Information processing systems, information processing methods, and programs

Abstract

This technology provides a more accurate analysis of the impact of content and other factors. [Solution] The first estimation unit 102 calculates the first estimated number of viewers of the video streaming service to be analyzed by multiplying a first coefficient by the number of subscribers of the video streaming service to be analyzed. The second estimation unit 104 calculates the second estimated number of viewers of the video streaming service to be analyzed by multiplying the viewing probability corresponding to the attribute of the target subscriber for each number of subscribers for each attribute of the video streaming service to be analyzed and summing them up. The third estimation unit 106 estimates the number of potential viewers for each attribute of the video streaming service to be analyzed based on the first estimated number of viewers calculated by the first estimation unit 102, the second estimated number of viewers calculated by the second estimation unit 104, and the correction value calculated by the correction value calculation unit 120.

Description

Technical Field

[0001] The present disclosure relates to an information processing system, an information processing method, and a program.

Background Art

[0002] In recent years, the value of analyzing the influence of content has been great, and data analysis has been performed in various forms and the results have been utilized. In this regard, for example, Non-Patent Document 1 discloses an example of the result of data analysis in a content distribution service.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Here, in the analysis of the prior art including the above Non-Patent Document 1, for example, data analysis is usually performed based on the numbers managed for each viewing device such as the number of accounts of the distribution service. However, in such a method, even if there are family members or friends who are viewing using the same device, it is not reflected in the result of the data analysis, and the data analysis is insufficient.

[0005] The present disclosure has been made in view of such a situation, and an object thereof is to provide a technique capable of analyzing the influence of content and the like with higher validity.

[0006] To achieve the above objectives, an information processing system in one aspect of this disclosure is: The first estimation unit estimates the first estimated number of users for the target content or broadcast by multiplying the number of registered users for the target content or broadcast by the average number of potential users per registration, A second estimation unit estimates the second estimated number of users for the target content or broadcast by summing the probabilities of use for each attribute, including the number of registered users for each attribute and the probability of use occurring for other attributes used simultaneously with the registered user. A third estimation unit estimates the number of potential users who have used the target content or broadcast for each user attribute, based on the first estimated number of users and the second estimated number of users. It is an information processing system equipped with [the following features].

[0007] An information processing method and program according to one aspect of this disclosure are also provided as an information processing method or program corresponding to an information processing system according to one aspect of this disclosure. [Effects of the Invention]

[0008] This disclosure provides technology that can analyze the impact of content and other factors in a more appropriate manner. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the configuration of an information processing system, which is one embodiment of the present disclosure. [Figure 2] This figure shows an example of the hardware configuration of an analysis server constituting an information processing system, which is one embodiment of the present disclosure. [Figure 3] This figure shows an example of the functional configuration of an analysis server and other components that constitute an information processing system, which is one embodiment of the present disclosure. [Figure 4] This figure shows an example of registered user information relating to an information processing system, which is one embodiment of the present disclosure. [Figure 5] This figure shows an example of the viewing occurrence probability for an information processing system, which is one embodiment of the present disclosure. [Figure 6] This figure shows an example of the viewing occurrence probability related to an information processing system, which is one embodiment of the present disclosure, and is a different example from the example in Figure 5. [Figure 7] This figure shows an example of the second estimated number of viewers for an information processing system according to one embodiment of the present disclosure. [Figure 8] This figure shows an example of a second estimated number of viewers for an information processing system, which is one embodiment of the present disclosure, and is a different example from the example in Figure 7. [Figure 9] This figure shows an example of the total number of potential viewers for each attribute related to an information processing system, which is one embodiment of this disclosure. [Figure 10] This figure shows an example of the total number of potential viewers for each attribute related to an information processing system, which is one embodiment of the present disclosure, and is a different example from the example in Figure 9. [Figure 11] This figure shows an example of the flow of various processes performed by the analysis server that constitutes the information processing system according to one embodiment of this disclosure. [Modes for carrying out the invention]

[0010] [Embodiment] Figure 1 is a diagram showing an example of the configuration of an information processing system according to one embodiment of the present disclosure (hereinafter referred to as "the System").

[0011] As shown in Figure 1, this system is composed of, for example, an analysis server 1, a content server 2, a receiving device 3, and a viewer terminal 4. The analysis server 1, content server 2, receiving device 3, and viewer terminal 4 are connected via a predetermined network such as the Internet. However, the network is not an essential component, and for example, NFC (Near Field Communication), Bluetooth (registered trademark), LAN (Local Area Network), etc. may be used. In the following description, the term "content" shall include various types of content such as TV programs, Internet programs, advertisements, music, etc.

[0012] The analysis server 1 is an information processing device managed by an administrator or the like of this system. The analysis server 1 is composed of, for example, a general-purpose PC (Personal Computer) or the like. The analysis server 1 executes processes related to various services provided by this system.

[0013] The content server 2 is a platform or the like managed by a business operator or the like that performs various IP (Internet Protocol) broadcasts or various TV broadcasts. The content server 2 sends out information regarding content or the like that is the target of IP broadcasts or TV broadcasts. Here, IP broadcast is a method of broadcasting video, audio, etc. through a network such as the Internet in a unicast or multicast manner. In IP broadcast, usually, content and the like are distributed in association with user ID information and the like, so it is particularly easy to manage situations such as the viewing history of users and the usage status of other network-related services. TV broadcast is a method of broadcasting video, audio, etc. using, for example, the communication method of television, and can mainly adopt a broadcast method.

[0014] The receiving device 3 is an information processing device used by a user (hereinafter referred to as "viewer") who wishes to receive the services provided by this system. The receiving device 3 is composed of, for example, a TV receiver equipped with a network function or the like. Specifically, the receiving device 3, for example, acquires information (hereinafter referred to as "usage information") regarding the viewer's use of various video distribution services and SNS (social networking service) via the Internet or the like, and transmits the acquired usage information to the analysis server 1. In particular, when receiving an IP broadcast or the like, the receiving device 3 may be equipped with, for example, a so-called STB (Set Top Box) or the like.

[0015] The viewer terminal 4 is an information processing terminal used by the viewer. The viewer terminal 4 is composed of various information processing terminals such as, for example, a smartphone, a tablet, a connected TV (CTV), etc. Specifically, similar to the receiving device 3, the viewer terminal 4 acquires usage information regarding the use of various video distribution services and SNS by the viewer via the Internet or the like, and transmits the acquired usage information to the analysis server 1. Note that the usage information may include, for example, information regarding the region where the receiving device 3 or the viewer terminal 4 is installed, information regarding the date and day of the week when various video distribution services and SNS are used, information regarding the time when the use of various video distribution services and SNS is started, information regarding the time when the use of various video distribution services and SNS is ended, information regarding the various video distribution services and SNS used (for example, the name and ID of the service, etc.), information regarding the device used (for example, the ID of the device, etc.), information regarding the viewer (for example, the ID of the viewer, etc.), a MAC address, and the like.

[0016] FIG. 2 is a diagram showing an example of the hardware configuration of an analysis server constituting an information processing system according to an embodiment of the present disclosure.

[0017] As shown in FIG. 2, the analysis server 1 includes a control unit 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20.

[0018] The control unit 11 is composed of a microcomputer including a CPU, a GPU, and a semiconductor memory. The control unit 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 to the RAM 13. The necessary information and the like for the control unit 11 to execute various processes are appropriately stored in the RAM 13.

[0019] The control unit 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14. An output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0020] The output unit 16 is composed of various displays, speakers, etc. The output unit 16 outputs various images, sounds, etc.

[0021] The input unit 17 is composed of, for example, a keyboard or mouse. The input unit 17 accepts input of various types of information.

[0022] The storage unit 18 consists of an HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores various types of information. The storage unit 18 stores various programs related to the provision of this service.

[0023] The communications unit 19 controls communications with other hardware, etc., via the network N, including the Internet.

[0024] A drive 20 is provided as needed. A removable media 31, consisting of a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately mounted on the drive 20. Programs for executing various processes are stored on the removable media 31. Programs read from the removable media 31 by the drive 20 are installed on the storage unit 18 as needed. Furthermore, the removable media 31 can store various types of information stored in the memory unit 18, just as the memory unit 18 does.

[0025] Since the hardware configurations of content server 2, receiving device 3, and viewer terminal 4 can all be basically the same as those of analysis server 1, we will omit their explanation here. However, for example, if content server 2 is broadcasting television, content server 2 may be equipped with various signal modulation functions in its communication unit, etc. Also, if receiving device 3 is receiving television broadcasts, receiving device 3 may be equipped with various signal demodulation functions. Furthermore, various hardware components (especially content server 2 and receiving device 3) may be equipped with so-called encryption functions, etc.

[0026] Figure 3 shows an example of the functional configuration of an analysis server and other components that constitute an information processing system according to one embodiment of the present disclosure. As shown in Figure 3, the control unit 11 of the analysis server 1 functions as an ID information management unit 100, a first estimation unit 102, a second estimation unit 104, a third estimation unit 106, and an output control unit 108 by executing various programs.

[0027] The ID Information Management Unit 100 manages various information concerning registered users of the target content or broadcast. Specifically, the ID information management unit 100 manages various information (for example, information such as registrant ID, contract type, contract period, and payment method, hereinafter referred to as "registrant information") concerning users (hereinafter referred to as "registrants") who have registered for or contracted for services related to the distribution of specified content or broadcasts (hereinafter referred to as "video distribution services"). The ID information management unit 100 may also store and manage the registration information in a database or the like (not shown) as appropriate. Furthermore, the ID information management unit 100 obtains subscriber information for the video streaming service to be analyzed from a database (not shown) or the like.

[0028] Here, with reference to Figure 4, a specific example of registration information will be briefly explained. Figure 4 is a diagram showing an example of registrant information related to an information processing system according to one embodiment of the present disclosure. In the example in Figure 4, the number of registered users of the video streaming service for each attribute, "under 19 years old", "male 20-34 years old", "male 35-49 years old", "male 50-64 years old", "male 65 years and older", "female 20-34 years old", "female 35-49 years old", "female 50-64 years old", and "female 65 years and older", is displayed, totaling 270,000 people (number of IDs). Furthermore, although not explained in the above embodiment, a registrant in this system may be, for example, a registrant for the entire service in a distribution service, or a registrant for specific content or some services within a distribution service.

[0029] The first estimation unit 102 estimates the first estimated number of users for the target content or broadcast by multiplying the number of registered users for the target content or broadcast by the average number of potential users per registration. Specifically, the first estimation unit 102 calculates a first value (hereinafter referred to as the "first estimated number of viewers") for estimating the number of potential viewers for a given content or broadcast by, for example, multiplying the number of registered users of the video distribution service to be analyzed by the average number of potential viewers per registration (hereinafter referred to as the "average potential viewership coefficient"). Here, the average potential audience coefficient is a coefficient value that estimates the number of viewers who are actually watching the target content for each registered user (ID assigned to each device, etc.) on a given video streaming service, etc. This average potential audience coefficient is statistically calculated from the results of various mechanical surveys or questionnaire surveys conducted by the applicant, etc. Specifically, the first estimation unit 102 can calculate the total number of first estimated viewers to be approximately "374,500" using the following formula 1, for example, if the number of registered users is "270,000" and the average potential audience coefficient is "1.387".

number

[0030] In summary, the first estimated audience size is an estimate of the total potential audience for the video streaming service being analyzed (independent of attributes, etc.), calculated based, for example, the number of registered users of the video streaming service being analyzed and the first coefficient obtained from various machine-based surveys.

[0031] The second estimation unit 104 estimates the second number of users for the target content or broadcast by multiplying the probability of use occurring for each registered user and for other attributes used simultaneously with the registered user, and then summing them up. Specifically, the second estimation unit 104 calculates a second value (hereinafter referred to as the "second estimated number of viewers") for estimating the number of potential viewers for a given content or broadcast by, for example, for each number of registered users for each attribute (for example, men aged 20 to 34, women aged 65 and over, etc.), accumulating the probability of potential viewing according to the attributes of the target registered user (hereinafter referred to as the "probability of viewing") and summing them up.

[0032] Here, with reference to Figures 5 to 8, the method for calculating the viewing probability and the second estimated number of viewers will be explained. Figures 5 and 6 show an example of the viewing probability of an information processing system according to one embodiment of the present disclosure. Figures 7 and 8 show an example of the method for calculating the second estimated number of viewers of an information processing system according to one embodiment of the present disclosure. In simple terms, viewing probability is the probability that when a registrant of a certain attribute starts watching content, other registrants of the same attribute (including the registrant themselves) or other attributes are also watching at the same time. In the example in Figure 5, for example, for a registrant of "Male 20-34 years old," the viewing probability for each attribute is displayed as follows: "Under 19 years old" is "0.010," "Male 20-34 years old" is "1.0," "Male 50-64 years old" is "0.101," "Male 65 years and older" is "0.020," "Female 20-34 years old" is "0.202," "Female 35-49 years old" is "0.030," "Female 50-64 years old" is "0.141," and "Female 65 years and older" is "0.010." In other words, in the example in Figure 5, when a registrant of "Male 20-34 years old" is watching the target content, it indicates that there are potential viewers who will watch the target content with the registrant, according to the probability for each attribute. Note that the values ​​shown in the example in Figure 5 were calculated by rounding the value to the fourth decimal place, and subsequent calculations may use values ​​that are not necessarily identical to those shown in Figure 5. Furthermore, this viewing probability is statistically calculated from the results of various mechanical surveys or questionnaire surveys conducted by the applicant, etc.

[0033] Figure 6 shows the application of this viewing probability as a probability matrix. In the example in Figure 6, the expected viewing probability for each of the following categories is shown, similar to the case where the registrant is "male, 20-34 years old": "19 years old and under", "male, 35-49 years old", "male, 50-64 years old", "male, 65 years and older", "female, 20-34 years old", "female, 35-49 years old", "female, 50-64 years old", and "female, 65 years and older". In the example in Figure 6, a viewing probability greater than 1 means, for example, that in addition to the registrant themselves, family members (siblings, etc.) or friends of the same age are watching together. This system can estimate the theoretical number of potential viewers (viewing probability) for each attribute by, for example, multiplying the viewing probability for each attribute by the number of registrants for each attribute. The values ​​shown in the example in Figure 6 are calculated by rounding the value to the fourth decimal place, similar to the case in Figure 5.

[0034] In the example in Figure 7, the potential number of viewers for each attribute is shown, specifically assuming that there are "20,000" registered users in the "Male 20-34 years old" category. In the example in Figure 7, the potential number of viewers for each attribute is calculated by multiplying the "20,000" registered users by the viewing probability for each attribute. For example, "20000" × "0.0101" (shown as "0.010" in the example in Figure 7) equals "202" (for those under 19 years old). Note that in the example in Figure 7, the viewing probability used in the calculation is calculated while retaining values ​​to four decimal places, and the values ​​shown in Figure 5 are not used directly, so there are slight differences in the numbers in the chart.

[0035] Figure 8 also shows the results of calculating the potential audience for other demographics, such as "Under 19," "Males 35-49," "Males 50-64," "Males 65 and over," "Females 20-34," "Females 35-49," "Females 50-64," and "Females 65 and over," using the same method as in Figure 7. When the total potential audience numbers shown in Figure 8 are added together, the second estimated audience number is approximately "450,743."

[0036] In summary, the second estimated audience count is an estimate of the potential audience for each attribute on the video streaming service being analyzed, calculated, for example, based on the number of subscribers to the video streaming service being analyzed and the probability of viewing based on the attributes of the viewers. This system can, for example, calculate these two different estimates, the first estimated audience count and the second estimated audience count, separately and then integrate them to ultimately estimate the potential audience for each attribute of the target content.

[0037] The third estimation unit 106 estimates the number of potential users who have used the target content or broadcast for each user attribute, based on the first estimated number of users and the second estimated number of users. Specifically, the third estimation unit 106 estimates, for example, the number of potential viewers who have viewed the target content or broadcast for each registered user or viewer attribute, based on the first estimated number of viewers calculated by the first estimation unit 102 and the second estimated number of viewers calculated by the second estimation unit 104. Here, the third estimation unit 106 is provided with a correction value calculation unit 120. The correction value calculation unit 120 calculates a "correction value" for estimating the number of potential viewers who have viewed the target content or broadcast, based on the first estimated number of users and the second estimated number of users. The correction value is a coefficient used to adjust the difference in the total number of potential viewers between the first estimated number of viewers calculated by the first estimation unit 102 and the second estimated number of viewers calculated by the second estimation unit 104 (the sum of potential viewers). For example, it is calculated by dividing the value obtained by subtracting the number of subscribers from the first estimated number of viewers by the value obtained by subtracting the number of subscribers from the second estimated number of viewers. Specifically, the correction value calculation unit 120 can calculate a correction value of approximately "0.578" using the following formula 2, for example, when the number of registered users is "270,000", the first estimated number of viewers is "374,500", and the second estimated number of viewers is "450,743".

number

[0038] The output control unit 108 performs control to output various types of output information, such as text information and image information, to the output unit 16, etc.

[0039] Figure 11 is a diagram showing an example of the flow of various processes performed by an analysis server that constitutes an information processing system according to one embodiment of the present disclosure. In step S1, the ID information management unit 100 obtains subscriber information for the video streaming service to be analyzed from a database (not shown) or the like.

[0040] In step S2, the first estimation unit 102 calculates the first estimated number of viewers of the video streaming service to be analyzed by multiplying the number of subscribers of the video streaming service to be analyzed by a first coefficient.

[0041] In step S3, the second estimation unit 104 calculates the second estimated number of viewers for the video streaming service under analysis by multiplying the viewing probability corresponding to the attributes of the target subscribers for each attribute of the video streaming service under analysis, and summing them up.

[0042] In step S4, the correction value calculation unit 120 calculates a correction value by dividing the value obtained by subtracting the number of subscribers from the first estimated number of viewers by the value obtained by subtracting the number of subscribers from the second estimated number of viewers.

[0043] In step S5, the third estimation unit 106 estimates the number of potential viewers for each attribute of the video distribution service to be analyzed, based on the first estimated number of viewers calculated by the first estimation unit 102, the second estimated number of viewers calculated by the second estimation unit 104, and the correction value calculated by the correction value calculation unit 120. This completes the various processes performed on the analysis server 1.

[0044] Although one embodiment of the present disclosure has been described above, the present disclosure is not limited to the embodiment described above, and modifications, improvements, etc., to the extent that they can achieve the purpose of the present disclosure are included in the present disclosure.

[0045] [Other embodiments] Here, I will provide some additional information about the features and advantages of this system. (1) This system can estimate a more reasonable (higher accurate) number of potential viewers by integrating the number of potential viewers calculated separately using two different methods, such as the first estimated number of viewers and the second estimated number of viewers. (2) This system can calculate the number of potential viewers (e.g., the second estimated number of viewers) by using a unique coefficient that shows the relationship between viewership triggers for each attribute, such as the probability of viewership occurring. (3) This system can estimate a more reasonable (more accurate) number of potential viewers by fixing the original number of registered users and applying a correction value only to the number of potential viewers (the increase in viewers). Because this system possesses these unique features and advantages, it can estimate potential viewers with greater originality and accuracy compared to conventional methods. Furthermore, since this system uses, for example, a matrix of viewing probability for each attribute, it can estimate the number of potential viewers for each attribute of the viewer, rather than simply estimating the total number of potential viewers, and is thought to be able to visualize a more realistic and detailed viewing environment.

[0046] Although not explained in the above embodiment, the analysis server 1 of this system may acquire information about various programs and content broadcast by the content server 2, etc. (hereinafter referred to as "content information") and use it for further analysis. Content information may include, for example, the name of the content, a summary of the content (e.g., cast, year of production, country of production, producer, etc.), the type of content (hereinafter also referred to as "genre"), an identifier, the broadcast time of the content (start time, end time, date and time of ad delivery, etc.), channel information, viewing period (e.g., limited-time distribution, etc.), viewing attributes (e.g., not viewable by those under 15 years of age, etc.), program schedule, content list, thumbnail, billing information including the amount, rights information, viewing area, and other such information. Content information may also be obtained by being transmitted from, for example, content server 2 to analysis server 1.

[0047] Furthermore, while the above-described embodiments have explained that attributes are the gender and age of the registrant or viewer, they are not limited to these attributes. The system may, for example, acquire information such as the registrant's or viewer's place of residence, family structure, occupation, income, and content preferences as attribute information. Furthermore, attribute information may be obtained, for example, through input operations by viewers or through various automated surveys or questionnaires conducted via the internet or other means.

[0048] Furthermore, while the above embodiments have described registered users or viewers as those who view various types of content, etc., they are not limited to this. Registered users or viewers may also engage in activities such as recreation, surveys, games, etc., using the device.

[0049] Furthermore, although not explained in the above embodiment, the registrant may be, for example, a free registrant for the target video streaming service, or a paid subscriber.

[0050] Furthermore, although not explained in the above embodiment, a registrant in this system may be, for example, a registrant for the entire service in a distribution service, or a registrant for specific content or some services within a distribution service.

[0051] Furthermore, the series of processes described above can be executed by hardware or by software. In other words, the functional configurations shown in Figure 3 are merely examples and are not particularly limiting. In other words, it is sufficient for the information processing system to have a function that can execute the series of processes described above as a whole, and the type of functional block used to realize this function is not limited to the examples shown in Figure 3. Furthermore, the location of the functional block is not limited to the examples shown in Figure 3, and can be arbitrary. That is, for example, some or all of the processes executed by the analysis server 1 in the above embodiment may be executed by another server or the like. Furthermore, a single functional block may consist of hardware alone, software alone, or a combination of both.

[0052] Furthermore, in this specification, the step of describing the program to be recorded on the recording medium may be performed not only chronologically according to its order, but also in parallel or individually. In addition, some of the steps described in the above embodiments may be omitted.

[0053] Furthermore, in this specification, the term "system" refers to an overall system composed of multiple devices, means, etc.

[0054] Furthermore, the system configuration shown in the above-described embodiment is illustrative and not limited to it. The number and types of hardware constituting this system are arbitrary, and other arbitrary hardware may be adopted as part of the system, or some of the hardware described above may be omitted.

[0055] Furthermore, some or all of the processes related to the system described above may be executed by other information processing devices managed by a so-called cloud service. If the system employs a so-called cloud server, the various information processing devices constituting the system may employ any hardware configuration different from, for example, the hardware configuration exemplified in the above-described embodiment.

[0056] Furthermore, although not explained in the above embodiment, this system may, for example, freely store various types of information it handles in any database, or it may simply hold them in memory without storing them in a database.

[0057] Furthermore, when a series of processes are executed by software, the programs that make up that software are installed on a computer or other device from a network or storage medium.

[0058] Furthermore, the computer may be one that is built into dedicated hardware. Also, the computer may be one that can perform various functions by installing various programs. In other words, for example, any computer, any mobile terminal such as a smartphone may be freely used as the various hardware in the above-described embodiment. Furthermore, any combination of types and contents of various input and output units may be adopted.

[0059] Furthermore, the recording medium containing such a program may consist not only of a removable media (not shown) provided separately from the main device body to provide the program to the user, but also of a recording medium that is pre-installed in the main device body and provided to the user. Moreover, some or all of the recording medium containing the program may be stored in, for example, another information processing device other than the analysis server 1 in the above-described embodiment.

[0060] The effects and advantages of this embodiment will also be achieved when these other embodiments are adopted. Furthermore, it is possible to combine this embodiment with other embodiments, and other embodiments with each other, as appropriate.

[0061] In summary, the information processing system to which this disclosure applies can take various forms having the following configurations. The aspects of the information processing system to which this disclosure applies are: A first estimation unit (for example, a first estimation unit 102) estimates the first estimated number of users for the target content or broadcast by multiplying the number of registered users for the target content or broadcast by the average number of potential users per registration, A second estimation unit (for example, a second estimation unit 104) estimates the second estimated number of users for the target content or broadcast by multiplying the probability of use occurring for each attribute, including the number of registered users for each attribute and the probability of use occurring for other attributes used simultaneously with the registered user, and then summing them up. A third estimation unit (for example, a third estimation unit 106) estimates the number of potential users who have used the target content or broadcast for each user attribute based on the first estimated number of users and the second estimated number of users, An information processing system equipped with these features would suffice.

[0062] Furthermore, the third estimation unit may use the correction values ​​calculated from the first estimated number of users and the second estimated number of users to estimate the number of potential users who have used the target content or broadcast for each user attribute.

[0063] Furthermore, the correction value may be a value calculated by dividing a first reference value, which is the value obtained by subtracting the number of users who use the target content or broadcast from the first estimated number of users, by a second reference value, which is the value obtained by subtracting the number of users who use the target content or broadcast from the second estimated number of users.

[0064] Furthermore, other aspects of this disclosure include, A method of information processing performed by a computer, The first estimation step involves estimating the first estimated number of users for the target content or broadcast by multiplying the number of registered users for the target content or broadcast by the average number of potential users per registration, The second estimation step involves estimating the second estimated number of users for the target content or broadcast by summing the probabilities of use for each attribute, including the number of registered users for each attribute and the probability of use occurring for other attributes used simultaneously with the registered user, and then summing them up. A third estimation step is performed to estimate the number of potential users who used the target content or broadcast for each user attribute, based on the first estimated number of users and the second estimated number of users. This may also be an information processing method that includes this.

[0065] Furthermore, other aspects of this disclosure include, In an information processing device, The first estimation step involves estimating the first estimated number of users for the target content or broadcast by multiplying the number of registered users for the target content or broadcast by the average number of potential users per registration, The second estimation step involves estimating the second estimated number of users for the target content or broadcast by summing the probabilities of use for each attribute, including the number of registered users for each attribute and the probability of use occurring for other attributes used simultaneously with the registered user, and then summing them up. A third estimation step is performed to estimate the number of potential users who used the target content or broadcast for each user attribute, based on the first estimated number of users and the second estimated number of users. It may also be a program that performs processing that includes [this]. [Explanation of symbols]

[0066] 1. Analysis Server 11 Control Unit 100 ID information management department 102 1st estimation part 104 Second estimation part 106 Third estimation part 120 Correction Value Calculation Unit 108 Output Control Unit 2. Content Server 3. Receiving device 4 Viewer terminals

Claims

1. A first estimation unit estimates the first estimated number of viewers for the target content or broadcast by adding the average number of potential viewers per registration, calculated through mechanical surveys or questionnaire surveys, to the number of registered users for viewing the target content or broadcast. A second estimation unit estimates the second estimated number of viewers for the target content or broadcast by summing the viewing probability for each age and gender attribute, calculated by machine surveys or questionnaire surveys, for each registered user and for each age and gender attribute who are simultaneously viewing with the registered user. A third estimation unit estimates the number of potential viewers who have viewed the target content or broadcast for each attribute of registered users or viewers, based on the first estimated number of viewers and the second estimated number of viewers. An information processing system equipped with the following features.

2. The number of potential viewers who viewed the target content or broadcast is estimated for each viewer attribute by using a correction value calculated by dividing the first baseline value, which is the value obtained by subtracting the number of registered users who viewed the target content or broadcast from the first estimated number of viewers, by the second baseline value, which is the value obtained by subtracting the number of registered users who viewed the target content or broadcast from the second estimated number of viewers. The information processing system according to claim 1.

3. A method of information processing performed by a computer, The first estimation step involves estimating the first estimated number of viewers for the target content or broadcast by adding the average number of potential viewers per registration, calculated through machine surveys or questionnaire surveys, to the number of registered users who view the target content or broadcast. The second estimation step involves estimating the second estimated number of viewers for the target content or broadcast by summing the viewing probabilities for each age and gender attribute, calculated through machine research or questionnaire research, for each registered user and for each age and gender attribute who are simultaneously viewing the content. A third estimation step is to estimate the number of potential viewers who watched the target content or broadcast for each attribute of registered users or viewers, based on the first estimated number of viewers and the second estimated number of viewers. Information processing methods including

4. In an information processing device, The first estimation step involves estimating the first estimated number of viewers for the target content or broadcast by adding the average number of potential viewers per registration, calculated through machine surveys or questionnaire surveys, to the number of registered users who view the target content or broadcast. The second estimation step involves estimating the second estimated number of viewers for the target content or broadcast by summing the viewing probabilities for each age and gender attribute, calculated through machine research or questionnaire research, for each registered user and for each age and gender attribute who are simultaneously viewing the content. A third estimation step is to estimate the number of potential viewers who watched the target content or broadcast for each attribute of registered users or viewers, based on the first estimated number of viewers and the second estimated number of viewers. A program that performs a process that includes this.

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