4 creates a predetermined number of POP candidate data, displays the created predetermined number of POP candidate data on the terminal display unit 24, and displays the predetermined number of POP candidate data displayed on the terminal display unit 24. From the POP candidate data, the user's desired POP candidate data is selected based on the user's input, and the user is based on the time from when a predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. A CPU 41 for deriving a learning value indicating the preference of the above is provided, and the CPU 41 creates a predetermined number of POP candidate data based on the derived learning value. Therefore, when the POP data is repeatedly obtained by repeatedly creating the POP data. In addition, POP data closer to the user's preference can be obtained in a short time.

Further, since the CPU 41 derives and updates the learning value every time the desired POP candidate data is selected, the POP data closer to the user's preference can be obtained.

Further, when the time until the desired POP candidate data is selected by the CPU 41 is less than the predetermined value, the amount of change in the learning value is larger than when the time is equal to or more than the predetermined value. It can be determined that the shorter the POP candidate data is, the closer the POP candidate data is to the user's preference, and the POP data closer to the user's preference can be obtained at the next POP data creation.

Further, when the time until the desired POP candidate data is selected by the CPU 41 is equal to or longer than the predetermined value, the amount of change in the learning value is smaller than when the time is less than the predetermined value, so that the selection time is reduced. It can be determined that the longer the POP candidate data is, the farther the POP candidate data is from the user's preference, and the POP data closer to the user's preference can be obtained at the next POP data creation.

Further, the CPU 41 displays any one of a predetermined number of POP candidate data on the terminal display unit 24, and the POP is the time from when the predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. Since the learning value is derived based on the display time of the candidate data, a more accurate learning value can be derived.

Further, when the CPU 41 includes a POP data component common to the desired POP candidate data in the POP candidate data whose display time is equal to or longer than a predetermined value, the common POP data component has a common POP. Since the amount of change in the learning value is larger than when the data component is not included, it can be determined that the common POP data component is the user's preference, and a more accurate learning value can be derived.

Further, a storage unit 45 for storing the learning value associated with each user is provided, and the CPU 41 stores the derived learning value in the storage unit 45 and acquires the learning value from the storage unit 45. It is possible to obtain POP data that is close to the user's preference.

《Others》
The description in the above embodiment is an example of a suitable information processing apparatus according to the present invention, and is not limited thereto.

For example, in the above-described embodiment, the server 4 stores various data in the storage unit 45 and displays various display screens on the terminal display unit 24 that functions as a display means, but the present invention is not limited to this. Absent. For example, the server 4 may be configured to store various data in the storage unit 45 and display various display screens on the server display unit 44 that functions as a display means. Further, the mobile terminal 2a may be configured to store various data in the storage unit 25 and display various display screens on the terminal display unit 24. In this case, the mobile terminal 2a itself serves as an information processing device. Function.

Further, in the above-described embodiment, the created plurality of types of POP candidate data are displayed one by one on the preview screen 6 in order, but the present invention is not limited to this, and for example, the created plurality of types are not limited to this. The POP data of the above may be displayed one by one in order, or may be displayed in a list.

Further, in the above-described embodiment, information about the product is input to the input screen 5 to create POP data for introducing the product, but the present invention is not limited to this, and for example, the input screen 5 relates to services. Information may be input to create POP data that introduces the service.

Further, in the above-described embodiment, the learning value is derived for each item of the POP data component, but the present invention is not limited to this. That is, the learning value may be derived for each POP data component. For example, learning values are derived for each of the layout types L1, L2, ..., L20, eye-catching types i1, i2, ..., I20, and each of the typeface, font color, background, frame / texture, and illustration. Similarly, the learning value may be derived for the POP data component.

Further, in the above-described embodiment, the learning value is derived according to the above equation (3), but the present invention is not limited to this, and the learning value is derived based on the time until the POP candidate data is selected. If so, the learning value may be derived under any condition. For example, points are given to the POP data components included in the POP candidate data selected by the user according to the time until the POP candidate data is selected, and the total value of the points given in the past is learned. It may be derived as a value.

Further, in the above-described embodiment, the above formula is based on the time from when a predetermined number of POP candidate data is displayed on the terminal display unit 24 until the desired POP candidate data is selected and the display time by the terminal display unit 24. The coefficient in (3) is set and the learning value is derived, but the present invention is not limited to this. For example, the coefficient may be set based on the breakdown of the time from when a predetermined number of POP candidate data is displayed on the terminal display unit 24 until the desired POP candidate data is selected, that is, the display time by the terminal display unit 24. , The POP candidate data may be ranked in descending order of display time, and a coefficient may be set for the POP data component included in the POP candidate data according to the rank.

Further, in the above-described embodiment, the CPU 41 acquires the POP data component based on the name of the target product input by the user, and the name of the target product in which the acquired POP data component is input by the user. A predetermined number of POP candidate data are created in combination with the above, and the data is displayed on the terminal display unit 24, but the present invention is not limited to this.
For example, the CPU 41 may create a predetermined number of POP candidate data by appropriately inputting information or selecting a POP data component by the user, and display them on the terminal display unit 24.
Further, for example, the CPU 41 may extract a predetermined number of POP candidate data from the created POP data and display the extracted predetermined number of POP candidate data on the terminal display unit 24. As a result, when the repeated POP data is obtained by repeatedly extracting the POP data, the POP data closer to the user's preference can be obtained in a short time. In this case, the CPU 41 functions as an extraction means. Further, the extraction destination of the POP candidate data may be the storage unit 45 or an external device (not shown).

Further, in the above-described embodiment, the CPU 41 derives a learning value for each POP data component based on the time from when a predetermined number of POP candidate data is displayed until the desired POP candidate data is selected. However, it is not limited to this. For example, a learning value may be derived for each POP data based on the time from the display of a predetermined number of POP candidate data to the selection of the desired POP candidate data. In that case, a predetermined number of POP candidate data may be extracted based on the derived learning value. For example, POP candidate data having a high learning value may be preferentially extracted.

Further, in the above-described embodiment, an example in which a hard disk, a non-volatile memory of a semiconductor, or the like is used as a computer-readable medium for the program according to the present invention has been disclosed, but the present invention is not limited to this example. As another computer-readable medium, a portable recording medium such as a CD-ROM can be applied. A carrier wave is also applied as a medium for providing the data of the program according to the present invention via a communication line.

Although some embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and the equivalent scope thereof.
The inventions described in the claims originally attached to the application of this application are added below. The claims in the appendix are as specified in the claims originally attached to the application for this application.
[Additional Notes]
<Claim 1>
A means of creating a predetermined number of ad candidates, and
A display control means for displaying the predetermined number of advertisement candidates created by the creation means on the display means,
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on the user's input.
A learning means for deriving a learning value indicating a user's preference based on the time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means. Prepare,
The information processing device is an information processing device characterized in that a predetermined number of advertisement candidates are created based on the learning value derived by the learning means.
<Claim 2>
The information processing apparatus according to claim 1, wherein the learning means derives and updates the learning value each time the desired advertising candidate is selected by the selection means.
<Claim 3>
The learning means is characterized in that when the time until the desired advertisement candidate is selected is less than a predetermined value, the amount of change in the learning value is larger than when the time is equal to or more than a predetermined value. The information processing apparatus according to claim 1 or 2.
<Claim 4>
The learning means is characterized in that when the time until the desired advertisement candidate is selected is equal to or longer than a predetermined value, the amount of change in the learning value is smaller than when the time is less than the predetermined value. The information processing apparatus according to any one of claims 1 to 3.
<Claim 5>
The display control means causes the display means to display any one of the predetermined number of advertisement candidates.
The learning means sets the display time of the advertisement candidates by the display control means in the time from the display of the predetermined number of advertisement candidates by the display control means to the selection of the desired advertisement candidates by the selection means. The information processing apparatus according to any one of claims 1 to 4, wherein the learning value is derived based on the above.
<Claim 6>
When the learning means includes an advertisement component common to the desired advertisement candidate among the advertisement candidates whose display time is equal to or longer than a predetermined value, the common advertisement is used for the common advertisement component. The information processing apparatus according to claim 5, wherein the amount of change in the learning value is made larger than when the component is not included.
<Claim 7>
A storage means for storing the learning value associated with each user is provided.
The learning means stores the derived learning value in the storage means, and causes the storage means to store the derived learning value.
The information processing device according to any one of claims 1 to 6, wherein the creating means acquires the learning value from the storage means.
<Claim 8>
An extraction method that extracts a predetermined number of advertisement candidates from a plurality of created advertisements,
A display control means for displaying the predetermined number of advertisement candidates extracted by the extraction means on the display means,
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on the user's input.
A learning means for deriving a learning value indicating a user's preference based on the time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means. Prepare,
The extraction means is an information processing apparatus characterized in that a predetermined number of advertisement candidates are extracted based on the learning value derived by the learning means.
<Claim 9>
Computer,
A means of creating a predetermined number of ad candidates,
A display control means for displaying the predetermined number of advertisement candidates created by the creation means on the display means,
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on the user's input.
A learning means for deriving a learning value indicating a user's preference based on the time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means.
It is a program to function as
The creating means is a program characterized in that a predetermined number of advertising candidates are created based on the learning value derived by the learning means.
<Claim 10>
Computer,
An extraction method that extracts a predetermined number of advertisement candidates from a plurality of created advertisements,
A display control means for displaying the predetermined number of advertisement candidates extracted by the extraction means on the display means,
A selection means for selecting a user's desired advertisement candidate from the predetermined number of advertisement candidates displayed on the display means by the display control means based on the user's input.
A learning means for deriving a learning value indicating a user's preference based on the time from when the predetermined number of advertisement candidates are displayed by the display control means until the desired advertisement candidate is selected by the selection means.
It is a program to function as
The extraction means is a program characterized in that a predetermined number of advertisement candidates are extracted based on the learning value derived by the learning means.

4 Server (information processing device)
24 Terminal display (display means)
41 CPU (creation means, display control means, selection means, learning means, extraction means)
45 Storage unit (storage means)

Claims (8)

A means of creating a predetermined number of ad candidates, and
First, the switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means is performed so that each of the predetermined number of advertisement candidates created by the creation means is individually displayed on the display means. Display control means to be executed according to the user operation of
In response to a second user operation in a state where any of the predetermined number of advertisement candidates is displayed on the display means, the displayed advertisement candidate is selected as the user's desired advertisement candidate. Selection method and
When the user's desired advertisement candidate is selected by the selection means, a learning means for deriving a learning value indicating a user's preference based on the time until the user's desired advertisement candidate is selected by the selection means. ,
With
The creation means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means .
In the learning means, an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the user's desired advertisement candidate until the user's desired advertisement candidate is selected by the selection means. In that case, if the advertisement candidate includes an advertisement component common to the advertisement candidate desired by the user, the amount of change in the learning value is made larger than in the case where the common advertisement component is not included.
An information processing device characterized by this. The learning means derives and updates the learning value each time the user- desired advertising candidate is selected by the selection means.
The information processing apparatus according to claim 1. When the time until the advertisement candidate desired by the user is selected by the selection means is less than a predetermined value, the learning means increases the amount of change in the learning value more than when the time is equal to or more than a predetermined value. to,
The information processing apparatus according to claim 1 or 2. When the time until the advertisement candidate desired by the user is selected by the selection means is equal to or longer than a predetermined value, the learning means makes the amount of change in the learning value smaller than when the time is less than the predetermined value. to,
The information processing apparatus according to any one of claims 1 to 3, wherein the information processing device is characterized by the above. A storage means for storing the learning value associated with each user is provided.
The learning means stores the derived learning value in the storage means, and causes the storage means to store the derived learning value.
The creating means acquires the learning value from the storage means.
The information processing apparatus according to any one of claims 1 to 4, wherein the information processing device is characterized by the above. An extraction method that extracts a predetermined number of advertisement candidates from a plurality of created advertisements,
The first is the switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means so that each of the predetermined number of advertisement candidates extracted by the extraction means is individually displayed on the display means. Display control means to be executed according to the user operation of
In response to a second user operation in a state where any of the predetermined number of advertisement candidates is displayed on the display means, the displayed advertisement candidate is selected as the user's desired advertisement candidate. Selection method and
When the user's desired advertisement candidate is selected by the selection means, a learning means for deriving a learning value indicating a user's preference based on the time until the user's desired advertisement candidate is selected by the selection means. ,
With
The extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means.
In the learning means, an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the user's desired advertisement candidate until the user's desired advertisement candidate is selected by the selection means. In that case, if the advertisement candidate includes an advertisement component common to the advertisement candidate desired by the user, the amount of change in the learning value is made larger than in the case where the common advertisement component is not included.
An information processing device characterized by this. Computer,
A means of creating a predetermined number of ad candidates,
First, the switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means is performed so that each of the predetermined number of advertisement candidates created by the creation means is individually displayed on the display means. Display control means, which is executed according to the user operation of
In response to a second user operation in a state where any of the predetermined number of advertisement candidates is displayed on the display means, the displayed advertisement candidate is selected as the user's desired advertisement candidate. Choice,
A learning means for deriving a learning value indicating a user's preference based on the time until the user's desired advertisement candidate is selected by the selection means when the user's desired advertisement candidate is selected by the selection means.
To function as
The creation means creates the predetermined number of advertisement candidates based on the learning value derived by the learning means.
In the learning means, an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the user's desired advertisement candidate until the user's desired advertisement candidate is selected by the selection means. In that case, if the advertisement candidate includes an advertisement component common to the advertisement candidate desired by the user, the amount of change in the learning value is made larger than in the case where the common advertisement component is not included.
A program characterized by that. Computer,
An extraction method that extracts a predetermined number of advertisement candidates from a plurality of created advertisements,
The first is the switching display of the advertisement candidates targeting the predetermined number of advertisement candidates to the display means so that each of the predetermined number of advertisement candidates extracted by the extraction means is individually displayed on the display means. Display control means, which is executed according to the user operation of
In response to a second user operation in a state where any of the predetermined number of advertisement candidates is displayed on the display means, the displayed advertisement candidate is selected as the user's desired advertisement candidate. Choice,
A learning means for deriving a learning value indicating a user's preference based on the time until the user's desired advertisement candidate is selected by the selection means when the user's desired advertisement candidate is selected by the selection means.
To function as
The extraction means extracts the predetermined number of advertisement candidates based on the learning value derived by the learning means.
In the learning means, an advertisement candidate whose display time on the display means is equal to or longer than a predetermined value in an advertisement candidate different from the user's desired advertisement candidate until the user's desired advertisement candidate is selected by the selection means. In that case, if the advertisement candidate includes an advertisement component common to the advertisement candidate desired by the user, the amount of change in the learning value is made larger than in the case where the common advertisement component is not included.
A program characterized by that.

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