Classification and selective filtering of a data set

Classifying and filtering data by categorizing a portion of a data segment according to the content of the portion, correlating each portion with a user content preference dictionary, assigning a content weight to each portion according to the correlation, and masking content portions having a weight below a threshold value.

BACKGROUND

The disclosure relates generally to the review, classification and filtering of data sets. The disclosure relates particularly to filtering data sets for presentation to a user according to automatic data content classification and user preferences.

Social media systems, and the internet in general, connect users to vast amounts of data. The available data may be categorized and classified in any number of ways, according to innumerable classification dimensions. Individual pieces of data may be linked according to these classification dimensions. Users are unique and have individualized needs and tastes in terms of the data they are willing to spend time reviewing.

SUMMARY

Aspects of the invention disclose methods, systems and computer readable media associated with classifying and filtering data by categorizing a portion of a data segment according to the content of the portion, correlating each portion with a user content preference dictionary, assigning a content weight to each portion according to the correlation, and masking content portions having a weight below a threshold value.

Aspects of the invention disclose methods, systems and computer readable media associated with classifying and filtering data by categorizing a portion of a data segment according to the content of the portion, presenting data associated with a new content category to a user, updating a user content preference dictionary according to user activity associated with the new content category, correlating each portion with the user content preference dictionary, assigning a content weight to each portion according to the correlation, and masking content portions having a weight below a threshold value.

DETAILED DESCRIPTION

In an embodiment, one or more components of the system can employ hardware and/or software to solve problems that are highly technical in nature (e.g., categorizing data according to content, correlating content to a user content reference dictionary masking low priority content, etc.). These solutions are not abstract and cannot be performed as a set of mental acts by a human due to the processing capabilities needed to analyze and weight data content, for example. Further, some of the processes performed may be performed by a specialized computer for carrying out defined tasks related to memory operations. For example, a specialized computer can be employed to carry out tasks related to subdividing data segments into portions according to content, weighting the portions according to user preferences, masking low priority—poorly weighted portions—during content presentation, or the like. Such selective content presentation—with portion of low interest pre-screened and masked from view, reduces the time needed to view the data segment as only high value portions of the segment are actually viewed. The time and computing resources available to a user are thus spent on high value content and not screening data segments for the high value content.

Social media networks afford users access to vast amounts of data, as does the internet in general. User have finite amounts of time to spend perusing data from the internet and social media systems. Users typically have defined interests, likes and dislikes, which can be expressed in view of the content of available data. Some data is of interest and some data is not. Time and computing resources spent on the presentation of data which is of no interest to the user is not only wasted but carries the additional effect of reducing time and computing resources available for presenting data of actual interest to the user and other system processing tasks. What is needed is a system and method to filter available data and mask data which is of no interest to a user, freeing system resources to present only data of interest and reducing or eliminating any need to present unwanted data, thereby increasing system resource efficiency by applying resources to high priority tasks.

In an embodiment, the method initializes the system of the invention. In this embodiment, the user creates an account and logs on to the system. In some embodiments, the system collects user preference information regarding like/dislikes associated with broad content categories—sports, entertainment, science and technology, political news, world events, etc. The system also collects, with permission from the user, demographic information regarding the user, geographic location, preferred language, user age, gender, etc. In this embodiment, the method of the invention builds a user profile containing user preferences and other collected data. In an embodiment, the method uses the user profile to associate a user with other users having similar profile dimensions, again, with appropriate user permission.

In an embodiment, the method collects social media information associated with the user, networks used, login information, etc., in order to enable the user to access social media networks through the system and method of the invention. In an embodiment, the method further collects on-line subscription accounts associated with the user to enable access to on-line data to which the user subscribes.

In an embodiment, the method collects user preferences regarding the extent of application of the methods to the users browsing and networking activities. In some embodiments, the method collects user preferences regarding applying the method to private group chat exchanges, direct message exchanges, and public group data exchanges, site comment sections, etc.

In an embodiment, the method builds a user content preference dictionary specific to each user. The dictionary includes the users content preferences. The method associates initial dictionary entries with the content preference collected from the user during the initialization of the system. In an embodiment, the method creates and maintains the user content preference dictionary locally on a user device. In an embodiment, the method creates and maintains the dictionary in a networked location such as cloud or edge cloud resources and is accessible by a user by way of a network connection. In this embodiment, the user accesses content using the method and the dictionary entry from mobile devices, smart phones, tablets, portable computers, etc., as well as fixed devices such as desktop computers.

Over time, as the user accesses data content via the methods and systems of embodiments of the invention, the methods modify and update the user content preference dictionary entry of the user. In some embodiments, the user provides additional direct input regarding content categories in response to system prompts, or more directly by accessing the dictionary entry and altering settings associated with data content categories. In some embodiments the method updates and modifies the dictionary entry automatically according to user activity in response to presented content.

In an embodiment, the method scans all content, whether originating from a social media feed, direct news feed, or general internet browsing activity, prior to presentation to the user. In this embodiment, the method analyzes the content using machine learning models, such as classification neural network models trained using image, video and textual data associated with differing content categories. In an embodiment, the machine learning models utilize natural language processing (NLP) algorithms to analyze the content and assign category designation(s) to the content. In an embodiment, the method subdivides the content segment into content portions according to content categories. In this embodiment, a content segment contains several portions, each portion associated with a unique and different category. For example, a world news video contains an embedded advertisement—a different category from that of world news itself. For this example, the method divides the segment into multiple portions, world news before the advertisement, the advertisement, and world news after the advertisement. The method assigns the first and third portions to the word news category—with possible sub-category assignments as well according to the world location and nature of the news—sports, entertainment, political, weather, etc.,—and assigns the advertisement to the advertisement category—with possible sub-categorization regarding the product or service advertised. As a further example, the method sub-divides sports news in general into sub-categories according to sport (baseball, football, golf, tennis, hockey, soccer, etc.), gender (men's, women's), level (professional, college, high school, amateur), regular season vs playoff season, etc. The method translates each sub-categorization identified by the NLP and classification neural network into an entry in the user's content preference dictionary.

As a further example, the method subdivides entertainment news into subcategories according to music, television and movie news as well as subcategories according recent and upcoming performance and individual performing artists and content genres. The method again translates each sub-categorization identified by the NLP and classification neural network into an entry in the user's content preference dictionary.

As a further example, the method evaluates science and technology content, subcategorizing the data segments according to field of study, recent developments, and researcher. The method translates each sub-categorization identified by the NLP and classification neural network into an entry in the user's content preference dictionary.

In an embodiment, the analysis of the content yields a set of metadata associated with the portions and overall segment of content. In this embodiment, the analysis metadata includes the percent each portion represents of the overall segment, the start and end points for each portion, (start time and end time for video, first line and last line for text). In this embodiment, the method then evaluates the metadata of the portions as a set to determine what, if anything will be presented to the user.

In an embodiment, the method evaluates the metadata of the set of content portions of each data segment according to the user's content preference dictionary entry. In this embodiment, the method presents content portions associated with categories having no corresponding entry in the user's content preference dictionary to the user, together with a user input prompt requesting a user preference. In this embodiment, the user input prompt requests, from the user, a preference selection for the new content category. As an example, the method provides a prompt asking the user to accept or decline presentation of the content portion according to the categorization of the content: “Would you like to see a video of the week's baseball highlights?”. The method then initializes a new entry in the user's content preference dictionary for the new category using the user input response to the request. The method assigns weights to all other content portions according to the user's content preference dictionary category and sub-category definitions. The method assigns high weights to content portions associated with preferred preference ratings according to the dictionary. The method assigns low weights to content portions which are not preferred by the user according to the dictionary.

In an embodiment, the method captures user activity as data segments and portions are presented. In this embodiment, the method tracks user activity including deleting, minimizing, or hiding the content, the time the user spends reading an article or viewing an image/video, images, article, or videos which the user copies or downloads, images within an album of images which the user views, the time the user spends viewing the image and any images, videos, articles, or other content the user likes, dislikes, upvotes, or downvotes after viewing the content, and any content the user shares across their social media network or by email or direct messaging applications.

In an embodiment, the method initializes each category with a nominal value on a scale of 0 to 100, for example the method assigns a value of 50 to each category. In this embodiment, the method collects user preference data as direct inputs and alters the values accordingly. For example, the method alters the preference value of categories which the user rejects in the initiation of the user content preference dictionary, from 50 to 0. In this embodiment, the method maintains all categories the user prefers at a value of 50. Over time, the method raises or lowers the values of preferred categories according to the monitored user activity. Activities indicating a user preference for a category result in a positive alteration of the value by a preset amount, e.g., an increase of 1 for each positive activity. Activities indicating refusal or rejection of the content category result in a reduction by a preset amount, e.g., decreasing the value by 1 for each such activity.

In an embodiment, the method continuously updates the user's content preference dictionary as user activity occurs. As an example, a user may have an initial generalized preference for sports news, the method assigns sports news a nominal value of 50. Over time, the method presents multiple sports news content portions having a variety of sub-categorizations, including football, tennis, auto racing, golf, baseball, basketball, hockey, soccer, men's, women's, professional, college, as well as portions associated with specific teams and athletes. Based upon user activity associated with the broad collection of sports news, the method generates new elevated positive values for sports news associated with the user's favorite baseball team, fantasy sports league players, playoff news for football, baseball, basketball, hockey, and world cup soccer. The method concurrently diminishes the values associated with tennis, auto racing, golf, teams other than the user's favorite and players other than the user's selections. Similarly, the method lowers the values associated with entertainment portions associated with genres, performers and entertainment types not favored by aa user, resulting in such items no longer being presented from an overall entertainment data segment.

In an embodiment, after categorizing each content portion, the method assigns weights to each portion according to the corresponding user content preference dictionary. In this embodiment, as sub-categorization values change, the content presented to the user also changes. The method ceases to present content from sub-categorizations having low or falling weight values. The method gradually drops updates related to golf tournaments, non-playoff games for teams other than the user's favorite, and all other non-preferred sports news from the sports category content being presented to the user.

In an embodiment, a threshold value is defined, the method presents only content having a weight above the threshold value. As an example, a threshold value of 30 on the 0 to 100 scale is set. In an embodiment, the user sets the threshold value. In an embodiment, an initial threshold value is set, and the model evaluates the initial value in terms of user activities. Negative user activities for defined category and subcategory content weighted at or near the threshold leads the method to raise the threshold value.

In an embodiment, content is ranked and ordered according to the assigned weight values, from highest to lowest weights. In this embodiment, the content is then presented to the user beginning with the highest weighted content and ending with the lowest non-zero weighted content or ending with the lowest weighted content having the threshold level weight.

In an embodiment, content is presented according to category and then according to sub-category according to weight. In this embodiment, the content associated with a highest ranked main category is all presented first, from highest to lowest weight based on sub-category weighting, then the next highest ranked main category of content is presented and so on.

In an embodiment, content having a low, below threshold or zero weighting is considered masked content, such content is not presented to the user. For segments including high weight value content portions as well as low or zero weighted portions, the segment is presented while skipping the low or zero weighted masked content. As an example, a video includes a zero-rated portion from time stamp 1:03 to time stamp 1:57, in this embodiment the method presents the portion from 0:00 to 1:03, and then presents the portion from 1:57 to the end of the segment, skipping the low or zero-rated portion.

FIG.1provides a schematic illustration of exemplary network resources associated with practicing the disclosed inventions. The inventions may be practiced in the processors of any of the disclosed elements which process an instruction stream. As shown in the figure, a networked Client device110connects wirelessly to server sub-system102. Client device104connects wirelessly to server sub-system102via network114. Client devices104and110comprise data content classification and filtering program (not shown) together with sufficient computing resource (processor, memory, network communications hardware) to execute the program. As shown inFIG.1, server sub-system102comprises a server computer150.FIG.1depicts a block diagram of components of server computer150within a networked computer system1000, in accordance with an embodiment of the present invention. It should be appreciated thatFIG.1provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made.

Server computer150can include processor(s)154, memory158, persistent storage170, communications unit152, input/output (I/O) interface(s)156and communications fabric140. Communications fabric140provides communications between cache162, memory158, persistent storage170, communications unit152, and input/output (I/O) interface(s)156. Communications fabric140can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric140can be implemented with one or more buses.

Memory158and persistent storage170are computer readable storage media. In this embodiment, memory158includes random access memory (RAM)160. In general, memory158can include any suitable volatile or non-volatile computer readable storage media. Cache162is a fast memory that enhances the performance of processor(s)154by holding recently accessed data, and data near recently accessed data, from memory158.

Program instructions and data used to practice embodiments of the present invention, e.g., the data content classification and filtering program175, are stored in persistent storage170for execution and/or access by one or more of the respective processor(s)154of server computer150via cache162. In this embodiment, persistent storage170includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage170can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage170may also be removable. For example, a removable hard drive may be used for persistent storage170. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage170.

Communications unit152, in these examples, provides for communications with other data processing systems or devices, including resources of client computing devices104, and110. In these examples, communications unit152includes one or more network interface cards.

Communications unit152may provide communications through the use of either or both physical and wireless communications links. Software distribution programs, and other programs and data used for implementation of the present invention, may be downloaded to persistent storage170of server computer150through communications unit152.

I/O interface(s)156allows for input and output of data with other devices that may be connected to server computer150. For example, I/O interface(s)156may provide a connection to external device(s)190such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)190can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., data content classification and filtering program175on server computer150, can be stored on such portable computer readable storage media and can be loaded onto persistent storage170via I/O interface(s)156. I/O interface(s)156also connect to a display180.

Display180provides a mechanism to display data to a user and may be, for example, a computer monitor. Display180can also function as a touch screen, such as a display of a tablet computer.

FIG.2provides a flowchart200, illustrating exemplary activities associated with the practice of the disclosure. As illustrated in the figure, at210, the method of data content classification and filtering program175, categorizes data segment portions. The method receives data segments from the user's social media, subscription data services, email and messaging applications and general internet browsing activities. Program175categorizes the data using trained machine learning models including classification neural networks and NLP algorithms. Program175subdivides data segments into portions according to content categories and sub-categories defined by the models and NLP algorithms. At220, program175correlates the categorized portions with entries in the user's defined content preference dictionary according to their assigned category. At230, program175assigns weights to the content portions according to the correlated entries in the dictionary. Program175monitors user activity and updates the weights associated with dictionary category and sub-category entries. Positive user activity—user dwell time while reading or viewing, liking content, sharing content, upvoting, etc., result in increased weight values. Hiding, deleting, disliking, downvoting, etc., result in lowered weights. The method also uses direct user input—selections made while initiating the system, and manually entered preferences, to adjust category and sub-category weight values.

At240, program175masks content portions having low weights or zero value weights. In this embodiment, program175does not present masked content to the user. In an embodiment, the method defines a threshold weight value below which the method masks all content. In an embodiment, the method monitors user behavior and adjusts the threshold according to user behavior related to content at or near the threshold value. At250, program175present content to the user according to the weighted values of the content. The method presents highly weighted categories before lower weighted categories and highly weighted sub-category content before lower weighted sub-category content. Program175is iterative and continuously evaluates new content streaming toward the user, categorizing, correlating, weighting, masking low weight content, and presenting content with a sufficiently high weighting value to the user.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The invention may be beneficially practiced in any system, single or parallel, which processes an instruction stream. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.