SYSTEMATIC AND ANALYTIC DATA SEGMENTATION BACKGROUND 5 Field of the Invention [0001] The subject matter described herein relates generally to information distribution:technology. More particularly, the 10 present invention relates to analytic data segmentation and recommendation based thereon. Description of Related Art [0002] A current system and method for analyzing data 15 utilizes fixed categories to generate a report. For instance, traditional patent watch services track and monitor patents and patent publications to generate analytic reporting of competitor's activity; Such.-tracking and monitoring is based _^ on the-legal status and timeline of the patents and patent 0)20 publications, therefore such system and method is limited by a TO J fixed categorization. Often times, data analysis of fixed categories provide the users with unnecessary information which increases the time it takes to further identify the meaningful data from the data analysis. [0003] Therefore, what is needed is a system and method that effectively produces analysis of a competitor's activity customized -to meet .the client's standard. DESCRIPTION OF THE PRESENT DISCLOSURE [0004] . The detailed description set forth below in connection with the appended drawings is in+pnHPH ^Q a Hn^rj pt-j ?n ?f 0) presently preferred embodiments of the invention and does not •X- ^ • :' ' • • represent the only forms in which the present invention may be constructed and/or utilized. The description sets forth the functions and the sequence of steps for constructing and operating the invention in connection with the illustrated 5 embodiments. [0005] To further aid in understanding the present invention, the appendices describing exemplary embodiments of the present invention are attached to the present disclosure. [0006] In the present disclosure, a system and method for . 10 analyzing competitor's activity data is provided. The system may comprise one or more processors, one or. more databases, a web- crawler, and one or more programs. The one or more programs may comprise instruction that, when executed, presents a user a user-specific data analysis of the 15' competitor's activity data by employing the methods described herein. Additionally, the above mentioned system may further comprise a network where multiple users may have access -J* thereto using a computing device. The system may be connected (tj • to the Internet. Q_ QJ 20 [0007] The system may comprise one or more computers or ^_» P computerized elements in communication working together to carry out the different functions of the system. The invention contemplated herein further may comprise non-transitory computer readable•media configured to instruct a computer or 25 computers to carry out the steps and functions of the system and method, as described herein. [0008] The computers contemplated herein, . may include, but are not limited to, desktop computers, laptop computers, tablet computers, handheld computers, smart phones and other co 30 cellular phones, and similar im-pr-n^t- pnahi.sH mn^-i \r, ^ e i r j r e e s— CM digital cameras, a customized computing device configured to •- • .eg.. * • 'T-31-10-*®i!i!_ 10 15 CD O) 03 Q_ ' client. Other examples may include: Corporate wanting insights on recent patent grant trends for a competitor; A product 10 manufacturing company may want to review the releases of new versions of products of their competitor. [0032] The grouped- data may be further analyzed. In one embodiment, weights -can be assigned to each of the multiple categories specific to client and competitor based on a set of 15 rules. Such rules may relate to relevancy and importance of the multiple categories customizable to the client. A relevancy score may be calculated for each of the multiple groups based on weight of the categories within a group and/or frequency of the categories in comparison to the competitor's 20 activity data. [0033] In another embodiment, weights can also be assigned to various groups based on a set of rules adapted to relevance and importance of these groups to the client. An-overall activity, score is calculated based on relevance score of each 25 group. A person having ordinary skill in the art would understand the various methods for ranking or arranging the data in order of relevancy or weights. [0034] Recommendation would be generated which would be based on the segmentation of activity data in.groups, relevance score of. groups, and overall art-i v-i 1-y qmrp Tho r^p^rtc with— scores and recommendation can be done using UI, dashboard and/or any other type of document (word, excel etc). [0035] The system can be configured to analyze activity data of multiple competitors simultaneously. Client and competitor specific data categories or grouping once generated can be reused. [0036] Fig. 3 shows an exemplary embodiment of recommendation logic based on four group model disclosed in Fig. 5. In this embodiment, the. competitor's activity data is to be competitor's patent activity data.- The client database comprises a product or service offerings by the client. And the competitor database comprises a product or service offerings by the competitor.. Group 2 (G2) is shared between the client database and the competitor's patent activity data. As.such, the client may be recommended to be involved in more patent activity themselves. Group 3 (G3) is shared between the competitor database and the competitor's patent activity'data. As such, Group 3 represents strong correlation between the competitor's.product Or services and the competitor's patent activity data, therefore the client may be recommended to approach related categories of Group 3 with caution. Group 1 is shared among the client database., the competitor database, and the competitor's activity data. As such, the recommendation to the client may be to inform that categories within Group 3 are competitive. Finally, Group 4 (G4) has no relation to the client and the competitor data, but exists in the competitor's activity data. As such the client may be recommended or informed that the competitor may be diversifying their product or services.. [0037] This example of recommendation logic may be varied depending on the circumstances. A person having ordinary skill in the art would understand such variations. [0038] While several variations of' the present, invention-have been illustrated by way of example in preferred or.particular embodiments, it is apparent•that further embodiments could be developed within the spirit and scope of the present invention, or the inventive concept thereof.. However, it is to be expressly understood that, such modifications and adaptations are within the spirit and scope of the present invention, and are inclusive, but not limited to the present .disclosure. [0039] Those-having ordinary skill in the art will readily observe that numerous modifications, applications~'and alterations of the device and method may be.made while retaining the teachings of the present invention'. claim 1. The method / interface / software for competitive technology watch: Segmenting client's portfolio into mutually exclusive comprehensive categories Segmenting competitor's p o r t f o l io into mutually exclusive comprehensive categories Extract competitor's activity data. In d a t a for each reference identify the category(s) of reference. Creating multiple groups based upon the intersection and uniqueness of categories. Associating each category of activity d a t a to a group based . Generating recommendations and analytic reporting based upon analysis on of data. 2. In claim 1, Segmenting portfolio into mutually exclusive comprehensive categories can done using product & services portfolio, technology offering and patent portfolio or combination. 3. In claim 1, Segmenting into categories/grouping/association can be automatic or manual. 4. In claim 1, Groups can be like overlapping categories, unique to client categories, unique to competitor categories, and other categories. 5. Activity data includes references which be patent filling data, patent granted data, research publication, F roduct release data, patent reassignment data available in public domain. 6. In claim 1, extraction of competitor's technology activity data can be done in time period or real time. 7 . In claim 1, reference is a unit of activity data. 8. In claim 1, category identification can done on the bases of keywords, cjasses, field of work etc or combination. 9. In claim 1, group can have hierarchy like having levels of super group. 10. In claim 1, in each group , sub-groups can be created based upon categories which are related or of high interest.