Claims:We claim:
1. A system for recommending videos using artificial intelligence is disclosed. The system for recommending videos using artificial intelligence comprising of;
a. Saving all the video files in the internal database of the website;
b. Analyzing unit for analysis of video files;
c. Converting the video files into desired format of sequences, audio files;
d. Extracting video descriptors from files;
e. establishing an output measure that relates said video descriptors with audience data;
f. an user interface for receiving recommendations;
g. Generating recommendations for selected consumers;
h. Sending the recommendations to the consumer based on artificial intelligence
2. The system as claimed in claim 1, wherein the said system (the chatbot) receives an input prompt, it analyzes the prompt and form context so that it can determine the desired output.
3. The system as claimed in claim 1, wherein the said system provides video recommendations to the customers.
, Description:FIELD OF THE INVENTION:
The present invention relates to a systems for analyzing and using digital video compositions. The present invention more particularly relates to a system for recommending videos using artificial intelligence.
BACKGROUND OF THE INVENTION:
Many people use internet and are dependent on internet for any news, stock information, research hobbies, educational information, and matrimonial information and so on. As internet provides a lot of information on any subject, but it is difficult for a user to remember the webpages in which the important content related to his/her topic, he/she wants to store or save which may be needed for them.
As internet information is constantly updated, changed and it is really difficult to store information. So users can store the information in the form of screenshots also. Screenshots do not necessarily store any searchable text and therefore are not easily searchable. Capturing information through a screenshot does not necessarily allow a user to copy/paste text from it either. Also, the saving of screenshots is not effectively integrated in the browser and thus requires the user to manually manage creating an image file from the screenshot, naming it, and saving it under categorized folders for further retrieval. Therefore the usefulness and effectiveness of screenshots as a tool to capture, preserve, share, and manage internet-based information and content is to limited extent.
Historically, a particular song is found in the basic structure of the music. Particular rhythms, changes in key and certain melodic patterns define the psychological and very human response we all have to music. In a similar fashion, particular characteristic variables of films, video-shorts, mobile videos, music videos, videogames, advertisings, video clips, and television programs affect how viewers respond to the production.
While the number of possible plot lines combined with all of the other variables in recorded films, video-shorts, mobile videos, music videos, videogames, advertisings, video clips and television programs allow for infinite number of combinations. By analyzing images, sound, script, and structure similar mathematical patterns can be discerned.
This is absolutely important for any product manager — to avoid the temptation of the tech enthusiast who marvels in the details of the data science / or ML for intellectual reasons without clearly identifying the problem or business need — potentially using up valuable technical resources with no business impact. Product managers need to properly connect a business problem to a data machine learning solution.
So there is a need for a system for recommending videos using artificial intelligence.
OBJECTS OF THE INVENTION:
An object of the present invention is to provide a system for recommending videos using artificial intelligence.
Another object of the present invention is to Increase / maintain viewership in terms of minutes consumed.
Yet another object of the present invention is to Overall increase in customers.
Other objects and benefits of the present invention will be more apparent from the following description, which is not intended to bind the scope of the present invention.
SUMMARY OF THE INVENTION:
Accordingly, a system for recommending videos using artificial intelligence is disclosed. The system for recommending videos using artificial intelligence comprising of;
a. Saving all the video files in the internal database of the website;
b. Analyzing unit for analysis of video files;
c. Converting the video files into desired format of sequences, audio files;
d. Extracting video descriptors from files;
e. establishing an output measure that relates said video descriptors with audience data;
f. an user interface for receiving recommendations;
g. Generating recommendations for selected consumers;
h. Sending the recommendations to the consumer based on artificial intelligence

BRIEF DESCRIPTION OF THE DRAWINGS:
Fig 1 is a system for recommending videos using artificial intelligence.
DETAILED DESCRIPTION OF THE INVENTION WITH RESPECT TO DRAWINGS:
The present invention is a system for recommending videos using artificial intelligence. The present invention Increases / maintains viewership in terms of minutes consumed. The present invention annotates these images then ranks each image in an effort to identify which thumbnails have the highest likelihood of resulting in your click. These calculations are based on what others who are similar to you have clicked on. One finding could be that users who like certain actors / movie genres /videos are more likely to click thumbnails with certain actors/image attributes.
In one embodiment, a system for recommending videos using artificial intelligence comprising of;
i. Saving all the video files in the internal database of the website;
j. Analyzing unit for analysis of video files;
k. Converting the video files into desired format of sequences, audio files;
l. Extracting video descriptors from files;
m. establishing an output measure that relates said video descriptors with audience data;
n. an user interface for receiving recommendations;
o. Generating recommendations for selected consumers;
a. Sending the recommendations to the consumer based on artificial intelligence.
Video descriptors comprise supervised cognitive analysis to evaluate scene structure and classification for each scene including scene character, scene intensity, and scene type. Audio parameters are selected from the sound power, maximum frequency and frequency centroid, power of low, medium, and high frequencies. Video descriptors with audience data being determined for each scene type.
Data is organized in a Database and allows detecting and correcting errors in the input data, both in the files and structure. In the case of TV programs, the data coming from the audience and from the programs are processed together. The videos are transformed (consisting of several recordings in QuickTime format, divided by advertisements in some cases) into sequences of frames/photograms and audio files.
A user is notified of recommendations based on his interests through emails, messages. The user profile is created which comprises identifier information of the user based on the interested topics and bookmarks created by the user, bookmarks are created based on the frequency of visits of user to internet webpages, evaluating the profile for showing most popular websites for the user and sending user the recommendations based on the popular websites for the user.
The chatboat scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database and sends recommendations about videos, movies to the customers. In another embodiment, Chatbots can answer everything from a simple product question to complex technical issues and can even take a customer through different actions on the site. These bots have rapid response times and sometimes personalities of their own, creating a clever marketing tactic that helps brands connect with their consumers on a more personal level.
When a chatbot receives an input prompt, it must analyze the prompt and form context so that it can determine the desired output. As the chatbot is trained by having data input, it searches for patterns, which it can save for reference. This is the “learning” process.