Information processing device

Abstract

In order to realize effective marketing in the medical field, the present invention generates data for accurately finding a feature of a prospective customer.　The present invention comprises: a factor analysis processing means that calculates a prescribed number of common factors by applying factor analysis processing to user action information; a user characteristics determination means that calculates, regarding the user action information, a factor score based on the prescribed number of common factors for each of user identification information, and determines one common factor having the highest score as user characteristics indicating a feature of a user identified by the user identification information; and a user integrated information generation means that integrates user attribute information and the user characteristics on the basis of the user identification information, and generates user integrated information for performing causal factor analysis by means of multivariate analysis of events which satisfy prescribed viewing or reading states regarding specified content information.

Description

[Technical field]

[0001] About digital marketing techniques in the medical field. [Background technology]

[0002] Whether in manufacturing or the service industry, marketing is a very important activity in corporate activities in achieving and maintaining a company's competitive advantage.

[0003] Meanwhile, in recent years, corporate advertising and sales activities are often carried out making full use of digital technologies such as the web, the internet, and AI (Artificial Intelligence), and corporate marketing activities are also tending to be based on digital technology.

[0004] Against this background, technical proposals relating to marketing have been actively made in various industries. For example, Patent Document 1 proposes a technology for extensively collecting various user behavior logs and performing highly accurate access analysis, and Patent Document 2 proposes an information processing device that displays information that allows the effects of learning to be appropriately grasped.

[0005] In addition, Patent Document 3 proposes a technology for improving advertising effectiveness and analytical accuracy in web advertising delivery according to user characteristics, and Patent Document 4 proposes an information distribution server that can improve advertising effectiveness through delivered information. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2022-009936 A [Patent Document 2] JP 2021-056364 A [Patent Document 3] Patent No. 5878218 [Patent Document 4] JP 2010-178176 A Summary of the Invention [Problem to be solved by the invention]

[0007] However, the above-mentioned conventional technology does not take into account the characteristics of customers in the medical field, and therefore has a problem in that it is difficult to apply it to marketing in the medical field.

[0008] In view of the above problems, the present invention aims to provide an information processing device that generates data for accurately identifying the characteristics of potential customers in order to realize effective marketing in the medical field. [Means for solving the problem]

[0009] A first aspect of the information processing device to be disclosed includes a content information storage means for storing, in association with each other, content information related to medical care that can be viewed or read on a website, and tag information for classifying the content information based on its contents; a user behavior information storage means for storing user behavior information that associates user identification information, the tag information corresponding to the content information viewed or read by a user identified by the user identification information, and the number of times the tag information has been viewed or read; a user attribute information storage means for storing user attribute information that associates the user identification information with attribute information including information regarding the viewing or reading status of specific content information of each of the users; a normalization means for normalizing the number of times the tag information has been viewed or read to represent the frequency of viewing or reading by the user; a factor analysis processing means for applying a factor analysis process to the normalized user behavior information and calculating a predetermined number of common factors; and a user behavior information storage means for calculating, for each of the user identification information, a factor analysis based on the predetermined number of common factors. the user characteristic determining means calculating factor scores for each of the user attributes and determining that the common factor with the highest score is the user characteristic representing the feature of the user identified by the user identification information; a user integrated information generating means integrating the user attribute information and the user characteristics based on the user identification information to generate user integrated information for performing factor analysis by multivariate analysis of the phenomenon of satisfying a predetermined viewing / viewing situation with respect to the specific content information; a multivariate analysis processing means specifying, from the user integrated information, attribute information items including information on the viewing / viewing situation with respect to the specific content information that contribute to the phenomenon of satisfying the predetermined viewing / viewing situation from which it can be inferred that the user has a deep interest in the specific content information; and a user extraction means for extracting, based on the user integrated information, the user identification information that has the predetermined user characteristic and satisfies the condition with respect to the attribute information specified by the multivariate analysis processing means. A second aspect of the disclosed information processing device includes a content information storage means for storing, in association with each other, content information relating to medical care that can be viewed or read on a website, and tag information for classifying the content information based on its contents; a user behavior information storage means for storing user behavior information that associates user identification information, the tag information corresponding to the content information viewed or read by a user identified by the user identification information, and the number of times the tag information has been viewed or read; a user attribute information storage means for storing user attribute information that associates the user identification information with a plurality of pieces of attribute information including information regarding the viewing or reading status of specific content information of each of the users; a normalization means for normalizing the number of times the tag information has been viewed or read so as to represent the frequency of viewing or reading by the user; a factor analysis processing means for applying a factor analysis process to the normalized user behavior information to calculate a predetermined number of common factors; The system is characterized by having a user characteristic determination means for calculating a factor score based on the predetermined number of common factors for each user identification information for behavioral information, and determining the common factor with the highest score as a user characteristic representing the characteristics of the user identified by the user identification information; a product usage information storage means for storing product usage information that associates the user identification information, the multiple attribute information of the user identified by the user identification information, and usage information for multiple differentiating targets of multiple medical products having the same effect that are used by the user identified by the user identification information; a correspondence storage means for storing a correspondence between the multiple differentiating targets and the multiple attribute information, and the user characteristics; and a factor extraction means for applying multivariate analysis based on the product usage information and extracting factors related to the user characteristics for the usage rate of a predetermined product from among the multiple differentiating targets and the multiple attribute information. Effect of the Invention

[0010] The disclosed information processing device generates data for accurately identifying characteristics of potential customers in order to realize effective marketing in the medical field. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an overview of an information processing device according to an embodiment of the present invention. [Diagram 2] 1 is a functional block diagram of an information processing device according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram illustrating an example of a user behavior information storage unit according to the present embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a user attribute information storage unit according to the present embodiment. [Diagram 5] 11 is a diagram for explaining processing by a factor analysis processing means according to the present embodiment. FIG. [Figure 6] FIG. 2 is a diagram illustrating an example of a user integrated information generating unit according to the present embodiment. [Figure 7] 1 is a diagram illustrating an example of a hardware configuration of an information processing device according to an embodiment of the present invention. [Figure 8] 1 is a flowchart showing a flow of a processing example (part 1) performed by the information processing device according to the present embodiment. [Figure 9] 13 is a flowchart showing a flow of a processing example (part 2) performed by the information processing device according to the present embodiment. [Figure 10] FIG. 11 is a functional block diagram of an information processing device according to a second embodiment of the present invention. [Figure 11] FIG. 11 is a diagram illustrating an example of a factor extraction unit according to the second embodiment. [Figure 12] 10 is a flowchart showing a flow of an example of processing by an information processing device according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS With reference to the drawings, an embodiment of the present invention will be described. (Operation principle of the information processing device according to the present embodiment)

[0013] The operation principle of an information processing device (hereinafter, simply referred to as "the device") 100 according to this embodiment will be described with reference to Figures 1 to 6. Figure 1 is a diagram showing the connection relationship between the device 100 and other devices, and Figure 2 is a functional block diagram of the device 100.

[0014] 1, the device 100 is connected to a user terminal 440 operated by a user 380 via a communication network 450. The communication network 450 may be wired or wireless. The user terminal 440 may be a desktop or laptop personal computer, or may be a mobile information terminal such as a smartphone.

[0015] As shown in FIG. 2, the device 100 has a content information storage means 110, a user behavior information storage means 120, a user attribute information storage means 130, a normalization means 140, a factor analysis processing means 150, a user characteristic determination means 160, a user integrated information generation means 170, an exclusion determination means 180, a multivariate analysis processing means 190, a user extraction means 200, and a second user extraction means 210.

[0016] The content information storage means 110 stores content information 310 relating to medical care (particularly, cardiology) that can be viewed or read on a website in association with tag information 320 for classifying the content information 310 based on the content. The content information 310 may be a text article or a video article.

[0017] The tag information 320 includes, for example, acute coronary syndrome, arrhythmia, literature, complex PCI (Percutaneous Coronary Intervention), peripheral endovascular therapy, coronary flow reserve, guidelines, heart failure, ischemic heart disease, diagnostic imaging, live, drug therapy, academic conference, structural heart disease, studying abroad, medical management, etc., which are contents related to cardiology. The tag information 320 can be changed, added, or deleted.

[0018] The user behavior information storage means 120 stores user behavior information 400 that associates user identification information 390, tag information 320 corresponding to content information 310 viewed or browsed by a user 380 identified by the user identification information 390, and the number of views / views 330 for each tag information 320. The number of views / views 330 may be the number of accesses by a user terminal 440, or may be the duration of the views / views.

[0019] Fig. 3 is a diagram showing an example of the user behavior information storage means 120. As shown in Fig. 3, the user behavior information storage means 120 stores user behavior information 390 that associates (tag information 320, number of views / views 330) = (acute coronary syndrome, 5 times), (arrhythmia, 10 times), ... with user identification information 390:11111, for example.

[0020] Furthermore, the user behavior information storage means 120 stores user behavior information 400 that associates (tag information 320, number of views / views 330)=(acute coronary syndrome, 0 times), (arrhythmia, 0 times), . . . with the user identification information 390:11115, for example.

[0021] The user attribute information storage means 130 stores user attribute information 410 that associates user identification information 390 with attribute information 350 including information 340 relating to the viewing / browsing status of each user 380 with respect to specific content information 310 .

[0022] The attribute information 350 is information regarding, for example, the place of work, age, sex, type of membership, subscription status to e-mail magazines, whether the person is a specialist in a medical association, whether the person is certified by a medical association, participation in events, viewing and viewing status of articles (either video articles or text articles), etc.

[0023] The information 340 relating to the viewing / browsing status of the specific content information 310 is information relating to whether the specific content information 310 has been viewed / browsed, the time and number of times the specific content information 310 has been viewed / browsed, and the like.

[0024] Fig. 4 is a diagram showing an example of the user attribute information storage means 130. As shown in Fig. 4, the user attribute information storage means 130 stores user attribute information 410 that associates, for example, user identification information 390:11111, workplace location: Tokyo (metropolis), age: 30 (years old), sex: male, membership type: paid member, ..., whether specific content information 310 was viewed / viewed: yes, time spent viewing / viewing specific content information 310: 5 minutes 0 seconds, ....

[0025] Furthermore, the user attribute information storage means 130 stores user attribute information 410 that associates, for example, for user identification information 390:11112, workplace location: Kanagawa (prefecture), age: 35 (years old), gender: female, membership type: paid member, ..., whether specific content information 310 was viewed / bought: no, time spent viewing / bought specific content information 310: 0 seconds, ...

[0026] The normalization means 140 normalizes the number of views 330 for each piece of tag information 320 stored in the user behavior information storage means 120 so as to represent the frequency of viewing or browsing by the user 380. The normalization method used by the normalization means 140 is, for example, to divide the number of views 330 by the number of all pieces of content information 310.

[0027] For example, for the user identification information 390:11111 in FIG. 3, if the number of all content information 310 is 500, the normalization means 140 performs normalization such as acute coronary syndrome: 5÷500=0.01, arrhythmia: 10÷500=0.02, literature: 0÷500=0, complex PCI: 20÷500=0.04, etc.

[0028] The factor analysis processing means 150 applies factor analysis processing to the normalized user behavior information 400, and calculates a predetermined number of common factors 360. The number of common factors 360 calculated by the factor analysis processing is not particularly limited.

[0029] Fig. 5 is a diagram for explaining the processing by the factor analysis processing means 150, and is a diagram that shows a model of the factor loadings obtained by the factor analysis processing. In Fig. 5, common factors 360 are taken horizontally, and tag information 320 is taken vertically. As shown in Fig. 5, the factor analysis processing means 150 calculates, for example, five common factors 360, and defines the contents shown (represented) by each common factor 360 based on tag information 320 that shows a relatively large value in the factor loading for each common factor 360 and common knowledge in the medical industry.

[0030] The user characteristic determination means 160 calculates a factor score 370 for each piece of user identification information 390 of the normalized user behavior information 400 based on a predetermined number of common factors 360 calculated by the factor analysis processing means 150. Then, the user characteristic determination means 160 determines one common factor 360 with the highest score as the user characteristic 420 that represents the characteristic of the user 380 identified by the user identification information 400.

[0031] As shown in FIG. 5, the user characteristics 420 include, for example, "Cardiologist interested in cardiac catheterization treatment (specialty area): F1", "Cardiologist interested in the general treatment of ischemic heart disease: F2", "Cardiologist interested in the general treatment of structural heart disease: F3", "Cardiologist with a general interest in the circulatory system (more interested in drug therapy, etc. than catheterization): F4", and "Cardiologist interested in peripheral intravascular treatment: F5".

[0032] The user integrated information generating means 170 integrates the normalized user behavior information 400, user attribute information 410, and user characteristics 420 based on the user identification information 390. In this way, the user integrated information generating means 170 generates user integrated information 430 for performing a factor analysis by multivariate analysis of the phenomenon that a predetermined viewing / browsing situation is satisfied with respect to specific content information 310.

[0033] Fig. 6 is a diagram showing an example of the user integrated information generating means 170. As shown in Fig. 6, the user integrated information generating means 170 integrates the user attribute information 410 in Fig. 4 and the user characteristics 420 determined by the user characteristic determining means 160 based on the user identification information 390. For example, for the user identification information 390:11111, the user integrated information generating means 170 generates user integrated information 430 that associates workplace location: Tokyo (metropolis), age: 30 (years old), gender: male, membership type: paid member, ..., user characteristics 420: F1.

[0034] When the attribute information 350 includes information regarding the time spent viewing or browsing specific content information 310, the exclusion determination means 180 determines whether or not to exclude each user attribute information 410 from being processed by the device 100 based on the length of the viewing or browsing time for the specific content information 310.

[0035] For example, when the viewing / viewing time for specific content information 310 is shorter or longer than a predetermined time, the exclusion determination means 180 determines that the user attribute information 410 should be excluded from processing by the present device 100. When the viewing / viewing time for the content information 310 is extremely short or long, it is determined that the user 380 has not been interested in viewing / viewing the content information 310, and determines that the content information 310 should be excluded from processing by the present device 100. Processing by the exclusion determination means 180 improves the accuracy and reliability of the analysis results by the present device 100.

[0036] 4, for example, the exclusion determination means 180 determines that user attribute information 410 having user identification information 390 of "11111" or "11113" should be processed by the device 100 because the viewing / browsing time is "5 minutes" and "5 minutes 30 seconds", respectively. On the other hand, for example, the exclusion determination means 180 determines that user attribute information 410 having user identification information 390 of "11115" should be excluded from processing by the device 100 because the viewing / browsing time is extremely short at "5 seconds". The threshold value for determination by the exclusion determination means 180 can be determined as appropriate.

[0037] The multivariate analysis processing means 190 uses multivariate analysis processing to identify items of the attribute information 350 that contribute to the event of satisfying a predetermined viewing / viewing situation with respect to the specific content information 310 in the user integrated information 430. The event of satisfying a predetermined viewing / viewing situation is assumed to be, but is not limited to, viewing / viewing the specific content information 310, viewing / viewing the specific content information 310 to the end, or the time spent viewing / viewing the specific content information 310 longer than a predetermined time.

[0038] As shown in FIG. 6, the multivariate analysis processing means 190, for example, applies multivariate analysis processing to the user integrated information 430, and identifies items of attribute information 350 that contribute significantly to the viewing / browsing of specific content information 310, such as membership type: paid membership, e-mail newsletter subscription status: yes, etc.

[0039] The user extraction means 200 extracts user identification information 390 that has a predetermined user characteristic 420 and satisfies the conditions related to the attribute information 350 specified by the multivariate analysis processing means 190, based on the user integrated information 430. This process makes it possible to effectively narrow down targets to which event participation invitations are sent or targets to which advertisements are placed.

[0040] 6, for example, when the multivariate analysis processing means 190 specifies "membership type: paid" and the predetermined user characteristic 420 is "F1", the user extraction means 200 extracts "11111" as the user identification information 390. On the other hand, when the multivariate analysis processing means 190 specifies "membership type: paid" and the predetermined user characteristic 420 is "F2", the user extraction means 200 extracts "11112" as the user identification information 390.

[0041] The second user extraction means 210 extracts user identification information 390 that has a predetermined user characteristic 420 and satisfies a predetermined viewing / viewing situation with respect to the specific content information 310, based on the user integrated information 430. Satisfying a predetermined viewing / viewing situation is assumed to mean, but is not limited to, having viewed / viewed the specific content information 310, having viewed / viewed the specific content information 310 to the end, or having viewed / viewed the specific content information 310 for a longer time than a predetermined time. This process makes it possible to effectively narrow down targets to which event participation invitations are sent or targets to which advertisements are placed.

[0042] In FIG. 6, for example, when the predetermined viewing / browsing situation is “has been viewed / browsed” and the predetermined user characteristic 420 is “F5”, the second user extraction means 210 extracts “11115” as the user identification information 390.

[0043] 6, it is assumed that the attribute information 350 includes information regarding the time spent viewing / watching the specific content information 310. In this case, the second user extraction means 210 may be configured to extract user identification information 390 that identifies a user 380 who has a predetermined user characteristic 420 and has viewed / watched the specific content information 310 for a longer period of time based on the user integrated information 430. It is presumed that the user 380 who has viewed / watched the specific content information 310 for a long period of time has a deep interest in the specific content information 310, and by targeting such a user 380 as a target for sending an invitation to participate in an event or a target for placing an advertisement, effective marketing activities can be carried out.

[0044] As shown in FIG. 6, for example, when the predetermined viewing / browsing situation is “viewing / browsing time is 5 minutes or more” and the predetermined user characteristic 420 is “F1”, the second user extraction means 210 extracts “11111” as the user identification information 390.

[0045] Based on the above-described operational principle, the present device 100 generates data 430 for accurately identifying the characteristics of potential customers in order to realize effective marketing in the medical field.

[0046] Furthermore, based on the above-described operational principle, the present device 100 can accurately extract customer candidates with a high response rate in order to realize effective marketing in the medical field. (Hardware configuration of the information processing device according to this embodiment)

[0047] An example of the hardware configuration of the present device 100 will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the hardware configuration of the present device 100. As shown in Fig. 7, the present device 100 has a CPU (Central Processing Unit) 510, a ROM (Read-Only Memory) 520, a RAM (Random Access Memory) 530, an auxiliary storage device 540, a communication I / F 550, an input device 560, a display device 570, and a storage medium I / F 580.

[0048] CPU 510 is a device that executes a program stored in ROM 520, performs arithmetic processing on data expanded (loaded) into RAM 530 in accordance with instructions from the program, and controls the entire device 100. ROM 520 stores programs and data to be executed by CPU 510. When CPU 510 executes a program stored in ROM 520, the program and data to be executed are expanded (loaded) into RAM 530, and RAM 530 temporarily holds the arithmetic data during calculation.

[0049] The auxiliary storage device 540 is a device that stores the OS (Operating System) which is basic software, the application program according to the present embodiment, and the like together with related data. The auxiliary storage device 540 is, for example, a HDD (Hard Disk Drive) or a flash memory.

[0050] The communication I / F 550 is an interface for connecting to a communication network 430 such as a wired or wireless LAN (Local Area Network) or the Internet, and for transmitting and receiving data to and from other devices that provide a communication function.

[0051] The input device 560 is a device such as a keyboard for inputting data to the present device 100. The display device (output device) 570 is a device constituted by an LCD (Liquid Crystal Display) or the like, and functions as a user interface when the user utilizes the functions of the present device 100 or when performing various settings. The storage medium I / F 580 is an interface for transmitting and receiving data to and from a storage medium 590 such as a CD-ROM, DVD-ROM, or USB memory.

[0052] Each of the means of the present device 100 may be realized by CPU 510 executing a program corresponding to each of the means stored in ROM 520 or auxiliary storage device 540. Each of the means of the present device 100 may be realized by hardware that performs processing related to each of the means. Alternatively, the present device 100 may be caused to read the program according to the present invention from an external server device via communication I / F 550, or to read the program according to the present invention from storage medium 590 via storage medium I / F 580, and cause the present device 100 to execute the program. (Processing example by the information processing device according to the present embodiment) (1) Processing example by the device 100 (part 1) A processing example (part 1) performed by the present device 100 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing the flow of the processing example (part 1) performed by the present device 100.

[0053] In S10, the normalization means 140 normalizes the number of views 330 for each tag information 320 stored in the user behavior information storage means 120 so as to represent the frequency of viewing or browsing by the user 380. The normalization method used by the normalization means 140 is, for example, to divide the number of views 330 by the number of all content information 310.

[0054] For example, for the user identification information 390:11111 in FIG. 3, if the number of all content information 310 is 500, the normalization means 140 performs normalization such as acute coronary syndrome: 5÷500=0.01, arrhythmia: 10÷500=0.02, literature: 0÷500=0, complex PCI: 20÷500=0.04, etc.

[0055] Furthermore, in S10, the factor analysis processing means 150 applies factor analysis processing to the normalized user behavior information 400 to calculate a predetermined number of common factors 360. The number of common factors 360 calculated by the factor analysis processing is not particularly limited.

[0056] As shown in FIG. 5, the factor analysis processing means 150 calculates, for example, five common factors 360, and defines the content that each common factor 360 indicates (represents) based on tag information 320 that indicates a relatively large value in the factor loading for each common factor 360 and common knowledge in the medical industry.

[0057] At S20, the user characteristic determination means 160 calculates a factor score 370 for each user identification information 390 of the normalized user behavior information 400 based on a predetermined number of common factors 360 calculated by the factor analysis processing means 150. Then, the user characteristic determination means 160 determines one common factor 360 with the highest score as the user characteristic 420 representing the feature of the user 380 identified by the user identification information 400.

[0058] As shown in FIG. 5, the user characteristics 420 include, for example, "Cardiologist interested in cardiac catheterization treatment (specialty area): F1", "Cardiologist interested in the general treatment of ischemic heart disease: F2", "Cardiologist interested in the general treatment of structural heart disease: F3", "Cardiologist with a general interest in the circulatory system (more interested in drug therapy, etc. than catheterization): F4", and "Cardiologist interested in peripheral intravascular treatment: F5".

[0059] In S30, the user integrated information generating means 170 integrates the normalized user behavior information 400, user attribute information 410, and user characteristics 420 based on the user identification information 390. As a result, the user integrated information generating means 170 generates user integrated information 430 for performing factor analysis by multivariate analysis of the phenomenon that a predetermined viewing / browsing situation is satisfied with respect to specific content information 310.

[0060] 6, the user integrated information generating means 170 integrates the user attribute information 410 in FIG. 4 with the user characteristics 420 determined by the user characteristic determining means 160 based on the user identification information 390. For example, for user identification information 390:11111, the user integrated information generating means 170 generates user integrated information 430 that associates workplace location: Tokyo (metropolis), age: 30 (years old), gender: male, membership type: paid member, ..., user characteristics 420:F1.

[0061] Furthermore, in S30, if the attribute information 350 includes information regarding the time spent watching / viewing specific content information 310, the exclusion determination means 180 determines whether or not to exclude each user attribute information 410 from being processed by the device 100 based on the length of the watching / viewing time for the specific content information 310.

[0062] For example, when the viewing / viewing time for specific content information 310 is shorter or longer than a predetermined time, the exclusion determination means 180 determines that the user attribute information 410 should be excluded from processing by the present device 100. When the viewing / viewing time for the content information 310 is extremely short or long, it is determined that the user 380 has not been interested in viewing / viewing the content information 310, and determines that the content information 310 should be excluded from processing by the present device 100. Processing by the exclusion determination means 180 improves the accuracy and reliability of the analysis results by the present device 100.

[0063] 4, for example, the exclusion determination means 180 determines that user attribute information 410 having user identification information 390 of "11111" or "11113" should be processed by the device 100 because the viewing / browsing time is "5 minutes" and "5 minutes 30 seconds", respectively. On the other hand, for example, the exclusion determination means 180 determines that user attribute information 410 having user identification information 390 of "11115" should be excluded from processing by the device 100 because the viewing / browsing time is extremely short at "5 seconds". The threshold value for determination by the exclusion determination means 180 can be determined as appropriate.

[0064] In S40, the multivariate analysis processing means 190 identifies, with respect to the user integrated information 430, items of the attribute information 350 that contribute to the event that a predetermined viewing / browsing situation is satisfied with respect to specific content information 310, by multivariate analysis processing.

[0065] As shown in FIG. 6, the multivariate analysis processing means 190, for example, applies multivariate analysis processing to the user integrated information 430, and identifies items of attribute information 350 that contribute significantly to the viewing / browsing of specific content information 310, such as membership type: paid membership, e-mail newsletter subscription status: yes, etc.

[0066] In S50, the user extraction means 200 extracts user identification information 390 that has predetermined user characteristics 420 and satisfies the conditions related to the attribute information 350 specified by the multivariate analysis processing means 190, based on the user integrated information 430. This process makes it possible to effectively narrow down targets to which event participation invitations are sent or targets to which advertisements are placed.

[0067] 6, for example, when the multivariate analysis processing means 190 specifies "membership type: paid" and the predetermined user characteristic 420 is "F1", the user extraction means 200 extracts "11111" as the user identification information 390. On the other hand, when the multivariate analysis processing means 190 specifies "membership type: paid" and the predetermined user characteristic 420 is "F2", the user extraction means 200 extracts "11112" as the user identification information 390.

[0068] By carrying out the above-mentioned processing, the present device 100 generates data 430 for accurately finding the characteristics of potential customers in order to realize effective marketing in the medical field.

[0069] Furthermore, by carrying out the above-mentioned processing, the present device 100 can accurately extract customer candidates with a high response rate in order to realize effective marketing in the medical field. (2) Processing Example by the Device 100 (Part 2) A second example of the process performed by the device 100 will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the flow of the second example of the process performed by the device 100.

[0070] Note that steps S110 to S130 are the same as steps S10 to S30, and therefore a description thereof will be omitted here. Below, a description will be given of the process after steps S110 to S130 (S10 to S30) have been performed.

[0071] In S140, the second user extraction means 210 extracts user identification information 390 that has a predetermined user characteristic 420 and satisfies a predetermined viewing / browsing situation with respect to the specific content information 310, based on the user integrated information 430. This process makes it possible to effectively narrow down targets to which event participation invitations are sent or targets to which advertisements are placed.

[0072] In FIG. 6, for example, when the predetermined viewing / browsing situation is “has been viewed / browsed” and the predetermined user characteristic 420 is “F5”, the second user extraction means 210 extracts “11115” as the user identification information 390.

[0073] 6, it is assumed that the attribute information 350 includes information regarding the time spent viewing / watching the specific content information 310. In this case, the second user extraction means 210 may be configured to extract user identification information 390 that identifies a user 380 who has a predetermined user characteristic 420 and has viewed / watched the specific content information 310 for a longer period of time based on the user integrated information 430. It is presumed that the user 380 who has viewed / watched the specific content information 310 for a long period of time has a deep interest in the specific content information 310, and by targeting such a user 380 as a target for sending an invitation to participate in an event or a target for placing an advertisement, effective marketing activities can be carried out.

[0074] As shown in FIG. 6, for example, when the predetermined viewing / browsing situation is “viewing / browsing time is 5 minutes or more” and the predetermined user characteristic 420 is “F1”, the second user extraction means 210 extracts “11111” as the user identification information 390.

[0075] By carrying out the above-mentioned processing, the present device 100 generates data 430 for accurately finding the characteristics of potential customers in order to realize effective marketing in the medical field.

[0076] Furthermore, by carrying out the above-mentioned processing, the present device 100 can accurately extract customer candidates with a high response rate in order to realize effective marketing in the medical field.

[0077] Next, an information processing device according to a second embodiment of the present embodiment will be described. An information processing device 600 according to the second embodiment of the present embodiment (hereinafter, simply referred to as "the device") differs from the above-mentioned embodiment of the present embodiment (hereinafter, sometimes referred to as "the first embodiment") in the processing after the user characteristic is determined by the user characteristic determination means 160. Note that the same reference numerals are used for the same configuration as the first embodiment, and the description thereof will be omitted.

[0078] (Operation principle of the information processing device according to the second embodiment) The operating principle of the present device 600 will be described with reference to Fig. 10. Fig. 10 is a functional block diagram of the present device 600.

[0079] As in the first embodiment, this device 600 is connected to a user terminal 440 operated by a user 380 via a communication network 450. This device 600 also has a CPU 510, a ROM 520, a RAM 530, an auxiliary storage device 540, a communication I / F 550, an input device 560, a display device 570, and a storage medium I / F 580.

[0080] As shown in FIG. 10, this device 600 has a content information storage means 110, a user behavior information storage means 120, a user attribute information storage means 130, a normalization means 140, a factor analysis processing means 150, a user characteristic determination means 160, a product usage information storage means 610, a correspondence relationship storage means 620, and a factor extraction means 630.

[0081] The product usage information storage means 610 stores product usage information that associates user identification information 390, multiple attribute information 350 of the user identified by the user identification information 390, and usage information for multiple targets of multiple medical products with the same effect that are used by the user identified by the user identification information 390. Medical products include medicines and medical devices. In the following explanation, "medicine" will be used as an example of a medical product. In the following explanation, "medical products" may be simply referred to as "products".

[0082] The multiple user attribute information 350 is information stored in the user attribute information storage means 130, and includes information on various user attributes such as the above-mentioned place of work, age, sex, membership type, subscription status to e-mail magazines, whether the doctor is a specialist in an academic society, whether the doctor is certified by an academic society, participation status in events, viewing and viewing status of articles, as well as the number of prescribed surgeries etc. performed, the number of times information has been provided by a medical information provider (MR), etc.

[0083] "Multiple medical products with the same effect" refers to, for example, "drugs" that are manufactured by different pharmaceutical companies and have different product names, but are of the same type and with the same efficacy, efficacy, pharmacology, and pharmacology. Also, in the case of "medical devices," it refers to medical devices that are manufactured by different pharmaceutical companies, medical device manufacturers, and product names, but have the same functions. In other words, "the same effect" does not mean exactly the same effect. For example, multiple direct anticoagulants (DOACs) distributed on the market by multiple pharmaceutical companies are multiple medical products (drugs) that have the "same effect" of suppressing the action of various coagulation factors that clot blood and preventing blood clots caused by blood stagnation in blood vessels with slow blood flow. In the following, we will explain the case where the drug is a "direct anticoagulant," but the drug is not limited to a "direct anticoagulant" and can be any drug that falls under the category of "multiple drugs with the same effect."

[0084] "Multiple target users" is information about targets (patients) for which multiple products (drugs) are used. For example, in the case of direct anticoagulants (DOACs), the multiple target users include "chronic kidney disease (CKD) patients," "liver disease patients," "diabetes patients," "patients after lower limb revascularization," "patients after TAVI (transcatheter aortic valve replacement)," "deep vein thrombosis patients," "pulmonary embolism patients," "patients with a high tendency to bleed," "elderly people," "young people," and "dementia patients."

[0085] That is, the "information on different uses" is information on how users use multiple products with the same effect for various different use targets. For example, for drugs A, B, and C with the same effect, user X uses drug A for "patients with high bleeding tendency," drug B for "patients after lower limb revascularization," and drug C for "patients with dementia." The "information on different uses" can be acquired, for example, by conducting a survey of users of a website that allows viewing and browsing of the content information 310. Specifically, multiple different uses of direct anticoagulants are listed in advance in the survey, and users of the website are asked to answer which direct anticoagulant they use among the direct anticoagulants of multiple pharmaceutical companies for each different use target. The different uses can be changed, added, or deleted.

[0086] In this way, the "product usage information" is information that associates (corresponds) the user identification information 390, the multiple pieces of user attribute information 350, and information on how to differentiate between multiple products and multiple targets.

[0087] The correspondence storage means 620 stores the correspondence between the multiple target devices and the multiple attribute information 350 and the user characteristics 420. For example, a predetermined number of common factors 360 when the factor analysis processing means 150 applies the factor analysis processing are specified in advance, and these common factors 360 and the user characteristics 420 correspond to each other. Therefore, the correspondence storage means 620 can store the correspondence between the multiple target devices and the multiple attribute information 350 and the user characteristics 420 by storing in advance the correspondence between these common factors 360 and both the multiple target devices and the multiple attribute information 350. For example, the user characteristics 420 of "cardiologist interested in peripheral endovascular treatment: F5" is associated with "patients after lower limb revascularization" among the multiple target devices. In addition, when "PCI (percutaneous transluminal coronary intervention (prescribed surgery))" is included in the multiple attribute information 350, the user characteristic 420 of "cardiologist interested in cardiac catheter treatment (specialty field): F1" is associated. Note that multiple user characteristics 420 may be associated with one target of use or attribute information 350.

[0088] The factor extraction means 630 applies multivariate analysis based on the product differentiation information, and extracts factors (hereinafter referred to as "usage rate factors") related to the user characteristics 420 for the usage rate of a specific product from among a plurality of differentiation targets and a plurality of attribute information 350. In this embodiment, an example in which the factor extraction means 630 applies decision tree analysis will be described. Note that the multivariate analysis is not limited to decision tree analysis, and may be, for example, Cox regression proportional hazard analysis, multiple regression analysis, etc.

[0089] In this embodiment, when the factor extraction means 630 extracts the use rate factors related to the user characteristics 420 from among the multiple use targets and the multiple attribute information 350, first, all users are divided into upper users whose use rate of the target product (e.g., "drug A") is equal to or higher than a predetermined value (e.g., 10% or more) and lower users whose use rate is less than the predetermined value (e.g., less than 10%). Then, the factor extraction means 630 executes a multivariate analysis on the multiple use targets and the multiple attribute information 350 based on the product use information, and extracts the use rate factors related to the user characteristics 420 for the use rate of drug A. The use rate factors related to the user characteristics 420 indicate what kind of user characteristics 420 are related as factors for the high (or low) use rate of drug A. Note that the above-mentioned predetermined value is not limited to 10%.

[0090] FIG. 11 is a diagram illustrating an example of the factor extraction means according to the second embodiment.

[0091] As shown in FIG. 11, the factor extraction means 630 applies (executes) a multivariate analysis (in this embodiment, a decision tree analysis) for multiple target users and multiple attribute information 350 based on the product usage information, and extracts usage rate factors related to the user characteristics 420 for the usage rate of drug A. In FIG. 11, the top users of "node 2" with a high number of PCI (percutaneous transluminal coronary intervention) cases per year for drug A are 99 people, and there are many users with high usage rates, so "number of PCI cases per year" is extracted as a factor of the high usage rate of drug A. The user characteristics 420 corresponding to "PCI" are associated with "cardiologists (specialty field) interested in cardiac catheterization: F1" in the correspondence stored in the correspondence storage means 620. As a result, the user characteristics 420 (F1) are extracted as a factor of the high usage rate of drug A. In this embodiment, decision tree analysis is applied to one item ("number of PCIs performed per year") (see FIG. 11), but decision tree analysis may be applied to other items (target of use and attribute information 350) for each of "node 1" and "node 2". In FIG. 11, "node 0" is branched into two nodes ("node 1" and "node 2") based on the "number of PCIs performed per year", but this is not limited to this and may be branched into three or more nodes. In this embodiment, factors for a high usage rate of drug A are extracted, but factors for a low usage rate of drug A may be extracted.

[0092] (Processing example by the information processing device according to the present embodiment) A processing example by the device 600 will be described with reference to Fig. 12. Fig. 12 is a flowchart showing the flow of a processing example by the information processing device according to the second embodiment. Note that S210 and S220 are the same as the processing in S10 and S20 in the first embodiment, and therefore the description thereof will be omitted here. Below, the processing after the processing in S210 and S220 (S10 and S20) is performed will be described.

[0093] In S230, the factor extraction means 630 applies at least a decision tree analysis based on the product differentiation information to extract usage rate factors related to the user characteristics 420 for the usage rate of a predetermined product (drug) from among a plurality of differentiation targets and a plurality of attribute information 350. For example, as shown in FIG. 11, the factor extraction means 630 extracts the user characteristics 420 (F1) as a factor of the high usage rate of drug A. This process makes it possible to effectively narrow down the user demographic (user characteristics 420) to which event participation invitations are sent or advertisements are placed.

[0094] In the device 600 configured as described above, the user characteristic determination means 160 determines the user characteristics 420 based on the normalized user behavior information 400, so that it is possible to determine whether the articles on the website have reached (are of interest to) a specific user group (user characteristics 420) that is the marketing target.

[0095] For example, a pharmaceutical company may target a specific user group (user characteristics 420) for marketing and conduct sales to that user group. Specifically, the pharmaceutical company may provide a WEB site with an advertisement article (an article that is expected to interest the specific user group) targeted at the specific user group as content information 310. The device 600 normalizes the user behavior information 400 based on the number of views / views 330 for each tag information 320 of the WEB site, and determines the user characteristics 420 based on the normalized user behavior information 400. Then, by checking the access log of the article provided by the pharmaceutical company and checking the user characteristics 420 of the accessing user, it is possible to determine whether the article on the WEB site has reached (is interested in) the specific user group (user characteristics 420) targeted by the pharmaceutical company in marketing. This allows the content of the article to be appropriately changed according to the determination result of the user characteristics 420, thereby improving the advertisement effect for the desired user group (user characteristics 420).

[0096] Furthermore, the factor extraction means 630 extracts usage rate factors related to the user characteristics 420 for the usage rate of a specific product from among the multiple target users and the multiple attribute information 350 based on the product usage information. This makes it possible to extract the user group (user characteristics 420) that will be a factor in improving the usage rate of the product, so that pharmaceutical companies can determine whether the user group they are targeting for marketing is really a user group that will be a factor in improving the usage rate of the product. This makes it possible to clarify the marketing target and improve marketing efficiency.

[0097] In this manner, according to this embodiment, it is possible to clearly narrow down the user demographic (user characteristics 420) to be targeted for marketing, and to determine whether the advertising and promotion is accurately reaching the desired user demographic (user characteristics 420).

[0098] By carrying out the above-mentioned processing, the present device 600 can generate data for accurately identifying the characteristics of potential customers in order to realize effective marketing in the medical field.

[0099] Furthermore, by carrying out the above-mentioned processing, the present device 600 can accurately extract customer candidates with a high response rate in order to realize effective marketing in the medical field.

[0100] In addition to the first and second forms, other analyses may be performed. For example, in the second form, the product usage information to be analyzed includes information on the usage of multiple products having the same effect for "multiple target users." This "multiple target users" is information on the target users (patients) for using multiple products, but in other analyses, other reasons for using multiple products (reason other than for patients) may be included in the product usage information. Examples of "other reasons" include "because it is a drug adopted by the clinic where I work," "because I got information at an academic conference," and "because my superiors and colleagues gave me opinions." In this way, the "other reasons" that are factors for improving the usage rate of the product may be extracted by logistic regression analysis or the like after including the "other reasons" in the product usage information.

[0101] In this embodiment, the device 600 does not include the user integrated information generating means 170, the exclusion determination means 180, the multivariate analysis processing means 190, the user extraction means 200, and the second user extraction means 210 of the first embodiment, but may include these. That is, the device 600 may perform the processing executed by the factor extraction means 630 in addition to all the processing of the first embodiment. Furthermore, in addition to all the processing of the first embodiment and the processing executed by the factor extraction means 630 of the second embodiment, the device 600 may perform the other analysis processing described above (analysis including the above "other reasons").

[0102] Furthermore, in the above first and second forms, an example of cardiology among medical specialties has been described, but the specialties are not limited to cardiology and can be applied to various medical specialties.

[0103] Although the embodiment of the present invention has been described in detail above, the present invention is not limited to such specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention described in the claims. [Explanation of symbols]

[0104] 100,600 Information processing equipment 110 Content information storage means 120 User behavior information storage means 130 User attribute information storage means 140 Normalization means 150 Factor Analysis Processing Method 160 User characteristic determination means 170 User Integrated Information Generation Means 180 Exclusion determination means 190 Multivariate analysis processing means 200 User Extraction Methods 210 Second user extraction means 310 Content Information 320 Tag Information 330 Content information viewed / viewed 340 Viewing and viewing status information 350 Attribute information 360 Common factors by factor analysis 370 Factor scores based on common factors 380 users 390 User Identification Information 400 User Behavior Information 410 User Attribute Information 420 User Characteristics 430 User Integration Information 440 User Terminals 450 Communication Network 510 CPU 520 ROM 530 RAM 540 Auxiliary storage 550 Communication Interface 560 Input Device 570 Output Device 580 Storage Media Interface 590 Storage medium 610 Product usage information storage means 620 Correspondence relationship storage means 630 Factor Extraction Method

Claims

1. a content information storage means for storing medical content information that can be viewed or read on a website and tag information for classifying the content information based on its content in association with each other; a user behavior information storage means for storing user behavior information that associates user identification information, the tag information corresponding to the content information viewed / browsed by a user identified by the user identification information, and the number of views / browsings for each tag information; a user attribute information storage means for storing user attribute information that associates the user identification information with a plurality of pieces of attribute information including information regarding a viewing / browsing situation of each of the users with respect to the specific content information; A normalization means for normalizing the number of times of viewing / browsing for each tag information item so as to represent the frequency of viewing / browsing by the user; A factor analysis processing means for applying a factor analysis process to the normalized user behavior information to calculate a predetermined number of common factors; a user characteristic determination means for calculating a factor score based on the predetermined number of common factors for each of the user identification information regarding the user behavior information after normalization, and determining one of the common factors with the highest score as a user characteristic representing a feature of the user identified by the user identification information; a product usage information storage means for storing product usage information that associates the user identification information, the multiple attribute information of the user identified by the user identification information, and usage information for multiple medical products having the same effect and used by the user identified by the user identification information, for multiple usage targets; a correspondence storage means for storing a correspondence between the plurality of objects of distinguishing use and the plurality of pieces of attribute information and the user characteristics; and a factor extraction means for applying multivariate analysis based on the product usage information and extracting factors related to the user characteristics regarding the usage rate of a specified product from among the multiple target usages and the multiple attribute information.

2. a content information storage means for storing, in association with each other, content information related to medical care that can be viewed or read on a website, and tag information for classifying the content information based on its contents; a user behavior information storage means for storing user behavior information that associates user identification information, the tag information corresponding to the content information viewed or read by a user identified by the user identification information, and the number of times each of the tag information has been viewed or read; a user attribute information storage means for storing user attribute information that associates the user identification information with a plurality of attribute information including information regarding the viewing or reading status of a specific piece of content information of each of the users; a product differentiation information storage means for storing product differentiation information that associates the user identification information, the plurality of attribute information of the user identified by the user identification information, and differentiation information for a plurality of differentiation targets of a plurality of medical care products having the same effect that are used by the user identified by the user identification information; and a correspondence relationship storage means for storing a correspondence relationship between the plurality of differentiation targets and the plurality of attribute information, and a user characteristic that represents a feature of the user identified by the user identification information; A normalization step of normalizing the number of times of viewing / browsing for each tag information item so as to represent the frequency of viewing / browsing by the user; A factor analysis processing means applies a factor analysis process to the normalized user behavior information to calculate a predetermined number of common factors; A user characteristic determination means calculates a factor score based on the predetermined number of common factors for each of the user identification information regarding the normalized user behavior information, and determines one of the common factors with the highest score as the user characteristic; a step of a factor extraction means applying multivariate analysis based on the product differentiation information to extract factors related to the user characteristics for the usage rate of a specified product from among the multiple differentiation targets and the multiple attribute information.

3. An information processing program for causing a computer to execute the method according to claim 2.

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