Programming plan generation method and system for the same

Abstract

The present invention relates to a method of generating a programming plan and a system therefor. Specifically, the present invention relates to a method and a system for searching for past contents that may replace new contents when there are no new movie contents scheduled to be released or the expected audience seating rate is low although movie contents are released, and providing a programming plan including the past contents to a programming plan generation manager.

Description

PROGRAMMING PLAN GENERATION METHOD AND SYSTEM FOR THE SAME

[0001] The present invention relates to a method of generating a programming plan and a system therefor. Specifically, the present invention relates to a method and a system for searching for past contents that may replace new contents when there is no new movie contents scheduled to be released or the expected audience seating rate is low although movie contents are released, and providing a programming plan including the past contents to a programming plan generation manager.

[0002] The movie theater business in a difficult situation while going through the pandemic era has not yet recovered even after the pandemic era, and recently, rapid growth of OTT markets and increase in the movie theater admission fees continuously decrease the audience seating rate of theaters. Accordingly, subjects that operate the movie theaters are making various efforts to improve the audience seating rate of theaters.

[0003] Meanwhile, operation of movie theaters is greatly affected by the quantity of new movie contents, especially quality movie contents, that can be produced. However, difficulties in the field of contents filming / production brough by the pandemic have led to low production of movie contents until present, and accordingly, new products to be released are insufficient, and the difficult situation of being unable to greatly expect production of quality movie contents meeting consumers' needs is continued. Accordingly, from the perspective of subjects that operate movie theaters, various methods are being sought to prepare for the cases where there are no new movie contents or the audience seating rate is expected to be low although there are new movie contents.

[0004] The present invention has been proposed to overcome these difficult situations of reality as described above, and relates to a method of generating and recommending a programming plan including contents released in the past in order to increase the theater audience seating rate when there are no new contents or a low audience seating rate of new contents is expected, and a system for the same.

[0005] An object of the present invention is to generate and recommend a programming plan that maximizes the audience seating rate in a theater to a user (e.g., a programming plan generation manager) to increase the audience seating rate.

[0006] In particular, another object of the present invention is to search for contents released and succeeded in the past and recommend a programming plan by reflecting the contents in the programming plan in the case where release of new contents is not scheduled or a low audience seating rate is expected although release of new contents is scheduled.

[0007] In addition, another object of the present invention is to increase reliability of a recommended programming plan by comprehensively reflecting information on the time of releasing or screening contents, similarities among the contents, commercial areas around a theater, and the like when a programming plan including past contents is generated.

[0008] Meanwhile, the technical problems of the present invention are not limited to the technical problems mentioned above, and unmentioned other technical problems may be clearly understood by those skilled in the art from the following description.

[0009] To accomplish the above objects, according to one aspect of the present invention, there is provided a method of generating a programming plan, the method comprising the steps of: (a) calculating a first expected audience seating rate expected when screening new contents on the basis of information on the new contents; (b) loading information on arbitrary past contents; (c) calculating a second expected audience seating rate expected when screening the past contents on the basis of information on the past contents; (d) generating the programming plan on the basis of the first expected audience seating rate and the second expected audience seating rate.

[0010] In addition, in the method of generating a programming plan, step (a) of calculating a first expected audience seating rate may calculate the first expected audience seating rate by utilizing at least one data among commercial area data, contents data, and customer data, wherein the commercial area data may include residential population around a theater, employed population around the theater, floating population around the theater, an income level of the residential population around the theater, or an income level of the employed population around the theater, the contents data may include a contents box office index, nationality of contents, or genre of contents, and the customer data may include ticket sales performance of each theater, preference for a special theater, theater occupancy rate by gender, or audience seating rate by genre.

[0011] In addition, in the method of generating a programming plan, step (c) of calculating a second expected audience seating rate may calculate the second expected audience seating rate by utilizing at least one data among commercial area data, contents data, and customer data, wherein the commercial area data may include residential population around a theater, employed population around the theater, floating population around the theater, an income level of the residential population around the theater, or an income level of the employed population around the theater, the contents data may include a contents rating, a cumulative number of viewers, a contents box office index, nationality of contents, or genre of contents, and the customer data may include ticket sales performance of each theater, preference for a special theater, theater occupancy rate by gender, or audience seating rate by genre.

[0012] In addition, in the method of generating a programming plan, step (a) of calculating a first expected audience seating rate may calculate the first expected audience seating rate by further utilizing summary analysis data that analyzes summary contents including summary information of the new contents.

[0013] In addition, in the method of generating a programming plan, the summary analysis data may be generated by a summary contents analysis process including the steps of: performing morphological analysis and embedding on the summary contents; searching for past contents, of which a similarity to the new contents is higher than a preset value, with reference to a result of performing the morphological analysis and embedding; and acquiring at least one among contents data and customer data of the past contents.

[0014] In addition, in the method of generating a programming plan, the summary content may include at least one among plot text, trailer video, and still image.

[0015] In addition, in the method of generating a programming plan, the arbitrary past contents are one among past contents of which a similarity to the new contents is higher than a reference value.

[0016] In addition, the method of generating a programming plan may further comprise, after step (a), the step of determining whether the first expected audience seating rate is lower than a reference value, wherein when the first expected audience seating rate is lower than the reference value, the steps after step (b) may be executed.

[0017] In addition, in the method of generating a programming plan, when the first expected audience seating rate is lower than the reference value, step (b) may select past contents of which a similarity to the new contents is higher than a preset value, and then load only information on the selected past contents.

[0018] In addition, in the method of generating a programming plan, after step (c), when the first expected audience seating rate is lower than the second expected audience seating rate, step (d) may generate a programming plan that increases the proportion of the past contents to be higher than the new contents.

[0019] According to another aspect of the present invention, there is provided a programming plan generation server comprising a central processing unit and a memory to generate a programming plan, wherein the central processing unit executes commands for executing a method of generating a programming plan stored in the memory, and the method of generating a programming plan may comprise the steps of: (a) calculating a first expected audience seating rate expected when screening new contents on the basis of information on the new contents; (b) loading information on arbitrary past contents; (c) calculating a second expected audience seating rate expected when screening the past contents on the basis of information on the past contents; (d) generating the programming plan on the basis of the first expected audience seating rate and the second expected audience seating rate.

[0020] According to the present invention, there is an effect of effectively increasing the audience seating rate in a theater, and in particular, there is an effect of increasing efficiency of operating a theater by utilizing past contents that can replace new contents during a period when production of new contents is low.

[0021] In addition, according to the present invention, there is an effect of increasing reliability of a recommended programming plan as the process of searching for past contents for replacing new contents is based on objective data.

[0022] Meanwhile, the effects of the present invention are not limited to the effects mentioned above, and unmentioned other technical effects will be clearly understood by those skilled in the art from the following description.

[0023] FIG. 1 is a view schematically describing a method and a system according to the present invention.

[0024] FIG. 2 is a view sequentially showing a method of generating a programming plan according to the present invention.

[0025] FIG. 3 is a view showing data types that a programming plan generation server refers to when executing the method of generating a programming plan.

[0026] FIGS. 4 and 5 are views showing the process of analyzing summary contents related to new contents by a programming plan generation server.

[0027] FIG. 6 is a view showing a first embodiment according to the present invention.

[0028] FIG. 7 is a view showing a second embodiment according to the present invention.

[0029] FIG. 8 is a view showing a third embodiment according to the present invention.

[0030] FIG. 9 is a view showing an example of utilizing the methodology utilized in the present invention in the e-commerce field.

[0031] FIG. 10 is a view showing an example of utilizing the methodology utilized in the present invention in the broadcasting field.

[0032] Details of the objects and technical configurations of the present invention and operational effects according thereto will be more clearly understood by the following detailed description based on the drawings attached in the specification of the present invention. An embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

[0033] The embodiments disclosed in this specification should not be construed or used as limiting the scope of the present invention. For those skilled in the art, it is natural that the description including the embodiments of the present specification have various applications. Accordingly, any embodiments described in the detailed description of the present invention are illustrative for better describing of the present invention, and are not intended to limit the scope of the present invention to the embodiments.

[0034] The functional blocks shown in the drawings and described below are merely examples of possible implementations. Other functional blocks may be used in other implementations without departing from the spirit and scope of the detailed description. In addition, although one or more functional blocks of the present invention are expressed as separate blocks, one or more of the functional blocks of the present invention may be combinations of various hardware and software configurations that perform the same function.

[0035] In addition, the expressions including certain components are expressions of "open type" and only refer to existence of corresponding components, and should not be construed as excluding additional components.

[0036] Furthermore, when a certain component is referred to as being "connected" or "coupled" to another component, it may be directly connected or coupled to another component, but it should be understood that other components may exist in between.

[0037] FIG. 1 is a view showing the overall system environment in which a method of generating a programming plan according to the present invention is executed. Referring to FIG. 1, the method of generating a programming plan according to the present invention may be executed by a programming plan generation server 100, and the programming plan generation server 100 may perform a task of generating a programming plan in response to a user's request, e.g., a programming plan generation manager who manages programming plans in a theater. As described above, the present invention is for generating a programming plan that can increase the audience seating rate in a situation where there are no new contents or performance of new contents is expected to be low. In reality, details of the invention may be implemented in a way that when an operator who operates a specific theater or a so-called programming plan generation manager who is involved in the operation of a theater and may manage generation of a programming plan of several movie contents requests the programming plan generation server 100 to generate a programming plan, the programming plan generation server 100 generates and provides a programming plan to the programming plan generation manager in response thereto.

[0038] The process of generating a programming plan by the programming plan generation server 100 may include a process of calculating an expected audience seating rate of a newly released movie, i.e., new contents, and may also include a process of calculating an expected audience seating rate when a movie released in the past, i.e., past contents, is re-released. The programming plan generation server 100 may perform an operation of calculating expected audience seating rates of both the new and past contents, and generate and provide an optimal programming plan to the programming plan generation manager on the basis of the result.

[0039] For reference, although it will be explained on the assumption that only the programming plan generation server 100 subjectively performs the operation and task in the detailed description to help understanding of the invention, it should be understood that the programming plan generation server 100 may not necessarily mean any single server computer. That is, the programming plan generation server 100 may be a collection of a plurality of computing devices connected by a network.

[0040] In addition, it is assumed that all types of computing devices, including the programming plan generation server 100, participating in the systematic environment that implements the method of generating a programming plan according to the present invention include a central processing unit and a memory. At this point, the central processing unit may also be referred to as a controller, a microcontroller, a microprocessor, a microcomputer, or the like. In addition, the central processing unit may be implemented as hardware, firmware, software, or a combination thereof. In the case of implementing the method using hardware, the central processing unit may be implemented as an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), or the like, and in the case of implementing the method using firmware or software, the firmware or software may be configured to include modules, procedures, functions, and the like that perform the functions or operations described above. In addition, the memory may be implemented as Read Only Memory (ROM), Random Access Memory (RAM), Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, Static RAM (SRAM), Hard Disk Drive (HDD), Solid State Drive (SSD), or the like.

[0041] There is no limitation on the type of the computing device, but representative examples of the computing devices include server computers, portable terminals (laptop computers, smartphones, or the like), desktop computers, and the like. In addition, in some cases, the computing device may be available in the form of a cloud server.

[0042] FIG. 2 is a view showing each of the steps in a method of generating a programming plan according to the present invention.

[0043] Referring to FIG. 2, the method of generating a programming plan includes, first of all, a step of receiving a request for generating a programming plan from a programming plan generation manager, i.e., a user, by the programming plan generation server 100 (S101). This step may be accomplished in a method of transmitting the request for generating a programming plan to the programming plan generation server 100 by the user by directly handling the programming plan generation server 100 (e.g., executes a programming plan generation program by handling an input device connected to the programming plan server) or handling a user terminal connected to the programming plan generation server 100 through a network.

[0044] After step S101, first, the programming plan generation server 100 may collect information needed to earnestly generate a programming plan, i.e., programming plan generation environment information (S103). The programming plan generation environment information means general information needed for generating a new programming plan, and for example, a period that requires a new programming plan (a period for which a programming plan is generated), currently released and screening contents, information on whether there are new contents scheduled to be released, information on new contents when there are new contents scheduled to be released, and the like may be included in the major programming plan generation environment information. In addition, the programming plan generation environment information may further include conditions that are input by the user when the user requests generation of a programming plan, such as a time zone that needs generation of a programming plan, characteristics of an audience group to be targeted, or the like.

[0045] Collection of program environment information may be accomplished as the programming plan generation server 100 acquires program environment information directly input by the user, or the programming plan generation server 100 directly collects program environment information from an external server through a network. In the former case, the programming plan generation server 100 may collect program environment information by accepting information directly input by the user as is, and in the latter case, the programming plan generation server 100 may implement step S103 by directly collecting program environment information, i.e., information needed for generating a programming plan, through a network when a request for generating a programming plan is received from the user.

[0046] Meanwhile, steps S101 and S103 may be omitted as needed. For example, the programming plan generation server 100 may be implemented to automatically start generation of a programming plan when the need for a new programming plan exceeds a preset value after calculating the need for a new programming plan by itself although there is no separate request from the user. In addition, the programming plan generation server 100 may be designed to collect information one by one whenever needed, rather than collecting programming plan generation environment information as shown in step S103 before earnestly starting generation of a programming plan.

[0047] Unlike steps S101 and S103 that can be omitted, steps S105 and S107 are essential steps that should be included in the method of generating a programming plan according to the present invention. Step S105 may be a step of calculating an expected audience seating rate of specific contents based on information on the specific contents, and step S107 may be a step of generating a new programming plan based on the expected audience seating rate calculated previously.

[0048] Step S105 may include a step of calculating a first expected audience seating rate, which is an expected audience seating rate expected when screening new contents (S1051), and a step of calculating a second expected audience seating rate, which is an expected audience seating rate expected when re-screening past contents (S1053).

[0049] In the step of calculating an expected audience seating rate of specific contents at step S105, the programming plan generation server 100 may refer to various types of data as shown in FIG. 3. Referring to FIG. 3, the programming plan generation server 100 may typically utilize at least one type of data among commercial area data, contents data, and customer data, and preferably utilize all three types of data. The commercial area data may include statistical data on commercial areas around a theater that the programming plan generation server 100 may acquire, and this may include detailed data such as residential population around the theater, employed population around the theater, floating population around the theater, the income level of the residential population around the theater, the income level of the employed population around the theater, and the like. The contents data may include various data related to the contents that are the target of calculation, and for example, detailed data such as a contents rating, a cumulative number of viewers, a contents box office index, nationality of contents, genre of contents, and the like may be included. The customer data may include data about customers (audience) who have used the theater, and this may include detailed data such as ticket sales performance of each theater, preference for a special theater, theater occupancy rate by gender, audience seating rate by genre, and the like. The programming plan generation server 100 may be equipped with a storage means capable of storing a large amount of data, and the programming plan generation server 100 may refer to the stored data when calculating an expected audience seating rate at step S105. On the other hand, the large amount of data may be stored in a separate database system, and the programming plan generation server 100 may access the database system through a network and acquire necessary data.

[0050] For reference, in calculating an expected audience seating rate of new contents and an expected audience seating rate of past contents, some types of contents data used by the programming plan generation server 100 may be different. Specifically, when calculating an expected audience seating rate of new contents, the programming plan generation server 100 may utilize the contents box office index, nationality of contents, or genre of contents among the contents data, and when calculating an expected audience seating rate of past contents, the programming plan generation server 100 may further refer to data recorded at the time of screening the contents in the past, such as a contents rating and a cumulative number of viewers, in addition to the types mentioned above.

[0051] Meanwhile, referring to FIG. 2 again, at step S105, when calculating an expected audience seating rate of new contents, the programming plan generation server 100 may further utilize summary analysis data that analyzes summary contents including summary information of the new contents (S1052).

[0052] A process of calculating an expected audience seating rate of new contents by further utilizing the summary analysis data by the programming plan generation server 100 is conceptually shown in FIG. 4. For reference, the summary contents mean all types of contents, such as plot text, trailer videos, or still images of new contents, that can estimate the story, genre, and characters of the new contents. Referring to FIG. 4, the process of calculating an expected audience seating rate by further utilizing the summary analysis data by the programming plan generation server 100 may first include a step of performing morphological analysis and embedding on the summary contents (S10521). The plot text of new contents, video analysis of trailer videos, text that can be extracted from voice analysis, text that can be extracted from image analysis of still images, and the like may be targets of the morphological analysis and embedding. The morphological analysis may be performed by utilizing a language model (BERT, Roberta, or the like) generated by the programming plan generation server 100 based on existing contents. Thereafter, the programming plan generation server 100 may search for past contents, of which the similarity to the new contents is higher than a preset value, with reference to the result of performing the morphological analysis and embedding (S10522). A cosine similarity operation may be performed in the process of calculating the similarity. When past contents similar to new contents are searched through the above step, contents data and / or customer data related to the past contents may be acquired (S10523), and the data acquired in this way may be utilized to calculate an expected audience seating rate of the new contents. The acquired data will be referred to as summary analysis data. Such summary analysis data may be input into a multimodal model (such as MT-DNN) of the programming plan generation server 100 as an input value and utilized for calculating an expected audience seating rate of the new contents.

[0053] Meanwhile, when whether the expected audience seating rate of the new contents (first expected audience seating rate) is calculated before calculating the expected audience seating rate of the past contents (second expected audience seating rate) or vice versa is not determined at step S105, the expected audience seating rates of both the new and past contents may be calculated simultaneously. However, it is preferable to implement to execute the step of calculating the expected audience seating rate of the new contents first, since whether or not to calculate the expected audience seating rate of the past contents may be determined according to whether the expected audience seating rate of the new contents is high or low. That is, since the programming plan generation server 100 calculates the expected audience seating rate of the new contents first, and only needs to generate a programming plan for the new contents scheduled to be released when the value of the expected audience seating rate of the new contents exceeds a reference value, it does not need to calculate the expected audience seating rate of the past contents, and on the contrary, when the expected audience seating rate of the new contents does not reach the reference value, the expected audience seating rate expected when the past contents are re-released may be calculated and used to generate a programming plan.

[0054] Meanwhile, when the expected audience seating rate of the new contents does not reach the reference value, the programming plan generation server 100 first selects arbitrary past contents, and then calculates an expected audience seating rate of the past contents. At this point, the criteria for choosing or selecting past contents may be determined in advance by an algorithm. For example, the programming plan generation server 100 may select past contents, of which the similarity to the new contents is higher than a preset value, among a plurality of past contents, and calculate an expected audience seating rate of the past contents. For reference, it is understood that the similarity between the new contents and the past contents described in this process is different from the similarity mentioned when the summary analysis data is described above. The similarity mentioned in this paragraph is calculated in the process of selecting past contents, which will be the target of analysis when the expected audience seating rate of the new contents does not reach the reference value, and the similarity mentioned in the explanation of summary analysis data is calculated in the process of calculating an expected audience seating rate of the past contents when any past contents have been determined as the target of analysis. It is understood that the two similarities are calculated for different needs although the terms are the same.

[0055] FIG. 5 is a view for explaining an embodiment in which the programming plan generation server 100 selects arbitrary past contents when the expected audience seating rate of new contents is lower than a reference value. When new contents A is scheduled to be released on June 14, 2023, and the result of calculating the expected audience seating rate of new contents A by the programming plan generation server 100 is expected not to reach a reference value (e.g., 15%), the programming plan generation server 100 may search for and select past contents having conditions similar to those of new contents A. FIG. 5 shows an embodiment in which past contents of high similarities are searched for and selected on the basis of the release date (screening period), genre, country in which the contents are produced, and running time. Referring to this, it can be seen that past contents #1 has a similarity of 90, past contents #2 has a similarity of 81, and past contents #3 has a similarity of 46, and as a result, past contents #1 and #2 have been selected as analysis targets, or more precisely, as targets for calculating an expected audience seating rate.

[0056] Referring to FIG. 2 again, after expected audience seating rates of the new contents and / or the past contents are calculated, the programming plan generation server 100 may generate a programming plan with reference to the calculation result (S107). Referring to the calculation result means that the programming plan is mainly configured of contents having a high expected audience seating rate value so that the audience seating rate can be maximized during the period when the contents are the target of analysis. When the expected audience seating rate of the new contents is higher than a predetermined level, the programming plan is configured so that the new contents can be screened as many times as possible, and when the expected audience seating rate of the new contents is expected to be lower than the expected audience seating rate of the past contents, the programming plan may be configured in the direction of eliminating or reducing the number of times of screening the new contents and increasing the number of times of screenings the past contents. The process of comparing the expected audience seating rates of the new contents and the past contents may consider the period, day of the week, time zone, and the like that will be the target of analysis. That is, the programming plan may be mainly configured of contents expected to have a higher expected audience seating rate among the new contents and the past contents under the same condition.

[0057] Step S107 may be implemented to generate a programming plan for each of a plurality of theaters existing within a region nationwide, as well as generating a programming plan only for any one theater. The programming plan generation server 100 may rank the expected audience seating rate of new contents and the expected audience seating rate of past contents for each theater, predict these expected audience seating rates by region and time zone, and then generate a programming plan prioritizing the contents with higher aggregated or combined value of the expected audience seating rates.

[0058] Representative embodiments of a method of generating a programming plan according to the present invention have been described above with reference to FIG. 2. Hereinafter, a detailed embodiment of each situation will be described with reference to FIGS. 6 to 8.

[0059] FIG. 6 is a view showing a method of generating a programming plan performed by a programming plan generation server 100 when there are no new contents scheduled to be released. Referring to FIG. 6, the programming plan generation server 100 may receive a request for generating a programming plan from a user (S601), collect programming plan generation environment information (S602), and grasp whether new contents are scheduled based on the collected programming plan generation environment information (S603). When it is determined that no new contents are scheduled to be released as a result, the programming plan generation server 100 may immediately search for and select appropriate past contents, for example, past contents having conditions the same as or similar to the current conditions (season, day of the week, and the like) (S604), calculate an expected audience seating rate expected at the time of re-releasing the selected past contents (S605), and then generate a programming plan with reference to the calculated expected audience seating rate values (S606).

[0060] FIG. 7 is a view for explaining a situation where there are new contents scheduled to be released, but the expected audience seating rate of the new contents does not reach a reference value. Referring to FIG. 7, the programming plan generation server 100 may start calculation of the expected audience seating rate of the new contents (S702) after receiving a request for generating a programming plan from a user (S701). When the expected audience seating rate is lower than the reference value, the programming plan generation server 100 may search for and select arbitrary past contents (S704), for example, past contents that have a similarity higher than a preset value when compared to the new contents (S703), or past contents that have conditions the same as or similar to the current conditions (season, day of the week, and the like), calculate an expected audience seating rate of the selected past contents (S705), and then generate a programming plan (S706) with reference to the calculated expected audience seating rate values. For reference, although the step of collecting programming plan generation environment information is intentionally omitted in this detailed embodiment, it is understood that the step may be included as needed.

[0061] Referring to FIG. 7 again, when the expected audience seating rate of the new contents exceeds the reference value, the programming plan generation server 100 may proceed directly to the step of generating a programming plan (S706) without searching for past contents or calculating the expected audience seating rate of past contents so that the programming plan may be generated using only the new contents.

[0062] FIG. 8 is a view showing another detailed embodiment, which shows a situation in which both an expected audience seating rate of new contents (first expected audience seating rate) and an expected audience seating rate of past contents (second expected audience seating rate) are calculated, and the second expected audience seating rate is calculated as a value higher than the first expected audience seating rate. Referring to FIG. 8, after the step of receiving a request for generating a programming plan from a user (S801), the programming plan generation server 100 may calculate an expected audience seating rate of new contents and an expected audience seating rate of arbitrary past contents (S802). When the expected audience seating rate of the past contents is higher than the expected audience seating rate of the new contents as a result of the calculation, the programming plan generation server 100 may generate a programming plan by increasing the screening ratio of the past contents (S803). On the contrary, when the expected audience seating rate of the new contents is higher, the programming plan generation server 100 will naturally generate a programming plan (S804) that increases the ratio of the new contents.

[0063] A method and a system for generating a programming plan have been described above.

[0064] Meanwhile, the method and the system described above through the drawings may be utilized in the field of e-commerce or broadcasting (OTT), as well as in predicting the audience seating rate in a movie theater and planning a movie contents listing.

[0065] FIG. 9 is a view showing an embodiment utilized in the field of e-commerce. In the field of e-commerce, a marketing server (not shown) may perform information collection and calculation instead of the programming plan generation server 100, and the marketing server may generate and recommend a promotion to a user (e.g., a marketing planner) by performing a series of steps as shown in FIG. 9. Referring to the drawing, the marketing server may first receive a promotion generation request from a user (S901).

[0066] After receiving the promotion generation request, the marketing server may collect market environment information (S903). The market environment information may include all basic information required for the marketing server to generate a promotion, and the market environment information may also include information directly input by the user when the marketing server is requested to generate a promotion. For example, a period for conducting the promotion, a target customer layer for conducting the promotion, whether the promotion will be conducted offline or online, stores in a region for conducting the promotion, new or old products for conducting the promotion, and whether release of a new product is scheduled may be included in the major market environment information. This step may be omitted according to freedom of system design.

[0067] After step S903, a step of calculating an expected sales volume may be executed by the marketing server (S905). In this step, the expected sales volume of a new product to be promoted may be calculated (S9051), and the expected sales volume of an old product may also be calculated as needed (S9053). For example, when a new product of a rice product is scheduled to be released, an expected sales volume of the new product may be calculated, and when the calculated expected sales volume does not reach a preset value, the expected sales volume of the old product that has already been released may be calculated under the same condition. Alternatively, when release of a new product is not scheduled, an expected sales volume of the old product may be calculated under the condition of the promotion. At this point, data referenced by the marketing server may include commercial area data (information indicating the level of distribution / sales of the product in the area targeted for promotion; residential population around the store, employed population around the store, floating population around the store or within the area, income level of residents around the store or within the area, income level of office workers, and the like), and in addition, product data (information related to the product that is the target of calculation; product name, price, product evaluation score, sales area, country of origin, and the like) and customer data (regions preferred by customers, cumulative sales volume, monthly average sales volume, share of the product in each region, and the like) may also be referenced.

[0068] After the expected sales volume is calculated in the above step, a promotion may be generated and recommended to the user (S907). In this step, product recommendation information indicating which product would be good as a target for promotion may be necessarily included as a result of the calculation, and in addition, the expected sales volume of new and old products, discount rates of the products, or recommendation rankings calculated by the marketing server may also be included. At step S907, numerical data indicating a period of time during which the new or / and old products should be sold to achieve good sales may be provided, and although the new product is scheduled to be released based on the expected sales rate, a promotion including only the old products may be recommended. In this way, the methodology according to the present invention shown in FIG. 2 may be applied to the field of e-commerce.

[0069] FIG. 10 is a view showing an example of utilizing the methodology described above in the field of broadcasting (OTT). FIG. 10 is described on the assumption that the calculation is performed by a broadcast server instead of the marketing server.

[0070] Referring to the drawing, the broadcast server may first receive a request for generating a broadcast programming plan from a user (S1001).

[0071] After receiving the request for generating a broadcast programming plan, the broadcast server may collect programming plan generation environment information (S1003). The programming plan generation environment information may include all basic information required when the broadcast server generates a programming plan, and the programming plan generation environment information may also include information directly input by the user when the user requests the broadcast server to generate a programming plan. For example, a period and a time zone for which the programming plan is generated, a viewer group for which the programming plan is generated, whether there are new broadcast contents, and the like may be included in the major programming plan generation environment information. This step may be omitted according to freedom of system design.

[0072] After step S1003, a step of calculating an expected viewership rate may be executed by the broadcast server (S1005). In this step, an expected viewership rate of new contents considered when a programming plan is generated may be calculated (S10051), and an expected viewership rate of past contents, i.e., contents that can be rebroadcast, may also be calculated as needed (S10053). For example, when generation of a programming plan including new entertainment contents is expected, an expected viewership rate of the new entertainment contents may be calculated, and when the calculated expected viewership rate does not reach a preset value, an expected viewership rate of the past contents that can be rebroadcast under the same condition may be calculated. Alternatively, when generation of a programming plan including new entertainment contents is not scheduled, an expected viewership rate for the past contents may be calculated under the programming condition desired by the user. At this point, characteristic data of a broadcast target region (information indicating the viewership level for both the new and past contents in a corresponding region; residential population in the region, employed population in the region, floating population in the region, income level of residents in the region, income level of employed population in the region, and the like) may be included in the data referenced by the broadcast server, and contents data (information related to the contents that are the target of calculation; contents rating, contents box office index, nationality of contents, genre of contents, and the like), customer data (regions preferred by customers, cumulative average viewership rating, viewership rating by region, and the like), and the like may also be referenced.

[0073] After the expected viewership ratings are calculated in the above step, a broadcast programming plan may be generated and recommended to a user (S1007).

[0074] A method of generating a programming plan and a system therefor according to the present invention have been described above. Meanwhile, the present invention is not limited to the specific embodiments and application examples described above, and various modifications can be made by those skilled in the art without departing from the gist of the present invention claimed in the claims, and these modifications should not be understood as being distinguished from the technical spirit or prospect of the present invention.

Claims

1.A method of generating a programming plan, the method comprising the steps of:(a) calculating a first expected audience seating rate expected when screening new contents on the basis of information on the new contents;(b) loading information on arbitrary past contents;(c) calculating a second expected audience seating rate expected when screening the past contents on the basis of information on the past contents;(d) generating the programming plan on the basis of the first expected audience seating rate and the second expected audience seating rate.2.The method according to claim 1, wherein step (a) of calculating a first expected audience seating rate calculates the first expected audience seating rate by utilizing at least one data among commercial area data, contents data, and customer data, whereinthe commercial area data includes residential population around a theater, employed population around the theater, floating population around the theater, an income level of the residential population around the theater, or an income level of the employed population around the theater,the contents data includes a contents box office index, nationality of contents, or genre of contents, andthe customer data includes ticket sales performance of each theater, preference for a special theater, theater occupancy rate by gender, or audience seating rate by genre.3.The method according to claim 1, wherein step (c) of calculating a second expected audience seating rate calculates the second expected audience seating rate by utilizing at least one data among commercial area data, contents data, and customer data, whereinthe commercial area data includes residential population around a theater, employed population around the theater, floating population around the theater, an income level of the residential population around the theater, or an income level of the employed population around the theater,the contents data includes a contents rating, a cumulative number of viewers, a contents box office index, nationality of contents, or genre of contents, andthe customer data includes ticket sales performance of each theater, preference for a special theater, theater occupancy rate by gender, or audience seating rate by genre.4.The method according to claim 1, wherein step (a) of calculating a first expected audience seating rate calculates the first expected audience seating rate by further utilizing summary analysis data that analyzes summary contents including summary information of the new contents.5.The method according to claim 4, wherein the summary analysis data is generated by a summary contents analysis process including the steps of:performing morphological analysis and embedding on the summary contents;searching for past contents, of which a similarity to the new contents is higher than a preset value, with reference to a result of performing the morphological analysis and embedding; andacquiring at least one among contents data and customer data of the past contents.6.The method according to claim 4, wherein the summary content includes at least one among plot text, trailer video, and still image.7.The method according to claim 1, wherein the arbitrary past contents are one among past contents of which a similarity to the new contents is higher than a reference value.8.The method according to claim 1, further comprising, after step (a), the step of determining whether the first expected audience seating rate is lower than a reference value, wherein when the first expected audience seating rate is lower than the reference value, the steps after step (b) are executed.9.The method according to claim 8, wherein when the first expected audience seating rate is lower than the reference value, step (b) selects past contents of which a similarity to the new contents is higher than a preset value, and then loads only information on the selected past contents.10.The method according to claim 1, wherein after step (c), when the first expected audience seating rate is lower than the second expected audience seating rate, step (d) generates a programming plan that increases the proportion of the past contents to be higher than the new contents.11.A programming plan generation server comprising a central processing unit and a memory to generate a programming plan, whereinthe central processing unit executes commands for executing a method of generating a programming plan stored in the memory, andthe method of generating a programming plan comprises the steps of:(a) calculating a first expected audience seating rate expected when screening new contents on the basis of information on the new contents;(b) loading information on arbitrary past contents;(c) calculating a second expected audience seating rate expected when screening the past contents on the basis of information on the past contents;(d) generating the programming plan on the basis of the first expected audience seating rate and the second expected audience seating rate.

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