Information processing device, information processing method, and information processing program

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program which provide a useful information.SOLUTION: The information processing apparatus comprises a generation unit and a specification unit. With respect to each co-occurrence word inputted as search queries together with a product word indicative of a first product, the generation unit generates relationship information indicating a relationship between the number of products which is the number of second products including the product word and the co-occurrence word in their product names and the number of searchers which is the number of searchers having searched search queries including the product word and the co-occurrence word. The specification unit specifies a product keyword relating to the first product out of the co-occurrence words on the basis of the relationship information.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In recent years, with the rapid spread of the Internet, for example, techniques related to analysis using various information on the Internet have been provided. For example, a technique has been proposed for a visualization presentation system for online auction history data, which extracts information related to past auctions from online auction sites under predetermined search conditions, analyzes the results based on multiple analytical elements, and visualizes the results in various graphs and tables to provide the results to users. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-138439 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned conventional technology does not necessarily provide useful information. For example, the above-mentioned conventional technology only provides information on the history of online auctions, and therefore cannot be said to be able to visually and appropriately present information on potential needs for a target provided by a specific business operator.

[0005] The present application has been made in consideration of the above, and has an object to provide an information processing device, an information processing method, and an information processing program that are capable of providing useful information. [Means for solving the problem]

[0006] The information processing device of the present application is characterized in that it comprises a generation unit that generates relationship information indicating, for each co-occurring word input as a search query together with a product word indicating a first product, the number of products, which is the number of second products that contain the product word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who searched for a search query including the product word and the co-occurring word, and an identification unit that identifies product keywords related to the first product from the co-occurring words based on the relationship information. Effect of the Invention

[0007] According to one aspect of the embodiment, it is possible to provide useful information. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an overview of information processing according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of an information providing device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of the user information database. [Figure 6] FIG. 6 is a diagram illustrating an example of the history information database. [Figure 7] FIG. 7 is a diagram illustrating an example of the keyword information database. [Figure 8] FIG. 8 is a diagram illustrating an example of the product information database. [Figure 9] FIG. 9 is a flowchart showing a processing procedure according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to the embodiments. In addition, the same components in the following embodiments are given the same reference numerals, and duplicated descriptions are omitted.

[0010] In addition, in the following embodiments, "user U" refers to a user (also referred to as a user of information provision service DS1) who uses information provision service DS1 that provides relationship information indicating the relationship between a product word indicating a specific product and a co-occurring word entered together with the product word as a search query, and also refers to an unspecified number of users.

[0011] [1. Overview of information processing method] Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. The information processing shown in Fig. 1 is realized by an information processing system 1. As shown in Fig. 1, the information processing system 1 includes a terminal device 10 and an information providing device 100. The terminal device 10 and the information providing device 100 are connected to each other via a network N (see Fig. 2) in a wired or wireless manner so as to be able to communicate with each other.

[0012] 1, the terminal device 10 transmits a request to transmit information to the information providing device 100 (step S1). For example, a user U of the information providing service DS1 specifies a product word (e.g., "soy meat") indicating a specific product (e.g., soy meat) that he / she wishes to analyze as a reference word. In accordance with the operation of the user U, the terminal device 10 transmits, together with the reference word, a request to transmit relationship information indicating a relationship between the number of products, which is the number of products that contain the reference word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who searched for a search query that contains the reference word and the co-occurring word, for each co-occurring word input together with the reference word as a search query.

[0013] Here, the name of the product according to the present invention is not limited to the official name of the product given by the manufacturer of the product (e.g., "soy meat"). For example, the name of the product according to the present invention may include, in addition to the official name of the product, a catchphrase, phrase, or keyword (e.g., "delicious," "low sugar," etc.) that expresses the characteristics or appeal of the product and is added by the distributor of the product.

[0014] When the information providing device 100 receives the information transmission request together with the reference word from the terminal device 10, the information providing device 100 acquires the co-occurring words input together with the reference word as a search query (step S2).

[0015] Next, when the information providing device 100 acquires the co-occurring words, it acquires product information on products that include the reference word and the co-occurring words in their product names. Next, the information providing device 100 calculates the number of products, which is the number of products that include the reference word and the co-occurring words in their product names, for each co-occurring word based on the product information. Furthermore, when the information providing device 100 acquires the co-occurring words, it acquires search information on searchers who searched for a search query that includes the reference word and the co-occurring words. Next, the information providing device 100 calculates the number of searchers, which is the number of searchers who searched for a search query that includes the reference word and the co-occurring words, for each co-occurring word based on the search information. Next, when the information providing device 100 calculates the number of products and the number of searchers, it calculates a score by multiplying the number of products and the number of searchers for each co-occurring word. Note that, instead of calculating a score by multiplying the number of products and the number of searchers, the information providing device 100 may calculate a score by multiplying the value of the number of products normalized with the maximum number of products set to 1 and the value of the number of searchers normalized with the maximum number of searchers set to 1 for each co-occurring word. Furthermore, the information providing apparatus 100 may calculate a score for each co-occurring word by multiplying the value obtained by dividing the number of products by the average number of products by the value obtained by dividing the number of searchers by the average number of searchers.

[0016] Next, after calculating the score, the information providing device 100 generates a graph GH1 showing the relationship between the number of products and the number of searchers for each co-occurring word (step S3). More specifically, the information providing device 100 generates a graph GH1 showing the relationship between the number of products (corresponding to the horizontal axis of the graph GH1 shown in FIG. 1) and the number of searchers (corresponding to the vertical axis of the graph GH1 shown in FIG. 1) for each co-occurring word. The information providing device 100 also generates a graph GH1 in which the size of the circle showing each co-occurring word indicates the magnitude of the score obtained by multiplying the number of products and the number of searchers.

[0017] In the example of the graph GH1 shown in FIG. 1, the size of the circle showing the co-occurring word #11 indicates "20", which is the score for the co-occurring word #11. The size of the circle showing the co-occurring word #12 indicates "150", which is the score for the co-occurring word #12. The size of the circle showing the co-occurring word #21 indicates "200", which is the score for the co-occurring word #21. The size of the circle showing the co-occurring word #22 indicates "50", which is the score for the co-occurring word #22. The size of the circle showing the co-occurring word #31 indicates "100", which is the score for the co-occurring word #31. The size of the circle showing the co-occurring word #41 indicates "70", which is the score for the co-occurring word #41. The size of the circle showing the co-occurring word #42 indicates "40", which is the score for the co-occurring word #42.

[0018] Next, after generating the graph GH1, the information providing device 100 identifies product keywords related to a specific product (for example, soy meat) (step S4).

[0019] Here, the vertical axis of the graph GH1 shown in FIG. 1 indicates the number of searchers, so that the higher the co-occurring word is located on the graph GH1, the more searchers who searched for the co-occurring word together with the reference word. Also, a large number of searchers means that there are a large number of users who are interested in the object corresponding to the co-occurring word. In other words, a large number of searchers can be interpreted as meaning that there is a high demand for products whose product names include the reference word and the co-occurring word. In other words, a large number of searchers means that if a product is given a name that combines the reference word and the co-occurring word, the product will have room to sell more. Alternatively, a large number of searchers means that if a new product is developed that combines an object that indicates the reference word and an object that indicates the co-occurring word, the product will have room to sell more.

[0020] In addition, the horizontal axis of the graph GH1 shown in FIG. 1 indicates the number of products, so the more co-occurring words are located on the right side of the graph GH1, the more products that contain the reference word and the co-occurring word in the product name. Also, a large number of products means that many products related to the objects corresponding to the co-occurring words have already been developed. In other words, a large number of products can be interpreted as meaning that there is a large supply of products that contain the reference word and the co-occurring word in the product name. In other words, a large number of products means that there are many products on the market that are given names that combine the reference word and the co-occurring word. Alternatively, a large number of products means that many products that combine the object indicating the reference word and the object indicating the co-occurring word have already been developed.

[0021] In view of the above, co-occurring words with a large number of searchers and a small number of products are considered to be useful product keywords to be combined with the reference word because the demand for products containing the reference word and the co-occurring word in their product names is high and the supply of products containing the reference word and the co-occurring word in their product names is low. Therefore, the information providing device 100 identifies co-occurring words with a large number of searchers compared to other co-occurring words and a small number of products compared to other co-occurring words as product keywords useful to be combined with the reference word. In the example shown in FIG. 1, the information providing device 100 identifies co-occurring words #21 and #22 included in the upper left area AR#2 of the graph GH1, which has a large number of searchers and a small number of products when the graph GH1 is divided into four parts vertically and horizontally, as product keywords useful to be combined with the reference word.

[0022] In addition, when the information providing device 100 identifies a product keyword that is useful to be combined with the reference word, the information providing device 100 may provide information (e.g., a message) recommending the use of a product name in which the identified product keyword is combined with the reference word. Alternatively, the information providing device 100 may provide information recommending the development of a product corresponding to a product name in which the identified product keyword is combined with the reference word.

[0023] In addition, the co-occurring words with a small number of searchers and a large number of products are considered to be product keywords that are not useful to be combined with the reference word because the demand for products containing the reference word and the co-occurring word in the product name is low and the supply of products containing the reference word and the co-occurring word in the product name is high. Therefore, the information providing device 100 identifies co-occurring words with a small number of searchers compared to other co-occurring words and with a large number of products compared to other co-occurring words as product keywords that are not useful to be combined with the reference word. In the example shown in FIG. 1, the information providing device 100 identifies co-occurring words #41 and #42 included in the lower right area AR#4 of the graph GH1 with a small number of searchers and a large number of products when the graph GH1 is divided into four parts vertically and horizontally as product keywords that are not useful to be combined with the reference word.

[0024] In addition, when the information providing device 100 identifies a product keyword that is not useful to combine with a reference word, the information providing device 100 may provide information encouraging the user not to use (or to change) a product name that combines the identified product keyword with the reference word.

[0025] In addition, a co-occurring word with a large number of searchers and a large number of products is considered to be a product keyword suitable for combination with the reference word, since the demand for products containing the reference word and the co-occurring word in the product name is high and the supply of products containing the reference word and the co-occurring word in the product name is large. Therefore, the information providing device 100 identifies a co-occurring word with a large number of searchers and a large number of products compared to other co-occurring words as a product keyword suitable for combination with the product word. In the example shown in FIG. 1, the information providing device 100 identifies a co-occurring word #31 included in the upper right area AR#3 of the graph GH1, which has a large number of searchers and a large number of products when the graph GH1 is divided into four parts vertically and horizontally, as a product keyword suitable for combination with the reference word.

[0026] In addition, when the information providing device 100 identifies a product keyword that is appropriate to be combined with the reference word, the information providing device 100 may provide information indicating that it is appropriate to use a product name that combines the identified product keyword with the reference word.

[0027] In addition, the co-occurring words with a small number of searchers and a small number of products are considered to be product keywords that need to be combined with the reference word to be widely recognized by users (i.e., advertised) because the demand for products containing the reference word and the co-occurring word in the product name is low and the supply of products containing the reference word and the co-occurring word in the product name is low. Therefore, the information providing device 100 specifies co-occurring words with a smaller number of searchers than other co-occurring words and with a smaller number of products than other co-occurring words as product keywords to be combined with the reference word to be advertised. In the example shown in FIG. 1, the information providing device 100 specifies co-occurring words #11 and #12 included in the lower left area AR#1 of the graph GH1 with a smaller number of searchers and with a smaller number of products when the graph GH1 is divided into four parts vertically and horizontally as product keywords to be combined with the reference word to be advertised.

[0028] In addition, when the information providing device 100 identifies a product keyword that should be advertised in combination with a reference word, it may provide information indicating that the identified product keyword needs to be combined with the reference word to be widely recognized by users (i.e., advertised).

[0029] When the information providing device 100 identifies the product keywords, it provides the terminal device 10 with information on the product keywords and the graph GH1 (step S5). For example, the information providing device 100 transmits to the terminal device 10 the graph GH1 in which the identified product keywords are displayed in a state in which they are visually distinguished and easily understood according to the type of the identified product keywords. In the example shown in FIG. 1, the information providing device 100 transmits to the terminal device 10 the graph GH1 in which the colors used to fill in the inside of the circle showing the co-occurring word #21 and the co-occurring word #22 identified as useful product keywords, the colors used to fill in the inside of the circle showing the co-occurring word #41 and the co-occurring word #42 identified as unuseful product keywords, the colors used to fill in the inside of the circle showing the co-occurring word #31 identified as an appropriate product keyword, and the colors used to fill in the inside of the circle showing the co-occurring word #11 and the co-occurring word #12 identified as product keywords to be advertised are all different.

[0030] This enables the information providing device 100 to provide relationship information indicating the relationship between the reference word and the co-occurring word in a visually easy-to-understand manner.

[0031] [2. Example of information processing system configuration] Fig. 2 is a diagram showing a configuration example of an information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to the embodiment includes a terminal device 10 and an information providing device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0032] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 2 is not limited to that shown in the figure. For example, in Fig. 2, for the sake of simplicity, only one terminal device 10 is shown, but this is merely an example and is not limiting, and two or more devices may be included.

[0033] The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game machine with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, or the like.

[0034] In addition, the terminal device 10 can connect to a network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network), and communicate with the information providing device 100.

[0035] The information providing device 100 is, for example, a PC, a server device, a mainframe, a workstation, etc. The information providing device 100 may be realized by cloud computing.

[0036] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 3, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

[0037] (Communications Department 11) The communication unit 11 is connected to a network N (see FIG. 2) by wire or wirelessly, and transmits and receives information to and from the information providing device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, or the like.

[0038] (Display section 12) The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 is a touch panel display, but is not limited to this.

[0039] Moreover, the display unit 12 displays the graph received from the information providing device 100 under the control of the display control unit .

[0040] (Input section 13) The input unit 13 is an input device that accepts various operations from the user U. The input unit 13 also has, for example, buttons for inputting characters, numbers, and the like. If the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. The input unit 13 may be a microphone that accepts voice input from the user U. The microphone may be wireless.

[0041] (Positioning unit 14) The positioning unit 14 receives a signal (radio wave) transmitted from a satellite of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10, which is the device itself, based on the received signal. That is, the positioning unit 14 measures the position of the terminal device 10. Note that the GPS is merely an example of a GNSS (Global Navigation Satellite System).

[0042] The positioning unit 14 can measure the position by various methods other than GPS. For example, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as described below as an auxiliary positioning means for position correction or the like.

[0043] (Wi-Fi positioning) For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point to measure the position of the terminal device 10.

[0044] (Beacon positioning) The positioning unit 14 may measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0045] (geomagnetic positioning) The positioning unit 14 also measures the position of the terminal device 10 based on a geomagnetic pattern of a structure measured in advance and a geomagnetic sensor included in the terminal device 10.

[0046] (RFID positioning) Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, stores, etc., or has a function of reading an RFID tag, the location of use is recorded together with information on a payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to measure the location of the terminal device 10. Furthermore, the location may be measured by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

[0047] The positioning unit 14 may, if necessary, measure the position of the terminal device 10 using one or a combination of the above-mentioned positioning means.

[0048] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices and wireless devices. In the example shown in FIG. 3, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, an air pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0049] The above-mentioned sensors 21 to 28 are merely examples and are not intended to be limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0050] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10, such as the moving direction, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10, such as tilts in three axial directions, based on the angular velocity, etc. of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

[0051] Since the terminal device 10 includes the above-mentioned acceleration sensor 21, gyro sensor 22, and air pressure sensor 23, it is possible to measure the position of the terminal device 10 using a technique such as Pedestrian Dead-Reckoning (PDR) that uses these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using a positioning system such as GPS.

[0052] For example, the number of steps, walking speed, and distance walked can be calculated by a pedometer using the acceleration sensor 21. In addition, the moving direction, line of sight direction, and body inclination of the user U can be known by using the gyro sensor 22. In addition, the altitude and floor number on which the terminal device 10 of the user U is located can be known from the air pressure detected by the air pressure sensor 23.

[0053] The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

[0054] The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the air pressure, temperature, sound, and illuminance, and capture images of the surroundings, thereby detecting the environment and situation around the terminal device 10. In addition, the accuracy of the location information of the terminal device 10 can be improved based on the environment and situation around the terminal device 10.

[0055] (Control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input / output port, and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31, a reception unit 32, a processing unit 33, and a display control unit 34.

[0056] (Transmitter 31) The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21-28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the information providing device 100 via the communication unit 11.

[0057] (Receiving unit 32) The receiving unit 32 can receive various information provided by the information providing device 100 and requests for various information from the information providing device 100 via the communication unit 11. For example, the receiving unit 32 receives information related to a graph from the information providing device 100 via the communication unit 11.

[0058] (Processing section 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12, etc. For example, the processing unit 33 can output various information transmitted by the transmission unit 31 and various information from the information providing device 100 received by the reception unit 32 to the display unit 12 for display. For example, the processing unit 33 can output information related to a graph received by the reception unit 32 to the display unit 12 for display.

[0059] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. Various programs and various data are stored in the storage unit 40.

[0060] [4. Example of the configuration of the information providing device] Next, a configuration of the information providing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of a configuration of the information providing device 100 according to the embodiment. As shown in Fig. 4, the information providing device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

[0061] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 2) in a wired or wireless manner.

[0062] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 4, the storage unit 120 has a user information database 121, a history information database 122, a keyword information database 123, and a product information database 124.

[0063] (User Information Database 121) The user information database 121 stores various information related to users. For example, the user information database 121 stores various information such as user attributes. Fig. 5 is a diagram showing an example of the user information database 121. In the example shown in Fig. 5, the user information database 121 has items such as "User ID (Identifier)", "Age", "Gender", "Home", "Workplace", and "Interests and Concerns".

[0064] "User ID" indicates identification information for identifying a user. Furthermore, "age" indicates the age of the user identified by the user ID. Note that "age" may be a specific age of the user identified by the user ID, such as 35 years old. Furthermore, "gender" indicates the gender of the user identified by the user ID.

[0065] Furthermore, "home" indicates the location information of the home of the user identified by the user ID. In the example shown in Fig. 5, "home" is illustrated as an abstract code such as "LC11", but it may also be latitude and longitude information or the like. Furthermore, for example, "home" may also be a region name or an address.

[0066] Furthermore, "workplace" indicates the location information of the work place (school in the case of a student) of the user identified by the user ID. Note that, in the example shown in FIG. 5, "workplace" is illustrated as an abstract code such as "LC12", but it may be latitude and longitude information or the like. Furthermore, for example, "workplace" may be a region name or an address.

[0067] Furthermore, "interests" indicate the interests or concerns of a user identified by a user ID. In other words, "interests" indicate subjects in which a user identified by a user ID has a high interest or concern. For example, "interests" indicate subjects that correspond to topics of interest of a user. Note that, although one "interest" is illustrated for each user in the example shown in FIG. 5, there may be multiple "interests."

[0068] For example, in the example shown in FIG. 5, the age of the user identified by the user ID "U1" is "20s" and the gender is "male." Furthermore, for example, the user identified by the user ID "U1" indicates that his / her home address is "LC11." Furthermore, for example, the user identified by the user ID "U1" indicates that his / her workplace is "LC12." Furthermore, for example, the user identified by the user ID "U1" indicates that he / she is interested in "vegan ideology."

[0069] Here, in the example shown in Figure 5, abstract values ​​such as "U1", "LC11", and "LC12" are used to illustrate the data, but "U1", "LC11", and "LC12" are assumed to store information such as specific character strings and numerical values. In the following figures relating to other information, abstract values ​​may also be illustrated.

[0070] The user information database 121 may store various information according to the purpose, not limited to the above. For example, the user information database 121 may store various information related to the user's terminal device 10. The user information database 121 may also store information related to the user's demographic (demographic attributes), psychographic (psychological attributes), geographic (geographical attributes), behavioral (behavioral attributes), and other attributes. For example, the user information database 121 may store information such as name, family structure, hometown (local area), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used station (other than the nearest station to home or workplace), extracurricular activities (location, time zone, etc.), hobbies, interests, lifestyle, and the like.

[0071] (Historical Information Database 122) The history information database 122 stores various information related to history information (log data) indicating user behavior. Fig. 6 is a diagram showing an example of the history information database 122. In the example shown in Fig. 6, the history information database 122 has items such as "user ID", "location history", "search history", "access history", "purchase history", and "post history".

[0072] "User ID" refers to identification information for identifying a user. "Location history" refers to location history, which is a history of a user's locations and movements. "Search history" refers to search history, which is a history of search queries entered by a user. "Access history" refers to access history, which is a history of content accessed by a user. "Purchase history" refers to purchase history, which is a history of purchases made by a user. "Post history" refers to posting history, which is a history of posts made by a user. "Post history" may include questions about the user's possessions.

[0073] For example, in the example shown in Figure 6, a user identified by user ID "U1" moved as shown in "Location History #1," searched as shown in "Search History #1," accessed content as shown in "Access History #1," purchased specified products etc. at specified stores etc. as shown in "Purchase History #1," and posted as shown in "Post History #1."

[0074] Here, in the example shown in Figure 6, abstract values ​​such as "U1," "Location History #1," "Search History #1," "Access History #1," "Purchase History #1," and "Post History #1" are used to illustrate the data; however, specific information such as character strings and numbers is stored in "U1," "Location History #1," "Search History #1," "Access History #1," "Purchase History #1," and "Post History #1."

[0075] The history information database 122 may store various information according to the purpose, not limited to the above. For example, the history information database 122 may store a usage history of a user of a predetermined service. For example, the history information database 122 stores a usage history of the user in the information providing service DS1.

[0076] Furthermore, the history information database 122 may store the user's visit history to a physical store or facility visit history, etc. Furthermore, the history information database 122 may store the user's payment history in electronic payments, etc.

[0077] (Keyword information database 123) The keyword information database 123 stores various information related to keywords searched by an unspecified number of users. Fig. 7 is a diagram showing an example of the keyword information database 123. In the example shown in Fig. 7, the keyword information database 123 has items such as "keyword", "co-occurring keyword", "category", "search volume", and "co-occurrence degree".

[0078] "Keyword" indicates a keyword searched for by an unspecified number of users on a search engine or the like. "Co-occurring keyword" indicates a co-occurring keyword of the keyword. For example, a co-occurring keyword is a keyword searched for at the same time or close to the time of the keyword. If there is no co-occurring keyword (such as when a search is performed using only a single keyword), the column for the co-occurring keyword is blank. "Category" indicates a category to which the keyword belongs. The category may be classified according to a pair of the keyword and the co-occurring keyword. "Search volume" indicates the search volume (number of searches) of the keyword. The search volume may be measured for each pair of the keyword and the co-occurring keyword. In this case, the total value of the search volumes measured for each pair of the keyword and the co-occurring keyword is the search volume of the keyword itself. "Co-occurrence" indicates the degree of co-occurrence of the keyword and the co-occurring keyword. The degree of co-occurrence may be calculated based on the search volume of the keyword. If there is no co-occurring keyword (such as when a search is performed using only a single keyword), the column for the degree of co-occurrence is blank.

[0079] For example, in the example shown in Figure 7, "Keyword #1" is searched for together with "co-occurring keyword #11," classified into "Category #11," has "search volume #11," and has a co-occurrence degree with the co-occurring keyword of "co-occurrence degree #11."

[0080] Here, in the example shown in Figure 7, abstract values ​​such as "keyword #1," "co-occurring keyword #11," "category #11," "search volume #11," and "co-occurrence degree #11" are used for the illustration, however, specific information such as character strings or numerical values ​​is stored in "keyword #1," "co-occurring keyword #11," "category #11," "search volume #11," and "co-occurrence degree #11."

[0081] The keyword information database 123 may store various information according to the purpose, without being limited to the above. For example, the keyword information database 123 may store information regarding the total value of search volumes measured for each pair of the keyword and a co-occurring keyword (search volume of the keyword itself). The keyword information database 123 may also store information regarding the category of the co-occurring keyword along with the category of the keyword. The keyword information database 123 may also store information regarding the number of page views (PV), click-through rate (CTR), conversion rate (CVR), etc. of a website selected as a search result for the keyword.

[0082] (Product Information Database 124) The product information database 124 stores various information related to products. Fig. 8 is a diagram showing an example of the product information database 124. In the example shown in Fig. 8, the product information database 124 has items such as "product ID" and "product name".

[0083] "Product ID" indicates identification information for identifying a product. "Product name" indicates the name of the product. For example, "Product name" includes the name of the product given by the product manufacturer (e.g., "soy meat"). Furthermore, "Product name" may include, in addition to the product name, a catchphrase, phrase, or keyword for the product added by the product seller (e.g., "delicious," "low carb," etc.).

[0084] (Control unit 130) Returning to Fig. 4, the description will be continued. The control unit 130 is a controller, and is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing device 100 using a storage area such as a RAM as a working area. In the example shown in Fig. 4, the control unit 130 has an acquisition unit 131, a generation unit 132, an identification unit 133, and a provision unit 134.

[0085] (Acquisition part 131) The acquisition unit 131 acquires information about keywords used by users in searches on search engines or the like from various servers or the like via the communication unit 110 .

[0086] At this time, the acquisition unit 131 acquires various pieces of information related to the user from the terminal device 10, various servers, etc., via the communication unit 110. For example, the acquisition unit 131 acquires information indicating the user (such as a user ID), location information of the user, attribute information of the user, etc. Then, the acquisition unit 131 registers the information in the user information database 121 of the storage unit 120.

[0087] Furthermore, the acquisition unit 131 acquires various types of history information (log data) indicating user behavior from various servers and the like via the communication unit 110. Then, the acquisition unit 131 registers the acquired history information in the history information database 122 of the storage unit 120.

[0088] The keywords used by the user in a search using a search engine or the like may be keywords included in various types of history information (keywords that appear in the history information).

[0089] The acquisition unit 131 also estimates co-occurring keywords for each keyword searched for by an unspecified number of users using a search engine or the like. For example, the acquisition unit 131 estimates that keywords searched for at the same time or in a similar time period are co-occurring keywords. The acquisition unit 131 then registers the keywords in the keyword information database 123 of the storage unit 120.

[0090] The acquisition unit 131 also estimates a corresponding category for each keyword searched for by an unspecified number of users using a search engine or the like. Then, the acquisition unit 131 registers the keyword in the keyword information database 123 of the storage unit 120. For example, the acquisition unit 131 compares and collates the acquired keyword with keywords previously classified into categories, and classifies the acquired keyword into the corresponding keyword category. At this time, the acquisition unit 131 may create a trained model in advance by machine learning that classifies input keywords into appropriate categories and outputs them.

[0091] For example, the acquisition unit 131 creates a trained model that classifies keyword categories by machine learning using a recurrent neural network (RNN) or a long short-term memory (LSTM). Note that the model may be of any type and any format. For example, the acquisition unit 131 may employ a support vector machine (SVM) or a deep neural network (DNN) as a model. Here, the DNN may be a convolutional neural network (CNN) or an RNN. The model may be realized by combining multiple models, such as a model that combines a CNN and an RNN.

[0092] The RNN and LSTM may be neural networks based on an attention mechanism. The acquisition unit 131 may use a similar natural language processing model. Data mining or other machine learning algorithms may also be used. The acquisition unit 131 can learn the model using the various learning methods described above.

[0093] In practice, the acquiring unit 131 may infer the category of a keyword based on a combination of a keyword and a co-occurring keyword, rather than on a single keyword.

[0094] The acquisition unit 131 also acquires product information related to products from various servers and the like via the communication unit 110. For example, the acquisition unit 131 acquires product information including the product names of products put up for sale on the electronic commerce service from a server providing the electronic commerce service. The acquisition unit 131 then registers the acquired product information in the product information database 124 of the storage unit 120.

[0095] Furthermore, the acquiring unit 131 receives an information transmission request together with the reference word from the terminal device 10 via the communication unit 110. When the acquiring unit 131 receives an information transmission request together with the reference word, the acquiring unit 131 refers to the keyword information database 123 and acquires co-occurring words input together with the reference word as a search query.

[0096] Furthermore, when acquiring the co-occurring words, the acquiring unit 131 refers to the product information database 124 to acquire, for each co-occurring word, product information on products whose product names include the reference word and the co-occurring word.

[0097] Furthermore, when acquiring the co-occurring words, the acquiring unit 131 refers to the history information database 122 and acquires, for each co-occurring word, search information related to a searcher who has searched for a search query including the reference word and the co-occurring word.

[0098] (Generation unit 132) The generating unit 132 calculates the number of products, which is the number of second products that include the product word and the co-occurring word in their product names, for each co-occurring word input as a search query together with the product word indicating a specific product. Specifically, the generating unit 132 calculates the number of products, which is the number of products that include the reference word and the co-occurring word in their product names, for each co-occurring word, based on the product information acquired by the acquiring unit 131.

[0099] Furthermore, the generation unit 132 calculates the number of searchers, which is the number of searchers who searched for a search query including the co-occurring word, for each co-occurring word input together with a product word indicating a specific product. Specifically, the generation unit 132 calculates the number of searchers, which is the number of searchers who searched for a search query including the reference word and the co-occurring word, for each co-occurring word, based on the search information acquired by the acquisition unit 131.

[0100] Furthermore, the generation unit 132 calculates a score indicating whether or not the co-occurring word is an appropriate product keyword to be combined with the product word, based on the number of products and the number of searchers, for each co-occurring word. Specifically, the generation unit 132 calculates a score based on the calculated number of products and the number of searchers, for each co-occurring word. For example, the generation unit 132 calculates a score by multiplying the calculated number of products and the number of searchers, for each co-occurring word. Note that, instead of calculating a score by multiplying the number of products and the number of searchers, the generation unit 132 may calculate a score by multiplying a value obtained by normalizing the number of products with the maximum number of products being 1 and a value obtained by normalizing the number of searchers with the maximum number of searchers being 1, for each co-occurring word. Furthermore, the generation unit 132 may calculate a score by multiplying a value obtained by dividing the number of products by the average number of products and a value obtained by dividing the number of searchers by the average number of searchers, for each co-occurring word.

[0101] Next, the generating unit 132 generates relationship information indicating the relationship between the number of products, which is the number of second products that include the product word and the co-occurring word in the product name, and the number of searchers, which is the number of searchers who searched for a search query that includes the product word and the co-occurring word, for each co-occurring word. For example, the generating unit 132 generates a graph indicating the relationship between the number of products and the number of searchers for each co-occurring word, as an example of the relationship information. More specifically, the generating unit 132 generates a two-dimensional graph indicating the relationship between the number of products and the number of searchers for each co-occurring word. In addition, the generating unit 132 generates a graph in which the size of a circle indicating each co-occurring word indicates the magnitude of a score obtained by multiplying the number of products and the number of searchers.

[0102] (Specific Section 133) The identification unit 133 identifies a product keyword related to a specific product from among the co-occurring words based on the relationship information generated by the generation unit 132. Specifically, the identification unit 133 identifies, as the product keyword, a co-occurring word that is associated with a smaller number of products than other co-occurring words. Also, the identification unit 133 identifies, as the product keyword, a co-occurring word that is associated with a larger number of searchers than other co-occurring words.

[0103] More specifically, the identifying unit 133 identifies, as a product keyword useful for combining with a product word, a co-occurring word that has a larger number of searchers than other co-occurring words and has a smaller number of products than other co-occurring words. For example, when the display area of ​​the graph generated by the generating unit 132 is divided into four parts, top, bottom, left, and right, the identifying unit 133 identifies, as a product keyword useful for combining with a product word, a co-occurring word included in the upper left area that has a larger number of searchers and has a smaller number of products.

[0104] Furthermore, the identifying unit 133 identifies, as product keywords that are not useful for combining with product words, co-occurring words that have a smaller number of searchers than other co-occurring words and have a larger number of products than other co-occurring words. For example, when the display area of ​​the graph generated by the generating unit 132 is divided into four parts, top, bottom, left, and right, the identifying unit 133 identifies, as product keywords that are not useful for combining with product words, co-occurring words included in the lower right area that has a smaller number of searchers and has a larger number of products.

[0105] Furthermore, the identifying unit 133 identifies, as a product keyword suitable for combination with a product word, a co-occurring word that has a larger number of searchers than other co-occurring words and has a larger number of products than other co-occurring words. For example, when the display area of ​​the graph generated by the generating unit 132 is divided into four parts, top, bottom, left, and right, the identifying unit 133 identifies, as a product keyword suitable for combination with a product word, a co-occurring word included in the upper right area that has a larger number of searchers and has a larger number of products.

[0106] Furthermore, the identifying unit 133 identifies, as a product keyword to be combined with the product word and advertised, a co-occurring word that has a smaller number of searchers than other co-occurring words and has a smaller number of products than other co-occurring words. For example, when the display area of ​​the graph generated by the generating unit 132 is divided into four parts, top, bottom, left, and right, the identifying unit 133 identifies, as a product keyword to be combined with the product word and advertised, a co-occurring word included in the lower left area that has a smaller number of searchers and has a smaller number of products.

[0107] (Provider 134) The providing unit 134 provides, for each product word, the relationship information generated for each co-occurring word by the generating unit 132. Specifically, the providing unit 134 transmits, as an example of the relationship information, a graph generated by the generating unit 132 to the user's terminal device 10. For example, the providing unit 134 transmits, to the terminal device 10, a graph in which the product keywords identified by the identifying unit 133 are displayed in a state in which they are visually distinguished and easily understood according to the type of the identified product keyword.

[0108] Furthermore, the providing unit 134 provides information related to the product keyword identified by the identifying unit 133. For example, when the providing unit 134 identifies a product keyword that is useful to be combined with a product word, the providing unit 134 transmits, to the terminal device 10, information (for example, a message) recommending the use of a product name in which the identified product keyword is combined with the product word. Alternatively, the providing unit 134 transmits, to the terminal device 10, information recommending the development of a product corresponding to a product name in which the identified product keyword is combined with the product word.

[0109] In addition, when the providing unit 134 identifies a product keyword that is not useful to combine with a product word, the providing unit 134 transmits information to the terminal device 10 to encourage the terminal device 10 not to use (or to change) the product name that combines the identified product keyword with the product word.

[0110] In addition, when the providing unit 134 identifies a product keyword that is appropriate to be combined with a product word, the providing unit 134 transmits information to the terminal device 10 indicating that it is appropriate to use a product name that combines the identified product keyword with the product word.

[0111] In addition, when the providing unit 134 identifies a product keyword that should be combined with a product word and advertised, the providing unit 134 transmits information to the terminal device 10 indicating that the identified product keyword needs to be combined with the product word to be widely recognized by users (i.e., advertised).

[0112] 5. Processing Procedure Next, a processing procedure by the terminal device 10 and the information providing device 100 according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 30 of the terminal device 10 and the control unit 130 of the information providing device 100.

[0113] As shown in FIG. 9, the acquisition unit 131 of the information providing device 100 acquires co-occurring words input as a search query together with a product word indicating a specific product from various servers or the like via the communication unit 110 (step S101).

[0114] Furthermore, the generating unit 132 of the information providing device 100 generates relationship information indicating the relationship between the number of products and the number of searchers for each co-occurring word acquired by the acquiring unit 131 (step S102). For example, the generating unit 132 generates a graph indicating the relationship between the number of products and the number of searchers for each co-occurring word as an example of the relationship information.

[0115] The identifying unit 133 of the information providing device 100 identifies a product keyword based on the relationship information (step S103). For example, the identifying unit 133 identifies a co-occurring word that has a larger number of searchers than other co-occurring words and has a smaller number of products than other co-occurring words as a product keyword useful for combining with the product word.

[0116] Furthermore, the providing unit 134 of the information providing device 100 provides information on the relationship information generated by the generating unit 132 and the product keywords identified by the identifying unit 133 (step S104). For example, the providing unit 135 transmits to the terminal device 10 a graph generated by the generating unit 132, in which the product keywords identified by the identifying unit 133 are displayed in a state in which they are visually distinguished and easily understood according to the type of the identified product keyword. 6. Modifications The above-described terminal device 10 and information providing device 100 may be implemented in various different forms other than the above-described embodiment. Therefore, modifications of the embodiment will be described below.

[0117] [6-1. Purchase ratio] In the above embodiment, the identification unit 133 identifies a product keyword from among co-occurring words based on the relationship information, but the identification unit 133 may identify a product keyword based on information other than the relationship information. Specifically, the identification unit 133 may identify a product keyword for each co-occurring word based on the ratio of the number of purchasers who purchased the product word and a product (hereinafter also referred to as a second product) whose product name includes the co-occurring word.

[0118] For example, the generating unit 132 calculates, for each co-occurring word, a first purchase ratio indicating the ratio of the number of purchasers who purchased the second product to the number of products. The identifying unit 133 identifies, as a product keyword, a co-occurring word having a higher first purchase ratio than other co-occurring words based on the first purchase ratio calculated by the generating unit 132.

[0119] Furthermore, the generating unit 132 calculates, for each co-occurring word, a second purchase ratio indicating the ratio of the number of purchasers who purchased the second product to the number of searchers. The identifying unit 133 identifies, as a product keyword, a co-occurring word having a higher second purchase ratio than other co-occurring words, based on the second purchase ratio calculated by the generating unit 132.

[0120] [6-2. Extracting differences based on comparison of graphs] In addition, in the above embodiment, a case has been described in which the identification unit 133 identifies a product keyword from among co-occurring words based on one graph related to product words generated by the generation unit 132, but the identification unit 133 may also identify a product keyword based on a comparison result between multiple graphs.

[0121] For example, when the acquiring unit 131 receives a request to transmit information together with a reference word (hereinafter also referred to as a first reference word), the acquiring unit 131 refers to the keyword information database 123 and identifies words related to the first reference word. For example, the acquiring unit 131 identifies words that share a common category with the first reference word. Next, the acquiring unit 131 refers to the history information database 122 and identifies, from among the identified words, a word that is searched by a large number of people as a second reference word (hereinafter also referred to as a second reference word). The information providing device 100 performs the same process on the second reference word as on the first reference word.

[0122] The generating unit 132 generates a graph GH1 showing the relationship between the number of products and the number of searchers for each co-occurring word related to the first base word. The generating unit 132 also generates a graph GH2 showing the relationship between the number of products and the number of searchers for each co-occurring word related to the second base word.

[0123] The identification unit 133 compares the graph GH1 and the graph GH2 generated by the generation unit 132, and identifies a combination of graphs in which the similarity between the two graphs exceeds a predetermined threshold. Next, the identification unit 133 compares the graph GH1 and the graph GH2 in which the similarity exceeds a predetermined threshold, and extracts the difference between the two graphs. For example, the identification unit 133 identifies a co-occurrence keyword that exists in the graph GH2 but does not exist in the graph GH1 as a product keyword related to the first reference word. For example, the identification unit 133 identifies a co-occurrence word that has a smaller number of searchers than other co-occurrence words and has a larger number of products than other co-occurrence words, among the co-occurrence keywords that exist in the graph GH2 but do not exist in the graph GH1.

[0124] The providing unit 134 provides information related to the identified product keyword. For example, the providing unit 134 identifies a product keyword useful for combining with the second reference word as a product keyword related to the first reference word, and transmits information recommending the combination with the first reference word to the terminal device 10.

[0125] This enables the information providing device 100 to identify product keywords that are useful to combine with a second reference word (e.g., "tofu") related to the first reference word (e.g., "soy meat"), and provide information that recommends combining the product keywords with the first reference word (e.g., "soy meat").

[0126] In the above embodiment, the processing executed by the information providing device 100 may actually be executed by the terminal device 10. That is, the processing may be completed in a standalone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the information providing device 100 in the above embodiment.

[0127] [7. Effects] As described above, the information processing device according to the present application (information providing device 100 in the embodiment) includes the generating unit 132 and the identifying unit 133. The generating unit 132 generates, for each co-occurring word input as a search query together with a product word indicating a first product, relationship information indicating the relationship between the number of products, which is the number of second products that include the product word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who searched for a search query including the product word and the co-occurring word, for each co-occurring word input as a search query together with a product word indicating a first product. The identifying unit 133 identifies a product keyword related to the first product from among the co-occurring words based on the relationship information.

[0128] This allows the information processing device to provide information on co-occurring words that are estimated to have high (or low) demand for products that include the product word and the co-occurring word in their product names based on the number of searchers from among multiple co-occurring words. Also, the information processing device can provide information on co-occurring words that are estimated to have high (or low) supply for products that include the reference word and the co-occurring word in their product names based on the number of products from among multiple co-occurring words. Therefore, the information processing device can provide useful information.

[0129] Furthermore, the generating unit 132 calculates, for each co-occurring word, a first purchasing ratio indicating the ratio of the number of purchasers who purchased the second product to the number of products or a second purchasing ratio indicating the ratio of the number of purchasers to the number of searchers. The identifying unit 133 identifies, as a product keyword, a co-occurring word having a higher first purchasing ratio or a higher second purchasing ratio than other co-occurring words.

[0130] This enables the information processing device to provide information on co-occurring words included in the product name of a popular product with a large number of purchasers, for example, from among a plurality of co-occurring words, based on the purchase ratio.

[0131] In addition, the identifying unit 133 identifies, as product keywords, co-occurring words that are associated with a smaller number of products than other co-occurring words.

[0132] Thereby, the information processing device can provide information on co-occurring words for which there is a small supply of products including the product word and the co-occurring word in the product name, for example. In other words, the information processing device can provide information on co-occurring words for which there are few products on the market with names that combine the reference word and the co-occurring word, and there is room for the development of new products that combine an object indicating the reference word and an object indicating the co-occurring word.

[0133] In addition, the identifying unit 133 identifies, as product keywords, co-occurring words that have a larger number of searchers than other co-occurring words.

[0134] This allows the information processing device to provide, for example, information on co-occurring words that are in high demand for products that include the reference word and the co-occurring words in their product names. In other words, the information processing device can provide information on co-occurring words that have potential to increase sales of a product when the product is given a name that combines the reference word and the co-occurring words. Alternatively, the information processing device can provide information on co-occurring words that have potential to increase sales of a new product that combines an object that indicates the reference word and an object that indicates the co-occurring words.

[0135] In addition, the identifying unit 133 identifies, as a product keyword useful for combining with a product word, a co-occurring word that has a larger number of searchers compared to other co-occurring words and has a smaller number of products compared to other co-occurring words.

[0136] This enables the information processing device to provide information regarding product keywords that are useful to combine with the reference word, for example, because there is a high demand for products that include the reference word and co-occurring words in their product names and there is a low supply of products that include the reference word and co-occurring words in their product names.

[0137] In addition, the identification unit 133 identifies, as product keywords that are not useful for combining with product words, co-occurring words that have a smaller number of searchers compared to other co-occurring words and that have a larger number of products compared to other co-occurring words.

[0138] This enables the information processing device to provide information about product keywords that are not useful for combining with the reference word because there is low demand for products that include the reference word and co-occurring words in their product names and there is a high supply of products that include the reference word and co-occurring words in their product names.

[0139] In addition, the identifying unit 133 identifies, as a product keyword suitable for combination with the product word, a co-occurring word that has a larger number of searchers than other co-occurring words and has a larger number of products than other co-occurring words.

[0140] This enables the information processing device to provide information regarding product keywords that are suitable to be combined with the reference word, since there is a high demand for products that include the reference word and co-occurring words in their product names, and there is a large supply of products that include the reference word and co-occurring words in their product names.

[0141] In addition, the identifying unit 133 identifies, as a product keyword to be combined with the product word and advertised, a co-occurring word that has a smaller number of searchers than other co-occurring words and has a smaller number of products than other co-occurring words.

[0142] This enables the information processing device to provide information regarding product keywords that need to be combined with the reference word to be widely recognized by users (i.e., advertised) because there is low demand for products that contain the reference word and co-occurring words in their product names and there is a small supply of products that contain the reference word and co-occurring words in their product names.

[0143] Furthermore, the generation unit 132 calculates, for each co-occurring word, a score indicating whether or not the co-occurring word is a product keyword appropriate for combination with a product word, based on the number of products and the number of searchers.

[0144] This enables the information processing device to provide an easy-to-visual understanding of the level (or low) of demand for products that contain the product word and co-occurring words in their product names, and the amount (or low) of supply of products that contain the reference word and co-occurring words in their product names.

[0145] The information processing device further includes a providing unit 134. The providing unit 134 provides the relationship information generated for each co-occurring word by the generating unit 132 for each product word.

[0146] This enables the information processing device to provide a visually easy-to-understand indication of the level (or low) of demand for products that contain the product word and co-occurring words in their product names, or the amount (or low) of supply for products that contain the reference word and co-occurring words in their product names.

[0147] [8. Hardware Configuration] Moreover, the terminal device 10 and the information providing device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration as shown in Fig. 10, for example. The information providing device 100 will be described below as an example. Fig. 10 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected by a bus 1090.

[0148] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0149] The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that primarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may be a removable storage medium such as a USB memory or a Secure Digital (SD) memory card. The secondary storage device 1050 may be a cloud storage device (online storage device), a NAS (Network Attached Storage), a file server, or the like.

[0150] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, a printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input I / F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, a keyboard, a keypad, a button, a scanner, etc., and is realized by a USB, etc.

[0151] Furthermore, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

[0152] The output device 1010 and the input device 1020 may be integrated into one device, such as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated into one device, such as an input / output I / F.

[0153] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0154] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0155] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0156] For example, when the computer 1000 functions as the information providing device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with the other device via the network I / F 1080 and use functions, data, etc. of a program by calling them from another program of the other device.

[0157] [9. Others] Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. The above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be appropriately combined. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

[0158] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0159] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0160] For example, the information providing device 100 described above may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0161] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0162] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, a specific section can be read as a specific means or a specific circuit. [Explanation of symbols]

[0163] 1. Information Processing Systems 10 Terminal Equipment 100 Information provision device 121 User Information Database 122 History Information Database 123 Keyword Information Database 124 Product Information Database 131 Acquisition Department 132 Generation part 133 Specific part 134 Provision Department

Claims

1. a generation unit that generates, for each co-occurring word input as a search query together with a product word indicating a first product, relationship information indicating a relationship between the number of products, which is the number of second products that include the product word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who have searched for a search query including the product word and the co-occurring word; an identification unit that identifies a product keyword related to the first product from among the co-occurring words based on the relationship information; An information processing device comprising:

2. The generation unit is Calculating a first purchase ratio indicating a ratio of the number of purchasers who purchased the second product to the number of products or a second purchase ratio indicating a ratio of the number of purchasers to the number of searchers for each of the co-occurring words; The identification unit is identifying a co-occurring word having a higher first purchase ratio or a higher second purchase ratio than other co-occurring words as the product keyword; 2. The information processing apparatus according to claim 1,

3. The identification unit is Identifying a co-occurring word that has a smaller number of products compared to other co-occurring words as the product keyword; 3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. The identification unit is Identifying a co-occurring word that has a larger number of searchers than other co-occurring words as the product keyword; 4. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. The identification unit is Identifying, as the product keyword useful for combining with the product word, a co-occurrence word that has a larger number of searchers than other co-occurrence words and has a smaller number of products than the other co-occurrence words; 5. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

6. The identification unit is Identifying, as the product keyword that is not useful for combining with the product word, a co-occurrence word that has a smaller number of searchers than other co-occurrence words and has a larger number of products than the other co-occurrence words; 6. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. The identification unit is Identifying, as the product keyword suitable for combination with the product word, a co-occurrence word that has a larger number of searchers than other co-occurrence words and has a larger number of products than the other co-occurrence words; 7. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

8. The identification unit is Identifying a co-occurrence word that has a smaller number of searchers than other co-occurrence words and has a smaller number of products than the other co-occurrence words as the product keyword to be advertised in combination with the product word; 8. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

9. The generation unit is calculating a score for each of the co-occurring words based on the number of products and the number of searchers, which indicates whether the co-occurring word is a product keyword appropriate for combination with the product word; 9. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

10. a provision unit for providing the relationship information generated for each co-occurring word by the generation unit for each of the product words; 10. The information processing device according to claim 1, further comprising:

11. 1. A computer-implemented information processing method, comprising: a generating step of generating relationship information indicating a relationship between the number of products, which is the number of second products that include the product word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who have searched for a search query that includes the product word and the co-occurring word, for each co-occurring word input as a search query together with a product word indicating a first product; a step of identifying a product keyword related to the first product from among the co-occurring words based on the relationship information; 13. An information processing method comprising:

12. a generation step of generating relationship information indicating a relationship between the number of products, which is the number of second products that include the product word and the co-occurring word in their product names, and the number of searchers, which is the number of searchers who have searched for a search query that includes the product word and the co-occurring word, for each co-occurring word input as a search query together with a product word indicating a first product; a step of identifying a product keyword related to the first product from among the co-occurring words based on the relationship information; An information processing program that causes a computer to execute the above.

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