Abstract:
The invention is concerned with a method and system for searching databases. It allows the user to get proper search results by classifying the user identity. A child, an adult, a Professor or even a labor worker, will get the searching results per their knowledge and expertise. A chosen weighted words applying the search engine and user databases are classifies to give these proper results. Each searched document is now mapping according these values and sent to the searcher according its score.

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
       [0001]     This application claims the benefit of U.S Provisional Patent Application No. 60/735,827 filled on 14 Nov. 2005 entitled Method and System Of Searching and claims the benefit of U.S Provisional Patent Application No. 60/773,352 filled on 15 Feb. 2006 entitled Method and System of Searching, which are hereby incorporated by references. 
     
    
     FIELD OF THE INVENTION  
       [0002]     The present invention relates to searching, in particular searching of electronic data whether in a database or on a network, including the Internet and Intranet.  
       BACKGROUND OF THE INVENTION  
       [0003]     There is an enormous amount of information on the Internet—Google™ today performs searches among 8 billion pages. However, while it may take just seconds to enter the chosen keyword(s) into the search engines and get the results, actually finding the desired information amongst all the results can take much longer. Even users using advanced search options still need to go through many pages of search results not relevant or appropriate to their needs. The problem is that there is currently no system that gives a good correlation between the person executing the search and the search results themselves.  
         [0004]     Actually, searching for information on the Internet is essentially a two-way process between the surfer and the database, such the Web. That is to say, between two databases. In other words, the problem is to say, how can the search engine provide exact information, when it is only actually confronted by a limited number of search words, and it is not exposed to the whole database (the brain) of the surfer.  
         [0005]     On the other hand, search engines today, gives the option to get a personal results based on the history of user searching. This brings a privacy problem, of using this data by the search engine company or by anyone else.  
       SUMMARY OF THE INVENTION  
       [0006]     The solution provided by means of the new invention is to map the two databases in a new way, and finds compatibility between the two. In order to acquire a characterization of the surfer, various features will be defined, such as: “child”, “adult”, “scientist”, “sportsman”, etc.  
         [0007]     On the other hand, preferences will be given to the words in the dictionary compatible to these features. As an example, the word “notebook” will get a high value for “child”, but its value will be lower for “sportsman”. This is a pre-defined system, which allows the user to get proper results without giving his searching history or any personal data.  
         [0008]     According to one embodiment of the invention, a professional user can have specific database words affecting the searching results. For example, a physics student will add to the common words appear in the database, specific words such as: “Black hole”, “Nebula”, “Einstein” and the like.  
         [0009]     According to another embodiment of the present invention, the user database is pre-defined by the search engine company using the invention.  
         [0010]     According to another embodiment of the present invention, the user can have a pre-defined profile such as: child, adult, musician and the like, with only one clicking on the proper icon on the user screen. This embodiment allows the user to get proper results without any need of putting specific words or using his searching history. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0011]     In the Drawings:  
         [0012]      FIG. 1  is a schematic illustration depicting one embodiment of the turbo searching system of the present invention.  
         [0013]      FIG. 2  is a schematic illustration depicting another embodiment of the turbo search engine of the present invention.  
         [0014]      FIG. 3  schematically illustrates an exemplary client system.  
         [0015]      FIG. 4  schematically illustrates an exemplary client system and insider components.  
         [0016]      FIG. 5  is a schematic illustration depicting the computer display screen of client.  
         [0017]      FIG. 6  is a schematic illustration depicting an exemplary set of defined weighted-database.  
         [0018]      FIG. 7  is a schematic illustration depicting an exemplary classified set of defined weighted-database.  
         [0019]      FIGS. 8   a - 8   f  depict an exemplary result obtained using the turbo method of the present invention  
         [0020]      FIG. 9  is a schematic illustration depicting the method of using the turbo unit of the turbo search engine of the present invention.  
         [0021]      FIG. 10  illustrates the flow of a search carried out using a turbo server.  
         [0022]      FIG. 11  illustrates an optional client usage of a private turbo unit.  
         [0023]      FIG. 12  shows the Home page of a website for searching the Internet.  
         [0024]      FIG. 13  shows the results Internet page for the turbo search method.  
         [0025]      FIG. 14  shows the database administration of the turbo search method.  
         [0026]      FIG. 15  shows an example of database results, which include words with initials letter ‘m’.  
         [0027]      FIG. 16  shows example database results, which include words with initials letter ‘h’.  
         [0028]      FIG. 17  shows example database results, which include words with initials letter ‘n’.  
         [0029]      FIG. 18  is an example of searching a specific word in the private turbo client. 
     
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS  
       [0030]      FIG. 1  is a schematic illustration depicting one embodiment of the turbo searching system of the present invention. Network  100  is connecting between clients  110  and servers  120 ; turbo server  130  comprises turbo unit  140  and search engine  150 . Each of clients  110  comprises a computer unit. Each of the servers  120  comprises a network server. Turbo server  130  receives client-specific parameters (which are selected by each client) and search keywords as an input data. Turbo server  130  analyses the input data by providing a weighted set of result documents (e.g., web site addresses, published documents) based on the client-specific parameters, the search key words and a defined weighted-database(s). Turbo unit  140  comprises the weights of words based on a defined weighted-database and search engine  150  comprises statistical and computational units used for data analysis.  
         [0000]     A turbo client  160  uses search results from a regular server or from a turbo server.  
         [0000]     Turbo database  170  comprises the weights of words based on a defined weighted-database. Turbo client  160  can chose using a private turbo search by activating the turbo database  170  or getting results of a regular search.  
         [0031]      FIG. 2  is a schematic illustration depicting another embodiment of the turbo search engine of the present invention. Network  100  is connecting between clients  110  and turbo client  160  and servers  120 ; Turbo server  130  comprises two separated servers: Server A, which comprises the turbo unit  140  and server B including the search engine  150 . The turbo server functions as described in  FIG. 1 .  
         [0032]      FIG. 3  schematically illustrates an exemplary client  110 ; Client  110  comprises a computer  300 , an external storage device  350 , a keyboard  310 , a mouse  320 , speakers  340 , a monitor  330  and a display screen  335 .  
         [0033]      FIG. 4  schematically illustrates an exemplary client  110 ; Client  110  comprises a computer  300 , an external storage device  350 , a keyboard  310 , a mouse  320 , speakers  340  and a monitor  330 . Computer  300  comprises BUS  301 , processor  302 , memory  303 , interface  304 , and storage  305 . Interface  304  can be a network interface and the like.  
         [0034]      FIG. 5  is a schematic illustration depicting the computer display screen  335  of client  110 . Display screen  335  includes a search key  510 , a ruler  520  for typing search keywords and multiple check buttons (such as check buttons  521 - 531 ) for selecting client-specific parameters. Display screen  335  further includes a personal profile unit  540  which includes a save push button  541  and a cancel push button  542  for saving or canceling the personal profile parameters, respectively. Push button  543  activating the private turbo unit of the client. By using the turbo unit, the client can affect the search results order by sorting them per the new method describes in this application.  
         [0035]      FIG. 6  is a schematic illustration depicting an exemplary set of defined weighted-database  600  in which the first row  620  includes a list of parameters (e.g., child, man, sport, present, company, game, nature, book, etc.) and the first column  610  includes a list of words from a dictionary, each of which has a distinct weight or value  630  when selected by one of the parameters listed in the first row. For example, the word “toy” has a value of “10” under the selection of a “child” parameter, while the same “toy” word has a value of “1” under the selection of a “company” parameter. Another example, the word “car” has a value of “9” under the selection of a “man” parameter, while the same “car” parameter has a value of “10” under the selection of a “child” parameter.  
         [0036]      FIG. 7  is a schematic illustration depicting an exemplary classified set of defined weighted-database  700 , which is a sorted version of the defined weighted-database  600  shown in  FIG. 6 . For example, under the selection of a “child” parameter, the heaviest word (i.e., which gets the highest value) is the word “toy”  610 , which appears at the top of “child” column  620 . The word “tree” under the selection of a: “child” appears at the bottom of the column with the weakest value.  
         [0037]      FIGS. 8   a - 8   c  depict an exemplary result obtained using the turbo method of the present invention, which uses turbo server  130  as shown in  FIG. 1 .  FIG. 8   a  illustrates three exemplary documents: document  1  (DOC  1 ,  810 ) which includes the words “toy” and “tree”document  2  (DOC  2 ,  820 ) which includes the words “toy” and “pencil” and document  3  (DOC  3 ,  830 ) which includes the words “toy” and “car”.  FIG. 8   b  presents Table  840 , which includes the calculated values of each of the documents depending on the parameters selected by the client for each search. These values can be calculated using various statistics and/or simple mathematics. For example, the value of document  1  (DOC  1 ) when the “child” parameter was selected is  15  which comprises the additive value of “toy” which is “10” and the value of “tree” which is “ -5 ”.  FIG. 8   c  presents Table  880 , which includes the sorted weighted documents under each selected parameter. For example, when the “child” parameter is selected, the first document which will be sent back to the client from the turbo server after typing the key word toy” is document  3  (DOC  3 ), which includes the words “toy” and “car”. On the other hand, when the parameter selected is “nature” the first document appear when the which will be sent back to the client from the turbo server after typing the key word toy” is document  1  (DOC  1 ) which includes the words “toy” and “tree”.  
         [0038]      FIGS. 8   d ,  8   e  and  FIG. 8   f  presents mapping of turbo server  130  exemplary data document  1  (DOC  1 ) and users turbo server  160  exemplary users CHILD  FIG. 8   e  and NATURE  FIG. 8   f .  
         [0039]     According to one embodiment of the present invention, both databases: the user database and the search engine database are sorted for getting proper results for the user search. For the searching word “ball”  3  in Doc.  1 , the category CHILD  1 , get score of 15 points per the words “toy” 4 score 10, and “tree” 5 score 5 as shown in  FIG. 600 . Category NATURE  2 , get a total score of 10 points per the words “toy”, and “tree” as shown in  FIG. 600 .  FIG. 8   e  is an illustration of a CHILD user category. The sorted words  10  are with high score from up to bottom.  FIG. 8   f  is an illustration of a NATURE user category. The sorted words  10  are with high score from up to bottom.  
         [0040]      FIG. 9  is a schematic illustration depicting the method of using the turbo unit of the turbo search engine of the present invention. In step  920 , the turbo determines the personalization parameters from the user. This is generally directly from the user entry or it could be from a previously saved personal profile. In step  930  the turbo assigns each parameter a weight dependent on the personalization parameters, such as shown in  FIG. 6 .  
         [0041]     In steps  940  and  950 , the option is provided to allow statistics to change the numbers in  FIG. 6 . Thus if it is noticed that many children preferred X to Y, it is possible to modify the table to reflect this choice.  
         [0042]     In stage  960 , turbo unit sorts the words according to their values, as in  FIG. 7 , to allow search requests to be ordered according to the values. The sorted words are now placed in memory ready to apply to actual searches.  
         [0043]      FIG. 10  illustrates the flow of a search carried out using a turbo server according to a preferred embodiment of the present invention.  
         [0044]     In stage  1000  the search parameters are received from the user client. In stage  1100  sorted words are received from the storage in which they placed in stage  970 . The search is now carried out using the words in order.  
         [0045]     In stage  1300 , calculations are being done. The searched words documents are getting values according the parameters.  
         [0046]     In stage  1400 , the documents are sorted per their calculated values.  
         [0000]     If the process is finished, activating stage  1500 , which the results are sending to client.  
         [0047]      FIG. 11  illustrates an optional client usage of a private turbo unit  170 .  
         [0048]     In stage  2000 , turbo client gets the search results from server or turbo server. If client didn&#39;t activate the turbo search, the results are displayed  2200 , while if turbo search is activate, the flow continue to stage  2300 .  
         [0049]     In stage  2300 , calculations of server results are being done. These calculations are per the private turbo database of the client, which include personal weighted words.  
         [0050]     In stage  2400 , turbo unit is sorting the results per the weighted values.  
         [0000]     If the process is finished, the results are displaying at the client monitor.  
         [0051]      FIG. 12  shows the Home page of a website for searching the Internet as shown in  FIG. 5 .  
         [0052]     The website offer four client specific parameters: Business  532 , Pleasure  533 , Education  534  and News  535 . My Turbo  453 , is a push button, which activate the personal turbo unit  170  of the client. By pressing one or more of the push buttons  532 - 535 , the client should get closer results for his searching. Optionally, the client can activate his personal turbo unit  170  by pressing My Turbo button  543 . This action causes to get results closer to the client taste and habits, cause his personal turbo unit is having his own chosen weighted words and expressions.  
         [0053]      FIG. 13  shows the results Internet page for the turbo search method.  
         [0000]     This figure compares Google™ results  3000  as appears at the left side, and the new method, which describes in this file at the right side.  
         [0000]     Each column brings the results  3060 ,  3070 ,  3080  and  3090  per the suitable chosen push button parameter  532 - 535 .  FIG. 13  is a search results for a searching word: New York.  
         [0000]     The first site achieved by a regular searching appears at no.  3050 , which is:  
         [0054]     The New York Times—Breaking News, World News &amp; Multimedia. While the results which getting from the turbo searching is depend of the chosen parameter. For Business  3100  for example, the first result is: “New York Board of Trade—an international marketplace For . . . ”. This result got the highest score in Business category and gets 2200 points. The score achieved by the calculation of the weighted words appears in this page, optional way is including its heading and abstract.  
         [0055]      FIG. 14  shows the database administration of the turbo search method. In this web page the data is inserting for the weighted words. For example, a client who likes Maccabi Tel-Aviv basketball team, can score the word ‘basketball’  4000  in category Pleasure  4200  with a high score of 100 points  4100 , and the word ‘Maccabi’  5000  at  FIG. 15  in the category of Pleasure  5200 , with a score  5100  of 100 points too.  
         [0056]      FIG. 18  is an example of searching the search word ‘Nadav’  8000  in the private turbo client  160 . The first search result, which appears at category Pleasure  8200 , is ‘Nadav Henefeld’. The reason that this is the result is that Nadav Henefeld is a basketball player at Maccabi Tel-Aviv. Cause the user like this basketball team and he already put in his private turbo  170  the words ‘basketball’  4000  and ‘Maccabi’  5000 , an associative result came with the name of his favorite team player: Nadav Henefeld (while the Google™  8300  first result is ‘Naday Kander’  8400 ).