JP2019193023A - Desired video information notification system - Google Patents

Abstract

To provide a desired video information notification system capable of encouraging users to view only useful video parts by informing the users that content that matches desired conditions contained in one content is output.SOLUTION: The desired video information notification system includes: determination means for determining whether content suitable for a desired condition is included in data that composes content being output while learning optimal conditions on the basis of accumulated results of summaries pre-stored on the basis of the previous content; and informing means for informing a user of the content being output when it is determined by the determination means that it is included.SELECTED DRAWING: Figure 9

Description

  The present invention relates to a desired video information notification system, and more particularly to a desired video information notification system for notifying a user of desired video information in content.

  2. Description of the Related Art Conventionally, for example, a technique for searching for a video portion similar to a certain section of a pre-registered video from videos output by television broadcasting as content is known.

  Such a search technique is, for example, the detection of a specific title roll from a television broadcast signal to start / stop real-time recording, or the same news material broadcast at different times / broadcast stations to detect the structure of the video. It is used for techniques such as analysis (see, for example, Patent Document 1).

  Moreover, such a search technique is not limited to television broadcasting, but can also target distribution data such as video content received through the Internet line (see, for example, Patent Document 2).

  Furthermore, such a search technique is not limited to video (moving images / still images), and can deal with text, for example. Specifically, in addition to the subtitle text included in the content, metadata such as the broadcast start time, broadcast end time, performers, and summary of the contents of the corner for each broadcast program corner is distributed after the broadcast program ends. Metadata provided by a service provider of a paid service (also referred to as a program metadata service), text describing the content input by the user operating a keyboard, etc. can be adopted (for example, , See Patent Document 2).

JP 2010-262413 A JP 2012-038239 A

  However, these techniques, for example, target one program or content, and have a problem that they are not fully compatible with user preferences.

  For example, in a television broadcast, a specific news program can be one of the preferences, but a specific corner in the news program and specific news information cannot be targeted.

  In order to solve the above-described problems, an object of the present invention is to provide a desired video information notification system that can prompt a user to view only a useful video portion.

  In order to achieve the above object, the desired video information notification system according to the present invention configures the content being output while learning the optimum conditions based on the accumulation result of the summary accumulated in advance based on the previous content. Determining means for determining whether or not the data includes content that meets a desired condition, and informing the user of the content being output when it is determined that the data is included in the determining means Notification means.

  According to the present invention, when the content is output, the determination unit is configured so that the content can be viewed only when the content that meets the desired condition is output, instead of viewing the entire content. The content being output can be monitored.

  Therefore, since it is only necessary to view the content being output that is suitable for the content being output, it is possible to perform “while viewing” such as performing other work during other output. it can.

  Similarly, the invention described in claim 2 is the desired video information notification system according to claim 1, further comprising an activation unit for displaying the content being output on the display unit, wherein the notification unit includes the determination unit. When it is determined that the content is included, the notification signal is output to the activation unit to display the content being output.

  That is, the content does not need to be output from the beginning to a display means (display screen) such as a monitor. For example, only the recording may be performed as an output.

  The determination means performs video data analysis and audio data analysis during the content recording to determine whether or not content that meets a desired condition is included.

  Then, when it is determined in the determination result by the determination means that the content suitable for the desired condition is included, the display means may be turned on by the activation means to start actual display as notification.

  Also, for example, when the main screen and the wipe screen are displayed on one display screen, when it is determined that a person corresponding to the desired condition is displayed on the wipe screen, the main screen and the wipe screen are displayed. The activation control may be performed so as to switch the display state with the screen.

  Similarly, the invention according to claim 3 is the desired video information notification system according to claim 1, wherein the determination means includes audio included in broadcast data being viewed in real time when the content is broadcast on television. It is determined in real time whether at least one of the data and the video data includes content that meets a desired condition, and the notification means is notified when it is determined that content that meets a desired condition is included. A signal is output.

  In the case where the content is broadcast on television, for example, even a single program is not limited to the entire program, but may be only a specific corner or performer.

  Therefore, according to the present invention, the determination unit determines in real time whether at least one of the audio data and the video data included in the broadcast data being viewed in real time includes content that meets a desired condition. As a result, the notification means outputs a notification signal so that the desired output can be viewed when the user reaches a portion that includes content that meets the desired condition that the user wants to view. Become.

  Similarly, the invention according to claim 4 is the desired video information notification system according to claim 1, wherein the determination unit views the content in real time when the content is distribution data received through a telecommunication line. When it is determined in real time whether at least one of the audio data or video data included in the distribution data includes content that meets the desired condition, and it is determined that content that meets the desired condition is included A notification signal is output to the notification means.

  When the content is distribution data such as video content received using a telecommunication line such as the Internet line, even if the video content is edited, what the user wants to view is not necessarily the entire content. , May be only part of it.

  Therefore, according to the present invention, the determination unit determines in real time whether at least one of the audio data and the video data included in the distribution data viewed in real time includes content that meets a desired condition. As a result, the notification means outputs a notification signal so that the desired output can be viewed when the user reaches a portion that includes content that meets the desired condition that the user wants to view. Become.

  Similarly, the invention according to claim 5 is the desired video information notification system according to claim 1, wherein the determination unit views the content in real time when the content is distribution data received through a telecommunication line. It is determined in advance whether at least one of the audio data or video data included in the distribution data received prior to the distribution data includes content that meets the desired condition, and content that meets the desired condition is included A notification signal is output to the notification means when a portion determined to be output is output.

  When the content is distribution data such as video content received using a telecommunication line such as the Internet line, for example, the number of accesses to the Internet server, the receiving speed of the telecommunication line, reception / reproduction of personal computers, smartphones, etc. Depending on the function of the terminal, etc., the delivery data of the part ahead of the output part may be received in advance.

  Therefore, according to the present invention, when the content is distribution data received via a telecommunication line, at least audio data or video data included in the distribution data received prior to the distribution data being viewed in real time. If it is determined in advance by the determining means whether or not the content suitable for the desired condition is included, it is possible to notify the notification means when it reaches the output of that part.

  Similarly, the invention according to claim 6 is the desired video information notification system according to claim 1, wherein the determination unit is configured to view in real time when the content is broadcast data or distribution data stored in advance in the storage unit. It is determined in advance whether at least one of the audio data and the video data included in the distribution data received prior to the distribution data being included includes content that meets the desired condition, and the desired condition is met A notification signal is output to the notification means when a portion determined to contain content is output.

  Some users may not be able to view the content in real time for other reasons, but may be stored (so-called recording) in the storage means.

  Therefore, according to the present invention, when the broadcast data or distribution data stored in the storage means is output (reproduced), the distribution data stored prior to the distribution data being viewed (reproduced) in real time. If at least one of the audio data or video data included in the image contains content that meets the desired condition, it is determined in advance by the determining means, and when the output of that portion is reached, the notification means notifies can do.

  Similarly, the invention according to claim 7 is the desired video information notification system according to claim 6, wherein the notification means outputs time data and broadcast data stored in the storage means in advance. If the output speed is a high-speed output that is faster than the same standard speed or a low-speed output that is slower than the standard speed, the output is being performed when it is determined by the determination means that content that meets the desired conditions is included. The output speed of the content is switched to the standard speed.

  When outputting (reproducing) the broadcast data or distribution data stored in the storage means in advance, the original output speed, that is, the high-speed output that is faster than the standard speed at which the time and the output speed coincide (or the standard speed) Output (playback) in some cases.

  Therefore, according to the present invention, when the output speed is not the standard speed, the output of the content being output when the determination unit determines that the content conforming to the desired condition is included as the notification by the notification unit By switching the speed to the standard speed, it is possible to view from a desired timing.

  Similarly, the invention according to claim 8 is the desired video information notification system according to any one of claims 1 to 7, wherein the notification means is a content adapted to a desired condition by the determination means. Is determined, the volume of the sound output from the sound output unit is increased based on the sound data.

  The viewer can easily recognize that the increase in the volume has led to the broadcast (reproduction) of the part that meets the desired condition.

  Similarly, the invention according to claim 9 is the desired video information notification system according to any one of claims 1 to 7, wherein the notification means is a content adapted to a desired condition by the determination means. When it is determined that the information is included, a notification voice different from the voice output from the voice output unit is output based on the voice data.

  The viewer can easily recognize that the part that has met the desired condition has been broadcast (reproduced) by outputting a sound different from the sound included in the content.

  As a result, viewers can watch only the video part they want or need without being restricted by the broadcast time, and notifications that can contribute to time efficiency, especially when busy. You can watch later videos.

  Also, in the video distribution service on the cloud, it is possible to view only the video portion that the user desires or needs without viewing all the videos of the program.

  According to the present invention, when content suitable for a desired condition included in one content is output, the user is notified so that only the video portion useful for the user is viewed. can do.

It is a block diagram which shows the whole structure in one Embodiment of the summary preparation system which concerns on this invention. The utterance text-izing part in one Embodiment of the summary preparation system which concerns on this invention is shown, (a) is a block diagram, (b) is a figure which shows the flow of a process. The telop text conversion part in one Embodiment of the summary production system which concerns on this invention is shown, (a) is a block diagram, (b) is a figure which shows the flow of a process. The background image text-izing part in one Embodiment of the summary preparation system which concerns on this invention is shown, (a) is a block diagram, (b) is a figure which shows the flow of a process. The logo mark text-izing part in one Embodiment of the summary preparation system which concerns on this invention is shown, (a) is a block diagram, (b) is a figure which shows the flow of a process. It is a block diagram which shows the text integration part in one Embodiment of the summary preparation system which concerns on this invention. It is a block diagram which shows the summary preparation part in one Embodiment of the summary preparation system which concerns on this invention. It is a flowchart which shows the operation | movement in one Embodiment of the summary preparation system which concerns on this invention. It is a block diagram which shows the whole structure of the desired image | video information alerting | reporting system in one Embodiment of the summary preparation system which concerns on this invention. The application example of the desired image | video information alerting | reporting system in one Embodiment of the summary preparation system which concerns on this invention is shown, (A) is explanatory drawing when it determines with satisfying desired conditions by character recognition, (B) These are explanatory drawings when it is determined by voice recognition that a desired condition is met.

  FIG. 1 is a block diagram showing an overall configuration of a summary creation system for realizing a desired video information notification system according to an embodiment of the present invention.

<Overall Configuration of Summary Production System 10>
As shown in FIG. 1, the summary generation system 10 includes a video signal separation unit 20, an utterance text conversion unit 100, a telop text conversion unit 200, a background image text conversion unit 300, a logo mark text conversion unit 400, a text integration unit 500, And a summary creation unit 600. In the present embodiment, the summary generation system 10 acquires a video signal from a broadcast program from the television broadcast station 30 or a moving image distributed on the Internet 40. The video signal can be acquired from video on the Internet. It should be noted that at least two parts selected from the telop text unit 200, the background image text unit 300, the logo mark text unit 400, and the text integration unit 500 may be provided.

  The video signal V including the audio signal and the video signal is separated into the audio signal A and the video signal B by the video signal separation unit 20. The audio signal A is input to the utterance text unit 100, and the video signal B is input to the telop text unit 200, the background image text unit 300, and the logo mark text unit 400.

<Speech text unit 100>
The speech text unit 100 receives the audio signal A and outputs speech text that is text describing the content of a person's speech in the content. The utterance text conversion unit 100 includes an utterance information extraction unit 110, an utterance content recognition unit 120, and an utterance content text conversion unit 130.

  The utterance information extraction unit 110 extracts utterance information from the audio signal A of the video signal V. That is, the noise in the voice signal A is removed, and the information of the human speech voice is extracted. The utterance information can include sound effects and characteristic music.

  The utterance content recognition unit 120 recognizes the utterance content from the utterance information. That is, the utterance information is acoustically and grammatically analyzed to recognize the utterance content as a language. Parameters, conditions, and the like used for this recognition can be generated by machine learning from the past speech text generation data accumulated as described later.

  The utterance content text conversion unit 130 converts the utterance content into text and outputs it. The parameters, conditions, and the like used for this recognition can be generated by machine learning from past speech text input data and generation data accumulated as described later.

<Telop text converter 200>
The telop text converting unit 200 receives the video signal B and outputs telop text that is text describing the telop content in the content. The telop text conversion unit 200 includes a telop information extraction unit 210, a telop content recognition unit 220, and a telop content text conversion unit 230.

  The telop information extraction unit 210 extracts telop information from the video signal B of the video signal V. That is, the background in the video signal B is removed, and only the telop image information is extracted.

  The utterance content recognition unit 120 recognizes the telop content from the telop image information. That is, the telop information is analyzed linguistically and grammatically to recognize the telop display content as a language. Parameters, conditions, and the like used for this recognition can be generated by machine learning from the past telop text input data and generation data accumulated as described later.

  The telop content text conversion unit 230 converts the telop content into text and outputs it. Parameters, conditions, and the like used for this recognition can be generated by machine learning from the past telop text input data and generation data accumulated as described later.

<Background image text unit 300>
The background image text conversion unit 300 receives the video signal B and outputs a background image text that is a text describing the background image content in the content. The background image includes at least one of a scene, a situation, an article, and an event, for example, a person, a person's belongings, a person's facial expression, a landscape, a building situation, an indoor situation, an animal, a vehicle, and other articles. Can be mentioned. The background image text conversion unit 300 includes a background image information extraction unit 310, a background image content recognition unit 320, and a background image content text conversion unit 330.

  The background image information extraction unit 310 extracts background image information from the video signal B of the video signal V. That is, the telop and unclear image in the video signal B are removed, and only the recognizable background image information is extracted.

  The background image content recognition unit 320 recognizes the content of the background image from the background image information. That is, a person, a personal belonging, a facial expression, a landscape, a building situation, an indoor situation, an animal, a vehicle, and other articles represented by analyzing background image information are recognized. Parameters, conditions, and the like used for this recognition can be generated by machine learning from input data and generation data of past background image text accumulated as will be described later.

  The background image content text unit 330 converts the background image content into text and outputs it. Parameters, conditions, and the like used for this recognition can be generated by machine learning from input data and generation data of past background image text accumulated as will be described later.

<Logo Mark Textification Unit 400>
The logo mark text converting unit 400 receives the video signal B and outputs a logo mark text which is a text describing the logo mark contents in the content. Examples of the logo mark include a mark, a mark including a mark, and a mark indicating the origin of the product, and other marks. The logo mark text conversion unit 400 includes a logo mark image information extraction unit 410, a logo mark content recognition unit 420, and a logo mark content text conversion unit 430.

  The logo mark image information extraction unit 410 extracts logo mark image information from the video signal B of the video signal V. That is, the telop and the background image in the video signal B are removed, and only the recognizable logo mark image information is extracted.

  The logo mark content recognition unit 420 recognizes the content of the logo mark from the logo mark image information. That is, it recognizes products, services, stores, facilities, etc. represented by analyzing logo mark image information. Parameters, conditions, and the like used for this recognition can be generated by machine learning from the past logo mark text input data and generation data accumulated as described later.

  The logo mark content text conversion unit 430 converts the logo mark image content into text and outputs it. Parameters, conditions, and the like used for this recognition can be generated by machine learning from the past logo mark text input data and generation data accumulated as described later.

<Text integration unit 500>
The text integration unit 500 integrates the utterance text from the utterance text conversion unit 100, the telop text from the telop text conversion unit 200, the background text from the background image text conversion unit 300, and the background text from the logo mark text conversion unit 400. . That is, inconsistencies and errors in each text are corrected, and an integrated text is generated. Parameters, conditions, and the like used for text integration can be generated by machine learning from past text integration input and output data accumulated as described later.

<Summary creation unit 600>
The summary creation unit 600 summarizes the integrated text from the text integration unit 500. In other words, the content of the summary text is summarized to the number of characters designated. Parameters, conditions, and the like used for this summarization can be generated by machine learning from input data and output data of combination processing such as the past accumulated as will be described later.

Next, machine learning processing of each unit will be described.
<Machine learning process of speech text unit 100>
2A and 2B show an utterance text conversion unit of the summary creation system, where FIG. 2A is a block diagram and FIG. 2B is a diagram showing a processing flow. As shown in FIG. 5A, the utterance text conversion unit 100 includes an utterance information extraction unit 110, an utterance content recognition unit 120, an utterance content text conversion unit 130, a machine learning unit 140, and a content recognition text creation setting unit 150. The comparison evaluation unit 160 is provided. Further, an existing data storage unit 700 is connected to the speech text unit 100.

  The utterance text conversion unit 100 performs machine learning based on the existing video data stored in the existing data storage unit 700 and the existing utterance text, and optimizes the utterance content recognition unit 120 and the utterance content text conversion unit 130. The existing data storage unit 700 stores an existing video data storage unit 710 that stores a large number of video data that has been used when a person has created an utterance text in the past, and an utterance text created from the utterance content of the video data. An existing utterance text storage unit 720 is provided. These video data and utterance texts are used as machine learning materials.

  Further, the utterance text conversion unit 100 includes switching units 170 and 180 that perform data output switching when machine learning is performed and when an utterance content text is created from new video data.

  The content recognition text creation setting unit 150 sets the speech information extraction processing of the speech information extraction unit 110, the speech content recognition processing setting of the speech content recognition unit 120, and the text conversion processing setting of the speech content text conversion unit 130. And are stored. The utterance information extraction unit 110, the utterance content recognition unit 120, and the utterance content text conversion unit 130 perform utterance information extraction, utterance content recognition, and text conversion according to the conditions and parameters set by the content recognition text creation setting unit 150.

  The comparative evaluation unit 160 includes a comparison unit 161 and an evaluation unit 162. The comparison unit 161 receives the existing video data from the existing video data storage unit 710 and compares the utterance text created by the utterance content text conversion unit 130 with the existing utterance text from the existing utterance text storage unit 720. The evaluation unit 162 performs an evaluation based on the comparison result of the comparison unit 161, and gives a high score when the values match well.

  The machine learning unit 140 receives the evaluation from the evaluation unit 162 and changes the setting state of the content recognition text creation setting unit 150. This process is repeated for the same video data to make the evaluation value of the evaluation unit 162 as high as possible. This process can be repeated for a plurality of video data.

  By performing such machine learning, the abilities of the utterance content recognition unit 120 and the utterance content text conversion unit 130 are improved. After the predetermined machine learning is completed, the utterance text converting unit 100 processes the new video data and is in a state where the optimum utterance text can be output.

  Processing of the utterance text unit 100 will be described. As shown in FIG. 2B, first, a feature amount for speech recognition and text conversion is set in the content recognition text creation setting unit 150 (step SA1). This setting is performed based on the learning result of the machine learning unit 140.

  Next, the utterance information extraction unit 110 extracts speech from a large amount of speech signals based on the set features (step SA2).

  Further, the utterance content recognition unit 120 analyzes the extracted voice based on the set feature (step SA3).

  Then, the utterance content text conversion unit 130 converts the voice into text based on the set feature and outputs the utterance text (step SA4).

<Machine learning of telop text unit 200>
FIG. 3 shows a telop text conversion unit of the summary creation system, where (a) is a block diagram and (b) is a diagram showing a flow of processing. As shown in FIG. 6A, the telop text conversion unit 200 includes a telop information extraction unit 210, a telop content recognition unit 220, and a telop content text conversion unit 230, as well as a machine learning unit 240 and a content recognition text creation setting unit 250. The comparison evaluation unit 260 is provided. An existing data storage unit 700 is connected to the telop text conversion unit 200.

  The telop text conversion unit 200 performs machine learning based on the existing video data stored in the existing data storage unit 700 and the existing telop text, and optimizes the telop content recognition unit 220 and the telop content text conversion unit 230. The existing data storage unit 700 stores an existing video data storage unit 710 that stores a large number of video data used when a telop text was created by a person in the past, and a telop text created from the utterance content of the video data. An existing telop text storage unit 730 is provided. These video data and utterance texts are used as machine learning materials.

  The telop text conversion unit 200 includes switching units 270 and 280 that perform data output switching when machine learning is performed and when an utterance content text is created from new video data.

  The content-recognized text creation setting unit 250 includes a setting of telop information extraction by the telop information extraction unit 210, a setting of text content recognition processing by the telop content recognition unit 220, and a setting of text conversion processing by the telop content text conversion unit 230. Stored. The telop information extraction unit 210, the telop content recognition unit 220, and the telop content text conversion unit 230 perform telop extraction, content recognition, and text conversion according to the conditions and parameters set by the content recognition text creation setting unit 250.

  The comparative evaluation unit 260 includes a comparison unit 261 and an evaluation unit 262. The comparison unit 261 receives the existing video data from the existing video data storage unit 710 and compares the telop text created by the telop content text unit 230 with the existing telop text from the existing telop text storage unit 730. The evaluation unit 262 performs an evaluation based on the comparison result of the comparison unit 261, and gives a high score when the values match well.

  The machine learning unit 240 receives the evaluation from the evaluation unit 262 and changes the setting state of the content recognition text creation setting unit 250. This process is repeated for the same video data to make the evaluation value of the evaluation unit 262 as high as possible. This process can be repeated for a plurality of video data.

  By performing such machine learning, the capabilities of the telop content recognition unit 220 and the telop content text conversion unit 230 are improved. After completing the predetermined machine learning, the telop text conversion unit 200 processes the new video data and is in a state where it can output the optimum telop text.

  The processing of the telop text conversion unit 200 will be described. As shown in FIG. 3B, first, telop information extraction, content extraction, and text conversion feature amounts are set in the content recognition text creation setting unit 250 (step SB1). This setting is performed based on the learning result of the machine learning unit 240 (step SB2).

  Next, the telop information extraction unit 210 extracts a telop from a large amount of video signals based on the set feature (step SB2).

  Further, the telop content recognition unit 220 analyzes the telop extracted based on the set feature (step SB3).

  Then, the telop content text conversion unit 230 converts the telop content into text based on the set feature and outputs it as telop text (step SB4).

<Machine learning of background image text unit 300>
4A and 4B show a background image text conversion unit of the summary creation system. FIG. 4A is a block diagram and FIG. 4B is a diagram showing a flow of processing. As shown in FIG. 6A, the background image text conversion unit 300 includes a background image information extraction unit 310, a background image content recognition unit 320, a background image content text conversion unit 330, a machine learning unit 340, a content recognition text. A creation setting unit 350 and a comparative evaluation unit 360 are provided. An existing data storage unit 700 is connected to the background image text unit 300.

  The background image text conversion unit 300 performs machine learning based on the existing video data stored in the existing data storage unit 700 and the existing background image text, and optimizes the background image content recognition unit 320 and the background image content text conversion unit 330. To do. The existing data storage unit 700 stores an existing video data storage unit 710 that stores a large number of video data used when a person created telop text in the past, and a background image text created from the utterance content of the video data. The existing background image text storage unit 740 is provided. These video data and background image text serve as machine learning materials.

  The background image text converting unit 300 includes switching units 370 and 380 that perform data output switching when machine learning is performed and when an utterance content text is created from new video data.

  The content recognition text creation setting unit 350 sets the background image extraction processing of the background image information extraction unit 310, the background image content recognition processing of the background image content recognition unit 320, and the text conversion of the background image content text conversion unit 330. Stores processing settings. The background image information extraction unit 310, the background image content recognition unit 320, and the background image content text conversion unit 330 perform background image extraction, background image content recognition, and text conversion according to the conditions and parameters set by the content recognition text creation setting unit 350. Do.

  The comparative evaluation unit 360 includes a comparison unit 361 and an evaluation unit 362. The comparison unit 361 compares the background image text generated by the background image content text unit 330 upon receiving the existing video data from the existing video data storage unit 710 and the existing background image text from the existing background image text storage unit 740. To do. The evaluation unit 362 performs an evaluation based on the comparison result of the comparison unit 361, and gives a high score when the values match well.

  The machine learning unit 340 receives the evaluation from the evaluation unit 362 and changes the setting state of the content recognition text creation setting unit 350. This process is repeated for the same video data to make the evaluation value of the evaluation unit 362 as high as possible. This process can be repeated for a plurality of video data.

  By performing such machine learning, the capabilities of the background image content recognition unit 320 and the background image content text conversion unit 330 are improved. After the predetermined machine learning is completed, the background image text converting unit 300 processes the new video data and is in a state where the optimum background image text can be output.

  The process of the background image text unit 300 will be described. As shown in FIG. 4B, first, background image information extraction, background image recognition, and text conversion feature quantities are set in the content recognition text creation setting unit 350 (step SC1). This setting is performed based on the learning result of the machine learning unit 340.

  Next, the background image information extraction unit 310 extracts a background image from a large amount of video signals based on the set feature (step SC2).

  Further, the background image content recognition unit 320 analyzes the background image extracted based on the set feature (step SC3).

  Then, the background image content text conversion unit 330 converts the content of the background image into text based on the set feature and outputs it as background image text (step SC4).

<Machine learning of logo mark text unit 400>
5A and 5B show a logo mark text conversion unit of the summary creation system, where FIG. 5A is a block diagram and FIG. 5B is a diagram showing a flow of processing. In addition to the logo mark image information extraction unit 410, the logo mark content recognition unit 420, and the logo mark content text conversion unit 430, the logo mark text conversion unit 400 includes a machine learning unit 440, a content recognition text creation setting unit 450, and a comparative evaluation unit 460. Is provided. Further, an existing data storage unit 700 is connected to the logo mark text unit 400.

  The logo mark text conversion unit 400 performs machine learning based on the existing video data stored in the existing data storage unit 700 and the existing logo mark text, and performs the logo mark image information extraction unit 410, the logo mark content recognition unit 420, and the logo mark. The content text unit 430 is optimized. In the existing data storage unit 700, an existing video data storage unit 710 storing a large number of video data used when a person has created logo mark text in the past, and a logo mark text created from the utterance content of the video data are stored. The stored existing logo mark text storage unit 750 is provided. These video data and logo mark text are used as machine learning materials.

  In addition, the logo mark text converting unit 400 includes switching units 470 and 480 that switch data output when machine learning is performed and when an utterance content text is created from new video data.

  The content recognition text creation setting unit 450 stores the settings of the logo mark image content recognition processing of the logo mark content recognition unit 420 and the settings of the text conversion processing of the logo mark content text conversion unit 430. The logo mark image information extraction unit 410, the logo mark content recognition unit 420, and the logo mark content text conversion unit 430 perform logo mark extraction, content recognition, and text conversion according to the conditions and parameters set by the content recognition text creation setting unit 450.

  The comparative evaluation unit 460 includes a comparison unit 461 and an evaluation unit 462. The comparison unit 461 receives the existing video data from the existing video data storage unit 710 and compares the text created by the logo mark content text conversion unit 430 with the existing background image text from the existing logo mark text storage unit 750. The evaluation unit 462 performs an evaluation based on the comparison result of the comparison unit 461, and gives a high score when the values match well.

  The machine learning unit 440 receives the evaluation from the evaluation unit 462 and changes the setting state of the content recognition text creation setting unit 450. This process is repeated for the same video data to make the evaluation value of the evaluation unit 462 as high as possible. This process can be repeated for a plurality of video data.

  By performing such machine learning, the ability of the logo mark content recognition unit 420 and the logo mark content text conversion unit 430 is improved. After the predetermined machine learning is completed, the logo mark text conversion unit 400 processes the new video data and is in a state where it can output the optimum background image text.

  Processing of the logo mark text conversion unit 400 will be described. As shown in FIG. 5B, first, a feature amount of a logo mark is set in the content recognition text creation setting unit 450 (step SC1). This setting is performed based on the learning result of the machine learning unit 340.

  Next, the logo mark image information extraction unit 410 extracts a logo mark from a large amount of video signals based on the set feature (step SD2).

  Further, the logo mark content recognition unit 420 analyzes the background image extracted based on the set feature, and automatically confirms and registers it (step SD3).

  Further, the logo mark content recognition unit 420 recognizes a registered logo mark or a thing that matches a specific logo mark as a logo mark (step SD4).

  Then, the logo mark content text converting unit 430 converts the logo mark content into text based on the set feature and outputs it as logo mark text (step SD5).

<Machine learning of text integration unit 500>
FIG. 6 is a block diagram showing a text integration unit of the summary creation system. The text integration unit 500 includes an integrated text creation unit 510, an integrated text creation setting unit 520, a machine learning unit 530, and a comparative evaluation unit 540. An existing data storage unit 700 is connected to the text integration unit 500.

  The text integration unit 500 performs machine learning based on various existing types stored in the existing data storage unit 700, that is, speech text, telop text, background text, logo mark text, and existing integrated text, and an integrated text creation unit 510. Optimize the operation. The existing data storage unit 700 stores an existing various text storage unit 760 that stores various text data used when an integrated text was created in the past, and an existing integrated text storage unit 770 that stores an integrated text created from the various texts. With. These various texts and integrated texts serve as machine learning materials.

  In addition, the text integration unit 500 includes switching units 570 and 580 that perform data output switching when performing machine learning and when creating a new integrated text from various new texts.

  The integrated text creation setting unit 520 stores text integration processing settings of the integrated text creation unit 510. The integrated text creation unit 510 performs text integration processing according to the conditions and parameters set by the integrated text creation setting unit 520.

  The comparative evaluation unit 540 includes a comparison unit 541 and an evaluation unit 542. The comparison unit 541 receives the existing various texts from the existing various text storage units 760 and compares the integrated text created by the integrated text creation unit 510 with the existing integrated texts from the existing integrated text storage unit 770. The evaluation unit 542 performs an evaluation based on the comparison result of the comparison unit 541, and gives a high score when the values match well.

  The machine learning unit 530 receives the evaluation from the evaluation unit 542 and changes the setting state of the integrated text creation setting unit 520. This process is repeated for the same various text data to make the evaluation value of the evaluation unit 542 as high as possible. This process can be repeated for a plurality of various text data.

  By performing such machine learning, the ability of the integrated text creation unit 510 is improved. After completing the predetermined machine learning, the text integration unit 500 processes the new video data and is in a state where it can output the optimum integrated text.

<Machine learning of summary creation unit 600>
FIG. 7 is a block diagram showing a summary creation unit of the summary creation system. The summary creation unit 600 includes a summary text creation unit 610, a summary creation setting unit 620, a machine learning unit 630, and a comparative evaluation unit 640. An existing data storage unit 700 is connected to the summary creation unit 600.

  The summary creation unit 600 performs machine learning based on the integrated text and summary text stored in the existing data storage unit 700, and optimizes the operation of the summary text creation unit 610. The existing data storage unit 700 includes an existing integrated text storage unit 770 that stores integrated text data used when a summary text was created in the past, and an existing summary text storage unit 780 that stores a summary text created from the integrated text. With. These integrated texts and summary texts serve as machine learning materials.

  The summary creation unit 600 includes switching units 670 and 680 that perform data output switching when machine learning is performed and when a new summary text is created from a new integrated text.

  The summary creation setting unit 620 stores the summary processing settings of the summary text creation unit 610. The summary text creation unit 610 performs text summary processing according to the conditions and parameters set by the summary creation setting unit 620.

  The comparative evaluation unit 640 includes a comparison unit 641 and an evaluation unit 642. The comparison unit 641 compares the summary text created by the summary text creation unit 610 in response to the existing integration text from the existing integration text storage unit 770 and the summary text from the existing summary text storage unit 780. The evaluation unit 642 performs an evaluation based on the comparison result of the comparison unit 641 and gives a high score when the values match well.

  The machine learning unit 630 receives the evaluation from the evaluation unit 642 and changes the setting state of the summary creation setting unit 620. This process is repeated for the same various text data to make the evaluation value of the evaluation unit 642 as high as possible. This process can be repeated for a plurality of integrated text data.

  By performing such machine learning, the capability of the summary text creation unit 610 is improved. After completing the predetermined machine learning, the summary creation unit 600 can process the new video data and output an optimum summary text.

Next, processing of the summary creation system 10 will be described. FIG. 8 is a flowchart showing the operation of the summary creation system.
First, the existing video data storage unit 710, the existing utterance text storage unit 720, the existing telop text storage unit 730, the existing background image text storage unit 740, the existing logo mark text storage unit 750, and the existing various text storage units of the existing data storage unit 700 760, an existing video signal and various text data are read into the existing integrated text storage unit 770 and the existing summary text storage unit 780 (step ST1).

  Next, machine learning processing is performed in the speech text unit 100, the telop text unit 200, the background image text unit 300, and the logo mark text unit 400 (steps ST2a, ST2b, ST2c, and ST2d). This learning process can also be performed sequentially.

  Next, the existing input data and output data are read into the existing data storage unit 550 of the text integration unit 500 and the existing data storage unit 650 of the summary creation unit 600 (step ST3). Next, machine learning processing is performed in the text integration unit 500 and the summary creation unit 600 (steps ST3a and 3b). This learning process can also be performed sequentially. Note that the machine learning processing of the utterance text conversion unit 100, the telop text conversion unit 200, the background image text conversion unit 300, and the logo mark text conversion unit 400 and the machine learning processing of the text integration unit 500 and the summary creation unit 600 are as follows. Regardless of the order of processing, it can be performed in the reverse order.

  When the learning process is completed (yes in step ST4), the video signal to be processed is input to the video signal separation unit 20 (step ST5). Thereby, the utterance text conversion unit 100, the telop text conversion unit 200, the background image text conversion unit 300, and the logo mark text conversion unit 400 execute text conversion processing (steps ST6a, ST6b, ST6c, ST6d).

  Then, each text is integrated by the text integration unit 500 (step ST7), the integrated text is further summarized by the summary creation unit 600 (step ST8), and the summary text is output. finish.

  From the next summary creation process, the optimum summary creation can be performed only by inputting the target video signal for summary creation (step ST5) immediately without performing the machine learning process (steps ST1 to ST4). The machine learning process can be performed as necessary.

  The above system includes a CPU (Central Processing Unit) as a processing device, a RAM (Random Access Memory), a ROM (Read Only Memory), a HDD (Hard Disc Drive), an SSD (Solid State Drive), etc. as a storage device. It can be realized by executing application software on a computer system. Moreover, each part does not need to be arrange | positioned in the same place, and it can implement | achieve by arrange | positioning a part on cloud and connecting with a network. In addition, since these processes target a large amount of data, it is preferable to perform processing using a GPU (Graphics Processing Unit).

  That is, the integrated text is simply converted into characters such as voice, characters, background video, etc., and is data about a huge amount of text. Therefore, GPU can be processed at high speed by specializing in text processing.

  Further, text input by the text integration unit 500 is not limited to speech text, telop text, background image text, and logo mark text.

  For example, when a TV program (terrestrial digital TV broadcast program) is targeted, text and sound acquired from an electronic program guide (EPG), caption broadcast, commentary broadcast (including bilingual broadcasts, etc.) are acquired as text. Can be entered. As a result, the quality and quantity of the integrated text can be improved, and the versatility and taste of the text can be improved.

  Similarly, in the case of Internet video distribution, third party evaluation (including comments) and reputation can be acquired and input as text. As a result, the quality and quantity of the integrated text can be improved, and the versatility and taste of the text can be improved.

  In addition, as a trigger for “notification” to be described later, these caption broadcasting and commentary broadcasting, or evaluation / reputation of a third party, etc. can be targeted.

[Other Embodiments]
In the present invention, data processing is summarized at high speed and appropriately by AI (Artificial Intelligence) processing. The AI process can be realized by the above-described machine learning (ML). Furthermore, supervised learning with existing data as correct answers can be adopted as machine learning. Further, it is effective to perform deep learning (DL: Deep Learning) as machine learning.

  In deep learning, a large number of existing video data, various text data corresponding to each video data, integrated text, and summary text are learned as big data. Each machine learning unit includes an input layer, a plurality of intermediate layers, and an output layer, and performs processing by a neural network including a large number of neurons. That is, an intermediate layer so that new video data input to the summary creation system according to the present invention, various texts based on the video data, integrated texts, and an output with the summary as input are close to existing various texts, integrated texts, and summaries. The weights and parameters in the neurons are optimized by a method such as the least square method.

  Taking the above basic configuration as an example, the present invention can, for example, inform the user (viewer / operator, etc.) when content suitable for a desired condition included in one program (content) is output. In order to urge the user to view only a useful video portion, the data constituting the content being output is referred to by referring to the summary accumulated in advance based on the previous content. A determination unit that determines whether or not content that meets the conditions is included; and a notification unit that notifies the user of the content that is being output when the determination unit determines that the content is included. , Are provided.

  FIG. 9 is a block diagram showing the overall configuration of the video information providing system according to the embodiment of the present invention.

  In FIG. 9, the video information providing system 1 includes the above-described summary creation system 10 by a dedicated management server or the like, and a video output system portion that operates based on the summary created by the management server, for example, You may implement | achieve with a television provided with a computer function, a personal computer, a smart phone, a tablet terminal, etc. (henceforth "reproducing apparatus 9" generically). In addition, the reproducing | regenerating apparatus 9 can be used not only by one but in multiple units. In this case, it is possible to change a desired condition for each unit.

  In the following description, the case of broadcasting on television will be mainly described, and the case of video distribution specific to the cloud will be described as appropriate, and the distribution of video on the cloud that is the same as or substantially the same as the usage form of television broadcasting. The description of the usage form is omitted.

  Broadcasting television and broadcasting are not particularly limited, such as terrestrial digital broadcasting, satellite broadcasting, one-segment broadcasting, Internet broadcasting, and the like.

  In FIG. 9, the video information providing system 1 includes a receiving unit 2 including a tuner that receives a video signal related to content from the above-described television station 30 (or a video distribution server on the cloud), and an operation unit ( 3 including a remote control) 3, a storage unit 4 storing applications for realizing various functions as the playback device 9, a control circuit unit 5 for processing various functions based on the applications stored in the storage unit 4, By the above-described summary creation system (video signal separation unit 20, speech text conversion unit 100, telop text conversion unit 200, background image text conversion unit 300, logo mark text conversion unit 400, text integration unit 500, and summary generation unit 600). A large-capacity storage unit 6 for storing the created summary and various data for content recording, and a sound And an output unit 7 including a speaker and a monitor for image output for output, a.

  In FIG. 9, the utterance text conversion unit 100, the telop text conversion unit 200, the background image text conversion unit 300, and the logo mark text conversion unit 400 are described as the video signal processing unit 8 as a whole. Accordingly, the playback device 9 includes the receiving unit 2, the operation unit 3, the storage unit 4, the control circuit unit 5, the large-capacity storage unit 6, and the output unit, excluding the video signal processing unit 8 among the configuration requirements shown in FIG. 9. 7. In addition, the control circuit unit 5 has a function as a determination unit, and the output unit 7 has a function of outputting the output result to a monitor or a printer based on the weighting calculation calculated by the control circuit unit 5 as a weighting unit, for example. It has.

<Production and distribution of video metadata>
Here, as an example of the case where video metadata is produced and distributed or can be distributed, a case where the contents of a television broadcast are processed into Japanese and converted into a database is described. In this case, the content is exemplified for one program (or corner).

  In TV programs, especially “news” and “broadcast programs” that are broadcast every moment, “immediateness” and “accuracy” are important.

  On the other hand, in such news / broadcast programs in television broadcasting, some news content may be broadcast on other news programs with different time zones (regardless of differences in broadcasting stations). However, it can be said that the same program is not rebroadcast on different days of the week and disappears.

  Such “immediate” and “accurate” information has continuity depending on the news content, social importance and needs, or new information becomes clear. In some cases, for example, it is desirable that the news is weighted according to the degree of importance / needs such that the number of appearances reaches a predetermined value.

  Here, the importance / needs level has short-term, long-term, and seasonal factors. For example, a graph weighted by weekly, monthly, seasonal (seasonal), annual, and other statistics. It is also possible to create. At this time, the created graph can be output from the output unit 7 as a monitor or a printer.

  As a result, the importance and needs in the short term are high. It is possible to prevent the importance / needs from becoming low over the course of the year, and to assign a weighting that the importance / needs at the corresponding time is high.

  Specifically, an effective weighting can be given when the degree of importance / needs increases in a specific cycle such as “forecasting of cherry blossoms”, “famous spots for cherry blossoms”, and “the Olympics”.

  Also, metadata for newly broadcast video information can be sequentially updated with a time lag of about 10 minutes, and can be updated to importance based on the latest information. At this time, it is also possible to simultaneously receive programs from a plurality of broadcasting stations and update them to the latest information.

  In addition to basic information such as broadcasting stations and broadcast times, metadata includes text information such as news titles, content abstracts, commentator names and prominent logos, image authentication of background images, caster face authentication, Finer video metadata can be produced and distributed by voiceprint analysis.

  Further, the result can be confirmed on the user side by Web or mail. Therefore, if the video metadata is stored and accumulated on a large-capacity storage medium such as a hard disk on the user side, it can be used in various utilization situations.

  Specifically, from daily news broadcasts, it is possible to close up the behavior of a particular commentator and “completely record” the details, and to verify the contents later. In addition, if that particular commentator is used as a notification condition (trigger), when the commentator is commenting on a news program currently being broadcast or a recorded news program, the volume is increased in a spot manner. Notification is also possible.

  In addition, when contents conforming to such notification conditions are included, notification to the user (viewer) of the content being output includes not only increasing the volume described above but also sounding a message or the like This can be done for the user's hearing. In addition to notification to the user's hearing, notification to the user's vision, such as repeated reversal of the brightness of the display screen 7a (see FIG. 10) or lighting / flashing of a dedicated lamp, may be used. Further, a combination of hearing and vision may be used. Furthermore, it can be used as a trigger signal for starting other operations (for example, recording) as well as mere notification.

  Furthermore, characters in the video that flows during the program, such as comment time and tone analysis of the specific commentator mentioned above, video of politicians (political parties) and sports players introduced in the broadcast content (during the program), etc. In addition to creating a database of video metadata such as broadcast time, including weighting such as calculating CM-converted values of people and objects by performing clustering (a function that automatically classifies data without external criteria, etc.) Granting is also possible.

  As described above using the summary creation process (AI process) that is a function of the summary creation system 10 that learns the optimum summary creation setting using the input data and output data of the past summary creation results accumulated as teaching materials. In addition to the AI processing, the CM conversion value is evaluated for each person by the manual input of the operator based on the audience rating or information not included in the video metadata such as newspapers and magazines. You may convert into a price (unit price per unit time).

  Furthermore, the weighted CM conversion value is generated as video metadata for a single broadcast station, a single program, a plurality of broadcast stations (for example, a key station in the Kanto area), and the weekly / monthly / seasonal ( (Quarterly) / half year / full year / unit. The collected data can be output from the output unit 7 as a monitor or a printer by a graph or a list (for example, for the top 100 people).

  In addition, text-formatted video metadata can be used as teletext broadcasting during simultaneous broadcasting. For example, TV news / broadcast programs, wide shows, discussion programs, political / economic programs, political / economic variety, etc. It is also possible to use the contents and the detailed information for each article as text information to be created by the operator for the information programs of the state-run broadcasting stations and private key stations that extend over 100 hours per day.

<Reproducing apparatus 9>
The playback device 9 has a tuner function for television broadcasting (terrestrial digital broadcasting / satellite broadcasting / one-segment broadcasting) or a receiving function for receiving Internet distribution video as the receiving unit 2, and as shown in FIG. If the video can be output to the display screen 7a of the output unit 7, a television, a personal computer, a smartphone, a tablet terminal, or the like can be used.

  The receiving unit 2 has a function of appropriately (or sequentially) receiving the summary created by the summary creation system 10. The summary received by the receiving unit 2 is stored (or updated) in the mass storage unit 6.

  The operation unit 3 can use various switches attached to the television, a remote control device attached to the television, a mouse and keyboard for computers, various switches and touch panels attached to smartphones and tablet terminals, and the like.

  Thereby, when the user wants to know only the result of the sports news in the news program, the user does not view the entire news program but monitors the control circuit unit (determination means) 5, for example, as shown in FIG. As shown in the figure, when the word “sports” is displayed in a telop on the display screen 7a, or when the caster reads an announcement document including “sports” as shown in FIG. can do.

  By the way, in the above-mentioned television broadcasting, when a certain incident occurs in news, a plurality of television broadcasting stations repeatedly broadcast the same scene. In such a case, it is often impossible to easily recognize the entire picture even if each TV media broadcasts what and when it broadcasts.

  Therefore, if such an incident is set as a desired condition, the contents of all designated news broadcast programs can be converted into text data in seconds and then compiled into a database to create a summary.

  Then, if the contents of the summary are classified (clustered) by the same theme and analyzed (for example, processing by the user or the operator of the contracted dedicated company), what will be broadcast at what station, when and how much. It is also possible to obtain quantified information.

  And by setting such quantified information as a desired condition, it becomes possible to set a more recent and accurate condition in subsequent news broadcasts. It is possible to switch between notification viewing and other news broadcasts such as normal viewing.

  At the time of this quantification, for example, an output function such as automatically recording the video portion of the incident (for example, the video portion regarding the progress of accident processing at the nuclear power plant) in the large-capacity storage unit 6 can be given as a weight. .

  In addition, as described above, weighting of usage patterns such as how much such broadcasts related to incidents and accidents were handled in television media, and which stations have handled which themes in terms of time and frequency, is also weighted. Is possible.

  Furthermore, such a summary is not limited to news broadcasting, but it is also possible to analyze the contents of various entertainment programs from various perspectives.

  Thereby, for example, based on an entertainment database constructed in an exhaustive manner, it is possible to analyze from various viewpoints such as contents of entertainment programs such as dramas, movies, variety, genre comparison, and time zone grasp.

  Therefore, for example, among the performers of a variety program, the time when the video of a specific performer by face authentication is output, the time and content when the sound of a specific performer by voice authentication (voice print) is output , Etc., and informs the user when the specific performer in the program in which the specific performer in subsequent broadcasts is broadcast or speaking on the screen It is possible to improve the versatility of the viewer.

  Furthermore, the performance time of the specific performer can be converted, and for example, the performer value can be easily calculated from the appearance ratio in units of days, weeks, and months.

  In addition, the voice of the performer described above can be given a weight for converting a dialect into a standard word when performing morphological analysis for text conversion after voice recognition.

  Therefore, the control circuit unit 5 receives the summary created by the summary creation system 10 as appropriate (or sequentially) and stores it in the large-capacity storage unit 6, and based on the storage result of the summary (for weighting). While learning the optimum conditions, weighting specialized for one or more predetermined conditions included in the summary can be given to a plurality of contents.

  Similarly, the control circuit unit 5 receives the summary created by the summary creation system 10 as appropriate (or sequentially) and stores it in the large-capacity storage unit 6, and optimizes it (for notification) based on the storage result of the summary. Learning conditions (for example, identification of a person by face authentication or voiceprint authentication) to improve the accuracy of determining whether or not content that meets the desired conditions is included, Thus, the output unit 7 can notify that the desired condition is met.

  In this way, the desired video information notification system 1 refers to the summary stored in advance based on the previous content, and whether or not the data constituting the content being output includes content that meets the desired condition. Is determined (monitored) by the control circuit unit 5, and if the determination result is included, the content of the content is notified by notifying the user of the content being output from the output unit 7. When outputting, it is possible not to view the entire content but only to view the content that meets the desired conditions.

  Therefore, since it is only necessary to view the content being output that is suitable for the content being output, it is possible to perform “while viewing” such as performing other work during other output. And versatility can be improved.

  Further, when the content is broadcast on television, for example, even a single program may not be limited to the entire program, but may be only a specific corner or performer.

  Therefore, the determination means determines in real time whether at least one of the audio data or the video data included in the broadcast data being viewed in real time includes content that meets a desired condition. If a notification signal is output when a portion that contains content that conforms to a desired condition to be viewed is reached, the desired output can be easily viewed.

  Also, if the content is distribution data such as video content received using a telecommunication line such as the Internet line, what the user wants to view is the entire content even if the video content is edited There is a case where it is not limited but only a part thereof.

  Therefore, the determination means determines in real time whether at least one of the audio data or the video data included in the distribution data that is viewed in real time includes content that meets a desired condition. When a portion that contains content that meets a desired condition to be viewed is reached, a desired output can be viewed by outputting a notification signal.

  In addition, when the content is distribution data such as video content received using a telecommunication line such as the Internet line, for example, the number of accesses to the Internet server, the receiving speed of the telecommunication line, the reception of a personal computer, a smartphone, etc. -Depending on the function of the playback terminal, the delivery data of the part ahead of the output part may be received in advance.

  Therefore, when the content is distribution data received through a telecommunication line, at least one of audio data and video data included in the distribution data received prior to the distribution data being viewed in real time satisfies a desired condition. If it is determined in advance by the determining means whether or not the adapted content is included, it is possible to notify when the output of that portion is reached.

  In addition, some users may not be able to view content in real time for other reasons, but may be stored (so-called recording) in the storage means.

  Therefore, when the broadcast data or distribution data stored in the storage means is output (reproduced), the audio data included in the distribution data stored prior to the distribution data being viewed (reproduced) in real time or If it is determined in advance by the determining means whether or not at least one of the video data includes content that meets a desired condition, the notifying means can notify when the output of that portion is reached.

  In addition, when outputting (reproducing) broadcast data or distribution data stored in the storage unit in advance, the original output speed, that is, a high-speed output that is faster than the standard speed where the time and the output speed match (or The output (reproduction) may be performed at a low-speed output slower than the standard speed).

  Therefore, when the output speed is not the standard speed, as an example of notification, when it is determined that the content suitable for the desired condition is included, the output speed of the content being output is switched to the standard speed. It becomes possible to view from the timing of.

  By such notification, the viewer can easily recognize that the portion that meets the desired condition has been broadcast (reproduced) due to the increase in volume.

  In addition, the viewer can easily recognize that the portion that has met the desired condition has been broadcast (reproduced) by outputting a sound different from the sound included in the content.

  Further, for example, a case where the output form is such that the content is not displayed on the display screen 7a and the video is recorded in the mass storage device 6 is also assumed.

  In such a case, the control circuit unit 5 analyzes the video data and audio data included in the content being output (recording) in real time, and includes contents that meet the desired conditions during the data analysis. The activation control may be performed so that the content is actually displayed on the display screen 7a when it is determined that the content is present.

  Further, for example, as shown in FIG. 10, when a main screen (caster screen) and a wipe screen (sports screen) are displayed on one display screen 7a, when it is determined that the sports corner has started. The activation control may be performed such that the display state is switched between the main screen and the wipe screen, or the wipe screen is switched to the full screen display.

1: Desired video information notification system 2: Receiving unit 3: Operation unit 4: Storage unit 5: Control circuit unit (determination means)
6: Mass storage section 7: Output section (output means)
7a: Display screen 8: Video signal processing unit 9: Playback device 10: Summary creation system 20: Video signal separation unit 30: TV station 100: Utterance text conversion unit 110: Utterance information extraction unit 120: Utterance content recognition unit 130: Utterance content Text conversion unit 140: Machine learning unit 150: Content recognition text creation setting unit 160: Comparison evaluation unit 161: Comparison unit 162: Evaluation unit 170: Switching unit 180: Switching unit 200: Telop text conversion unit 210: Telop information extraction unit 220 : Telop content recognition unit 230: telop content text conversion unit 240: machine learning unit 250: content recognition text creation setting unit 260: comparative evaluation unit 261: comparison unit 262: evaluation unit 270: switching unit 280: switching unit 300: background image Text conversion unit 310: Background image information extraction unit 320: Background image content recognition unit 330: Background image content Kisting unit 340: Machine learning unit 350: Content recognition text creation setting unit 360: Comparison evaluation unit 361: Comparison unit 362: Evaluation unit 370: Switching unit 380: Switching unit 400: Logo mark text conversion unit 410: Logo mark image information Extraction unit 420: Logo mark content recognition unit 430: Logo mark content text conversion unit 440: Machine learning unit 450: Content recognition text creation setting unit 460: Comparison evaluation unit 461: Comparison unit 462: Evaluation unit 470: Switching unit 480: Switching Unit 500: text integration unit 510: integrated text creation unit 520: integrated text creation setting unit 530: machine learning unit 540: comparative evaluation unit 541: comparison unit 542: evaluation unit 550: existing data storage unit 570: switching unit 580: switching Unit 600: summary creation unit 610: summary text creation unit 620: summary creation setting unit 630: machine learning unit 6 40: comparative evaluation unit 641: comparison unit 642: evaluation unit 650: existing data storage unit 670: switching unit 680: switching unit 700: existing data storage unit 710: existing video data storage unit 720: existing utterance text storage unit 730: existing Telop text storage unit 740: Existing background image text storage unit 750: Existing logo mark text storage unit 760: Existing various text storage unit 770: Existing integrated text storage unit 780: Existing summary text storage unit

Claims (9)

  The data that constitutes the content being output includes content that meets the desired conditions while learning the optimal conditions based on the accumulated results of the summaries stored in advance based on the previous contents that can be displayed on the display means. A determination means for determining whether or not the content is included, and a notification means for notifying the user of the content being output when the determination means determines that the content is included. Video information notification system.   An activation unit configured to display the content being output on the display unit, and the notification unit outputs a notification signal to the activation unit when the determination unit determines that the content is being output; The desired video information notification system according to claim 1, wherein the content is displayed.   The determination means determines whether at least one of audio data and video data included in broadcast data being viewed in real time includes content that meets a desired condition when the content is broadcast on television. 2. The desired video information notification system according to claim 1, wherein a notification signal is output to the notification means when it is determined in real time and it is determined that content suitable for a desired condition is included.   In the case where the content is distribution data received through a telecommunication line, the determination means includes at least one of audio data and video data included in the distribution data viewed in real time that includes content that meets a desired condition. 2. The desired video according to claim 1, wherein: a desired image is output to the notification means when it is determined in real time whether or not content suitable for a desired condition is included. Information reporting system.   When the content is distribution data received through a telecommunication line, the determination means may be set to at least one of audio data and video data included in the distribution data received prior to the distribution data viewed in real time. It is determined in advance whether or not content that conforms to the condition is included, and when a portion that is determined to include content that conforms to the desired condition is output, a notification signal is output to the notification unit. The desired video information notification system according to claim 1, wherein:   In the case where the content is broadcast data or distribution data stored in advance in the storage unit, the determination unit includes at least audio data or video data included in the distribution data received prior to the distribution data viewed in real time. A determination signal is output to the notification means when it is determined in advance whether content suitable for the desired condition is included on one side, and a portion determined to include content suitable for the desired condition is output. The desired video information notification system according to claim 1, wherein:   The notification means is a high-speed output whose time and output speed are faster than the same standard speed or a low-speed output which is slower than the standard speed when outputting broadcast data or distribution data stored in advance in the storage means. 7. The request according to claim 6, wherein the output speed of the content being output is switched to the standard speed when it is determined by the determination means that the content conforming to a desired condition is included. Video information notification system.   The notification means increases the volume of the sound output from the sound output unit based on the sound data when it is determined by the determination means that content suitable for a desired condition is included. The desired video information notification system according to any one of claims 1 to 7.   The notification means outputs a notification sound different from the sound output from the sound output unit based on the sound data when the determination means determines that the content that meets the desired condition is included. The desired video information notification system according to any one of claims 1 to 7, wherein the desired video information notification system is provided.