The present invention relates to an apparatus and method for a category-based content recommendation using a Neuro-Linguistic Programming (NLP) Module. The invention more particularly related to the apparatus and systems for recommending content items, such as news stories, that reference entities in an indicated category and method thereof.
BACKGROUND OF THE INVENTION
[002] In recent years, various methods for automated categorization (or classification) of texts and strings into predefined categories exist. One approach to this problem uses machine learning and artificial intelligence, in which a general inductive process spontaneously builds a classifier by learning, from a set of pre-classified documents that are represented as vectors of key terms, the characteristics of the categories. Various Al and ML based techniques is used says for example, for each category, a set of human-labeled examples are collected as training data in order to build classifiers, such as Decision Tree classifiers, Naive Bayes classifiers, Support Vector Machines, Neural Networks, and the like. A separate classifier typically must be built for each new category. [003] The aforesaid approaches is not scalable and utilize when processing a large quantity of documents, and requires facilitation like, to add a new category, a new classifier may need to be built. Then, every document is needed to be execute through the resulting classifier.

[004] In addition, various approaches to providing computer-generated news Web sites exist. In another method, which aggregates headlines from news sources worldwide, and groups similar stories together. Further, the stories are grouped into a handful of broad, statically defined categories, such as Business, Sports, Entertainment, and the like. In few methods, the presentation of news items may be customized, such as by allowing users to specify keywords and strings to filter the requisite news items. [005] While, in the present invention, these issues has been resolved in by using a Neuro-Linguistic Programming (NLP) Module methods to categorized each of the content items. This allows users to use any number of documents and further categorization of that is provided in the output. The implemented method and apparatus uses a new symmetric Al and ML based algorithm that enhance the space utility and less time-consuming. [006] Accordingly, there remains a need in the prior art for a technical convergence to make the system, apparatus and method compact, it is in this context that the present invention provided with a main difference between the proposed work and the previous related works described in the present invention, which is based on the Neuro-Linguistic Programming (NLP) techniques. Therefore, it would be useful and desirable to have an apparatus and method to meet the above-mentioned needs.
SUMMARY OF THE PRESENT INVENTION
[007] In the view of the foregoing disadvantages inherent in the known types
of methods and techniques to provide automated categorization (or

classification) of texts into predefined categories, are now present in the prior art. The present invention provides an apparatus for a category-based content recommendation using a Neuro-Linguistic Programming (NLP) Module and method thereof, which has all the advantages of the prior art and none of the disadvantages. The system and method can also be optionally implemented with the existing hardware and related system application or electronic devices such as Desktop Computer, laptop and the like devices and able to process the designed algorithms as are known to those skilled in the art. The system and method can be provided electronically over the Internet or the Virtual Private Network VPN to the user's desktop, PDA, or a digital cell, smart phone, or other devices for receiving and processing the predetermined set of data as are known to those skilled in the art.
[008] Another important aspect of the present invention, the proposed method and apparatus works with a processing unit to process content items provided with predefined services to determine, for each of the content items, multiple corresponding entities referenced by the content item, each of the determined entities being represented by the NLP system. The system determines, for each of at least some of the content items, at least one corresponding category that is part of a taxonomy that is represented as a graph stored by the NLP system and that is associated with one of the multiple corresponding entities referenced by the content item.

[009] The system provides an overview of the use of the Neuro-Linguistic Programming (NLP) algorithm on a server or cloud platform, as shown in Figure 1. The particular category is determined by aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by defining a plurality of taxonomic paths associated with each of the determined entities, each of the dedicated path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity with the other involved entity. [010] In addition, The processing unit in conjunction with NLP system determines all taxonomic path associated with a second one of determined entities, and determining a common node among the plurality of taxonomic paths by using the NLP system, the common node representing the at least one corresponding category, such that the involved entities are in a relationship with the particular category and receive from a search query an indication of a category. The processing unit selects a content item from the content items having a particular category that matches the indicated category, the particular category being one of the determined categories, which transmits an indication of the selected content item. [011] Another important aspect of the present invention, which is implemented on, but not limited to, the Field Programmable Gate Arrays (FPGAs) and the like, PC, Microcontroller and with other known processors to have computer algorithms and instruction up gradation for supporting

many applications domain where the aforesaid problems to solution is required.
[012] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. [013] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[014] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:

[015] FIGS. 1 illustrates an apparatus for a category-based content recommendation using a Neuro-Linguistic Programming (NLP) Module, in accordance with an embodiment of the present invention; and [016] FIG. 2 illustrates a block diagram, which is showing an interaction between various implemented hardware modules, in accordance with another embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[017] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used

herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[018] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa. [019] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be

embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. [020] FIG. 1 illustrates an apparatus for a category-based content recommendation using an Artificial Intelligence/Machine learning & Neuro-Linguistic Programming (NLP) Modules and method thereof. The apparatus includes, but not limited to, one or more processor provided in a computer network; computer memory holding computer program instructions that when executed by the processor perform a method comprising: processing, content items provided with predefined services to determine, for each of the content items, multiple corresponding entities referenced by the content item, each of the determined entities being represented by the NLP module; and determining, for each of at least some of the content items, at least one corresponding category that is part of a taxonomy that is represented as a graph stored by the NLP module and that is associated with one of the multiple corresponding entities referenced by the content item. Best mode & enablement and further validation preferably, but not limited to,
for the present invention

[021] The system provides an overview of the use of the AI/ML & NLP based algorithms on a server or a cloud platform. The particular category is determined by aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by defining a plurality of taxonomic paths associated with each of the determined entities, each of the dedicated path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity with the other involved entity.
[022] In accordance with an exemplary embodiment of the present invention, the processing unit is configured in conjunction with the NLP module to determine all taxonomic path associated with a second one of determined entities. Further, the processing unit is configured in conjunction with the NLP module to determine a common node among the plurality of taxonomic paths.
[023] Implemented algorithm stored data security. The methods have used a symmetrical algorithm for security and safe categorization of the documents. This algorithm can be used to easily encrypt data that is saving on the server or cloud. The common node representing at least one corresponding category, such that the involved entities are in a relationship with the particular category and receive from a search query an indication of a category. The processing unit further selects a content item from each of the content items having a particular category that matches the indicated category.

[024] The implemented algorithm the particular category being one of the determined categories, which transmits an indication of the selected content item. Further, the processing unit is configured to protect or expand a data storage function for storing the data in an online database provided on a computation server. Therefore, it reliably protects processing documents in the expanded cloud.
[025] The word "module," "model" "algorithms" and the like as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, Python or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of computer-readable medium or other computer storage device. Further, in various embodiments, the processor is one of, but not limited to, a general-purpose processor, an application specific integrated circuit (ASIC) and a field-programmable gate array (FPGA) processor. Furthermore, the data repository may be a cloud-based storage or a hard disk drive (HDD), Solid state drive (SSD), flash drive, ROM or any other data storage means.

[026] Further, an exemplary computer system for implementing aforesaid embodiments consistent with the present disclosure can be used with various variations of computer system that may be used for implementing the apparatus for a category-based content recommendation using a Neuro-Linguistic Programming (NLP) Module. Computer system may comprise a central processing unit ("CPU" or "processor"). Processor may comprise at least one data processor for executing program components for executing user or system-generated requests. A user may include a person, a person using a device such as such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as Field Programmable Gate Arrays FPGA, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM'S application, embedded or secure processors, IBM PowerPC, Intel's Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc. [027] Furthermore various exemplary hardware is shown in FIG. 5 that can be, but not limited to, resided in the computation server / computer system is comprised of the processor may be disposed in communication with one

or more input/output (I/O) devices via I/O interface. The I/O interface may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n/b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.
[028] It is to be cleared from the above disclosure that the present invention that the participating parties will need a computing device like, but not limited to, desktop, laptop, mobile, or PDA etc. for computing. All these devices will be arranged in a network. Therefore, all the components for a specific type of network will be needed. Preferably, the AI/ML & NLP modules are also implemented by using a computation server machine connected in a real¬time network.
[029] In some embodiments, the processor may be disposed in communication with one or more memory devices (e.g., RAM, ROM, etc.) via a storage interface. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI),

etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. The memory devices may store a collection of program or database components, including, without limitation, an operating system, user interface application, web browser, mail server, mail client, user/application data(e.g., any data variables or data records discussed in this disclosure), etc. The operating system may facilitate resource management and operation of the computer system. Examples of operating systems include, without limitation, Apple Macintosh OS X, Unix, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.
[030] The above-mentioned invention is provided with the preciseness in its real-world applications, and is devised a new apparatus, system and method for a category-based content recommendation using AI/ML & Neuro-Linguistic Programming (NLP) Modules. The Future Scope of the present invention includes, but not limited to, for recommending textual content items, the apparatus, system and method may be used to automatically recommend other types of items, such as music or other audio items, videos, applications (e.g., mobile applications), online activities, or the like, which has been achieved.

[031] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[032] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments. [033] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.

We Claim:
1. An apparatus for a category-based content recommendation using a Neuro-
Linguistic Programming (NLP) Module, comprising:
one or more processor provided in a computer network;
computer memory holding computer program instructions that when executed by
the processor perform a method comprising:
processing, content items provided with predefined services to determine, for each
of the content items, multiple corresponding entities referenced by the content
item, each of the determined entities being represented by the NLP module; and
determining, for each of at least some of the content items, at least one
corresponding category that is part of a taxonomy that is represented as a graph
stored by the NLP module and that is associated with one of the multiple
corresponding entities referenced by the content item.
2. The apparatus as claimed in claim 1, wherein the particular category is determined by aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by defining a plurality of taxonomic paths associated with each of the determined entities, each of the dedicated path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity with the other involved entity.
3. The apparatus as claimed in claim 1, wherein the processing unit is configured in conjunction with the NLP module to determine all taxonomic path associated with a second one of determined entities.

4. The apparatus as claimed in claim 1, wherein the processing unit is configured in conjunction with the NLP module to determine a common node among the plurality of taxonomic paths.
5. The apparatus as claimed in claim 4, wherein the common node representing at least one corresponding category, such that the involved entities are in a relationship with the particular category and receive from a search query an indication of a category.
6. The apparatus as claimed in claim 1, wherein the processing unit further selects a content item from each of the content items having a particular category that matches the indicated category.
7. The apparatus as claimed in claim 6, wherein the particular category being one of the determined categories, which transmits an indication of the selected content item.
8. The apparatus as claimed in claim 1, wherein the processing unit is configured to protect or expand a data storage function for storing the data in an online database provided on a computation server.