JP2011124655A - Video content recommendation management device, method for executing video content recording, and program thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an automatic program timer-recording technique for giving unconscious "awareness" of a viewer. <P>SOLUTION: A management server 1 connected to an STB5 for receiving a broadcast signal and causing the STB5 to record video content includes: a customer information storage section 181 for storing customer information; a customer-specific timer-recording setting section 105 for acquiring content recommendation information 20 defined in accordance with the details of video content and indicating the degree of recommendation for each customer attribute for each piece of video content, and then specifying video content to be recommended for a certain customer in accordance with the attributes of the certain customer, on the basis of the acquired program recommendation information and customer information; and a timer-recording instructing section 107 for outputting an instruction to record the specified video content to the STB5 of a customer. The management server 1 causes the STB5 to record the video content. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a technique for automatically recording or storing video content such as a broadcast program, and more particularly to a technique for automatically recording recommended content based on potential preferences or needs that the viewer himself / herself does not notice.

  In recent years, in addition to terrestrial broadcasting, there are various broadcast channels, and the number of broadcast programs has become enormous. Therefore, a technique for automatically recording a broadcast program without requiring the viewer to make a recording reservation by himself / herself is known. For example, Patent Document 1 describes a technique for automatically recording a program that is presumed to be a viewer's preference based on a keyword specified by the viewer and a preference value determined by a content reproduction history.

JP 2006-86638 A

  In Patent Document 1, a viewer's preference is estimated based on keywords explicitly specified by the viewer, content that the viewer views on his / her own will, and the like. Thus, inferring from actions based on the viewer's own consciousness, it is not possible to obtain so-called “awareness” and “exposure” that match potential preferences or needs that the viewer himself / herself does not notice.

  Therefore, an object of the present invention is to provide an automatic program reservation technology that provides “awareness” that the viewer is not aware of.

  A video content recommendation management device according to one embodiment of the present invention is connected to a receiving device for a customer via a network, and causes the receiving device to record video content, including identification information of the customer's receiving device, and Storage means for storing customer information including attributes, program recommendation information acquisition means for acquiring program recommendation information indicating the degree of recommendation for each video content, which is determined according to the content of the video content, and the acquired program Based on the recommended information and the customer information, a specifying means for specifying video content recommended for the customer according to an attribute of the one customer, and the specified video for the receiving device of the one customer Output means for outputting a content recording instruction, and causing the one customer's receiving device to record the specified video content.

  In a preferred embodiment, it may include gender, age, and favorite content type.

  In a preferred embodiment, the program recommendation information acquisition unit acquires writing information including an expectation degree for video content, and the program recommendation is based on the evaluation of the other person to the writer of the writing information and the expectation degree. Information may be generated.

  In a preferred embodiment, the information processing apparatus further comprises means for acquiring viewing history information of video content from the receiving device of the one customer, and the specifying means refers to the acquired viewing history with respect to the one customer. The recommended video content may be specified.

1 shows an overall configuration of a video content management system according to an embodiment of the present invention. The functional structure of STB5 is shown. The function structure of PC7 is shown. An example of customer information 1810 stored in the customer information storage unit 181 is shown. 1 is a configuration diagram of an advanced reservation system 100. FIG. An example of the content recommendation information 20 is shown. An example of the basic data 30 is shown. It is a flowchart which shows an example of the production | generation procedure of the content recommendation information. An example of a score table is shown. It is a flowchart which shows an example of the procedure of content scoring and addition. It is an example of the score according to contents of a certain customer (male 40s). It is a flowchart which shows an example of the production | generation procedure of a video recording reservation schedule. The example in the case of producing | generating a recording reservation according to the flowchart of FIG. 1 is a configuration diagram of an optimum knitting system 150. FIG. An example of the scene information 40 is shown. It is a flowchart which shows an example of the production | generation procedure of scene information. It is a flowchart which shows an example of the production | generation procedure of scene information. It is explanatory drawing of the optimal organization list. An example of the composition instruction information 60 is shown. It is a flowchart which shows an example of the procedure of an optimal organization list | wrist production | generation process. An example of a viewing screen 600 displayed on the television receiver 6 at the time of optimal organization viewing is shown.

  Hereinafter, a management system for managing video content represented by a broadcast program or the like according to an embodiment of the present invention will be described with reference to the drawings. Video content includes content including still images in addition to various broadcast programs and video content such as VOD content.

  FIG. 1 shows the overall configuration of the present system. This system provides video content such as broadcast programs to a plurality of customers and services related to the use of video content.

  As shown in the figure, this system includes a management server 1 that manages a receiving apparatus such as an STB (set top box) 5 and one or more transmitting apparatuses 2 (2a to 2d) that transmit broadcast signals. An STB 5, a television receiver 6, and a PC (personal computer) 7 are installed at each customer's home.

  The transmitter 2 includes, for example, a transmitter 2a of a terrestrial broadcast station, a transmitter 2b of a satellite broadcast station such as BS or CS, a transmitter 2c of a CATV station, and a VOD server 2d.

  The management server 1 and the transmission device 2 are communicably connected to an STB 5 installed at a customer's home or the like via a predetermined communication means. The STB 5 receives and records (stores in a storage device) a broadcast signal transmitted from the transmission device 2 in accordance with an instruction from the management server 1 or an instruction from a customer. Further, the STB 5 outputs the received or recorded broadcast signal to the television receiver 6 in accordance with the instruction from the management server 1 or the instruction from the customer, and reproduces the video content.

  FIG. 2 shows a functional configuration of the STB 5. The STB 5 includes one or more tuners 550 for receiving broadcast signals, and a control unit 500 that performs communication with the management server 1 and controls the tuner 550.

  The control unit 500 is configured by a computer system including a predetermined processor and memory, for example, and individual components or functions in the control unit 500 described below are realized by executing a computer program, for example. This computer program can be stored in a computer-readable recording medium.

  The control unit 500 includes an action history acquisition unit 501, a recording reservation management unit 503, a storage device 505, a recorded content operation unit 507, and a reproduction management unit 509.

  The action history acquisition unit 501 acquires information related to the content viewed by the customer on the television receiver 6 as action history data. For example, the action history acquisition unit 501 acquires identification information of content received by the tuner 550 and viewed in real time on the television receiver 6 and recorded (stored in the storage device 505). The action history acquisition unit 501 transmits the acquired action history data to the management server 1.

  The recording reservation management unit 503 stores (records) the content transmitted from the transmission device 2 in the storage device 505 in accordance with the recording reservation instruction from the recording reservation instruction unit 107 of the management server 1. For example, the recording reservation management unit 503 may store the content in the advanced recording area 505a described below.

  The storage device 505 is a storage device for storing the content transmitted from the transmission device 2. In the storage device 505, a pre-recording area 505a for recording according to an instruction from the recording reservation management unit 503 may be secured in advance. In the advance recording area 505a, the content that has received a recording reservation instruction from the recording reservation management unit 503 is stored. In addition to this, the storage device 505 also stores content for which the customer has instructed recording.

  The recorded content operation unit 507 deletes recorded content stored in the storage device 505. For example, the recorded content operation unit 507 deletes the content stored in the storage device 505 based on the delete command transmitted from the recorded content management unit 109.

  The reproduction management unit 509 reproduces the content according to the instruction from the reproduction instruction unit 155. For example, the reproduction management unit 509 acquires an optimal organization list described later from the reproduction instruction unit 155. Then, upon receiving an instruction for optimal composition viewing from the customer, the reproduction management unit 509 causes the content stored in the storage device 505 to be displayed on 6 in accordance with the optimal composition list.

  FIG. 3 shows a functional configuration of the PC 7.

  The PC 7 includes an action history acquisition unit 701 and a composition correction unit 703.

  The action history acquisition unit 701 transmits to the server 1 action history data including information on a Web site or a moving image site that a customer has searched for, browsed, or viewed using a Web browser (not shown) of the PC 7.

  In response to an instruction from the customer, the composition correcting unit 703 generates composition instruction information 60 (see FIG. 19). Then, the composition correcting unit 703 transmits the generated composition instruction information 60 to the management server 1.

  Referring to FIG. 1 again, the management server 1 is configured by, for example, a general-purpose computer system, and individual components or functions in the management server 1 described below are realized by, for example, executing a computer program. The This computer program can be stored in a computer-readable recording medium.

  The management server 1 has acquired the advanced reservation system 100, the optimum scheduling system 150, the customer information storage unit 181 for storing customer information, the behavior history receiving unit 182 for acquiring customer behavior history data from the STB 5 or the PC 7, and And an action history storage unit 183 that stores action history data.

  The customer information storage unit 181 stores customer information including customer attributes and the like. FIG. 4 shows an example of customer information 1810 stored in the customer information storage unit 181. As shown in the figure, customer information 1810 includes at least customer ID 1811, STB-ID 1812 which is identification information of each STB 5, customer gender 1813, age 1814, STB tuner number 1815, and favorite content type. 1816 is included as a data item. In addition, data items such as a customer's name, address, occupation, and family structure may be included.

  The action history receiving unit 182 receives action history data transmitted from one or both of the customer STB 5 and the PC 7 together with the customer identification information. The action history data acquired from the STB 5 includes, for example, a recording history of video content (hereinafter simply referred to as content), a viewing history on the television receiver 6 and the like. The acquired action history data acquired from the PC 7 includes, for example, keywords used for browsing or viewing history of web sites and moving image sites using a web browser (not shown) and searching.

  The behavior history storage unit 183 stores the behavior history data acquired by the behavior history reception unit 182 for each customer.

  FIG. 5 is a configuration diagram of the advanced reservation system 100. The advance reservation system 100 makes a recording reservation for content recommended to be viewed by each customer.

  The management server 1 includes a content information receiving unit 101 that acquires information related to content, a scoring processing unit 103 that scores content, a customer-specific recording reservation setting unit 105 that sets a recording reservation schedule for each customer, a customer A recording schedule storage unit 106 that stores the recording reservation schedule for each customer generated by the separate recording reservation setting unit 105, and a recording reservation instruction unit that instructs the STB 5 of the customer to make a recording reservation (advance reservation) according to the set recording reservation schedule. 107 and a recorded content management unit 109 that manages recorded content stored in the STB 5.

  The content information receiving unit 101 receives content broadcast information 10 that is information related to content. The content broadcast information 10 is, for example, content metadata, such as content title, performer, genre (news, drama, variety, etc.), broadcast schedule (broadcast station, broadcast start and end times, etc.), etc. Including.

  The scoring processing unit 103 and the customer-specific recording reservation setting unit 105 specify content recommended for the customer based on the content recommendation information 20 and the customer information 1810 according to the attribute of a certain customer.

  The scoring processing unit 103 performs scoring of each content. First, the scoring processing unit 103 acquires the content recommendation information 20 from the outside. The scoring processing unit 103 then scores each content based on the content broadcast information 10 and the content recommendation information 20 acquired by the content information receiving unit 101.

  The content recommendation information 20 is information indicating a recommendation level for each content for each customer attribute determined according to the content. For example, the content recommendation information 20 is obtained by assigning points indicating the degree of recommendation to each content according to attributes such as the gender, age and favorite content type of the viewer (customer).

  FIG. 6 shows an example of the content recommendation information 20. In the example shown in the figure, for example, the content recommendation information 20 includes content identification information (title name, broadcast date, station name / start and end time) 21, viewer attributes 23, and a score 25 indicating a recommendation level. including.

  The scoring processing unit 103 may acquire the basic data 30 for generating the content recommendation information 20 and generate the content recommendation information 20 by itself. For example, the scoring processing unit 103 obtains writing information (basic data) including the degree of expectation for the content, and generates content recommendation information based on the evaluation and the degree of expectation of others of the writing information by the writer. Also good.

  For example, an example of the basic data 30 is shown in FIG. In the example shown in the figure, the basic data 30 is, for example, writing to content operated on the Internet such as SNS (Social Networking Service). The basic data 30 includes, for example, the number of respondents 37 who answered “helpful” by looking at the title 31, the writer 32, the writer's gender 33 and age 34, the comment 35, and the expectation level 36 of the content. Including. The scoring processing unit 103 generates content recommendation information 20 based on the basic data 30.

  The procedure for generating the content recommendation information 20 is shown in the flowchart of FIG. Hereinafter, a procedure in which the scoring processing unit 103 generates the content recommendation information 20 from the basic data 30 will be described according to this flowchart.

  First, the scoring processing unit 103 acquires the basic data 30 described above from a site on the Internet (S11). The scoring processing unit 103 may acquire only the data written in a certain period based on the time when the basic data 30 was written to the site on the net by using a technique such as crawl.

  In the basic data 30 acquired here, the scoring processing unit 103 scores the credibility of the writer 32 on the basis of the number of respondents 37 who are “referenced” (S13). For example, the scoring processing unit 103 obtains the sum of the number of respondents 37 “referenced” for each writer 32, and evaluates the credit rating into five levels based on this. This is because the more people who answered “helpful”, the higher the credibility of the writer.

  The scoring processing unit 103 calculates points for each content and each attribute (S15). For example, the score obtained by multiplying the expectation 36 of the basic data 30 and the credibility of the writer calculated in step S13 is added for each writer's attribute (gender 33, age 34), and this is added to each content, attribute Use another score. For example, in the example of FIG. 7, the number of respondents 37 who were “referenced” for Mr. A is 230, and, for example, when the credit rating is calculated as “5” in five stages in step S13, Since the expectation degree 36 is “5”, the score for the customer attribute “40's male” of the content “A Jiro 1st” is 5 × 5 = 25.

  The scoring processing unit 103 normalizes the content-specific and attribute-specific scores calculated in step S15 so as to be distributed in a range of, for example, 1 to 100, and sets the normalized scores as respective recommended scores (S17). ).

  Steps S15 and S17 are repeatedly executed for each content and each attribute (S19).

  As a result, the scoring processing unit 103 can also generate content recommendation information 20 by content and by attribute from the basic data 30.

  The scoring processing unit 103 may further add points to the content score based on the action history data.

  For example, the scoring processing unit 103 generates a score table for each customer based on the behavior history data for each customer in the behavior history storage unit 183. FIG. 9 shows an example of the point addition table. FIG. 6A shows a score table for each genre based on the viewing history, and FIG. 5B shows a score table for each keyword based on the search history in the Web browser. In each score table, a high score is assigned to a genre that a viewer is viewing more, and a high score is assigned to a keyword that is used more frequently in a search. The scoring processing unit 103 adds points to the content according to the addition table.

  FIG. 10 is a flowchart illustrating an example of a content scoring and adding procedure performed by the scoring processing unit 103 for each customer. The procedure for scoring and adding content by the scoring processing unit 103 will be described below according to this flowchart.

  First, the scoring processing unit 103 selects unscored content (S21).

  The scoring processing unit 103 determines whether there is content recommendation information 20 for the selected content (S23). When there is the content recommendation information 20 (S23: Yes), the scoring processing unit 103 adds the score 25 as the score of the content if the attribute 21 of the content recommendation information 20 matches the attribute of the target customer (S25). ). When there is no content recommendation information 20 (S23: No), step S25 is skipped.

  Next, the scoring processing unit 103 adds points based on the action history (S27). For example, the scoring processing unit 103 creates the above-described score table based on the behavior history data of the target customer, and adds the score of the content that matches the genre or keyword based on the score table. When performing this addition, the scoring processing unit 103 may appropriately weight a specific genre or keyword.

  The scoring processing unit 103 applies the above processing to all the contents (S29). As a result, scoring and points are added to all contents.

  FIG. 11 is an example of a scoring result in the scoring processing unit 103, and is an example of a score for each content of a certain customer (male in his 40s).

  Referring to FIG. 5 again, the customer-specific recording reservation setting unit 105 generates a customer-specific recording schedule based on the scoring result (see FIG. 11) in the scoring processing unit 103, and corresponds to the customer identification information. At the same time, it is stored in the recording schedule storage unit 106. For example, the customer-specific recording reservation setting unit 105 may generate a recording schedule for each customer with content having a score equal to or higher than a predetermined value, or a predetermined number of content items in order from the highest score. The customer-specific recording reservation setting unit 105 is not what the customer himself / herself desires, but the recording for making a recording reservation ahead of “recommended content that the customer would like” determined by the management server 1 based on the customer attributes. Generate a schedule.

  FIG. 12 is a flowchart showing an example of a procedure for generating a recording reservation schedule, and FIG. 13 shows an example of generating a recording reservation according to this flowchart.

  Hereinafter, the generation of the recording reservation schedule will be described with reference to FIGS.

  First, the customer-specific recording reservation setting unit 105 refers to the customer information storage unit 181 and confirms the number of tuners 1815 of the target customer's STB 5 (S111).

  Next, the customer-specific recording reservation setting unit 105 advances the time by unit time (S113). Based on the scoring result in the scoring processing unit 103, the customer-specific recording reservation setting unit 105 selects the content having a high score at this time as the recording candidate content (S115).

  For example, in the example of FIG. 13, when the number of tuners is “2” and the time is 20:00, ch1 content (score: 100) and ch2 content (score: 80) are selected.

  At this time, if the lowest content is the same, and if all the content of the same score is selected and the number of tuners is exceeded (S117: Yes), the customer-specific recording reservation setting unit 105 selects from the content of the same score. The content that has been selected from before is prioritized (S119).

  Also, it is determined whether or not there is a content replacement in which new content with a high score becomes a candidate and content with a low score that has been a candidate so far is excluded from the candidate (S121).

  In the example of FIG. 13, when the time is 20:40, the ch3 content (score: 90) has a higher score than the ch2 content (score: 80), so content replacement occurs at this point. When the content is replaced (S121: Yes), it is determined whether or not the reservation time so far for the content that is not a candidate is equal to or less than a predetermined reference (S123). When the reservation time is equal to or less than the predetermined reference (S123: Yes), the content replacement is canceled and the content recording performed so far is continued (S125). If the reservation time is not less than or equal to the predetermined reference (S123: No), the content replacement is validated and the process returns to step S113.

  In the example of FIG. 13, at the time of 20:40, the recording of the content of ch2 has already been completed for 40 minutes out of the total of 50 minutes. Here, if the determination criterion in step S123 is 1/3 of the time of the entire content, in the example of FIG. 13, recording has been performed exceeding this criterion. Therefore, in this case, the recording of ch2 is interrupted and the switching to ch3 is performed without canceling the replacement of contents.

  The customer-specific recording reservation setting unit 105 stores the recording reservation schedule set by the above processing in the recording schedule storage unit 106.

  The recording reservation instruction unit 107 instructs the customer's STB 5 to make a recording reservation based on the customer-specific schedule stored in the recording schedule storage unit 106.

  The recorded content management unit 109 manages content stored in the storage device 505 (see FIG. 2) of the STB 5. For example, the recorded content management unit 109 refers to the recording schedule storage unit 106 to determine the capacity of the content stored in the advance recording area 505a of the storage device 505 of the STB 5. Then, a command for deleting the content recorded in the recording area 505a is issued so that the capacity of the advance recording area 505a of the storage device 505 of the STB 5 is not filled. For example, the recorded content management unit 109 refers to the recording schedule storage unit 106, and selects the content to be deleted from the content stored in the previous recording area 505a in the order of oldest recording date or lowest score. May be.

  Next, FIG. 14 is a configuration diagram of the optimum knitting system 150.

  As shown in the figure, the optimum composition system 150 includes a scene information reception unit 151, a scene information generation unit 152, an optimum composition unit 153, a reproduction instruction unit 155, a scene information storage unit 157, and an optimum composition list storage. Part 159.

  The scene information receiving unit 151 receives the scene information 40 from the outside. The scene information receiving unit 151 stores the acquired scene information 40 in the scene information storage unit 157.

  Here, a scene refers to a part of content divided into predetermined units. For example, in the case of a news program, a video block divided by individual news becomes one scene, and in the case of a music program, each appearance video block by artist becomes one scene. Even in the case of dramas, the main part of the drama and the drama trailer, etc., which are separated by CM, are each one scene. Furthermore, each CM is one scene. The scene information is meta information including information specifying each scene, information indicating the characteristics of each scene, and the like.

  FIG. 15 shows an example of the scene information 40. In the example shown in the figure, the scene information 40 includes a title 41, a scene name 42, a scene start time 43, a scene end time 44, a title classification 45, a scene classification 46, and an organization flag 47 as data items.

  A title 41 is a title of content including a scene. The scene name 42 is the name of each scene cut out from the content. The scene start time 43 and scene end time 44 are the start time and end time of each scene. The title classification 45 is a content title classification. The scene classification 46 is a classification indicating the contents of the scene. The organization flag 47 indicates scene attributes.

  In the optimal organization system 150, instead of acquiring the scene information 40 from the outside, the scene information generation unit 152 may acquire content data from the transmission device 2 and generate the content data using the content data. 16 and 17 show a procedure for generating scene information. Hereinafter, the generation procedure of the scene information 40 will be described with reference to FIGS. If the scene information 40 can be acquired from the outside, the scene information generation unit 152 may not exist.

  The scene information generation unit 152 scans the content data, and if the content video includes a telop, the scene information generation unit 152 recognizes the telop as characters (S31).

  When the telop recognized here is different from the telop recognized immediately before, or when a CM break is detected, the scene information generation unit 152 divides the content here, before the division point. Are cut out as one scene (S33).

  The scene information generation unit 152 sets the title 41 and title classification 45 of the scene extracted here based on the title and title classification of the content including the scene, and specifies the start time and end time (S34). ).

  The scene information generation unit 152 determines whether or not a telop is included in the scene extracted in step S33 (S35). If a telop is included (S35: Yes), the telop recognized in step S31 is set as the scene name 42 (S37). When the cut-out scene does not include a telop (S35: No), a scene name 42 is generated using a predetermined rule engine (S39). In the rule engine, for example, in the main part of the drama separated by CM, there are rules for naming “times + serial numbers”, rules for naming the opening of “variety programs” as “opening”, and “ending” at the end. ing.

  The scene information generation unit 152 sets the scene classification 46 based on the scene name 42 determined in the above process with reference to the scene dictionary in which keywords for each scene classification are defined (S41). Here, in the scene dictionary, for example, keywords (for example, “Prime Minister”, “National Diet”, etc.) closely related to a predetermined scene classification (for example, “politics”) are associated. That is, the scene information generation unit 152 sets the scene classification associated with the recognized character in the scene dictionary as the classification of this scene.

  The scene information generation unit 152 stores the scene information cut out by the above processing in the scene information storage unit 157 (S43).

  The scene information generation unit 152 repeats the above processing for all target content data, and performs scene segmentation (S45).

  The scene information generation unit 152 sets the composition flag 47 for each scene cut out by the above processing. For this purpose, first, the scene information generation unit 152 selects one scene from the scene information storage unit 157 (S47). When the scene classification 46 of the scene immediately after the selected scene is “CM”, the scene information generation unit 152 has the same scene name as the scene immediately after “CM”, and the length of the scene is within 30 seconds. (S49: Yes), “post-CM notice” is set in the composition flag 47 of the scene (S51). This is because, in news or variety programs, the content broadcast after the CM may be notified immediately before the CM, so the scene is classified as “notice after the CM”.

  When the selected scene does not match the “post-CM notice” condition (S49: No), the scene information generation unit 152 refers to the scene information storage unit 157, and has the same title name 41 as the selected scene. When there are more than two minutes and the scene is within the first two minutes (S53: Yes), “digest” is set in the composition flag 47 of the scene (S55).

  When the selected scene does not meet the condition of “digest” (S53: No), the scene information generating unit 152, when “notice” is included in the scene name 42 of the selected scene (S57: Yes). ) “Next notice” is set in the organization flag 47 of the scene (S59).

  When the selected scene does not meet the “next notice” condition (S57: No), the scene information generation unit 152 organizes the scene when the selected scene is “CM” (S61: Yes). “CM” is set in the flag 47 (S63).

  Until the knitting flag is set for all scenes, the processing from step S47 is repeated (S65).

  By the above processing, scene information can be automatically generated.

  Next, referring again to FIG. 14, the optimum organization unit 153 selects one or more scenes included in the content recorded in the STB 5 based on the recording instruction from a plurality of scenes obtained by dividing the content. It functions as a generation unit that selects and generates a reproduction list (optimum organization list) including one or more selected scenes. For example, the optimum organization unit 153 generates an optimum organization list for each customer and stores the generated optimum organization list in the optimum organization list storage unit 159. When there is a notice scene or digest scene for the main scene included in the optimum composition list, the optimum composition unit 153 may preferentially include the notice scene or digest scene in the optimum composition list.

  The optimum organization list is a scene list for providing a recommended content or a recommended scene for a customer as a digest.

  FIG. 18 is an explanatory diagram of the optimum organization list. For example, it is a list of recommended scenes for each customer, which are a part of scenes extracted and collected from the content that has already been recorded by the advanced reservation system 100 described above.

  The optimum organization unit 153 organizes an optimum organization list so as to suit each customer's preference. Therefore, for example, when a customer refers to the optimal organization list or views a video according to the optimal organization list, an outline of content that is likely to be interesting among the content broadcasted on a certain day is known. be able to. The order of scenes to be included in the optimal organization list may be the order of shortest scene time (or longest order), early order of broadcast time (or late order), or order of high viewer interest.

  For example, the optimum knitting unit 153 knitting the optimum knitting list as follows. The optimum knitting unit 153 acquires the recommended knitting information 50 and knitting the optimum knitting list based on the recommended knitting information 50. The recommended organization information 50 is a template of an optimum organization list composed of recommended scenes according to customer attributes. For example, the recommended organization information 50 includes recommended title classification and scene classification. The optimum organization unit 153 refers to the customer information, selects the recommended organization information 50 corresponding to the customer attribute, and organizes the optimum organization list including the scenes of the title classification and scene classification specified there. Further, the optimum organization unit 153 may select a scene from the content stored in the customer's STB 5 with reference to the recording schedule storage unit 106.

  Further, the optimum knitting unit 153 may acquire the knitting instruction information 60 from the customer's input to the PC 7 or the STB 5, and may perform knitting according to the preference of each customer according to the acquired knitting instruction information 60.

  FIG. 19 shows an example of the composition instruction information 60. As in the example of the figure, the composition instruction information 60 includes an instruction as to whether or not to include the composition time, title classification 45, and scene classification 46 in the optimum composition list. When the customer inputs his / her preference regarding the composition instruction information 60 to his / her PC 7, the PC 7 generates the composition instruction information 60 and transmits it to the management server 1.

  FIG. 20 is a flowchart illustrating an example of the procedure of the optimum organization list generation process performed by the optimum organization unit 153 for each customer. Hereinafter, the procedure for the optimum knitting unit 153 to generate the optimum knitting list will be described according to this flowchart.

  First, the optimum organization unit 153 acquires recommended organization information 50 corresponding to the attribute of the target customer (S71). The optimum organization unit 153 refers to the recording schedule storage unit 106 and confirms the recorded content in the STB 5 of the target customer (S73).

  In the following process, a scene is selected according to the recommended composition information 50 from the recorded content confirmed here.

  The optimum composition unit 153 determines whether or not a scene whose composition flag 47 is “next notice” corresponding to a scene to be included in the optimum composition list determined based on the recommended composition information 50 has been recorded (S75). For example, a scene of “next notice” (usually broadcast at the time of the previous broadcast) for the broadcast time of the current drama is extracted. When there is a scene whose composition flag 47 is “next notice” (S75: Yes), the scene is included in the optimum composition list (S76).

  Next, it is determined whether or not a scene whose organization flag 47 is “digest” corresponding to a scene to be included in the optimum organization list determined based on the recommended organization information 50 has been recorded (S77). For example, in the case of a drama, a digest scene for the current broadcast of about 1 minute that is broadcast before the main part is extracted. When there is a scene whose composition flag 47 is “digest” (S77: Yes), the scene is included in the optimum composition list (S78).

  The optimum knitting unit 153 selects a scene in which the knitting flag 47 is “main part” for a scene that does not have “next notice” or “digest” among scenes to be included in the optimum knitting list determined based on the recommended knitting information 50 (S79).

  The optimum composition unit 153 selects a scene to be included in the optimum composition list from all the scenes selected in the processes so far according to the composition instruction information 60 (S81). A CM may be subscribed according to the composition instruction information 60.

  The optimum composition unit 153 confirms whether or not the total time of the scenes included in the optimum composition list is within the configuration time specified by the composition instruction information 60 (S83). If the total time of the scene exceeds the designated configuration time (S83: No), the scenes are deleted in the order of the content having the lowest score or the scene having the longer time in the recording schedule (S84).

  The optimum organization unit 153 generates an optimum organization list for each customer through the processing as described above.

  The reproduction instruction unit 155 instructs the STB 5 of the customer to reproduce the scenes included in the optimum organization list of each customer in accordance with the optimum organization list stored in the optimum organization list storage unit 159. For example, the reproduction instruction unit 155 may transmit the optimum organization list to the STB 5 of each customer. In this case, the STB 5 displays the video of the scene included in the optimum organization list on the television receiver 6.

  FIG. 21 shows an example of viewing screens 600 and 610 displayed on the television receiver 6 when viewing the optimal composition.

  The optimal composition viewing screen 600 shown in FIG. A includes a list display unit 601 and a video display unit 602. The list display unit 601 displays the scene names included in the optimum organization list in chronological order from the earliest broadcast time. When the viewer selects automatic reproduction of the optimum organization list, the reproduction management unit 509 causes the video display unit 602 to sequentially reproduce and display each scene in the order displayed on the list display unit 601. When the viewer performs a predetermined operation, the menu viewing screen 610 is displayed.

  The menu viewing screen 610 illustrated in FIG. B includes a menu display unit 611 and a video display unit 612. In the menu display unit 611, the optimum organization list is displayed for each category. The reproduction management unit 509 causes the video display unit 612 to automatically reproduce and display scenes included in the list of the list display unit 611. Alternatively, when the customer selects a desired scene from the list of the list display unit 611, the reproduction management unit 509 causes the video display unit 612 to reproduce the selected scene.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

1 management server 2 transmitting device 5 STB
6 Television receiver 10 Content broadcast information 20 Content recommendation information 30 Basic data 40 Scene information 50 Recommended organization information 60 Composition instruction information 100 Reservation system 101 Content information receiving unit 103 Scoring processing unit 105 Customer-specific recording reservation setting unit 106 Recording schedule storage Section 107 Recording reservation instruction section 109 Recording content management section 150 Optimal composition system 151 Scene information reception section 152 Scene information generation section 153 Optimal composition section 155 Playback instruction section 157 Scene information storage section 159 Optimal composition list storage section

Claims (6)

A video content recommendation management device that is connected to a receiving device for a customer via a network and causes the receiving device to record video content,
Storage means for storing customer information including identification information and attributes of the customer receiving device;
Program recommendation information acquisition means for acquiring program recommendation information indicating a recommendation degree for each customer attribute for each video content determined according to the content of the video content;
Based on the acquired program recommendation information and the customer information, a specifying means for specifying video content recommended for the customer according to an attribute of one customer;
Output means for outputting a recording instruction of the specified video content to the receiving device of the one customer;
A video content recommendation management device that causes the receiving device of the one customer to record the specified video content.   The video content recommendation management apparatus according to claim 1, wherein the customer attributes include gender, age, and a favorite content type.   The program recommendation information acquisition means acquires write information including an expectation level for video content, and generates the program recommendation information based on the evaluation of the others to the writer of the write information and the expectation level. Item 3. The video content recommendation management device according to Item 1 or 2. Means for obtaining viewing history information of video content from the one customer receiving device;
The video content recommendation management device according to claim 1, wherein the specifying unit specifies video content recommended for the one customer with reference to the acquired viewing history. A video content recommendation management device connected to a receiving device for a customer via a network, the video content recommendation management device comprising storage means for storing customer information including identification information and attributes of the customer's receiving device Is a method of causing the receiving device to record video content,
Obtaining program recommendation information indicating the degree of recommendation by customer attribute for each video content determined according to the content of the video content;
Based on the acquired program recommendation information and the customer information, identifying video content recommended for the customer according to the attribute of one customer;
Outputting a recording instruction of the specified video content to the receiving device of the one customer;
A method of causing the receiving device of the one customer to record the specified video content. A video content recommendation management device connected to a receiving device for a customer via a network, the video content recommendation management device comprising storage means for storing customer information including identification information and attributes of the customer's receiving device Is a computer program for causing the receiving device to record video content,
Obtaining program recommendation information indicating the degree of recommendation by customer attribute for each video content determined according to the content of the video content;
Based on the acquired program recommendation information and the customer information, identifying video content recommended for the customer according to the attribute of one customer;
Outputting a recording instruction of the specified video content to the receiving device of the one customer;
A computer program for causing the receiving device of the one customer to record the specified video content.

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