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

Abstract

To learn the tendency of a user input mode from a search keyword input pattern.SOLUTION: An information processing device comprises: an identification unit which identifies features of a time-series change of a search keyword input mode; a learning unit which learns a cluster of users searching for a search keyword using the features per pair of a search keyword and features of a time-series change of an input mode; an estimation unit which estimates a cluster of a user searching for the search keyword using the features on the basis of a pair of the specified search keyword and features of a time-series change of an input mode; and a provision unit which provides information showing the cluster of the estimated user.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] In recent years, with the remarkable spread of the Internet, for example, technologies related to analysis using various information on the Internet have been provided. For example, a technique for extracting information regarding needs for a target provided by a predetermined business operator based on a search query input by a user has been proposed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, with the above prior art, useful information cannot always be obtained. For example, with the above prior art, only information regarding needs for a target provided by a predetermined business operator is extracted, so the tendency of the user's input mode cannot be learned from the input pattern of the search keyword.

[0005] The present application has been made in view of the above, and an object thereof is to learn the tendency of the user's input mode from the input pattern of the search keyword.

Means for Solving the Problems

[0006] The information processing device according to the present application includes an extraction unit that extracts users who have searched a certain number of times or more using the same keyword as target users, a classification unit that classifies the target users into multiple clusters based on the user input patterns based on the input patterns of search keywords excluding the same keyword, an identification unit that identifies the characteristics of the waveform pattern of the time-series change in the search volume of search keywords entered by target users belonging to each of the multiple clusters during a predetermined period, and the search keywords entered during the predetermined period. Category For each pair of the waveform pattern characteristics of the time-series changes in search volume, the characteristic is Belonging to the category A learning unit that trains a model on clusters of users who search using search keywords, and a specified search keyword Category The characteristics of the waveform pattern of the time-series change in search volume are input into the model, and these characteristics are used Belonging to the category The system is characterized by comprising an estimation unit that estimates the cluster of users who perform searches using search keywords, and a provision unit that provides information indicating the estimated cluster of users. [Effects of the Invention]

[0007] According to one embodiment, the user's input patterns can be learned from the input patterns of search keywords. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is an explanatory diagram showing an overview of the information processing method according to the embodiment. [Figure 2] Figure 2 is an explanatory diagram showing an overview of clustering according to the embodiment. [Figure 3] Figure 3 is an example of information showing the ranking of search query categories based on the characteristics of time-series changes in input patterns. [Figure 4] Figure 4 shows an example of the configuration of an information processing system according to the embodiment. [Figure 5] Figure 5 shows an example of the configuration of a terminal device according to this embodiment. [Figure 6]Figure 6 shows an example of the configuration of a server device according to the embodiment. [Figure 7] Figure 7 shows an example of a user information database. [Figure 8] Figure 8 shows an example of a historical information database. [Figure 9] Figure 9 shows an example of a feature information database. [Figure 10] Figure 10 is a flowchart showing the processing procedure according to the embodiment. [Figure 11] Figure 11 shows an example of a hardware configuration. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, embodiments for implementing the information processing device, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing device, information processing method, and information processing program according to the present application. Furthermore, the same parts are denoted by the same reference numerals in the following embodiments, and redundant descriptions are omitted.

[0010] [1. Overview of Information Processing Methods] First, with reference to Figure 1, an overview of the information processing method performed by the information processing device according to the embodiment will be described. Figure 1 is an explanatory diagram showing an overview of the information processing method according to the embodiment. In Figure 1, the example of finding trends in the user's input patterns from the input patterns of search keywords will be used for explanation.

[0011] As shown in Figure 1, the information processing system 1 includes a terminal device 10 and a server device 100. The terminal device 10 and the server device 100 are connected to each other via a network N (see Figure 4) by wire or wireless means so that they can communicate with each other. In this embodiment, the terminal device 10 cooperates with the server device 100.

[0012] The terminal device 10 is a smart device such as a smartphone or a tablet terminal used by the user U, and is a portable terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). Further, the terminal device 10 has a screen such as a liquid crystal display, and has a screen having a touch panel function, and receives various operations on display data such as content, such as a tap operation, a slide operation, and a scroll operation, from the user U using a finger or a stylus. Note that an operation performed on the area of the screen where the content is displayed may be regarded as an operation on the content. Further, the terminal device 10 may be not only a smart device but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

[0013] The server device 100 is an information processing device that cooperates with the terminal device 10 of each user U and provides various API (Application Programming Interface) services and various data to the terminal device 10 of each user U, and is realized by a computer, a cloud system, or the like.

[0014] Further, the server device 100 may be an information processing device that provides some kind of web service online to the terminal device 10 of each user U. For example, as a web service, the server device 100 may provide services such as Internet connection, search service, SNS (Social Networking Service), electronic commerce (EC: Electronic Commerce), electronic payment, online game, online banking, online trading, accommodation / ticket reservation, video / music distribution, news, map, route search, route guidance, route information, operation information, weather forecast, etc. Actually, the server device 100 may cooperate with various servers that provide the above web services and mediate the web services, or may be in charge of the processing of the web services.

[0015] The server device 100 can acquire user information regarding the user U. For example, the server device 100 acquires information regarding the attributes of the user U, such as the gender, age, and residential area of the user U. Then, the server device 100 stores and manages the information regarding the attributes of the user U together with the identification information (such as user ID) indicating the user U.

[0016] In addition, the server device 100 acquires various types of history information (log data) indicating the actions of the user U from the terminal device 10 of the user U or from various servers or the like based on the user ID or the like. For example, the server device 100 acquires a location history, which is a history of the location and time of the user U, from the terminal device 10. Also, the server device 100 acquires a search history, which is a history of the search queries input by the user U, from a search server (search engine). Further, the server device 100 acquires a browsing history, which is a history of the content browsed by the user U, from a content server. Additionally, the server device 100 acquires a purchase history (settlement history), which is a history of the user U's product purchases and settlement processes, from an e-commerce server or a settlement processing server. Moreover, the server device 100 may acquire a posting history or a sales history, which is a history of the user U's postings to a marketplace, from an e-commerce server or a settlement processing server. Also, the server device 100 acquires a posting history, which is a history of the user U's postings, from a posting server or an SNS server that provides a word-of-mouth posting service.

[0017] 〔1-1. Analysis of Cluster Tendencies〕 In the present embodiment, the server device 100 finds out the tendency of the user's input mode from the input pattern of the search keyword. That is, it clarifies the tendency of the input mode of the user's cluster and analyzes the tendency.

[0018] As shown in Figure 1, the server device 100 extracts target users (step S1). In this embodiment, the server device 100 extracts users who have searched for the same search keyword as target users. For example, the server device 100 extracts users who have searched for keywords containing soy meat a certain number of times or more as a user group interested in soy meat (soy meat interested group). The reason for setting the number of searches to a certain number of times or more is to exclude users who have little interest in soy meat, even if they are interested in it.

[0019] In this case, the server device 100 may accept search queries (search keywords) from each user U's terminal device 10 via the network N (see Figure 4), collect logs (search history) of each user U's search queries, and extract user segments that are interested in specific matters. In practice, the server device 100 may also obtain information about the search queries entered by each user U from the search engine that received the search queries. In other words, the server device 100 may accept search queries (search keywords) from each user U's terminal device 10 either directly or indirectly.

[0020] Next, the server device 100 extracts keywords of interest for the target audience (step S2). For example, the server device 100 extracts keywords of interest for the soy meat-interested audience, such as "vegan shampoo," "vegetarian diet," and "strength training." The server device 100 excludes "soy meat" from the keywords of interest for the soy meat-interested audience, as it is already clear that the soy meat-interested audience is interested in "soy meat." In other words, the server device 100 extracts search keywords other than "soy meat" as keywords of interest for the soy meat-interested audience.

[0021] In this case, the server device 100 may extract (obtain) information related to the 5W1H, such as "Who," "When," "Where," "What," "Why," and "How," from the search query logs (search history) of each user U. Note that the server device 100 may extract (obtain) information related to any one or any combination of the 5W1H, rather than all of them.

[0022] Next, the server device 100 clusters (groups) the target users based on their search trends (step S3). For example, as shown in Figure 2, the server device 100 classifies the target users interested in soy meat into segments such as the "beliefs" group, the "health and food" group, the "diet" group, the "fashion" group, and the "industry interest" group. Figure 2 is an explanatory diagram showing an overview of clustering according to this embodiment. In this way, even users who are all interested in soy meat (soy meat interested users) can be classified into multiple clusters based on their search trends.

[0023] At this time, the server device 100 mechanically classifies the keywords of interest of the users extracted as targets and assigns each user to the topic with the highest number of searches. Alternatively, the server device 100 may assign each user to the topic with the highest number of searches. Furthermore, a single user may belong to multiple clusters.

[0024] Next, the server device 100 receives a specification of the cluster to be analyzed and the period to be analyzed from the terminal device 10 of user U who wishes to perform / will perform an analysis of cluster trends via the network N (see Figure 4) (step S4).

[0025] Next, the server device 100 identifies the characteristics of the time-series changes in the input patterns of each category of search queries (search keywords) during the specified analysis period for the specified cluster (waveform pattern of changes in search volume) (step S5). For example, the server device 100 identifies the characteristics of the time-series changes in the input patterns of each category of keywords of interest during the specified analysis period for the specified cluster.

[0026] In this case, the server device 100 classifies the search queries entered by users belonging to a specified cluster during a specified analysis period into categories, and for each classified category, it aggregates and analyzes the number of times and the number of people (search volume) that entered the search queries belonging to that category to identify the characteristics of the time-series changes in input patterns for each category. Alternatively, instead of the categories of search queries, the server device 100 may identify the characteristics of the time-series changes in input patterns for each brand corresponding to the store name or product name entered as a search query.

[0027] For example, server device 100 identifies "Feature 1" if the number of entries or the number of people (search volume) has been steadily decreasing over time. Server device 100 also identifies "Feature 2" if there is a slight sharp increase followed by a larger decrease than the increase. Furthermore, server device 100 identifies "Feature 3" if the number of entries or the number of people increases and then decreases back to the original level. Server device 100 also identifies "Feature 4" if there is an increase and then decreases to a level higher than the original. Finally, server device 100 identifies "Feature 5" if the increase continues. In this way, server device 100 identifies the time-series characteristics of the input patterns over the analysis period for each category of search queries. Server device 100 may also classify data into more features than those listed above (Features 1-5).

[0028] Next, the server device 100 classifies the categories of search queries according to the characteristics of the time-series changes in the input patterns (step S6), and then ranks the categories of search queries according to their characteristics (step S7).

[0029] Next, the server device 100 outputs information showing the ranking of search query categories for each characteristic of the time-series change in the input pattern to the terminal device 10 of user U who wishes to perform / implement cluster trend analysis via the network N (see Figure 4) (step S8). For example, as shown in Figure 3, the server device 100 outputs information showing the ranking of search query categories for each of the time-series change characteristics 1 to 5 of the input pattern. Figure 3 is a diagram showing an example of information showing the ranking of search query categories for each characteristic of the time-series change in the input pattern.

[0030] (Cluster breakdown) Furthermore, the server device 100 may accept the specification of an analysis target query, such as "soy meat," from the user U's terminal device 10 via the network N (see Figure 4), instead of the cluster to be analyzed. For example, the server device 100 may accept the specification of keywords of interest to the "target person," which includes each cluster.

[0031] Furthermore, when the server device 100 outputs information showing the ranking of search query categories for each characteristic of the time-series changes in the input pattern, it may also present a breakdown of the number of inputs and the number of people (search volume) for each cluster of each search query category, such as "Characteristic 1: Rank 1 categories - Cluster B (10,000 inputs), Cluster A (5,000 inputs), ...", "Characteristic 1: Rank 2 categories - Cluster F (5,000 inputs), Cluster G (2,000 inputs), ...", etc.

[0032] In other words, the server device 100 may output information showing the ranking of search query categories for each characteristic of the time-series change in input patterns, not for a specified cluster, but for a specified target person. In this case, the server device 100 may also aggregate and present the breakdown of the number of times and the number of people (search volume) entered for each user cluster for each search query category.

[0033] Alternatively, the server device 100 may focus on user clusters instead of search query categories and output information showing the ranking of user clusters for each characteristic of the time-series changes in input patterns. For example, the server device 100 may present information showing the ranking of user clusters for each characteristic, such as "Characteristic 1: Rank 1 - Cluster B, Rank 2 - Cluster A, Rank 3 - Cluster D, ...".

[0034] As described above, in this embodiment, the server device 100 identifies the characteristics of the time-series changes in the input pattern for each category of search queries (search keywords) over a predetermined period, and outputs information indicating the identified characteristics.

[0035] In other words, the server device 100 concretizes the characteristics of the time-series changes in input patterns within a predetermined period, and identifies and provides a category of search query (search keyword) for each characteristic. The server device 100 also concretizes other aspects as appropriate. The search query category may be at the brand level (e.g., frequently buying or searching for that brand). The pair of search query category and the characteristics of the time-series changes in input patterns may be in tabular format. Furthermore, in the ranking, the server device 100 displays similar categories and brands using similar colors.

[0036] [1-2. Learning cluster tendencies] In this embodiment, the server device 100 learns cluster trends for each input pattern of search keywords (search queries) and estimates (infers) the cluster of users likely to input the target keywords.

[0037] As shown in Figure 1, the server device 100 learns user clusters and creates a model based on the characteristics of the time-series changes in the input patterns of search keywords (search queries) (step S11). For example, the server device 100 vectorizes the search keywords using W2V (Word2Vec) (step S11-1). In this case, similar keywords become similar vectors. Then, the server device 100 uses machine learning to create a model that outputs "information indicating the cluster classified by Feature 1" when "search keyword vectors" and "Feature 1" are input (step S11-2). Alternatively, the server device 100 may learn user clusters based on the characteristics of the time-series changes in the input patterns for each category of search keywords (search queries).

[0038] Next, the server device 100 receives input of a target keyword from the terminal device 10 of user U, who wishes to perform cluster determination for a specific keyword, via the network N (see Figure 4) (step S12). In other words, the server device 100 receives the specification of the search keyword to be determined.

[0039] Next, the server device 100 vectorizes the received target keyword using W2V (step S13). That is, the server device 100 converts the search keyword to be judged into a vector using natural language processing.

[0040] Next, the server device 100 inputs a vector (vectorized target keyword) and each of the features 1 to 5 into the model, and for each feature, it estimates (infers) the cluster of users who are likely to input that target keyword using that feature (step S14).

[0041] Next, the server device 100 provides information indicating the estimated cluster to the terminal device 10 of user U, who wishes to have cluster estimation performed, via the network N (see Figure 4) (step S15). For example, if the target keyword is "brand", the server device 100 can suggest brands that are a good match for the products indicated by that target keyword (brands that are likely to resonate with people searching for those characteristics).

[0042] As described above, in this embodiment, the server device 100 identifies the time-series characteristics of how a predetermined search query was entered over a predetermined period, for each cluster of user U. At this time, the server device 100 learns the cluster of user U that searches for the predetermined search query using the given characteristics, for each pair of the predetermined search query and the time-series characteristics. The model learning method can be arbitrarily changed.

[0043] Furthermore, if the search query is for a brand or product, the server device 100 can suggest products that are compatible with the brand indicated by the search query, or brands that are compatible with the product indicated by the search query.

[0044] Furthermore, the server device 100 takes a specified search query as input and provides information indicating clusters for each feature. The server device 100 may also learn information indicating the ranking of search query categories for each feature of the time-series change in the input pattern, create a model, and perform inference using the model. The server device 100 also provides information indicating clusters with high (top) rankings for the search query categories. The server device 100 also learns clusters of clusters and proposes clusters of clusters. In addition, the search queries are first vectorized using W2V and learned for each vector-feature pair.

[0045] [2. Example of an information processing system configuration] Next, the configuration of the information processing system 1, which includes the server device 100 according to the embodiment, will be described using Figure 4. Figure 4 is a diagram showing an example of the configuration of the information processing system 1 according to the embodiment. As shown in Figure 4, the information processing system 1 according to the embodiment includes a terminal device 10 and a server device 100. These various devices are connected to each other via a network N, either by wire or wireless communication. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0046] Furthermore, the number of devices included in the information processing system 1 shown in Figure 4 is not limited to those illustrated. For example, in Figure 4, only one terminal device 10 is shown for the sake of illustration, but this is merely an example and not limiting; there may be two or more.

[0047] Terminal device 10 is an information processing device used by user U. For example, terminal device 10 may be a smart device such as a smartphone or tablet, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console or AV equipment with communication functions, a car navigation system, a wearable device such as a smartwatch or head-mounted display, or smart glasses.

[0048] Furthermore, the terminal device 10 can connect to the network N via wireless communication networks such as LTE (Long Term Evolution), 4G (4th Generation), and 5G (5th Generation), or via short-range wireless communication such as Bluetooth (registered trademark) and Wi-Fi (Local Area Network), and communicate with the server device 100.

[0049] The server device 100 is, for example, a computer such as a PC or blade server, or a mainframe or workstation. The server device 100 may also be implemented through cloud computing.

[0050] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be explained using Figure 5. Figure 5 is a diagram showing an example of the configuration of the terminal device 10. As shown in Figure 5, the terminal device 10 comprises 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.

[0051] (Communications Section 11) The communication unit 11 is connected to the network N (see Figure 4) by wire or wireless connection and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 can be implemented using a NIC (Network Interface Card) or an antenna.

[0052] (Display section 12) The display unit 12 is a display device that displays various information such as location information. For example, the display unit 12 may be a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 may also be a touch panel display, but is not limited to this.

[0053] (Input section 13) The input unit 13 is an input device that receives various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. The input unit 13 may also be an input / output port (I / O port) or a USB (Universal Serial Bus) port. 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 also be a microphone that receives voice input from the user U. The microphone may be wireless.

[0054] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from GPS (Global Positioning System) satellites and, based on the received signals, acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10. In other words, the positioning unit 14 determines the position of the terminal device 10. Note that GPS is just one example of a GNSS (Global Navigation Satellite System).

[0055] Furthermore, the positioning unit 14 can determine its position using various methods other than GPS. For example, the positioning unit 14 may use various communication functions of the terminal device 10 to determine its position as an auxiliary positioning means for position correction, etc., as described below.

[0056] (Wi-Fi positioning) For example, the positioning unit 14 determines the location of the terminal device 10 by utilizing the Wi-Fi® communication function of the terminal device 10 and the communication network provided by each telecommunications company. Specifically, the positioning unit 14 determines the location of the terminal device 10 by performing Wi-Fi communication, etc., and determining the distance to nearby base stations and access points.

[0057] (Beacon positioning) Furthermore, the positioning unit 14 may determine the location using the Bluetooth® function of the terminal device 10. For example, the positioning unit 14 determines the location of the terminal device 10 by connecting to a beacon transmitter connected via the Bluetooth® function.

[0058] (Geomagnetic positioning) Furthermore, the positioning unit 14 determines the position of the terminal device 10 based on the geomagnetic pattern of the structure, which has been measured in advance, and the geomagnetic sensor provided by the terminal device 10.

[0059] (RFID positioning) Furthermore, if, for example, the terminal device 10 is equipped with an RFID (Radio Frequency Identification) tag function equivalent to that of a contactless IC card used at a train station ticket gate or in a store, or if it is equipped with a function to read RFID tags, the location where it was used will be recorded along with the information on the payment or other transactions made by the terminal device 10. The positioning unit 14 may determine the location of the terminal device 10 by acquiring such information. Alternatively, the location may be determined by an optical sensor or infrared sensor equipped in the terminal device 10.

[0060] The positioning unit 14 may, if necessary, determine the position of the terminal device 10 using one or a combination of the positioning means described above.

[0061] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection can be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices or wireless devices. In the example shown in Figure 5, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a barometric 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.

[0062] The sensors 21-28 described above are merely examples and not limiting. In other words, the sensor unit 20 may be configured to include some of the sensors 21-28, or it may include other sensors such as humidity sensors in addition to or instead of the sensors 21-28.

[0063] The acceleration sensor 21 is, for example, a 3-axis acceleration sensor and detects the physical movement of the terminal device 10, such as its direction of movement, velocity, and acceleration. The gyro sensor 22 detects the physical movement of the terminal device 10, such as its tilt in the three axes, based on its angular velocity. The barometric pressure sensor 23 detects the atmospheric pressure around the terminal device 10, for example.

[0064] Since the terminal device 10 is equipped with the acceleration sensor 21, gyroscope 22, barometric pressure sensor 23, etc., it becomes possible to determine the position of the terminal device 10 using technologies such as pedestrian dead-reckoning (PDR) that utilize these sensors 21 to 23. This makes it possible to obtain indoor location information that is difficult to obtain with positioning systems such as GPS.

[0065] For example, a pedometer using an accelerometer 21 can calculate the number of steps, walking speed, and distance walked. Additionally, a gyroscope 22 can be used to determine the user U's direction of movement, gaze direction, and body tilt. Furthermore, the barometric pressure detected by the barometric pressure sensor 23 can be used to determine the altitude and floor number of the user U's terminal device 10.

[0066] The temperature sensor 24 detects, for example, the ambient temperature around the terminal device 10. The sound sensor 25 detects, for example, the ambient sound around the terminal device 10. The light sensor 26 detects the ambient illumination around the terminal device 10. The magnetic sensor 27 detects, for example, the Earth's magnetic field around the terminal device 10. The image sensor 28 captures an image of the area around the terminal device 10.

[0067] The aforementioned pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the surrounding environment and conditions of the terminal device 10 by detecting atmospheric pressure, temperature, sound, and illuminance, respectively, and by capturing images of the surroundings. Furthermore, it becomes possible to improve the accuracy of the location information of the terminal device 10 based on the surrounding environment and conditions.

[0068] (Control Unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM, input / output ports, and various circuits. Alternatively, the control unit 30 may be composed of hardware such as an integrated circuit (ASIC) or FPGA (Field Programmable Gate Array). The control unit 30 comprises a transmission unit 31, a reception unit 32, and a processing unit 33.

[0069] (Transmitter 31) The transmission unit 31 can transmit various information, such as information input by the user U using the input unit 13, various information detected by sensors 21-28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 determined by the positioning unit 14, to the server device 100 via the communication unit 11.

[0070] (Receiver 32) The receiving unit 32 can receive various information provided by the server device 100 and requests for various information from the server device 100 via the communication unit 11.

[0071] (Processing 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output and display various information transmitted by the transmission unit 31 and various information received from the server device 100 by the reception unit 32 to the display unit 12.

[0072] (Storage unit 40) The storage unit 40 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDD (Hard Disk Drive), SSD (Solid State Drive), and optical discs. Various programs and various data are stored in this storage unit 40.

[0073] [4. Example of Server Device Configuration] Next, the configuration of the server device 100 according to the embodiment will be described using Figure 6. Figure 6 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Figure 6, the server device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

[0074] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N (see Figure 4) by wire or wireless connection.

[0075] (Storage unit 120) The storage unit 120 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDDs, SSDs, and optical discs. As shown in Figure 6, the storage unit 120 has a user information database 121, a history information database 122, and a feature information database 123.

[0076] (User Information Database 121) The user information database 121 stores user information about user U. For example, the user information database 121 stores various information such as user U's attributes. Figure 7 shows an example of the user information database 121. In the example shown in Figure 7, the user information database 121 has items such as "User ID (Identifier)", "Age", "Gender", "Home", "Workplace", and "Interests".

[0077] "User ID" refers to identification information used to identify user U. Note that "User ID" may be user U's contact information (telephone number, email address, etc.) or identification information used to identify user U's terminal device 10.

[0078] Furthermore, "Age" indicates the age of user U, identified by the user ID. Note that "Age" may be information indicating user U's specific age (e.g., 35 years old), or information indicating user U's age group (e.g., 30s), or "Age" may be information indicating user U's date of birth, or information indicating user U's generation (e.g., born in the 1980s). Furthermore, "Gender" indicates the gender of user U, identified by the user ID.

[0079] Furthermore, "Home" indicates the location information of user U's home, which is identified by the user ID. In the example shown in Figure 7, "Home" is represented by an abstract code such as "LC11," but it could also be latitude and longitude information, etc. Also, for example, "Home" could be a regional name or address.

[0080] Furthermore, "Workplace" indicates the location information of the workplace (or school in the case of a student) of user U, identified by the user ID. In the example shown in Figure 7, "Workplace" is illustrated with an abstract code such as "LC12," but it may also be latitude and longitude information, etc. Also, for example, "Workplace" may be a regional name or address.

[0081] Furthermore, "Interests" indicate the interests of user U, who is identified by their user ID. In other words, "Interests" indicate the subjects of high interest to user U, who is identified by their user ID. For example, "Interests" may be search queries (keywords) that user U enters into a search engine. In the example shown in Figure 7, one "Interest" is shown for each user U, but there may be multiple interests.

[0082] For example, in the example shown in Figure 7, user U, identified by user ID "U1", is in their 20s and is male. Also, for example, user U, identified by user ID "U1", has their home address at "LC11". Furthermore, for example, user U, identified by user ID "U1", has their workplace at "LC12". Finally, for example, user U, identified by user ID "U1", is interested in "sports".

[0083] In the example shown in Figure 7, abstract values ​​such as "U1," "LC11," and "LC12" are used to illustrate the information, but it is assumed that "U1," "LC11," and "LC12" actually store specific strings, numbers, or other information. In the following diagrams relating to other information, abstract values ​​may also be used to illustrate the information.

[0084] The user information database 121 is not limited to the above and may store various types of information depending on the purpose. For example, the user information database 121 may store various types of information about user U's terminal device 10. In addition, the user information database 121 may store information about user U's demographic, psychographic, geographic, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, place of origin (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not a car is owned, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used stations (other than the nearest station to home / workplace), lessons / classes (location, time, etc.), hobbies, interests, and lifestyle.

[0085] (History Information Database 122) The history information database 122 stores various information related to the history information (log data) that shows the user U's actions. Figure 8 shows an example of the history information database 122. In the example shown in Figure 8, the history information database 122 has items such as "User ID", "Location History", "Search History", "Browsing History", "Purchase History", and "Posting History".

[0086] "User ID" indicates identification information used to identify user U. "Location History" indicates the location history, which is the history of user U's location and movements. "Search History" indicates the search history, which is the history of search queries entered by user U. "Browsing History" indicates the browsing history, which is the history of content viewed by user U. "Purchase History" indicates the purchase history, which is the history of purchases made by user U. "Posting History" indicates the posting history, which is the history of posts made by user U. Note that "Posting History" may include questions about user U's possessions.

[0087] For example, in the example shown in Figure 8, user U, identified by user ID "U1", moves as described in "Location History #1", searches as described in "Search History #1", views content as described in "Browsing History #1", purchases specified goods at specified stores as described in "Purchase History #1", and posts as described in "Posting History".

[0088] In the example shown in Figure 8, abstract values ​​such as "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Posting History #1" are used for illustration. However, it is assumed that "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Posting History #1" will actually store specific strings, numbers, and other information.

[0089] The history information database 122 is not limited to the above and may store various types of information depending on the purpose. For example, the history information database 122 may store the usage history of user U for a specified service. The history information database 122 may also store the visit history of user U to a physical store or a facility. The history information database 122 may also store the payment history of user U using the terminal device 10 for payments (electronic payments).

[0090] (Feature Information Database 123) The feature information database 123 stores various information regarding the characteristics of the time-series changes in the input patterns of search keywords. Figure 9 shows an example of the feature information database 123. In the example shown in Figure 9, the feature information database 123 displays the categories of search keywords in a tabular ranking for each user cluster, based on the characteristics of the time-series changes in the input patterns of search keywords.

[0091] In practice, the target group may not be limited to user clusters, but rather to an unspecified large number of people, or even to a group of multiple clusters. Furthermore, the feature information database 123 may indicate searched brands or products, or user clusters, instead of search keyword categories.

[0092] Furthermore, the feature information database 123 may store a breakdown of the number of times each user has entered a search keyword and the number of users (search volume) for each user cluster, for each category of search keyword.

[0093] Furthermore, the feature information database 123 is not limited to the above and may store various types of information depending on the purpose. For example, the feature information database 123 may store a model that has learned clusters of users who search for a particular search keyword based on the characteristics of the time-series changes in the search keyword and the input method.

[0094] (Control unit 130) Returning to Figure 6, let's continue the explanation. The control unit 130 is a controller, and is realized by various programs (corresponding to an example of an information processing program) stored in the internal memory of the server device 100, such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), ASIC (Application Specific Integrated Circuit), or FPGA (Field Programmable Gate Array), executing them using a memory area such as RAM as the working area. In the example shown in Figure 6, the control unit 130 has an acquisition unit 131, a classification unit 132, a specific unit 133, an aggregation unit 134, a conversion unit 135, a learning unit 136, an estimation unit 137, and a provision unit 138.

[0095] (Acquisition part 131) The acquisition unit 131 acquires the search query entered by user U. For example, when user U enters a search query into a search engine or the like and performs a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. In other words, the acquisition unit 131 acquires the keyword entered by user U into the search box of a search engine, website, or app via the communication unit 110.

[0096] Furthermore, the acquisition unit 131 acquires user information about user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as user ID), location information, and attribute information of user U from user U's terminal device 10. The acquisition unit 131 may also acquire identification information and attribute information of user U when user U is registered. The acquisition unit 131 then registers the user information in the user information database 121 of the storage unit 120.

[0097] Furthermore, the acquisition unit 131 acquires various historical information (log data) indicating the user U's actions via the communication unit 110. For example, the acquisition unit 131 acquires various historical information indicating the user U's actions from the user U's terminal device 10, or from various servers based on the user ID, etc. The acquisition unit 131 then registers the various historical information in the history information database 122 of the storage unit 120.

[0098] (Classification section 132) The classification unit 132 further clusters users who have searched for the same search keyword based on differences in their keywords of interest. The classification unit 132 also clusters users by brand based on their search history and purchase history. In practice, the classification unit 132 may cluster users by brand not only based on their search history and purchase history, but also on factors such as their visit history to physical stores and their social media posting history.

[0099] (Specific Section 133) The identification unit 133 identifies the characteristics of the time-series changes in the input patterns of search keywords. For example, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns of search keywords for each category.

[0100] Furthermore, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns of the searched brands. For example, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns for each searched brand.

[0101] Furthermore, the identification unit 133 identifies the characteristics of the time-series changes in the input methods of the searched products. For example, the identification unit 133 identifies the characteristics of the time-series changes in the input methods for each searched product.

[0102] Furthermore, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns of the user clusters. For example, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns for each user cluster.

[0103] (Aggregation Section 134) The aggregation unit 134 ranks the categories of search keywords according to the characteristics of the time-series changes in the identified input patterns. The aggregation unit 134 also ranks the brands according to the characteristics of the time-series changes in the identified input patterns.

[0104] (Conversion unit 135) The conversion unit 135 vectorizes the search keywords using natural language processing. For example, the conversion unit 135 vectorizes the search keywords using W2V (Word2Vec).

[0105] (Learning Section 136) The learning unit 136 learns clusters of users who search for a given search keyword based on the characteristics of the time-series changes in the search keyword and the input method. Furthermore, the learning unit 136 learns clusters of users who search for a given search keyword based on the characteristics of the search keyword category and the input method.

[0106] Furthermore, the learning unit 136 learns clusters of users who search for a given search keyword based on the characteristics of the time-series changes in the input pattern, for each pair of search keyword vectors and characteristics of the time-series changes in the input pattern. The learning unit 136 also learns a ranking of search keyword categories for each identified characteristic of the time-series changes in the input pattern.

[0107] Furthermore, the learning unit 136 learns clusters of users who search for the searched brand using the characteristics of the time-series changes in the searched brand and the input method, for each pair of the searched product and the characteristics of the time-series changes in the input method.

[0108] Furthermore, the learning unit 136 learns clusters of users who search for the search keyword using the time-series characteristics of the search keyword and input method for each pair of those characteristics, and creates a model.

[0109] (Estimation Department 137) The estimation unit 137 estimates (infers) clusters of users who search for the specified search keyword based on the characteristics of the time-series changes in the input method, based on the combination of the specified search keyword and the characteristics of the time-series changes in the input method. The estimation unit 137 also estimates clusters of users who search for the specified search keyword based on the combination of the category to which the specified search keyword belongs and the characteristics of the time-series changes in the input method, based on the combination of the categories to which the specified search keyword belongs.

[0110] Furthermore, the estimation unit 137 estimates clusters of users who search for the specified search keyword based on a combination of a vector corresponding to the specified search keyword and the characteristics of the time-series changes in the input pattern. The estimation unit 137 also estimates clusters of users whose search keyword category ranks highly in the characteristics of the time-series changes in the input pattern.

[0111] Furthermore, the estimation unit 137 estimates clusters of users who search for the searched brand based on the characteristics of the specified brand and the time-series changes in the input method. Furthermore, the estimation unit 137 estimates clusters of users who search for the searched product based on the characteristics of the specified product and the time-series changes in the input method.

[0112] Furthermore, the estimation unit 137 inputs the specified search keyword and the characteristics of the time-series changes in the input method into the model and estimates the cluster of users who search for the search keyword based on those characteristics.

[0113] (Provider 138) The provisioning unit 138, via the communication unit 110, provides the user U's terminal device 10 with information indicating the characteristics of the time-series changes in the specified input pattern. For example, the provisioning unit 138, via the communication unit 110, provides the user U's terminal device 10 with information indicating the ranking of search keyword categories for each characteristic of the time-series changes in the input pattern. Alternatively, the provisioning unit 138, via the communication unit 110, provides the user U's terminal device 10 with information indicating the ranking of brands for each characteristic of the time-series changes in the input pattern.

[0114] Furthermore, the provisioning unit 138, via the communication unit 110, provides the user U's terminal device 10 with information showing the characteristics of the time-series changes in input patterns for each cluster of identified users. In addition, the provisioning unit 138, via the communication unit 110, provides the user U's terminal device 10 with information showing the breakdown of search volumes for each cluster of users for each characteristic of the time-series changes in input patterns.

[0115] Furthermore, when the provision unit 138 provides information to the user U's terminal device 10 via the communication unit 110, showing a ranking of search keyword categories for each characteristic of the time-series change in input patterns, it displays similar categories in a table format, color-coding them with similar colors.

[0116] Furthermore, the information provider 138 provides information indicating estimated user clusters. The information provider 138 also provides information indicating clusters with high rankings in the search keyword category characteristics of the time-series changes in input patterns.

[0117] [5. Processing Procedure] Next, the processing procedure by the server device 100 according to the embodiment will be described using Figure 10. Figure 10 is a flowchart of the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the server device 100.

[0118] As shown in Figure 10, the acquisition unit 131 of the server device 100 receives search keywords from each user U's terminal device 10 via the communication unit 110 (step S101). In other words, the acquisition unit 131 collects search keywords via the communication unit 110. The acquisition unit 131 may also acquire attribute information, history information, etc., of each user U from an external server device via the communication unit 110.

[0119] Next, the classification unit 132 of the server device 100 extracts the target individuals from the search history of each user U (step S102).

[0120] Next, the classification unit 132 of the server device 100 extracts keywords of interest to the subject (step S103).

[0121] Next, the classification unit 132 of the server device 100 clusters (groups) the target individuals based on their search trends (step S104). For example, the classification unit 132 classifies the target individuals into multiple clusters based on keywords of interest.

[0122] Next, the identification unit 133 of the server device 100 identifies the characteristics of the time-series changes in the input patterns of search keywords (step S105). For example, the identification unit 133 identifies the characteristics of the time-series changes in the input patterns of each category of search keywords for each user cluster.

[0123] Next, the aggregation unit 134 of the server device 100 ranks the search keywords according to the characteristics of the time-series changes in the identified input patterns (step S106). For example, the aggregation unit 134 ranks the categories of search keywords according to the characteristics of the time-series changes in the identified input patterns for each user cluster.

[0124] Next, the conversion unit 135 of the server device 100 vectorizes the search keywords using W2V (Word2Vec) (step S107). The conversion unit 135 may also vectorize the search keywords using natural language processing other than W2V.

[0125] Next, the learning unit 136 of the server device 100 learns clusters of users who search for the search keyword for each pair of the vector of the search keyword and the characteristics of the time-series change in the input pattern (step S108).

[0126] Next, the estimation unit 137 of the server device 100 estimates (infers) a cluster of users who search for the specified search keyword based on a combination of a vector corresponding to the specified search keyword and the characteristics of the time-series changes in the input pattern (step S109).

[0127] Next, the provision unit 138 of the server device 100 provides the user U's terminal device 10, via the communication unit 110, with information showing the ranking of search keyword categories for each characteristic of the time-series change in input patterns, and information showing the estimated user cluster (step S110).

[0128] [6. Variant Example] The terminal device 10 and server device 100 described above may be implemented in various other forms besides those of the embodiment described above. Therefore, the following describes modifications of the embodiment.

[0129] In the above embodiment, some or all of the processing performed by the server device 100 may actually be performed by the terminal device 10. For example, 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 server device 100 in the above embodiment. Furthermore, in the above embodiment, since the terminal device 10 is in cooperation with the server device 100, from the perspective of the user U, it appears as if the processing of the server device 100 is also being performed by the terminal device 10. In other words, from another perspective, it can be said that the terminal device 10 is equipped with the server device 100.

[0130] Furthermore, in the above embodiment, the server device 100 extracts users who have searched using the same search keyword and then clusters them based on their keywords of interest. However, in practice, it is not necessary to extract users. For example, the server device 100 may cluster an unspecified number of user groups based on their keywords of interest.

[0131] Furthermore, in the above embodiment, the server device 100 ranks (orders) the categories of search queries for each user cluster based on the characteristics of the time-series changes in the input patterns. However, in practice, the server device 100 may rank the user clusters based on the characteristics of the time-series changes in the input patterns. For example, the server device 100 may cluster users based on the categories of search queries.

[0132] [7. Effects] As described above, the information processing device (terminal device 10 and server device 100) according to the present invention includes: an identification unit 133 that identifies the characteristics of the time-series changes in the input manner of a search keyword; a learning unit 136 that learns a cluster of users who search for the search keyword using the characteristics for each pair of a search keyword and the characteristics of the time-series changes in the input manner; an estimation unit 137 that estimates a cluster of users who search for the search keyword using the characteristics based on a specified pair of a search keyword and the characteristics of the time-series changes in the input manner; and a providing unit 138 that provides information indicating the estimated cluster of users.

[0133] The identification unit 133 identifies the characteristics of the time-series changes in input patterns for each category of search keywords. The learning unit 136 learns clusters of users who search for the search keyword using the given characteristics for each pair of a search keyword category and a characteristic of the time-series changes in input patterns. The estimation unit 137 estimates clusters of users who search for the search keyword using the given characteristics, based on the pair of a category to which the specified search keyword belongs and a characteristic of the time-series changes in input patterns.

[0134] Furthermore, the information processing device according to the present invention further comprises a conversion unit 135 that vectorizes search keywords using natural language processing. The learning unit 136 learns clusters of users who search for the search keyword for each pair of a vector of the search keyword and a characteristic of the time-series change in the input pattern. The estimation unit 137 estimates clusters of users who search for the search keyword based on the pair of a vector corresponding to the specified search keyword and a characteristic of the time-series change in the input pattern.

[0135] Furthermore, the information processing device according to the present invention further comprises an aggregation unit that ranks the categories of search keywords for each characteristic of the time-series changes in the specified input pattern. The providing unit 138 provides information indicating clusters in which the categories of search keywords have a high ranking in the characteristics of the time-series changes in the input pattern.

[0136] The learning unit 136 learns the ranking of search keyword categories for each characteristic of the time-series changes in the identified input pattern. The estimation unit 137 estimates clusters with high rankings for search keyword categories in the characteristics of the time-series changes in the input pattern.

[0137] The identification unit 133 identifies the characteristics of the time-series changes in the input patterns of the searched brand. The learning unit 136 learns clusters of users who search for the searched brand using the characteristics for each pair of the searched brand and the characteristics of the time-series changes in the input patterns. The estimation unit 137 estimates clusters of users who search for the searched brand using the characteristics based on the specified pair of the brand and the characteristics of the time-series changes in the input patterns. The provision unit 138 provides information indicating the estimated clusters of users.

[0138] The identification unit 133 identifies the characteristics of the time-series changes in the input method of the searched product. The learning unit 136 learns clusters of users who search for the searched product using the characteristics for each pair of the searched product and the characteristics of the time-series changes in the input method. The estimation unit 137 estimates clusters of users who search for the searched product using the characteristics based on the specified pair of the product and the characteristics of the time-series changes in the input method. The provision unit 138 provides information indicating the estimated clusters of users.

[0139] The learning unit 136 learns a model of clusters of users who search for a given search keyword based on the characteristics of the time-series changes in the search keyword and input method, for each pair of such characteristics. The estimation unit 137 inputs the specified search keyword and the characteristics of the time-series changes in the input method into the model and estimates the clusters of users who search for that search keyword based on those characteristics.

[0140] Through any or a combination of the above-described processes, the information processing device according to the present invention can learn the user's input patterns from the input patterns of search keywords.

[0141] [8. Hardware Configuration] Furthermore, the terminal device 10 and server device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration such as that shown in Figure 11. The following explanation will use the server device 100 as an example. Figure 11 is a diagram showing an example of the hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which an arithmetic unit 1030, a primary storage device 1040, a secondary storage device 1050, an output interface 1060, an input interface 1070, and a network interface 1080 are connected by a bus 1090.

[0142] The arithmetic unit 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, as well as programs read from the input device 1020, and executes various processes. The arithmetic unit 1030 can be implemented using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array).

[0143] The primary storage device 1040 is a memory device, such as RAM (Random Access Memory), that temporarily stores data used by the arithmetic unit 1030 for various calculations. The secondary storage device 1050 is a storage device where data used by the arithmetic unit 1030 for various calculations and various databases are registered, and can be implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, etc. The secondary storage device 1050 may be internal storage or external storage. The secondary storage device 1050 may also be a removable storage medium such as USB (Universal Serial Bus) memory or SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), NAS (Network Attached Storage), file server, etc.

[0144] The output I / F 1060 is an interface for transmitting information to be output to output devices 1010, such as displays, projectors, and printers, and is implemented using connectors of standards such as USB (Universal Serial Bus), DVI (Digital Visual Interface), and HDMI (High Definition Multimedia Interface). The input I / F 1070 is an interface for receiving information from various input devices 1020, such as mice, keyboards, keypads, buttons, and scanners, and is implemented using, for example, USB.

[0145] Furthermore, the output interface 1060 and input interface 1070 may be wirelessly connected to the output device 1010 and input device 1020, respectively. In other words, the output device 1010 and input device 1020 may be wireless devices.

[0146] Furthermore, the output device 1010 and the input device 1020 may be integrated as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as an input / output I / F.

[0147] The input device 1020 may also be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), DVD (Digital Versatile Disc), or 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.

[0148] The network interface 1080 receives data from other devices via network N and sends it to the computing unit 1030, and also transmits data generated by the computing unit 1030 to other devices via network N.

[0149] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output interface 1060 and the input interface 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.

[0150] For example, when computer 1000 functions as a server device 100, the arithmetic unit 1030 of computer 1000 realizes the functions of the control unit 130 by executing a program loaded onto the primary storage device 1040. Alternatively, the arithmetic unit 1030 of computer 1000 may load a program obtained from another device via the network interface 1080 onto the primary storage device 1040 and execute the loaded program. Furthermore, the arithmetic unit 1030 of computer 1000 may cooperate with other devices via the network interface 1080 and call and use program functions, data, etc., from other programs on other devices.

[0151] [9. Other] Although embodiments of the present invention have been described above, the present invention is not limited by the content of these embodiments. Furthermore, the aforementioned components include those that can be easily conceived by those skilled in the art, those that are substantially the same, and those that fall within the so-called equivalent range. Moreover, the aforementioned components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the gist of the embodiments described above.

[0152] 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 known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0153] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0154] For example, the server device 100 described above may be implemented using multiple server computers, and the configuration can be flexibly changed, such as by calling external platforms via APIs (Application Programming Interfaces) or network computing depending on the function.

[0155] Furthermore, the embodiments and modifications described above can be combined as appropriate, provided that the processing content is not inconsistent.

[0156] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of symbols]

[0157] 1. Information Processing System 10 Terminal devices 100 Server Devices 110 Communications Department 120 Storage section 121 User Information Database 122 History Information Database 123 Feature Information Database 130 Control Unit 131 Acquisition Department 132 Classification Department 133 Specific part 134 Aggregation Department 135 Conversion section 136 Learning Department 137 Estimation Department 138 Provision Department

Claims

1. An extraction unit that extracts users who have searched using the same keyword a certain number of times or more as target users, A classification unit classifies target users into multiple clusters based on the trends in user input patterns, which are derived from the input patterns of search keywords excluding the aforementioned identical keywords. An identification unit that identifies the characteristics of the waveform pattern of the time-series changes in the search volume of search keywords entered by target users belonging to each of the aforementioned multiple clusters during a predetermined period, A learning unit that trains a model to identify clusters of users who use search keywords belonging to a given category based on the characteristics of the waveform pattern of the time-series changes in search volume, for each pair of categories of search keywords entered during a predetermined period. An estimation unit inputs the category of a specified search keyword and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who search using the search keyword belonging to that category based on those characteristics. A provision unit that provides information indicating the estimated user cluster, An information processing device characterized by comprising:

2. The aforementioned identification unit classifies the search keywords entered during a predetermined period into categories, and identifies the characteristics of the waveform pattern of the time-series change in search volume for each category of search keywords. The learning unit trains the model to identify clusters of users who use search keywords belonging to a given category based on the characteristics of the search keyword category and the waveform pattern of the time-series change in search volume, for each pair of these characteristics. The estimation unit inputs the category to which the specified search keyword belongs and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who use the search keyword belonging to that category based on these characteristics. The information processing apparatus according to feature 1.

3. A conversion unit that vectorizes search keywords using natural language processing, Furthermore, The learning unit trains the model to identify clusters of users who use the search keyword in a search, based on the characteristics of the waveform pattern of the time-series change in search volume, for each pair of a search keyword vector, which is one of the pieces of information related to the search keyword. The estimation unit inputs a vector corresponding to the specified search keyword and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who use the search keyword to perform searches based on these characteristics. The information processing apparatus according to claim 1 or 2.

4. An aggregation unit that ranks the categories of search keywords based on the characteristics of the waveform pattern of the time-series changes in identified search volume, Furthermore, The aforementioned provision unit provides information indicating estimated user clusters, along with information indicating user clusters whose search keyword categories rank highly in the waveform pattern characteristics of the time-series changes in search volume. An information processing apparatus according to any one of features 1 to 3.

5. The learning unit further trains the model to rank the search keyword categories for each characteristic of the waveform pattern of the time-series change in search volume, for each pair of the search keyword category, which is one of the pieces of information related to the search keyword, and the characteristics of the waveform pattern of the time-series change in search volume. The estimation unit inputs the category to which the specified search keyword belongs and the characteristics of the waveform pattern of the time-series change in search volume into the model, outputs a ranking of the search keyword category for each characteristic of the waveform pattern of the time-series change in search volume, and estimates the clusters in which the search keyword category ranks highly in the characteristics of the waveform pattern of the time-series change in search volume. The aforementioned provision unit provides information indicating estimated user clusters, along with information indicating user clusters whose search keyword categories rank highly in the waveform pattern characteristics of the time-series changes in search volume. An information processing apparatus according to any one of features 1 to 3.

6. The aforementioned identification unit uses the brand entered as the search keyword as the category of the search keyword, and identifies the characteristics of the waveform pattern of the time-series change in the search volume of said brand. The learning unit trains the model to identify clusters of users who use a particular brand in their search, based on the characteristics of the waveform pattern of the time-series change in search volume, which is one of the pieces of information related to the search keywords. The estimation unit inputs the characteristics of the waveform pattern of the specified brand and the time-series change in search volume into the model, and estimates the cluster of users who search using the brand based on these characteristics. The aforementioned provisioning unit provides information indicating the estimated user cluster. An information processing apparatus according to any one of features 1 to 5.

7. The aforementioned identification unit identifies the characteristics of the waveform pattern of the time-series change in the search volume of the brand corresponding to the searched product name. The learning unit trains the model to identify clusters of users who use the brand corresponding to the searched product name for each pair of the brand corresponding to the searched product name and the waveform pattern features of the time-series change in search volume. The estimation unit inputs the brand corresponding to the specified product name and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who search using the brand corresponding to the searched product name based on these characteristics. The aforementioned provisioning unit provides information indicating the estimated user cluster. An information processing apparatus according to any one of features 1 to 6.

8. The learning unit learns a cluster of users who use the search keyword to perform searches, based on the characteristics of the waveform pattern of the time-series change in search volume, for each pair of the search keyword itself (which is one of the pieces of information related to the search keyword) and the characteristics of the search volume change over time, and creates a model. The estimation unit inputs the specified search keyword and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who use the search keyword to perform searches based on these characteristics. An information processing apparatus according to any one of features 1 to 7.

9. An information processing method performed by an information processing device, An extraction process that identifies users who have searched using the same keyword a certain number of times or more as target users, A classification process for target users, which involves classifying them into multiple clusters based on the trends in user input patterns, excluding the aforementioned identical keywords, and A process to identify the characteristics of the waveform pattern of the time-series changes in the search volume of search keywords entered by target users belonging to each of the aforementioned multiple clusters during a predetermined period, A learning process in which a model is trained to identify clusters of users who use search keywords belonging to a given category based on the characteristics of the waveform pattern of the time-series changes in search volume, for each pair of categories of search keywords entered during a predetermined period. An estimation step is performed by inputting the category of a specified search keyword and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimating the cluster of users who search using the search keyword belonging to that category based on the characteristics. A provision process that provides information indicating the estimated user cluster, An information processing method characterized by including

10. An extraction procedure to identify users who have searched using the same keyword a certain number of times or more as target users, A classification procedure for users who are the target group, which classifies them into multiple clusters based on the trends in their input patterns, which are derived from the input patterns of search keywords excluding the aforementioned identical keywords, A procedure for identifying the characteristics of the waveform pattern of the time-series changes in the search volume of search keywords entered by target users belonging to each of the aforementioned multiple clusters during a predetermined period, A learning procedure that trains a model to identify clusters of users who use search keywords belonging to a given category based on the characteristics of the waveform pattern of the time-series changes in search volume, for each pair of categories of search keywords entered during a predetermined period. An estimation procedure that inputs the category of a specified search keyword and the characteristics of the waveform pattern of the time-series change in search volume into the model, and estimates the cluster of users who search using the search keyword belonging to that category based on those characteristics, A procedure for providing information indicating estimated user clusters, An information processing program that causes a computer to execute something.

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