Information processing equipment, information processing methods, and information processing software.

Abstract

Provided are an information processing device, an information provided method, and an information processing program with which it is possible to highly accurately extract a related broadcast that is used for predicting the audience rating of a broadcast of interest. An image processing device (30) according to one embodiment of the present invention has an acquisition unit (31), an extraction unit (32), and a prediction unit (33). The acquisition unit (31) acquires past broadcasts serving as candidates for a related broadcast that is used for predicting the audience rating of a broadcast of interest. The extraction unit (32) extracts the related broadcast from the past broadcasts on the basis of the broadcast stations, broadcast dates / times, broadcast names, and broadcast content of the past broadcasts. The prediction unit (33) predicts the audience rating of the broadcast of interest using the related broadcast.

Description

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

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

[0002] For example, Patent Literature 1 describes an advertising space optimization system that optimizes advertising spaces broadcast on broadcast media. The advertising space optimization system includes an advanced audience rating prediction device (SHARE). The advanced audience rating prediction device (SHARE) is configured to predict target audience ratings with higher accuracy than a predetermined accuracy and / or input audience rating data measured in real time by a third party, and transmit predicted audience rating data for the advertising space and / or audience rating data immediately before broadcast to a target actual optimization device (TAOS).

[0003] International Publication No. 2018 / 131576

[0004] However, the conventional techniques including Patent Document 1 have room for improvement in terms of extracting related programs to be used for predicting the audience rating of a target program with high accuracy.

[0005] Therefore, one object of the present disclosure is to provide an information processing device, an information processing method, and an information processing program that can extract related programs to be used for predicting the audience rating of a target program with high accuracy.

[0006] An information processing device according to one aspect of the present disclosure is characterized by having an acquisition unit that acquires past programs that are candidates for related programs to be used in predicting the viewership ratings of a target program, and an extraction unit that extracts the related programs from the past programs based on the broadcasting station, broadcast date and time, program name, and program content of the past programs.

[0007] According to one aspect of the present disclosure, it is possible to provide an information processing device, an information processing method, and an information processing program that can extract related programs to be used for predicting the audience rating of a target program with high accuracy.

[0008] FIG. 1 is a diagram showing an example of a schematic configuration of a system including an information processing device (audience rating prediction device). FIG. 2 is a functional block diagram showing an example of the internal configuration of the information processing device (audience rating prediction device). FIG. 3 is a diagram showing an example of extraction of related programs based on edit distance. FIG. 4 is a diagram showing an example of extraction of related programs based on the broadcasting station, broadcast date, start time, end time, and program name of a target program and past programs. FIG. 5 is a diagram showing a first example of extraction of related programs based on BERT. FIG. 6 is a diagram showing a second example of extraction of related programs based on BERT. FIG. 7 is a diagram showing an example of functional configurations of a cost calculation device, a broadcast distribution device, and an information processing device (audience rating prediction device). FIG. 8 is a diagram showing an example of hardware configurations of the cost calculation device, the broadcast distribution device, and the information processing device (audience rating prediction device).

[0009] For example, commercials (CM: Commercial Messages) are broadcast as advertisements on television broadcasts. In television commercials, messages are repeatedly conveyed through audio and video, thereby giving viewers an impression of a product, service, etc.

[0010] Television commercials are usually distributed through media time slot transactions, where advertising companies sell media time slots provided by television stations as advertising space to advertisers. Television commercials are broadly divided into time commercials and spot commercials.

[0011] A time commercial is a commercial that is broadcast during a program in exchange for the advertiser bearing the program production costs, etc. (a commercial that is broadcast in a time slot that is bought and sold together with the program). A time commercial may also be called a program commercial, a program commercial, a sponsored commercial, etc.

[0012] A spot commercial is a commercial that is broadcast mainly between programs without sponsoring the program (a commercial that is broadcast in a commercial time slot set by a television station). Spot commercials are divided into station breaks (SBs) that are broadcast between programs, and participation commercials (PTs) (or participating commercials) that are inserted into programs but do not carry a sponsorship indication.

[0013] Here, the cost of a television commercial is calculated using a formula that includes the audience rating (predicted audience rating) of the target program. For example, spot commercial planning is performed in units of gross rating points (GRP). The cost per GRP is called the per cost. Generally, the per cost differs depending on the advertising pattern (e.g., inverted L-shaped, all-day, square-shaped, square-shaped, late-night, all-day daytime, etc.). The fee for a spot commercial when a certain advertising pattern is selected may be calculated based on GRP x per cost.

[0014] Furthermore, the predicted audience rating of a target program is calculated using the audience ratings of related programs related to the target program (for example, previous broadcast episodes of a drama series, previous broadcast episodes of a special program, etc.) Therefore, in order to extract related programs to be used in predicting the audience rating of the target program with high accuracy, the inventors have come up with the idea of ​​an information processing device (audience rating prediction device) having the following configuration.

[0015] That is, an information processing device (audience rating prediction device) according to one aspect of the present disclosure has an acquisition unit that acquires past programs that are candidates for related programs to be used in predicting the audience rating of a target program, and an extraction unit that extracts related programs from the past programs based on the broadcasting station, broadcast date and time, program name, and program content of the past programs.

[0016] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same parts are designated by the same reference numerals. Since the same parts have the same names, functions, etc., detailed description thereof will not be repeated.

[0017] 1 is a diagram showing an example of a schematic configuration of a system including an information processing device (audience rating prediction device) according to an embodiment. The system 1 includes a cost calculation device 10, a broadcast distribution device 20, and an information processing device (audience rating prediction device, audience rating management device) 30. The cost calculation device 10, the broadcast distribution device 20, and the information processing device 30 are connected to each other via a network 40 so as to be able to communicate with each other.

[0018] The cost calculation device 10 calculates the cost of, for example, a television commercial based on a calculation formula that includes the audience rating (predicted audience rating) of a target program into which the television commercial is inserted.

[0019] The cost calculation device 10 acquires, for example, the cost for each advertising pattern of spot commercials for a certain broadcasting station (which may be replaced with the channel) over a predetermined period (e.g., one week). Here, the cost in this disclosure may refer to cost per rating, which indicates the fee per GRP (1%). The advertising pattern may include existing (arbitrary) advertising patterns (e.g., inverted L-shaped, all-day, square-shaped, square-shaped, square-shaped, late-night, all-day daytime, etc.). Furthermore, the cost calculation device 10 may acquire the cost for each advertising pattern, the time periods that make up the advertising pattern, etc., based on external input (e.g., settings by the broadcasting station).

[0020] The cost calculation device 10 may acquire (e.g., receive) information for cost calculation. For example, the cost calculation device 10 may acquire (receive) information on viewership ratings for each advertising slot (e.g., a household viewership rating, an individual viewership rating, and an overall individual viewership rating that is a combined viewership rating of real-time viewing and time-shifted viewing up to seven days after broadcast (which may be called "All & P + C7 (Program + Commercial 7)")) from the information processing device 30. The cost calculation device 10 may derive the cost of one A-time slot, for example, based on the unit price set by the broadcast station for one A-time slot and viewership rating information acquired from the information processing device 30. Here, the "viewership rating information acquired from the information processing device 30" may correspond to the "viewership rating (predicted viewership rating) of a target program in which a television commercial is inserted."

[0021] The cost calculation device 10 may be a mobile terminal (mobile communication terminal) such as a mobile phone, smartphone, or tablet terminal, or may be a fixed communication terminal such as a personal computer (PC) or server. In other words, the cost calculation device 10 in the present disclosure can be interpreted as a communication device. The cost calculation device 10 may also communicate with a network (such as the Internet) via wired and / or wireless communication (e.g., Long Term Evolution (LTE), New Radio (NR), Wi-Fi (registered trademark), etc.).

[0022] The broadcast distribution device 20 is a device that distributes broadcasts to televisions (TVs). Here, the television (television receiver) may be a device that has the function of receiving at least one of terrestrial broadcasts, broadcasts via broadcasting satellites (BS) / communications satellites (CS), internet broadcasts (internet television), etc. For example, the television may be a multi-function television, a smart TV, an IP (Internet Protocol) TV, a set-top box, etc. Note that broadcasts refer to the transmission of telecommunications intended for direct reception by the public, and may include wireless broadcasts, internet broadcasts, etc.

[0023] The information processing device 30 is a device that tallyes actual audience ratings of programs broadcast on television. The information processing device 30 is also a device that predicts audience ratings of programs (target programs) that will soon be broadcast on television. The audience ratings may be stored in predetermined time units (e.g., seconds, minutes, hours). The information processing device 30 may transmit information related to audience ratings (e.g., the audience rating (predicted audience rating) of a target program in which a television commercial is inserted) to another device (e.g., the cost calculation device 10) via a network.

[0024] The information processing device 30 may be, for example, a device managed by a television audience rating researcher. The information processing device 30 may acquire and manage viewing history data from a measuring device connected to the television of a predetermined household (e.g., a household cooperating in the survey). Viewership ratings may be calculated or predicted from the data. The information processing device 30 may record audience ratings (individual audience ratings of the target) for each predetermined target (users corresponding to predetermined attributes). In the present disclosure, the targets are assumed to be gender and age categories (C (Child) demographic, T (Teen) demographic, M (Male) 1-M3 demographic, F (Female) 1-F3 demographic) used in the field of marketing, but are not limited to these categories.

[0025] The information processing device 30 is configured to use a new analytical method for handling big data to predict television "viewer ad viewership ratings" with high accuracy. The information processing device 30 may include a data input unit that inputs data source information, an explanatory variable conversion unit that applies specific processing to the data source information input from the data input unit to convert the data source information into explanatory variables, a program implementation / execution unit that implements a program of a predetermined algorithm, and a data output unit that outputs the results of the program implementation / execution unit. The program implementation / execution unit executes the algorithm of the program using the data source information input from the data input unit and the explanatory variables obtained by converting the data source information into explanatory variables by the explanatory variable conversion unit, and outputs predicted viewership rating data obtained by executing the algorithm from the output unit. Advertising slots can be optimized based on the output predicted viewership rating data. Here, viewership ratings of related programs related to the target program (e.g., previous broadcasts of a drama series, previous broadcasts of a special program, etc.) can be used as data source information for calculating the viewership rating (predicted viewership rating) of a target program in which a television commercial is inserted.

[0026] Note that this system configuration is an example and is not limited to this. For example, while FIG. 1 shows a configuration including one of each device, the number of each device is not limited to this and multiple devices may exist. The system 1 may be configured not to include some devices, or may be configured such that the functions of one device are realized by multiple devices. Conversely, the system 1 may be configured such that the functions of multiple devices are realized by one device. For example, at least two of the cost calculation device 10, the broadcast distribution device 20, and the information processing device 30 may be implemented on a single server.

[0027] 2 is a functional block diagram showing an example of the internal configuration of the information processing device (audience rating prediction device) 30. The information processing device 30 has an acquisition unit 31, an extraction unit 32, and a prediction unit 33. The information processing method and information processing program disclosed herein are realized by causing a computer included in the information processing device 30 (the acquisition unit 31, the extraction unit 32, and the prediction unit 33) to execute predetermined processing steps.

[0028] The acquisition unit 31 acquires past programs that are candidates for related programs to be used in predicting the audience rating of a target program. The acquisition unit 31 can acquire all past programs that are candidates for related programs without imposing any time or content restrictions.

[0029] The extraction unit 32 narrows down the past programs acquired by the acquisition unit 31 to those that were broadcast a predetermined period before the broadcast date and time of the target program and extracts them. For example, when calculating the predicted audience rating three days before the broadcast date of the target program, the extraction unit 32 narrows down the past programs to those that were broadcast four days before the target program. When the system is implemented, all past programs that can be acquired at the time of prediction can be used. Furthermore, there is a degree of freedom in the time period for calculating the predicted audience rating of the target program, and various design changes are possible. Furthermore, it is not necessary to narrow down the past programs according to the time period for calculating the predicted audience rating of the target program.

[0030] The extraction unit 32 extracts related programs to be used for predicting the audience rating of the target program from the past programs based on the broadcasting station, broadcast date and time, program name, and program content of the past programs. The broadcasting station, broadcast date and time, program name, and program content of the past programs can be obtained, for example, from VR program information in which Video Research has compiled a database of program names, broadcast minutes, broadcast start times, broadcast dates, broadcasting station names, etc. after the end of broadcast. Furthermore, the broadcasting station, broadcast date and time, program name, and program content of the past programs can be obtained, for example, from television metadata, which is a database created by Wire Action Co., Ltd. after the television broadcast by recording, via a unique input interface, the content that was actually broadcast in the program, such as companies, products, shops, music, and performers introduced in the program.

[0031] Television metadata can include EPG (Electronic Program Guide) program information and the number of EPG pre-views. EPG program information is official broadcast station program information, and Interactive Program Guide (IPG) Inc. obtains program-related information directly from broadcast stations nationwide and centrally manages this data. Specifically, it includes program title, broadcast date and time, program synopsis, program details, genre, channel ID, program ID, etc. Furthermore, when viewing an EPG electronic program guide on a PC homepage or an application on a mobile smartphone, the entire electronic program guide (i.e., timetable) is initially displayed, but by selecting (e.g., clicking) individual program columns from this display, detailed program content can be viewed. The number of EPG pre-views is the aggregate number of accesses to each individual program detail page before the program is broadcast.

[0032] The extraction unit 32 extracts related programs from past programs based on whether the target program and past programs are broadcast on the same station. For example, the extraction unit 32 extracts past programs that are broadcast on the same station as the target program as candidates for related programs. On the other hand, past programs may include related programs even if they are broadcast on different stations (for example, programs that change stations depending on the episode, such as professional baseball games or the Tokyo Marathon, may not be able to be linked). Therefore, even if the broadcast stations for the target program and past programs are different, the extraction unit 32 may retain past programs that are broadcast on different stations as candidates for related programs. In other words, the extraction unit 32 does not need to discard candidates for related programs from past programs based solely on whether the target program and past programs are broadcast on the same station (whether the broadcast stations for the target program and past programs are the same may be used as a secondary criterion).

[0033] The extraction unit 32 extracts related programs from past programs based on whether or not there are predetermined commonalities between the broadcast dates of the target program and past programs, and whether or not the start time, end time, and broadcast minutes of the target program and past programs are the same or within a predetermined range.

[0034] For example, the extraction unit 32 recognizes that the broadcast dates of the target program and past programs are the same day of the week, or the same date in a different year but in a different month (for example, New Year's Day or New Year's Eve every year) as predetermined commonalities, and extracts from past programs those that have the predetermined commonalities with the target program as candidates for related programs.

[0035] For example, when at least one of the start time, end time, and broadcast minutes of a target program and a past program is the same, the extraction unit 32 extracts, from among the past programs, those programs that have the same start time, end time, and broadcast minutes as the target program as candidates for related programs. Alternatively, the extraction unit 32 uses the start time, end time, and broadcast minutes of the target program as criteria and extracts, from among the past programs, those programs that fall within a predetermined range of the start time, end time, and broadcast minutes of the target program as candidates for related programs. For example, when the start time is used as criteria, past programs that start within two hours before and after the start time of the target program may be extracted as candidates for related programs. When the end time is used as criteria, past programs that end within two hours before and after the end time of the target program may be extracted as candidates for related programs. When the broadcast minutes are used as criteria, past programs whose broadcast minutes fall within a predetermined relative value of the broadcast minutes of the target program may be extracted as candidates for related programs. For example, if the target program has a broadcast duration of 60 minutes and the predetermined relative value is 0.6 to 1.6, past programs whose broadcast duration falls within the range of 36 to 96 minutes may be extracted as candidates for related programs.

[0036] The extraction unit 32 extracts related programs from the past programs based on whether the amount of editing required to make the program names of the target program and past programs identical exceeds a predetermined amount. The extraction unit 32 can extract related programs from the past programs using an "edit distance," which is an index indicating how many edits are required to turn an original string into a string to be compared.

[0037] FIG. 3 is a diagram showing an example of related program extraction based on edit distance. In the example of FIG. 3, a comparison is made between the broadcast of the program title "XXXXXX Countdown 2019-2020 [Live Broadcast from △△△△△! ] [Subtitles]" which aired from 11:45 PM on December 31, 2019 to 12:45 AM on January 1, 2020, and the broadcast of the program title "XXXXXX Countdown 2016-2017 [Once a Year! □□□□□ Grand Gathering!]" which aired from 11:45 PM on December 31, 2016 to 12:45 AM on January 1, 2017. As a preprocessing step, character strings enclosed in brackets [ ] are deleted, and tags such as [Subtitles] and [Video] are also deleted. This preprocessing removes the "XXXXXX Countdown 2019-2020 [Live Broadcast from △△△△△! The string "XXX Countdown 2019-2020" is converted to "XXX Countdown 2016-2017 [Once a year! □□□□□ Grand Gathering!" and the string "XXX Countdown 2016-2017" are converted to "XXX Countdown 2016-2017." Comparing the converted strings, three characters are different: "201<9>-20<2><0>" in the year section of the former string and "201<6>-20<1><7>" in the year section of the latter string. Therefore, the edit distance between the two is 3.

[0038] The following is an example of a rule for determining related programs based on edit distance. If the preprocessed program name is less than 20 characters, a program with an edit distance of 2 or less is determined to be a related program. If the preprocessed program name is 20 characters or more, a program with an edit distance of 3 or less for the first 20 characters of the program name can be determined to be a related program. Since "XXX Countdown 2019-2020" and "XXX Countdown 2016-2017" in Figure 3 each contain 21 characters, if determined based on the latter rule, the program has an edit distance of 3 or less for the first 20 characters of the program name, and is therefore determined to be related to the target program. Of course, the rule for determining related programs based on edit distance described here is merely an example, and various design modifications are possible. Furthermore, for example, if a program name has a [rerun] tag, which indicates a rerun, a related program can be searched for among past programs that have the [rerun] tag. On the other hand, if the program name does not have the [rerun] tag, a related program can be searched for among past programs that do not have the [rerun] tag.

[0039] FIG. 4 is a diagram showing an example of extraction of related programs based on the broadcasting station, broadcasting date, start time, end time, and program name of the target program and past programs.

[0040] 4 exemplifies a target program as follows: the broadcast station is A, the broadcast date is XX / XX / 2019, the start time is 9:00 PM, the end time is 10:00 PM, and the program title is XXXXX. Also, FIG. 4 exemplifies past programs that are candidates for related programs: the broadcast station is A, the broadcast date is △ / △ / 2017, the start time is 9:00 PM, the end time is 10:00 PM, and the program title is YYYYY, and the broadcast station is B, the broadcast date is XX / XX / 2016, the start time is 9:00 PM, the end time is 10:00 PM, and the program title is ZZZZZ.

[0041] The extraction unit 32 extracts related programs from past programs based on the degree of similarity and relevance between the broadcasting station, broadcast date, start time, end time, and program name of the target program and past programs. The related programs extracted by the extraction unit 32 are optimized for predicting the audience rating of the target program with high accuracy, so it is possible to extremely reduce the difference (predicted-actual difference ■■■%) between the predicted audience rating of the target program (predicted value three days before, which is ●●●%) and the actual audience rating of the target program (▲▲▲%).

[0042] The extraction unit 32 uses BERT (Bidirectional Encoder Representations from Transformers) to vectorize the program names, program summaries, and program details of the target program and past programs, and extracts related programs from past programs based on the cosine similarity between the vectorized target program and past programs. In other words, it analyzes the text of the program names, program summaries, and program details in detail, and extracts related programs from past programs based on the similarity and relevance of the words and semantic content that appear (sentence vectorization). BERT excels at natural language processing tasks such as translation, document classification, and question answering, and incorporates an architecture called Transformer, which enables it to "read context" by learning from sentences in both directions (beginning and ending).

[0043] 5 is a diagram showing a first example of extraction of related programs based on BERT. As shown in Fig. 5, for each of the target program and past programs, the program name text (here, XXXXXX...) is converted into a 768-dimensional vector of the program name, the program summary text (here, △△△△△...) is converted into a 768-dimensional vector of the program summary, and the program details text (here, □□□□□...) is converted into a 768-dimensional vector of the program details.

[0044] 6 is a diagram showing a second example of related program extraction based on BERT. In FIG. 6 , the program name cosine similarity (0.99954 here) is calculated between the vectorized data of the program title text of the target program (here, XXXXXX...) and the vectorized data of the program title text of a past program that is a candidate for a related program (here, ●●●●●...). The program synopsis cosine similarity (0.97011 here) is also calculated between the vectorized data of the program synopsis text of the target program (here, △△△△△...) and the vectorized data of the program synopsis text of a past program that is a candidate for a related program (here, ▲▲▲▲▲...). The program detail cosine similarity (0.98688 here) is also calculated between the vectorized data of the program details text of the target program (here, □□□□□...) and the vectorized data of the program details text of a past program that is a candidate for a related program (here, ■■■■■...). 6 shows only one past program as a candidate for a related program, but in reality, the cosine similarity is calculated for all past programs (test programs) narrowed down by, for example, broadcast station, start time, end time, broadcast duration, etc. The larger the cosine similarity value, the higher the similarity with the target program, and the smaller the value, the lower the similarity with the target program.

[0045] From a group of past programs that are candidates for related programs, those with high cosine similarity to the target program are extracted for each of the program name vector, program summary vector, and program detail vector, and a ranking is calculated. Then, related programs are extracted from the group of past programs based on, for example, the following criteria: (1) A past program that ranks first in cosine similarity with the target program and has a cosine similarity of 0.99 or more is extracted as a related program. If the cosine similarities of the program name vector, program summary vector, and program detail vector differ, the cosine similarity of the program name vector is given priority. (2) A threshold is set for each of the cosine similarities of the program name vector, program summary vector, and program detail vector. If there are two or more items whose cosine similarity does not exceed the threshold, that past program is not extracted as a related program. If there are two or more items whose cosine similarity exceeds the threshold, that past program is extracted as a related program. For example, the threshold for the cosine similarity of the program name vector can be set to 0.97, the threshold for the cosine similarity of the program summary vector to 0.98, and the threshold for the cosine similarity of the program detail vector to 0.99. In other words, the thresholds for the cosine similarity of the program name vector, program summary vector, and program detail vector can be made different. (3) The past program that ranked first in the total cosine similarity of the program name vector, program summary vector, and program detail vector is extracted as a related program.

[0046] The prediction unit 33 predicts the audience rating of the target program using the related programs extracted by the extraction unit 32. More specifically, the prediction unit 33 predicts the audience rating of the target program by applying the audience ratings of the related programs extracted by the extraction unit 32 to a predetermined calculation formula (algorithm). Parameters of the audience rating of the related programs extracted by the extraction unit 32 used to predict the audience rating of the target program include, for example, program broadcast slot factors (program broadcast week, average of the previous four weeks, average of the previous four weeks of the back slot, average of the previous four weeks of the front slot, average of the previous four weeks of the back slot, audience rating of the previous week, etc.). Parameters other than the audience rating of the related programs extracted by the extraction unit 32 used to predict the audience rating of the target program include, for example, program content factors (viewing impression cluster, performer information, keyword category, topic category, information type, maximum person weight, total person weight, VR program category, number of advance EPG views by target, number of advance views of the opposing program by target, etc.).

[0047] The calculation formula (algorithm) used by the prediction unit 33 to predict the audience ratings of a target program is not limited to a specific algorithm as long as it produces a "target output," i.e., a "highly accurate predicted audience rating." For example, "random forest" can be used as the calculation formula (algorithm). "Random forest" is a type of "ensemble learning" technique that aggregates and learns from multiple prediction results derived by weak learners that train using simple models. It is a machine learning technique that achieves "high prediction accuracy" and "interpretable results." "Random forest" uses "decision trees" for each weak learner, and can be said to be a technique that solves the problems of "decision trees" while taking advantage of the advantages of "decision trees." A decision tree is a prediction method that divides space by lines perpendicular to the axis and assigns predicted values ​​to each interval. Because decision trees linearly decompose space, they are widely used for analyzing data with simple distributions, which makes it easy to visualize the reasons for prediction results.

[0048] The audience rating of the target program predicted by the prediction unit 33 is transmitted from the information processing device 30 to the cost calculation device 10 via the network 40. The cost calculation device 10 calculates the cost of a television commercial to be inserted into the target program based on the transmitted predicted audience rating of the target program (for example, by multiplying the predicted audience rating by the per cost per 1 GRP (1%)).

[0049] The information processing device, information processing method, and information processing program described in the above-described embodiments are applied to television commercials, but are not limited thereto. The above-described embodiments of the present disclosure may also be applied to, for example, radio commercials, commercials distributed over the Internet, and the like. Those skilled in the art can understand the above-described embodiments by reinterpreting them as necessary. For example, when applied to radio commercials, the audience rating of the present disclosure may be interpreted as the audience rating.

[0050] In the present disclosure, "GRP" may be interpreted as any other indicator related to total viewership rating.

[0051] Note that the present disclosure also covers content in which "viewership rate" is replaced with a specific index (or the degree of improvement of a specific index, offset, etc.). The specific index may be an index for determining advertising effectiveness, an intermediate index for improving the advertiser's key performance indicators (KPIs), etc., and may include, for example, awareness rate, reach, ROAS (Return On Advertising Spend), ROI (Return on Investment), etc. At least a part of the term "viewership rate" may be replaced with any of the above specific indexes.

[0052] FIG. 7 is a diagram showing an example of the functional configuration of the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30. As shown in FIG. 7, the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30 each include a control unit 110, a storage unit 120, a communication unit 130, an input unit 140, and an output unit 150. Note that FIG. 7 shows an example of functional blocks of the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30, and they may also include other functional blocks not shown in FIG. 7. Furthermore, they may be configured not to include some of the functional blocks.

[0053] The control unit 110 controls the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30. The control unit 110 can be configured by a controller, a control circuit, or a control device that is explained based on common understanding in the technical field related to the present invention.

[0054] The storage unit 120 stores information used by the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30. The storage unit 120 can be configured, for example, by a memory, storage, storage device, or the like that is explained based on common understanding in the technical field related to the present invention.

[0055] The communication unit 130 performs communication (e.g., mutual communication) between the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30. The communication unit 130 can be configured by a transmitter / receiver, a transmission / reception circuit, or a transmission / reception device that is explained based on common understanding in the technical field related to the present invention. Note that the communication unit 130 may be configured by a transmission unit and a reception unit.

[0056] The input unit 140 accepts inputs from the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30. The input unit 140 may also be connected to a predetermined device, storage medium, etc., and accept data input. The input unit 140 may output the input results to, for example, the control unit 110. The input unit 140 may be configured with input devices such as a keyboard, a mouse, and buttons, input / output terminals, input / output circuits, etc., which are explained based on common understanding in the technical field related to the present invention. The input unit 140 may also be configured as an integrated unit with a display unit (for example, a touch panel).

[0057] The output unit 150 performs output in the cost calculation device 10, the broadcast distribution device 20, and the information processing device (viewership prediction device) 30. For example, the output unit 150 may be configured to include a display unit that displays images, an audio output unit that outputs audio, and the like. The display unit may be configured, for example, by a display device such as a monitor or the like that is described based on common understanding in the technical field related to the present invention. Furthermore, the audio output unit may be configured by an output device such as a speaker that is described based on common understanding in the technical field related to the present invention.

[0058] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the means for realizing each functional block is not particularly limited. That is, each functional block may be realized by a single physically coupled device, or may be realized by two or more physically separate devices connected by wire or wirelessly.

[0059] For example, the devices (cost calculation device 10, broadcast distribution device 20, information processing device (viewership prediction device) 30) according to an embodiment of the present disclosure may function as a computer that performs the information processing (viewership prediction processing) of the present disclosure. FIG. 8 is a diagram illustrating an example of the hardware configuration of the cost calculation device 10, broadcast distribution device 20, and information processing device (viewership prediction device) 30. The above-described cost calculation device 10, broadcast distribution device 20, and information processing device (viewership prediction device) 30 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0060] In the present disclosure, the terms apparatus, circuit, device, unit, server, etc. may be interchangeable. The hardware configurations of the cost calculation apparatus 10, the broadcast distribution apparatus 20, and the information processing apparatus (audience rating prediction apparatus) 30 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0061] For example, although only one processor 1001 is shown, there may be multiple processors. Furthermore, processing may be performed by one processor, or processing may be performed by two or more processors simultaneously, serially, or in other ways. Furthermore, processor 1001 may be implemented by one or more chips.

[0062] Each function of the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30 is realized by loading predetermined software (programs) onto hardware such as a processor 1001, a memory 1002, etc., so that the processor 1001 performs calculations and controls communication via a communication device 1004, reading and / or writing of data in the memory 1002 and storage 1003, etc.

[0063] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Note that each unit, such as the control unit 110 described above, may be realized by the processor 1001.

[0064] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 110 may be implemented by a control program stored in the memory 1002 and executed by the processor 1001, and similar implementations may be made for other functional blocks.

[0065] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically programmable programmable read-only memory (EEPROM), a random access memory (RAM), or other suitable storage medium. The memory 1002 may also be referred to as a register, a cache, a main memory, or the like. The memory 1002 may store executable programs (program codes), software modules, and the like for implementing a method according to an embodiment.

[0066] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, a flexible disk, a floppy (registered trademark) disk, a magneto-optical disk (e.g., a compact disk (CD-ROM (Compact Disc ROM)), a digital versatile disk, a Blu-ray (registered trademark) disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, a stick, a key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be called an auxiliary storage device. Note that the above-mentioned storage unit 120 may be realized by memory 1002 and / or storage 1003.

[0067] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may include a SIM card. Note that the above-mentioned communication unit 130 may be realized by the communication device 1004.

[0068] The input device 1005 is an input device (e.g., a keyboard, a mouse, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one unit (e.g., a touch panel). The above-mentioned input unit 140 and output unit 150 may be realized by the input device 1005 and the output device 1006, respectively.

[0069] Furthermore, each device such as the processor 1001 and the memory 1002 is connected by a bus 1008 for communicating information. The bus 1008 may be configured as a single bus or may be configured as different buses between the devices.

[0070] Furthermore, the cost calculation device 10, the broadcast distribution device 20, and the information processing device (audience rating prediction device) 30 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented by at least one of these pieces of hardware.

[0071] (Modifications) Note that terms explained in this disclosure and / or terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0072] The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or other corresponding information. Furthermore, the names used for parameters, etc. in this disclosure are not limiting in any way.

[0073] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0074] Information, signals, etc. may be input / output via multiple network nodes. The input / output information, signals, etc. may be stored in a specific location (e.g., memory) or may be managed using a table. The input / output information, signals, etc. may be overwritten, updated, or added. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0075] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0076] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0077] Software, instructions, information, etc. may also be transmitted and received via a transmission medium and / or signal waveform. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0078] As used in this disclosure, the terms "system" and "network" may be used interchangeably.

[0079] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, the order of the processing procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0080] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0081] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0082] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0083] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0084] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The invention according to the present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the invention as defined by the description of the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not impose any limiting meaning on the invention according to the present disclosure.

[0085] This application is based on Japanese Patent Application No. 2022-112231, filed on July 13, 2022, the contents of which are incorporated herein in their entirety.

Claims

DEPCT681. Information processing equipment comprising: an acquisition unit configured to provide a pool of previously published programs as a selection for related programs used for popularity prediction of the target program, and a sorting unit configured to sort related programs from a pool of previously published programs based on broadcasting station, broadcast date and time, program name, and program content of the previously published program.

2. Additional information processing equipment under claim 1 comprising: a prediction unit configured to predict the popularity of the target program using related programs.

3. Information processing equipment under claim 1 or 2 where the sorting unit sorts related programs from a pool of previously published programs based on whether the broadcasting station of the target program and the broadcasting station of the previously published program are the same. 4.Information processing equipment under claim 1 or 2 in which the sorting unit separates related programs from a number of previously passed programs based on whether they share a pre-defined common element during the broadcasting date of the target program and previous programs, and whether the start time, end time, and broadcasting duration of the target program and previous programs are the same or within a pre-defined range.

5. Information processing equipment under claim 1 or 2 in which the sorting unit separates related programs from a number of previously passed programs based on whether the amount of modification for converting the program name of the target program and previous programs into a similar string exceeds the pre-defined amount of modification. 6.The information processing equipment under claim 1 or 2, in which the sorting unit performs vectorizing of the program name, program summary, and program details of the target program and previous programs using bidirectional encoder representation from the transformer (BERT), and sorts related programs from a number of previous programs based on the cosine similarity between the vectorized target program and the previous programs.

7. The information processing method comprises: a step of providing a selection of previous programs for related programs used for popularity prediction of the target program, and a step of sorting related programs from a number of previous programs based on the broadcasting station, broadcast date and time, program name, and program content of the previous programs. 8.An information processing program that enables a computer to perform the following steps: the process of providing a list of previously published programs as options for related programs used for predictive evaluation of the target program's popularity, and the process of filtering related programs from a number of previously published programs based on broadcasting station, broadcast date and time, program name, and program content of the previously published programs.

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