Information processing system, information processing method and program

Abstract

The purpose is to make it easier to provide useful information for television program production. [Solution] An information processing system is provided in which a data acquisition unit is configured to acquire viewing data, the viewing data having identification data and situation data related to the identification data, the identification data being data for identifying a viewer of a television program, and the situation data being data corresponding to the viewer's viewing of the television program, a pattern data generation unit is configured to generate pattern data based on the viewing data, the pattern data having viewing classification data associated with a plurality of viewing patterns for classifying the situation data, each viewing pattern defining which of a plurality of set periods during the broadcast period of the television program the situation data corresponds to, and the viewing classification data having data corresponding to the number of identification data related to the situation data classified as each viewing pattern.

Description

[Technical Field]

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

[0002] Distributed media content includes a variety of types, such as audio content such as radio and podcasts, and television programs that contain video and audio. For example, data indicating television viewing status for television programs is acquired using various methods (see, for example, Patent Document 1). Such data includes information such as the viewer's viewing start time, viewing end time, and viewing channel, and is useful for analyzing what programs viewers are watching. By accumulating and analyzing such data, it is possible to provide television program producers with information corresponding to viewers' evaluations of television programs, for example. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-192390 Summary of the Invention [Problem to be solved by the invention]

[0004] For analyzing viewer viewing behavior, there are common indicators such as viewer ratings, but even if a certain program has a high viewer rating, this viewer rating alone does not allow for understanding, for example, which parts of the program the viewers watched, and the information that can be obtained regarding viewers' evaluations of the television program is limited. As such, depending on the content of the analysis of viewing behavior, it may be difficult to provide television program producers, etc. with information that is useful for television program production. The same applies to other media content, such as audio content like radio and podcasts. In other words, there is a problem in that depending on the content of the analysis of usage behavior (the viewing behavior of viewers of content or the listening behavior of listeners to content), it may be difficult to provide program producers, etc. with information that is useful for program production.

[0005] The present invention aims to make it easier to provide information that is useful in program production. [Means for solving the problem]

[0006] According to the present invention, there is provided an information processing system for understanding viewing or listening patterns of media content, comprising a data acquisition unit and a pattern data generation unit, wherein the data acquisition unit is configured to acquire usage data, the usage data having identification data and situation data related to the identification data, the identification data being data for identifying a user of the media content, the user corresponding to a viewer or listener of the media content, the situation data being data corresponding to the user's usage status of the media content, the usage status corresponding to the viewing status of the media content or the listening status of the media content, the pattern data generation unit being configured to generate pattern data based on the usage data, the pattern data having usage classification data associated with a plurality of usage patterns for classifying the situation data, each of the usage patterns defining which of a plurality of set periods during the distribution period of the media content the situation data corresponds to the viewing or listening to, and the usage classification data having data corresponding to the number of identification data related to the situation data classified as each of the usage patterns.

[0007] According to the present invention, pattern data is generated based on usage data. Here, the pattern data has usage classification data (data corresponding to the number of identification data related to the situation data classified as each usage pattern) associated with a plurality of usage patterns for classifying situation data. In other words, according to the present invention, usage classification data (data corresponding to the number of identification data) associated with usage patterns can be generated, making it easier to provide information useful for program production. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 shows an example of a system configuration of an information processing system 100 according to the embodiment. [Figure 2]FIG. 2 is a block diagram showing the hardware configuration of the information processing device 1. As shown in FIG. [Figure 3] FIG. 3 is a functional block diagram of the control unit 12 shown in FIG. [Figure 4] FIG. 4 is an explanatory diagram that shows a model of a screen for setting information such as predetermined selection items. [Figure 5] FIG. 5 is an explanatory diagram that schematically shows a detailed screen for setting the aggregation time frame shown in FIG. [Figure 6] FIG. 6 is an explanatory diagram that schematically shows a detailed screen for the block division setting shown in FIG. [Figure 7] FIG. 7 is an example of a graph visualizing the viewing classification data (an example of usage classification data) of the pattern data Dp. [Figure 8] FIG. 8 is an example of a graph visualizing viewer composition data (an example of user composition data) of the pattern data Dp. [Figure 9] FIG. 9 is an example of a graph visualizing the viewed program breakdown data (an example of the used program breakdown data) of the pattern data Dp. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other. Furthermore, each feature can be an invention independently.

[0010] 1. System Configuration of Information Processing System 100 The information processing system 100 according to the embodiment is a system for ascertaining patterns of viewing or listening to media content. Here, in the embodiment, a case where the media content is a television program will be described as an example. That is, in the embodiment, the information processing system 100 is a system for ascertaining patterns of viewing television programs. Note that, as will be described in detail in a modified example below, the information processing system 100 can also be applied when the media content is audio distribution content such as radio or podcasts. In addition, when the media content is a television program as video content, it is predetermined that specific content will be distributed at a specific time. The same is true for radio.

[0011] 1, the information processing system 100 includes an information processing device 1 and a user terminal 2. These are connected to each other so as to enable the exchange of information via a communication network 3 (e.g., the Internet, etc.). Note that the communication network 3 may be a closed network, partly or entirely separated from the Internet.

[0012] In this embodiment, a television program refers to content broadcast by a broadcaster, and is a concept that can include, for example, content with a specific program name, content separated by broadcast time slots, and broadcast channels themselves (e.g., channel units such as Channel 1 and Channel 2). Furthermore, television programs can include content provided by any distribution means, such as terrestrial broadcasting, BS broadcasting, CS broadcasting, cable television broadcasting, and internet distribution. Television programs can be broadcast in any form, such as live broadcasting, recorded broadcasting, rebroadcasting, or on-demand distribution. Furthermore, television programs can be treated as including not only the main programs but also connecting portions between programs, such as commercials, or excluding such connecting portions (although in one embodiment, they are treated as including such connecting portions).

[0013] Each component of the information processing system 100, such as the information processing device 1, has one or more functions (functional units). Each component may be configured as a single device as shown in FIG. 1, or may be configured as multiple independent devices configured to be able to exchange information. The same applies to each functional unit, such as the control unit 12 of the information processing device 1, which will be described later. Each component included in the information processing system 100 will be further described below.

[0014] 1-1. Information processing device 1 2, the information processing device 1 has a communication unit 10, a storage unit 11, a control unit 12, an output unit 13, and an input unit 14, and these components are electrically connected via a communication bus 15 inside the information processing device 1. Also, as shown in FIG. 3, the control unit 12 has a data acquisition unit 121, a data selection unit 122, a data acceptance unit 123, a pattern data generation unit 124, and a data presentation unit 125.

[0015] Each of the above components may be implemented by software or hardware. When implemented by software, various functions can be realized by a CPU executing a computer program. The program may be stored on a non-transitory computer-readable recording medium, provided as a downloadable file from an external server, or implemented by so-called cloud computing, in which a program stored in an external storage unit is read and functions are realized. When implemented by hardware, it can be implemented by various circuits, such as an ASIC, FPGA, or DRP. The embodiments deal with various information and concepts encompassing such information. These are represented by high and low signal values ​​or quantum bits as a binary bit set consisting of 0s or 1s, and communication and calculations can be performed by the above software or hardware aspects. The software may be a general-purpose OS or a dedicated OS.

[0016] The communication unit 10 can employ wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc. The communication unit 10 may employ a configuration in which it is connected to the communication network 3 via wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark) communication, etc. The communication unit 10 may also employ a configuration in which it uses both the wired communication means and wireless communication means described above.

[0017] The storage unit 11 stores various values ​​such as various programs, constants, coefficients, variables, setting values, formulas, and tables of the information processing device 1 executed by the control unit 12. The storage unit 11 also stores data acquired by communicating with the user terminal 2, for example. The storage unit 11 may be, for example, a storage device such as a solid state drive (SSD), or a storage medium such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to program calculations. The information processing device 1 may also use an external storage unit (for example, an external storage medium, a cloud, etc.) in addition to the storage unit 11.

[0018] The control unit 12 is configured to execute information processing of the information processing device 1. The control unit 12 can be configured, for example, by a central processing unit (CPU), and in the embodiment, the control unit 12 is an example of a processor capable of executing programs related to the operations (steps) of the information processing device 1, which will be described later. The control unit 12 realizes various functions related to the information processing device 1, for example, by reading out programs stored in the storage unit 11. Furthermore, the information processing of the software in the information processing device 1 is realized, for example, by the control unit 12 as hardware processing the various programs stored in the storage unit 11.

[0019] The output unit 13 is, for example, a display unit of the information processing device 1. The output unit 13 may be included in the housing of the information processing device 1 or may be externally attached. The output unit 13 displays a graphical user interface (GUI) screen that can be operated by a user. The output unit 13 may be, for example, a display device such as a CRT display, a liquid crystal display, an organic EL display, a plasma display, or an electronic paper display, as well as a display device such as an illuminable light or a projector. It is optional whether or not the information processing device 1 includes the output unit 13. For example, the output of the information processing device 1 may be displayed on a display unit located at a location independent of the location where the information processing device 1 is installed. The output unit 13 may also have a device that outputs audio.

[0020] The input unit 14 is configured to receive, for example, an operation input made by an administrator of the information processing device 1. The input unit 14 may be included in the housing of the information processing device 1 or may be attached externally. For example, the input unit 14 may be a touch panel, a switch button, a mouse, a keyboard, a camera, a scanner, or the like. It is optional whether or not the information processing device 1 includes the input unit 14. For example, the information processing device 1 may receive an operation input to the information processing device 1 via an information processing terminal located at a location separate from the location where the information processing device 1 is installed.

[0021] 1-2. User terminal 2 1 is an information processing device (for example, a personal computer, a smartphone, or a tablet terminal) used by a person who receives information for understanding a viewing pattern (an example of a usage pattern), and is a terminal that can access the information processing device 1. The user terminal 2 is capable of information processing such as inputting and outputting various types of data to and from the information processing device 1. Note that the user terminal 2 is not essential to the information processing system 100, and the information processing system 100 may be completed with only the information processing device 1. However, in the embodiment, a form in which a user uses a user terminal 2 independent of the information processing device 1 will be described as an example.

[0022] 2. Functional configuration The functional configuration of the information processing device 1 according to this embodiment will be described with reference to Fig. 3. Information processing by software stored in the storage unit 11 is specifically realized by the control unit 12, which is an example of hardware, and each functional unit included in the control unit 12 is executed.

[0023] 2-1. Data Acquisition Unit 121 The data acquisition unit 121 is configured to acquire various types of data transmitted from the user terminal 2. The data acquisition unit 121 is also configured to acquire various types of data received from the input unit 14. Furthermore, the data acquisition unit 121 can also acquire data that is pre-stored in the storage unit 11, for example. Furthermore, the data acquisition unit 121 may be configured to collect (acquire) various types of data via the communication unit 10 from any external server or the like connected to the Internet.

[0024] The data acquisition unit 121 is configured to acquire viewing data Dv (an example of usage data). The viewing data Dv, which is an example of usage data, is data that may include identification data and situation data, which will be described later. The data acquisition unit 121 is also configured to acquire program metadata Dm for multiple television programs (see, for example, the program guide in the lower part of FIG. 9). These data will be described later in "3. Description of Data in the Information Processing System 100." The function of the data acquisition unit 121 may include a function to receive similar data and transmit it to another functional unit of the control unit 12, a function to preprocess the received data to create desired data and then transmit it to another functional unit, or both of these functions. In other words, acquisition may not only involve simply acquiring data, but may also include processing to modify the data through preprocessing, etc.

[0025] 2-2. Data selection unit 122 The data selection unit 122 is configured to select the viewing data Dv according to predetermined selection items. The data selection unit 122 then selects the viewing data Dv according to the selection items, which causes the pattern data generation unit 124, described later, to generate pattern data Dp based on the selection items. In an example embodiment, the selection items (selection data Dd) include at least one of a region, a counting period, a counting time frame, a distributor, and a counting category. For example, the region in the selection items (selection data Dd) corresponds to the region where a television program or the like was viewed if the media content is video content such as a television program, or corresponds to the region where the program was listened to if the media content is audio content (e.g., radio, podcasts, etc.) described in a modified example below. Furthermore, if the media content is video content such as a television program, the distributor is, for example, a broadcasting station.

[0026] 2-3. Data reception unit 123 The data receiving unit 123 is configured to receive the setting period determination data Ds. In one embodiment, the data receiving unit 123 is configured to receive the setting period determination data Ds, and is capable of adjusting the content of the generated pattern data Dp to content that meets the user's request.

[0027] 2-4. Pattern data generation unit 124 The pattern data generation unit 124 is configured to generate pattern data Dp based on the viewing data Dv. In one example of an embodiment, the pattern data generation unit 124 generates the pattern data Dp by performing information processing based on the set period determination data Ds on the viewing data Dv selected by the data selection unit 122. The pattern data Dp includes three perspectives (viewing classification data, viewer composition data, and viewed program breakdown data), which will be explained later in "3. Description of Data in Information Processing System 100."

[0028] 2-5. Data presentation section 125 The data presentation unit 125 is configured to present an object Ob corresponding to the pattern data Dp. For example, the data presentation unit 125 outputs data corresponding to the object Ob to the display unit of the information processing device 1 or the display unit of the user terminal 2. The object Ob has objects Ob1 to Ob3 corresponding to the three aspects of the pattern data Dp described above. The object Ob is presented so that the user can grasp the pattern data Dp analyzed by the information processing device 1 as visual information. Note that the presentation mode in the data presentation unit 125 is premised on visual information, but audio information may be added. Furthermore, the data presentation unit 125 can present, for example, a display screen for receiving various data from the user (for example, screens W1 to W3 (see FIGS. 4 to 6) for receiving data in the data receiving unit 123). As a result, the screen is displayed on the display unit of the user terminal 2.

[0029] 3. Data Description in Information Processing System 100 Here, various types of data used in the information processing system 100 will be described.

[0030] 3-1. Viewing data Dv (an example of usage data) The viewing data Dv includes identification data and situation data related to the identification data. Furthermore, the viewing data Dv includes demographic data of the viewer (an example of a user). Viewing data Dv, which is an example of usage data, can be composed of, for example, data collected from a measuring device installed in a television receiver, data provided by a broadcasting station, data acquired from a data collection company, or a combination of these data. The data format of the viewing data Dv is not particularly limited, but for example, CSV format data can be used. Furthermore, the viewing data Dv can be acquired by any method, such as real-time collection, periodic collection by batch processing, or on-demand collection.

[0031] 3-1-1. Identification data of viewing data Dv Identification data is data for identifying viewers of television programs. Identification data is data for uniquely identifying individual viewers (users), and examples of identification data that can be used include viewer IDs (user IDs), household IDs, personal IDs, device IDs, and account IDs. Identification data can be composed of numbers, character strings, symbols, or combinations of these. Furthermore, identification data may be managed in an encrypted, hashed, or pseudonymized format, and may, of course, be anonymized for privacy reasons.

[0032] 3-1-2. Viewing data Dv status data The situation data is data corresponding to the viewing situation (an example of usage situation) of a television program by a viewer (an example of a user). The situation data is data associated with identification data, and in an example of an embodiment, the viewing situation of an arbitrary viewing channel (an arbitrary broadcast station) is indicated. Here, in the example of an embodiment, for the sake of convenience of explanation, it is described as if an arbitrary one viewing channel and an arbitrary one broadcast station correspond one-to-one and there is no strict distinction between the two, but this is not necessarily the case. This is because an arbitrary one broadcast station may have multiple viewing channels.

[0033] The situation data indicates the transition of viewing situations over time. In one embodiment, the situation data includes data for specifying the viewing situations (usage situations) of multiple television programs by a viewer (user). That is, the viewing situations of multiple television programs are indicated at predetermined time intervals (1 minute in this example), such as "viewing channel A at a certain time (e.g., 6:00 PM)" and "viewing channel B one minute later (6:01 PM)."

[0034] 3-1-3. Demographic Data Demographic data is data that represents attribute information of viewers (users). Demographic data can include information such as age, gender, occupation, residential area, household composition, income level, educational background, or hobbies and preferences. Age can be expressed, for example, as a specific age value, an age group (teens, twenties, etc.), or an age category (minor, adult, etc.). Gender may be expressed as, for example, male, female, or some other category. The residential area can be expressed, for example, by prefecture, city, town, or village, or by regional division (Kanto region, Kansai region, etc.). Household structure can be expressed as categories such as single-person households, married couple households, households with children, three-generation households, etc. These demographic data are used to generate viewer structure data of the pattern data Dp, and are used to understand the characteristics of viewer segments in analyzing viewing patterns.

[0035] 3-2. Pattern data Dp In one embodiment, the pattern data Dp has three components: viewing classification data Dp1 (an example of usage classification data), viewer composition data Dp2 (an example of user composition data), and viewed program breakdown data Dp3 (usage program breakdown data). These are data sets for multifaceted analysis of viewer viewing patterns, and can provide comprehensive viewer analysis information to television program producers, for example. These can be used independently for analysis, or can be correlated with each other for combined analysis. They can be used to understand changes and trends in viewing patterns.

[0036] In the exemplary embodiment, the pattern data Dp is described as including all three of the viewing classification data Dp1, the viewer composition data Dp2, and the viewed program breakdown data Dp3, but is not limited to this. For example, the viewing classification data Dp1 may be required, and either the viewer composition data Dp2 or the viewed program breakdown data Dp3 may be included. Alternatively, the pattern data Dp may include only the viewing classification data Dp1.

[0037] 3-2-1. Viewing classification data (an example of usage classification data) An example of a visualization of viewing classification data Dp1 is object Ob1 shown in Fig. 7. In this way, viewing classification data Dp1 can be visualized in a graph format with the number of identification data (or a value corresponding thereto) on the vertical axis and time transition on the horizontal axis. This graph (object Ob1) allows users to intuitively grasp the changes over time in each viewing pattern, peak times, trends in the increase and decrease in the number of viewers, etc. That is, the viewing classification data Dp1 is data associated with a plurality of viewing patterns (see viewing patterns PT1 to PT7 in FIG. 7) for classifying situation data. The viewing classification data Dp1 corresponds to the number of identification data related to situation data classified into each of the viewing patterns PT1 to PT7.

[0038] Note that the viewing classification data "corresponding to the number of identification data" does not necessarily mean that the viewing classification data represents the number of identification data itself, but may also include the case where the viewing classification data is a ratio based on the number of identification data, or a statistical value (e.g., average, median, standard deviation, etc.) based on the number of identification data. The viewing classification data can be expressed numerically as any numerical expression, such as a real number, integer value, ratio, exponential expression, logarithmic expression, etc.

[0039] The viewing classification data indicates a transition (a temporal transition) during each set period. The viewing classification data is expressed as a transition at a predetermined time interval, for example. The predetermined time interval (seconds) may be, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, or 300 seconds, and may be specified within the range of the two values ​​exemplified here.

[0040] Next, the viewing patterns and set periods in the pattern data Dp will be described in detail. The viewing patterns PT1 to PT7 in the pattern data Dp define which of a plurality of set periods during the broadcast period of a television program the situation data corresponds to. Note that in the embodiment, the media content is a television program as video content, so the term "broadcast period" is used here, but since the media content also includes audio content as will be explained in a modified example below, the broadcast period may also be referred to as a "delivery period" as a more comprehensive expression. A plurality of set periods are defined by a plurality of divided periods (e.g., first half, middle, second half, and last half). In other words, the divided periods are components of a period that define the set period.

[0041] Here, the set period, divided period, individual period, and total period are defined. The period of a television program to be analyzed (corresponding to the time slots for the aggregation of selection items, described later) is defined as the broadcast period (e.g., 18:10-19:00). In addition, the smallest time divisions into which the broadcast period is arbitrarily divided are defined as divided periods (e.g., first half, middle, second half, end). Each divided period is a period within the broadcast period of a television program, but is shorter than the broadcast period. The divided periods (e.g., first half, middle, second half, and end) are independent of each other. The divided periods do not necessarily have to be independent; they may overlap to some extent, but it is preferable that the overlap be small (e.g., 10% or less of the length of each period). In the embodiment, the set periods (that is, the periods corresponding to each viewing pattern) include individual periods and total periods that are combinations of individual periods. The independent period is a period in which the divided period itself is a division. The total period is the period of a segment that is formed by combining two or more divided periods. For example, the set period of viewing pattern PT1, which will be described later, is made up of all periods (divided periods) from the first half to the last, and corresponds to the total period. In this way, in the embodiment, since there are individual periods and total periods, the user can arbitrarily specify the granularity of the set period, and the pattern data generation unit 124 classifies the viewing patterns based on the specified set period.

[0042] Next, seven viewing patterns will be described as an example of an embodiment. (1) Viewing pattern PT1: This is a pattern in which the entire period (first half, middle, second half, and end) was viewed. (2) Viewing pattern PT2: This is a pattern in which the first half was viewed. (3) Viewing pattern PT3: This is the pattern in which the middle part of the program was viewed. (4) Viewing pattern PT4: This is the pattern in which the second half was viewed. (5) Viewing pattern PT5: This is the last pattern viewed. (6) Viewing pattern PT6: This is a zapping pattern. (7) Viewing pattern PT7: This is a pattern in which all parts except the last part (first half, middle, and second half) were viewed.

[0043] Viewing pattern PT1 is when the entire program is viewed from the first half to the end. The set period of viewing pattern PT1 consists of everything from the first half to the end. The set period of viewing pattern PT1 is defined as the total period. In other words, the set period of viewing pattern PT1 is the total period, and is a period that is made up of a combination of divided periods that are components of multiple divided periods (here, the first half, middle, second half, and end). More specifically, the set period of viewing pattern PT1 is a period that is made up of a combination of all divided periods (the first half, middle, second half, and end).

[0044] Viewing pattern PT2 is based on the premise that the first half of a program is viewed. The set period for viewing pattern PT2 is defined as a single period. Note that viewing pattern PT2 is not limited to cases where the first half is viewed and the entire program from the middle onward is not viewed. For example, in the embodiment, when the first half is viewed and the middle or second half is viewed, the viewing pattern is not identified as a viewing pattern other than the seven classifications described above, but is processed as belonging to viewing pattern PT2. In other words, the viewing pattern is processed as belonging to the period viewed first (the first half). By processing in this manner, it is possible to prevent the number of viewing patterns from becoming enormous, making the analysis results of information processing system 100 complicated and difficult for users to grasp and understand.

[0045] Viewing pattern PT3 is based on the premise that the first half is not viewed, and the middle part is viewed. The set period of viewing pattern PT3 is defined as a single period. Note that with regard to viewing pattern PT3, if the first half is not viewed, the middle part is viewed, and at least one period from the second half onwards is viewed, this is treated as belonging to viewing pattern PT3.

[0046] Viewing pattern PT4 is based on the premise that the second half will be viewed. The set period for viewing pattern PT4 is defined as a single period. Note that with regard to viewing pattern PT4, even if the first half is not viewed, the middle part is not viewed, the second half is viewed, and the final part is viewed or not, it is processed as belonging to viewing pattern PT4.

[0047] Viewing pattern PT5 is a case where the viewer does not view the first half to the second half, but views only the last half. The set period of viewing pattern PT5 is defined as a single period.

[0048] Viewing pattern PT6 is, for example, a viewing pattern in which a viewer frequently switches between a plurality of television programs, and is a pattern in which none of the first half, middle half, second half, or last half is specified as having been viewed.

[0049] Viewing pattern PT7 is a case where the first half to the second half are viewed, and only the last half is not viewed. The set period of viewing pattern PT7 is made up of the period from the first half to the second half. The set period of viewing pattern PT7 is defined as a total period, similar to viewing pattern PT1. In other words, the total period of viewing pattern PT7 is a period made up of a combination of divided periods (here, the first half, middle, second half, and last half) that are components of multiple divided periods (here, the first half, middle, second half, and last half).

[0050] It should be noted that the seven viewing patterns and the corresponding set periods given here as examples are merely examples, and may be omitted or added as appropriate.

[0051] The pattern data Dp also includes viewer composition data Dp2. The viewer composition data Dp2 is associated with a plurality of viewing patterns and includes the demographic data described above. An example of the visualized content of the viewer composition data Dp2 is the object Ob2 shown in FIG. 8. Furthermore, the pattern data Dp includes viewed program breakdown data Dp3. An example of a visualization of the viewed program breakdown data Dp3 is the object Ob3 shown in FIG. 9. The viewed program breakdown data Dp3 is based on situation data classified as one of a plurality of viewing patterns. The viewed program breakdown data Dp3 is data indicating to which of a plurality of television programs the viewing related to the situation data has shifted throughout a set attention period. The set attention period is a period arbitrarily designated by the user and corresponds to any set period among a plurality of set periods.

[0052] 3-2-2. Viewer configuration data Dp2 (an example of user configuration data) The viewer configuration data Dp2 will be described with reference to FIG. The viewer composition data Dp2 is data indicating the distribution of viewer attributes related to demographic data (in one embodiment, age and gender). The viewer composition data Dp2 is data indicating the age distribution for each viewing pattern, as shown in FIG. 8, for example, and makes it possible to grasp trends such as a high prevalence of teenage males in the viewing pattern of viewing the first half. The viewer composition data Dp2 can also be used for multidimensional analysis combining multiple demographic items (e.g., regional distribution of women in their 20s, analysis of viewing time slots for high-income households, etc.). The representation format of the viewer composition data Dp2 is not limited to bar graphs, and other formats such as pie charts may also be used.

[0053] 3-2-3. Viewing program breakdown data Dp3 (example of viewing program breakdown data) The viewed program breakdown data Dp3 will be described with reference to FIG. 9 and other figures. Here, any of the multiple set periods described above is defined as the attention set period. In the example of FIG. 9, the attention set period is a set period corresponding to viewing pattern PT4, which is the "second half." The attention set period is a period specified by the user. In other words, in this case, the user wants to analyze the "second half."

[0054] The viewed program breakdown data Dp3 is based on situation data classified as one of the viewing patterns PT1 to PT7 (viewing pattern PT4 in this case). The viewed program breakdown data Dp3 indicates which of the multiple television programs the viewing related to this situation data has shifted (temporally) to throughout the attention setting period (the latter half in this case). The viewed program breakdown data Dp3 is data for analyzing the viewer's channel shifting behavior, or so-called channel zapping behavior, in detail. For example, it is possible to grasp the viewing fluidity, such as whether a viewer who was watching television program L2 on station L in the first half of the program shifted to program M1 or program M2 on station M, program N1 on station N, program P2 on station P, or program Q1 on station Q in the middle of the program. In an example embodiment, the viewed program breakdown data Dp3 may be expressed as a time-series graph, as shown in the upper part of FIG. 9, but is not limited thereto. In an example embodiment, the viewed program breakdown data Dp3 is accompanied by program metadata Dm and presented together with the program metadata Dm.

[0055] 3-3. Set period determination data Ds The set period determination data Ds includes at least one of the length of each set period, the start timing of each set period, the end timing of each set period, and the number of set periods. In an example embodiment, as shown in FIG. 6 , the user interface allows the user to specify equal divisions (in the embodiment, 1 / 3 or 1 / 4) or the last 5-minute division. For example, if an equal division of 1 / 3 is specified and the last division is not specified, the lengths, start timings, and end timings of the first half (viewing pattern PT2), middle part (viewing pattern PT3), and second half (viewing pattern PT4) of the set period are respectively determined. The number of set periods is also determined to be three. The set period determination data Ds allows the pattern data generation unit 124 to flexibly set the analysis target period in response to user requests.

[0056] The number of set periods can be set from 2 to any number of periods. For example, 2 periods (first half, second half), 3 periods (first half, middle, second half), 4 periods (first half, middle, second half, last), or more detailed period settings are also possible.

[0057] Furthermore, the set period determination data Ds can be divided by a user's own designation, as shown in Fig. 6. In other words, it is possible to customize a unique period setting. In this case, as shown in Fig. 6, the period setting is accepted through an input box object b1 for inputting each set period.

[0058] 3-4. Program metadata Dm The program metadata Dm is data including attribute information about multiple television programs, and is associated with the pattern data Dp. As shown in the lower part of Fig. 9, the program metadata Dm may include information such as the program name, program genre, broadcasting station name, broadcasting channel, broadcast start time, broadcast end time, program duration, cast information, program summary, target audience age, production company, and rebroadcast information.

[0059] The program metadata Dm may include additional information that describes the characteristics of the program (e.g., whether it is a live broadcast or a recorded broadcast, whether it has subtitles or audio description, actual viewer ratings, program evaluation score, number of mentions on social media, etc.). The program metadata Dm can be managed and displayed in a program guide format as shown in the lower part of Figure 9, and is used to analyze the correlation between the results of viewing pattern analysis and program attributes. By using the program metadata Dm in conjunction with the viewed program breakdown data Dp3, it is possible to grasp not only viewing fluidity (tendencies to move between programs), but also analysis based on program attributes (for example, grasping trends to move from dramas to variety shows).

[0060] 4. Information Processing in Information Processing System 100 The program of the information processing system 100 according to the embodiment executes the steps (information processing method) described below. The information processing in the information processing system 100 includes a data acquisition step, a data reception step, a data selection step, a pattern data generation step, and a data presentation step. Note that the steps may be executed in parallel as necessary, or some steps may be omitted.

[0061] 4-1. Information processing by the data acquisition unit 121 (data acquisition step) The data acquisition unit 121 acquires the viewing data Dv. The acquisition method may be a method of directly acquiring the viewing data Dv from the user terminal 2 via a network, a method of reading out the viewing data Dv pre-stored in the storage unit 11, or a method of acquiring the viewing data Dv from an external server.

[0062] The data acquisition unit 121 acquires program metadata Dm for multiple television programs. The source from which the program metadata Dm is acquired is not particularly limited, but may be, for example, an external server that holds a program information database. Alternatively, a user of the information processing system 100 may create program metadata Dm by himself / herself by referring to data in the program information database, a data feed provided by a broadcaster, etc., store the program metadata Dm in advance in the storage unit 11, and then read it out.

[0063] The process of acquiring the above-described viewing data Dv and the like by the data acquisition unit 121 may be executed as a periodic batch process, or may be executed as an on-demand process in response to a user request.

[0064] The data acquisition unit 121 may have a function to perform preprocessing to convert viewing data Dv provided in different formats into a unified format. Furthermore, the data acquired by the data acquisition unit 121 may contain missing values, abnormal values, etc. Therefore, the data acquisition unit 121 may have a function to perform preprocessing to correct or remove such missing values, abnormal values, etc. as necessary.

[0065] In this way, the data acquisition unit 121 has the role of acquiring viewing data Dv and program metadata Dm having a quality and format suitable for subsequent processing, and providing them to other functional units of the control unit 12.

[0066] 4-2. Information processing by the data reception unit 123 (data reception step) The data accepting unit 123 executes a process of accepting selection items (selection data Dd) and set period determination data Ds. Here, as shown in FIGS. 4 to 6, the data presenting unit 125 can display screens W1 to W3 (object screens) as user interfaces. Through these screens, the user can specify selection items for the viewing data Dv and can also specify a set period for the viewing data Dv. In an example of an embodiment, the data accepting unit 123 accepts the selection items (selection data Dd) and set period determination data Ds through such a user interface.

[0067] 4-2-1. Acceptance of selection items Here, the reception of selection items will be described. As shown in FIG. 4, the selection items include viewing area (in this example, it is area-based, and Z area is specified), aggregation period (in this example, April 4, 2024 is specified), aggregation time frame (in this example, time section selection is specified, 18:10 to 19:00 is specified as the time slot, and Thursday is specified as the day of the week), broadcasting station (in this example, L station is specified), and aggregation section (in this example, there are daily and period averages, but daily is specified). By appropriately setting these selection items, the user can specify the range of data to be analyzed. The viewing area, aggregation period, and broadcasting station can be specified through the screen W1 shown in FIG. 4.

[0068] When object B1 shown in FIG. 4 is selected (for example, by a selection operation such as clicking a mouse or tapping a touch panel), a screen W2 for specifying the aggregation time frame shown in FIG. 5 is displayed. The aggregation time frame can be specified by program selection or time segment selection. When program selection is selected, a program name can be specified, and when time segment selection is specified, a time period is specified. In this example, the user has specified time segment selection and 18:10 to 19:00 as the time period. Since April 4, 2024 has already been specified as the aggregation period, Thursday is automatically selected as the day of the week. On the other hand, if the aggregation period is, for example, April 1 to April 30, 2024, viewing data Dv for a total of four days, namely, April 4, 11, 18, and 25, 2024, which are Thursdays, is targeted. When input is completed through screen W2 shown in FIG. 5, object B11 is selected to complete the setting, and to cancel the input, object B12 is selected.

[0069] 4-2-2. Acceptance of setting period determination data Ds Next, the reception of the set period determination data Ds will be described. When the object B2 shown in FIG. 4 is selected, a screen W3 for setting the set period shown in FIG. 6 is displayed. As shown in FIG. 6, when setting the set period, it is possible to select a designation method such as equal division (in this example, 1 / 3 or 1 / 4) or division by specifying it yourself. For example, if division into 1 / 3 is specified, the time period selected for the time segment is divided into 1 / 3 each (i.e., first half, middle, and second half), and each is set as a period (divided period). When dividing by specifying it yourself, the user specifies it themselves through the input box object b1. In one embodiment, dividing the last 5 minutes is done in the form of a check box, and by specifying this, the last 5 minutes of a TV program are also set as a period (divided period). When the last 5 minutes are specified, 5 minutes are subtracted from the time slot selected for the time segment, and the time is then equally divided (here, 1 / 3 division). In the embodiment, a case where the last 5 minutes are specified as the period is described as an example, but the present invention is not limited to this, and a value other than 5 minutes may be used. When input is completed through the screen W3 shown in FIG. 6, the object B21 is selected to complete the setting, and when input is canceled, the object B22 is selected.

[0070] 4-3. Information processing by the data selection unit 122 (data selection step) The data selection unit 122 executes a process of selecting the viewing data Dv acquired from the data acquisition unit 121 in accordance with predetermined selection items accepted by the data acceptance unit 123. In other words, the data selection unit 122 extracts data that matches the conditions from the viewing data Dv based on the selection items designated (set) by the user, and appropriately narrows down the amount of data to be processed by the pattern data generation unit 124, enabling efficient and purposeful analysis.

[0071] 4-4. Information Processing of Pattern Data Generator 124 (Pattern Data Generation Step) The pattern data generation unit 124 generates pattern data Dp based on the viewing data Dv selected by the data selection unit 122. The pattern data generation unit 124 generates the pattern data Dp using a set period based on the set period determination data Ds. In other words, the pattern data generation unit 124 performs data processing using the set period determination data Ds on the viewing data Dv selected by the data selection unit 122, and generates the pattern data Dp.

[0072] 4, when object B3 on screen W1 is selected, pattern data generation unit 124 automatically starts data processing using set period determination data Ds on viewing data Dv selected by data selection unit 122. As a result, viewing classification data Dp1, viewer composition data Dp2, and viewed program breakdown data Dp3 are generated.

[0073] As explained in Section 3-2-1, viewing pattern PT2 is not limited to cases where the first half is viewed but the middle or latter half is not viewed at all; cases where the first half is viewed and the middle or latter half is viewed are also processed as belonging to viewing pattern PT2. In other words, pattern data generation unit 124 classifies each piece of situation data into one of multiple viewing patterns PT1 to PT7 based on the set period to which the viewing start timing for each piece of situation data belongs. This makes it possible to avoid an enormous number of viewing patterns and to avoid the analysis results becoming complicated and difficult for users to grasp and understand.

[0074] 4-4-1. Generation process of viewing classification data Dp1 The pattern data generation unit 124 classifies the situation data of the viewing data Dv selected based on the selection items into a plurality of viewing patterns PT1 to PT7, and can generate the viewing classification data Dp1 by tallying up the number of identification data (number of viewers) corresponding to the situation data classified into each viewing pattern. Note that the pattern data generation unit 124 analyzes and processes the viewing situation at predetermined time intervals (for example, one-minute intervals), and tally up the number of identification data (number of viewers) for each viewing pattern at each time.

[0075] Here, the pattern data generation unit 124 classifies each piece of situation data into one of a plurality of viewing patterns based on the viewing time length during the set period (an example of usage time length). For example, the pattern data generation unit 124 classifies the situation data into viewing patterns related to divided periods with relatively short viewing times (viewing patterns PT2 to PT5) and viewing patterns related to total periods with relatively long viewing times (viewing patterns PT1 and PT7). In this way, by performing an analysis according to the viewing time length, it becomes possible to analyze the viewer's viewing behavior in more depth.

[0076] As shown in Fig. 7, the viewing classification data Dp1 is expressed in a graph format with the number of IDs (number of identification data) on the vertical axis and time on the horizontal axis. The viewing classification data Dp1 visualized in a graph format is the object Ob1. In this graph, the time transition of the number of viewers corresponding to each of the viewing patterns PT1 to PT7 is expressed as a stacked graph. For example, viewers who fit viewing pattern PT1 (viewing the entire program) tend to peak around 18:35 and then decline. Similarly, peaks at different times and characteristic transition patterns are visualized for viewing patterns PT2 (viewing the first half), PT3 (viewing the middle), PT4 (viewing the second half), PT5 (viewing the end), and PT6 (zapping). Note that for ease of explanation, PT7 (viewing the entire program except for the end) is not shown in Figure 7.

[0077] 4-4-2. Generation process of viewer configuration data Dp2 The pattern data generator 124 generates viewer composition data Dp2 using demographic data included in the viewing data Dv. The viewer composition data Dp2 is data indicating the attribute distribution of viewers classified into each of the viewing patterns PT1 to PT7.

[0078] As shown in FIG. 8, the viewer composition data Dp2 is displayed as a bar graph showing, for example, the composition ratio for each viewing pattern. The viewer composition data Dp2 visualized in a graph format is the object Ob2. Each bar graph shows the viewer composition by gender and age group (C: children, T: teenagers, M1: teenage men, M2: men in their 20s, M3: men in their 30s, M4: men in their 40s, F1: teenage women, F2: women in their 20s, F3: women in their 30s, F4: women in their 40s).

[0079] The pattern data generation unit 124 first extracts the identification data classified into each viewing pattern and refers to the demographic data corresponding to each identification data. The pattern data generation unit 124 then aggregates the age and gender distribution within each viewing pattern and calculates the composition ratio. By generating the viewer composition data Dp2, television program producers and the like can specifically grasp which parts of a program appeal to which age and gender demographics, and can use this data to optimize program composition according to the target demographic.

[0080] 4-4-3. Generation process of program viewing breakdown data Dp3 The pattern data generation unit 124 generates viewed program breakdown data Dp3 based on the attention set period. The viewed program breakdown data Dp3 is data indicating what television programs a viewer classified as having one of the plurality of viewing patterns PT1 to PT7 is watching throughout the attention set period, in other words, data indicating viewing fluidity. Note that, on the user interface screens such as Fig. 4, the display of an object for the user to input the attention setting period is omitted, but it would be preferable if the user could input it on, for example, screen W1 of Fig. 4 or the screen of Fig. 9. Note that in this example, the attention setting period is the latter half corresponding to viewing pattern PT4 (watching in the latter half).

[0081] The pattern data generation unit 124 extracts the identification data and situation data classified as the viewing pattern PT4 corresponding to the attention setting period (the latter half in this example). Next, the situation data is analyzed to determine which broadcasting station and program each viewer is viewing along the time axis, and the transition of the number of identification data (number of viewers) is tallied.

[0082] 9, the viewed program breakdown data Dp3 is expressed as a time-series graph. The viewed program breakdown data Dp3 visualized in a graph format is an object Ob3. The example of FIG. 9 shows the viewing status of viewers classified into viewing pattern PT4 (viewing in the latter half) for station L on Thursday, April 4, 2024. The vertical axis is the number of IDs (number of identification data). The horizontal axis represents the time corresponding to the broadcast period (18:00-19:10), and shows how the number of viewers classified as viewing pattern PT4 at station L on Thursday, April 4, 2024 has changed for each broadcasting station (station M, station P, station N, station L, station O, OFF (non-terrestrial broadcasting)).

[0083] As shown in the lower part of Fig. 9, the program metadata Dm is displayed together with the viewed program breakdown data Dp3. In this way, by combining the program metadata Dm and the viewed program breakdown data Dp3, it becomes easier to analyze the correlation between the program content and the viewer's behavior.

[0084] 4-5. Information processing of the data presentation unit 125 (data presentation step) The data presentation unit 125 is configured to present objects Ob corresponding to the pattern data Dp. The objects Ob include an object Ob1 (see FIG. 7) corresponding to the viewing classification data Dp1, an object Ob2 (see FIG. 8) corresponding to the viewer configuration data Dp2, and an object Ob3 (see FIG. 9) corresponding to the viewed program breakdown data Dp3.

[0085] As shown in FIG. 4, on the screen W1, the user can select to present an object B4 corresponding to a pattern data graph, an object B5 corresponding to a viewer configuration, or an object B6 corresponding to a breakdown of programs viewed. When object B4 is selected, the data presentation unit 125 presents object Ob1 shown in FIG. 7, and displays the time transition for each viewing pattern in a stacked graph. When object B5 is selected, object Ob2 shown in FIG. 8 is presented, and the attribute composition of the viewer is displayed using a bar graph. When object B6 is selected, object Ob3 shown in FIG. 9 is presented, and information regarding the viewing fluidity between programs is displayed using a time series graph.

[0086] The data presentation unit 125 receives the pattern data Dp generated by the pattern data generation unit 124, and displays an appropriate object Ob in accordance with the user's selection on the display unit of the user terminal 2 or the output unit 13 of the information processing device 1. This allows users, such as television program producers, to intuitively understand the viewing pattern analysis results according to their own analysis purposes and use them in program production.

[0087] 5. Functions and Effects of the Embodiments The information processing system 100 can classify and visualize detailed viewing behavior, such as "which parts of a program viewers watched," into multiple viewing patterns (PT1-PT7), which is difficult to grasp using conventional indicators such as audience ratings. Specifically, it is possible to analyze viewer behavior based on multiple viewing patterns, such as viewing the entire program, viewing the first half, viewing the middle part, viewing the second half, viewing the end, zapping, and viewing the entire program except for the end. In particular, by visualizing the temporal progression of each viewing pattern as a stacked graph using the viewing classification data Dp1, it is possible to grasp what viewing behaviors occurred during which time slots of a program. This makes it possible to grasp detailed viewing trends that are not visible using simple indicators such as conventional audience ratings, such as the tendency for viewers to drop out shortly after a program starts or the tendency for viewership to increase toward the end of a program.

[0088] Furthermore, by understanding the demographic distribution of age, gender, etc. for each viewing pattern using the viewer composition data Dp2, it is possible to analyze trends such as "there are more young people in the first half viewing patterns" or "there is a bias towards a specific age group in the entire viewing pattern." This allows television program producers to optimize program composition according to the target demographic.

[0089] Furthermore, the viewing program breakdown data Dp3 makes it possible to grasp the viewing fluidity of viewers classified into specific viewing patterns, i.e., which programs viewers move to during a set attention period. This allows for a detailed analysis of viewers' zapping behavior and tendency to switch to other programs, which can be used to improve program scheduling and program content.

[0090] In this way, according to the information processing system 100 of the embodiment, it is possible to effectively provide television program producers and the like with multifaceted and detailed viewing analysis information that was difficult to provide using a single indicator such as the conventional audience rating, and to provide information that is useful for television program production.

[0091] 6. Variations In the embodiment, the media content is described as a television program as video content, but is not limited to this. The video content may be, for example, a video distribution service program via the Internet. Furthermore, the media content is not limited to video content, but may also be audio content such as radio or podcasts.

[0092] When the media content is audio content, the terms described in the embodiments can be interpreted as follows. Viewers can be read as listeners, which are an example of users. The viewing situation can be read as the listening situation, which is an example of the usage situation. Viewing data can be read as listening data, which is an example of usage data. The viewing classification data can be read as listening classification data, which is an example of usage classification data. The term "viewing pattern" can be interpreted as "listening pattern," which is an example of a usage pattern. The viewed program breakdown data can be read as listened-to program breakdown data, which is an example of used program breakdown data. Viewing time can be read as listening time, which is an example of usage time. Additionally, viewer ratings can be read as listener ratings.

[0093] If the media content is a radio program, the listening data may consist of, for example, data collected from internet radio server logs, data collected from handheld measuring devices (e.g., portable people meters), data transmitted from car radio systems, data recorded by listeners (e.g., listener interviews or survey results), or any combination thereof. In the case of podcasts, listening data can be obtained from the server log of the distribution source (distribution platform), the playback log of the application, or the usage history of the streaming service.

[0094] For on-demand media content that is not time-dependent, such as podcasts, the distribution period can correspond to the playback time of the content. For example, for a 60-minute podcast episode, the distribution period can correspond to the playback time from 0 to 60 minutes, and the set period is determined based on this playback time. Furthermore, divided periods can be set based on the playback position, such as the first half (0-20 minutes), the middle (20-40 minutes), and the second half (40-60 minutes). Furthermore, situation data in podcast listening data can be identified as the position of a podcast being played at a certain time. For example, listening situations for multiple programs are identified for each predetermined time interval (1 minute in this example), such as "listening to podcast X at a certain time, 5 minutes into the podcast," "listening to podcast Y one minute later, 0 minutes into the podcast," etc. In other words, the pattern data generation unit 124 classifies the listening patterns based on the playback position information.

[0095] Podcasts allow listeners to start, pause, and resume listening at any time, and collected listening data includes information such as the start time, end time, playback position, and playback speed of each playback session. Context data may also include special situations such as intermittent listening behavior or playback at double speed. Movement between sources can be understood as, for example, movement from one podcast distribution platform to another, or movement between programs on the same platform.

[0096] 7. Supplementary Notes Various embodiments are exemplified below, and the embodiments shown below can be combined with each other. [Appendix 1] An information processing system for understanding patterns of viewing or listening to media content, comprising: A data acquisition unit and a pattern data generation unit are provided, the data acquisition unit is configured to acquire usage data; the usage data includes identification data and situation data related to the identification data; the identification data is data for identifying a user of the media content; The user corresponds to a viewer of the media content or a listener of the media content; the situation data is data corresponding to a usage situation of the media content by the user, The usage status corresponds to a viewing status of the media content or a listening status of the media content; the pattern data generation unit is configured to generate pattern data based on the usage data; the pattern data includes usage classification data associated with a plurality of usage patterns for classifying the situation data; Each of the usage patterns defines which of a plurality of set periods during a distribution period of the media content corresponds to the viewing or listening activity of the situation data, and An information processing system, wherein the usage classification data includes data corresponding to the number of identification data related to the situation data classified as each of the usage patterns. [Appendix 2] 10. The information processing system of claim 1, An information processing system, wherein the usage classification data is data showing trends during each of the set periods. [Appendix 3] 10. The information processing system according to claim 1, the plurality of set periods are defined by a plurality of divided periods, An information processing system, wherein each of the divided periods is a period within the distribution period of the media content but is shorter than the distribution period, and the plurality of divided periods are periods independent of one another. [Appendix 4] 4. The information processing system according to claim 3, The plurality of set periods further includes a total period; An information processing system, wherein the total period is a period formed by combining the divided periods that make up the plurality of divided periods. [Appendix 5] 5. The information processing system according to claim 4, An information processing system, wherein the total period is a period formed by combining all of the divided periods that make up the plurality of divided periods. [Appendix 6] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 5, Further comprising a data receiving unit, the data receiving unit is configured to receive setting period determination data; the set period determination data includes at least one of a length of each set period, a start timing of each set period, an end timing of each set period, and a number of set periods; The pattern data generating unit generates the pattern data using the set period based on the set period determination data. [Appendix 7] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 6, the pattern data generation unit classifies each of the situation data into one of the plurality of usage patterns based on a usage time length during the set period; The information processing system, wherein the usage time length corresponds to a viewing time length of the media content or a listening time length of the media content. [Appendix 8] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 7, An information processing system in which the pattern data generation unit classifies each of the situation data into one of the multiple usage patterns based on the set period to which the timing at which viewing or listening related to each of the situation data began belongs. [Appendix 9] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 8, the usage data includes demographic data of the user; The pattern data includes user configuration data associated with the plurality of usage patterns and incorporating the demographic data. [Appendix 10] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 9, the situation data includes data for identifying a usage situation of the plurality of media contents by the user; The pattern data includes program breakdown data; When any one of the plurality of set periods is defined as a set period of interest, An information processing system in which the program usage breakdown data is based on the situation data classified as one of the multiple usage patterns, and is data indicating which of the multiple media contents the viewing or listening related to the situation data is shifting to throughout the attention setting period. [Appendix 11] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 10, the data acquisition unit is configured to acquire program metadata for a plurality of the media contents; An information processing system, wherein the program metadata is associated with the pattern data. [Appendix 12] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 11, Further comprising a data selection unit, the data selection unit is configured to select the usage data in accordance with predetermined selection items; The selection items include at least one of a region, a counting period, a counting time frame, a source, and a counting category; The pattern data generating unit generates the pattern data based on the selected usage data. [Appendix 13] An information processing system according to any one of Supplementary Note 1 to Supplementary Note 12, Further comprising a data presentation unit, The information processing system, wherein the data presentation unit is configured to present an object corresponding to the pattern data. [Appendix 14] 1. An information processing method for understanding patterns of viewing or listening to media content, comprising: The method includes a data acquisition step and a pattern data generation step, In the data acquisition step, usage data is acquired, the usage data includes identification data and situation data related to the identification data; the identification data is data for identifying a user of the media content; the user is a viewer or listener of the media content; the situation data is data corresponding to a usage situation of the media content by the user, The usage status is a viewing or listening status of the media content, In the pattern data generating step, pattern data is generated based on the usage data, the pattern data includes usage classification data associated with a plurality of usage patterns for classifying the situation data; Each of the usage patterns defines which of a plurality of set periods during a distribution period of the media content corresponds to the viewing or listening activity of the situation data, and An information processing method, wherein the usage classification data has data corresponding to the number of identification data related to the situation data classified as each of the usage patterns. [Appendix 15] A program that causes a computer to execute the information processing method described in Appendix 14.

[0097] Although the embodiments have been described above, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made. The embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0098] 100: Information Processing Systems 1: Information processing equipment 2: User terminal 3: Communication network 10: Communications Department 11: Storage section 12: Control section 121: Data acquisition section 122: Data selection section 123: Data reception department 124: Pattern data generation unit 125: Data presentation section 13: Output section 14: Input section 15: Communication bus Dd: Selection data Dm: Program metadata Dp: Pattern data Dp1: Viewing classification data Dp2: Viewer composition data Dp3: Breakdown of viewing programs Ds: Setting period determination data Dv: Viewing data Ob, Ob1~Ob3: Object PT1~PT7: Viewing patterns W1~W3: Screen b1: Input box object

Claims

1. An information processing system for understanding patterns of viewing or listening to media content, comprising: A data acquisition unit and a pattern data generation unit are provided, the data acquisition unit is configured to acquire usage data; the usage data includes identification data and situation data related to the identification data; the identification data is data for identifying a user of the media content; The user corresponds to a viewer of the media content or a listener of the media content; the situation data is data corresponding to a usage situation of the media content by the user, The usage status corresponds to a viewing status of the media content or a listening status of the media content; the pattern data generation unit is configured to generate pattern data based on the usage data; the pattern data includes usage classification data associated with a plurality of usage patterns for classifying the situation data; Each of the usage patterns defines which of a plurality of set periods during a distribution period of the media content corresponds to the viewing or listening activity of the situation data, and An information processing system, wherein the usage classification data includes data corresponding to the number of identification data related to the situation data classified as each of the usage patterns.

2. 2. The information processing system according to claim 1, An information processing system, wherein the usage classification data is data showing trends during each of the set periods.

3. 2. The information processing system according to claim 1, the plurality of set periods are defined by a plurality of divided periods, An information processing system, wherein each of the divided periods is a period within the distribution period of the media content but is shorter than the distribution period, and the plurality of divided periods are periods independent of one another.

4. 4. The information processing system according to claim 3, The plurality of set periods further includes a total period; An information processing system, wherein the total period is a period formed by combining the divided periods that make up the plurality of divided periods.

5. 5. The information processing system according to claim 4, An information processing system, wherein the total period is a period formed by combining all of the divided periods that make up the plurality of divided periods.

6. 2. The information processing system according to claim 1, Further comprising a data receiving unit, the data receiving unit is configured to receive setting period determination data; the set period determination data includes at least one of a length of each set period, a start timing of each set period, an end timing of each set period, and the number of set periods; The pattern data generating unit generates the pattern data using the set period based on the set period determination data.

7. 2. The information processing system according to claim 1, the pattern data generation unit classifies each of the situation data into one of the plurality of usage patterns based on a usage time length during the set period; The information processing system, wherein the usage time corresponds to a viewing time of the media content or a listening time of the media content.

8. 2. The information processing system according to claim 1, An information processing system in which the pattern data generation unit classifies each of the situation data into one of the multiple usage patterns based on the set period to which the timing at which viewing or listening related to each of the situation data began belongs.

9. 2. The information processing system according to claim 1, the usage data includes demographic data of the user; The pattern data includes user configuration data associated with the plurality of usage patterns and incorporating the demographic data.

10. 2. The information processing system according to claim 1, the situation data includes data for identifying a usage situation of the plurality of media contents by the user; The pattern data includes program breakdown data; When any one of the plurality of set periods is defined as a set period of interest, An information processing system in which the program usage breakdown data is based on the situation data classified as one of the multiple usage patterns, and is data indicating which of the multiple media contents the viewing or listening related to the situation data is shifting to throughout the attention setting period.

11. 2. The information processing system according to claim 1, the data acquisition unit is configured to acquire program metadata for a plurality of the media contents; An information processing system, wherein the program metadata is associated with the pattern data.

12. 2. The information processing system according to claim 1, Further comprising a data selection unit, the data selection unit is configured to select the usage data in accordance with predetermined selection items; the selection items include at least one of a region, a counting period, a counting time frame, a source, and a counting category; The pattern data generating unit generates the pattern data based on the selected usage data.

13. An information processing system according to any one of claims 1 to 12, Further comprising a data presentation unit, The information processing system, wherein the data presentation unit is configured to present an object corresponding to the pattern data.

14. 1. An information processing method for understanding patterns of viewing or listening to media content, comprising: The method includes a data acquisition step and a pattern data generation step, In the data acquisition step, usage data is acquired, the usage data includes identification data and situation data related to the identification data; the identification data is data for identifying a user of the media content; the user is a viewer or listener of the media content; the situation data is data corresponding to a usage situation of the media content by the user, The usage status is a viewing or listening status of the media content, In the pattern data generating step, pattern data is generated based on the usage data, the pattern data includes usage classification data associated with a plurality of usage patterns for classifying the situation data; Each of the usage patterns defines which of a plurality of set periods during a distribution period of the media content corresponds to the viewing or listening activity of the situation data, and An information processing method, wherein the usage classification data has data corresponding to the number of identification data related to the situation data classified as each of the usage patterns.

15. A program causing a computer to execute the information processing method according to claim 14.

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