Abstract:
A method for generating marketing materials using filtered reviews including the step of receiving a set of reviews for a business and filtering the reviews into a set of reviews containing a quantitative rating and a set of reviews without such a rating. The method further includes determining a set of reviews containing quantitative ratings that exceed a quantitative threshold. The method further semantically filters these reviews along with the set of reviews without a quantitative rating to generate a set of reviews that exceed a semantic threshold of satisfaction for the business. The method then ranks these reviews based on quantitative rating, and further ranks the reviews based on semantic analysis. Reviews include professional reviews, user-generated reviews, aggregate ratings, commentary, rankings, etc. Marketing materials include print advertisements, online advertisements, brochures, pamphlets, websites, flyers, videos, etc.

Description:
[0001]    This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/116,117 and U.S. Provisional Patent Application Ser. No. 61/116,123, both filed on Nov. 19, 2008, the contents of which are incorporated by reference herein. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present disclosure relates to a system and method for filtering professional reviews, user-generated reviews, aggregate ratings, commentary, rankings, etc. for use in generating print advertisements, online advertisements, brochures, pamphlets, websites, flyers, videos, etc. (“Marketing Materials”). More particularly, the present disclosure relates to a system and method for filtering a set of reviews using pre-defined criteria to identify those that portray the business most favorably. Among other uses, the system may be used to automate design aspects of Marketing Materials. 
         [0004]    2. Description of Related Art 
         [0005]    As the Internet continues to develop, businesses are seeking opportunities to use information available on the Internet for use in Marketing Materials. Many websites make available professional reviews, user-generated reviews, aggregate ratings, commentary, rankings, etc. (“Reviews”). For example, user-generated reviews may include reviews, ratings, and other commentary posted by patrons and clients of such business to share experiences about shopping, dining, movies, concerts, hotels, or vacation spots. Such Reviews can take the form of text, images, audio, or video. 
         [0006]    While many websites make available such Reviews, there does not exist a system or methodology to search disparate sources that contain Reviews and generate Marketing Materials using Reviews that are determined to portray the business most favorably (“Selected Marketable Reviews”), for example the best or most positive Reviews. Such disparate sources may include websites, databases, and structured data feeds, which may be internal or maintained by third parties (“Content Sources”). 
         [0007]    Accordingly, there is a need for a system and a software system that would search disparate Content Sources containing Reviews and filter such Reviews using predefined filtering criteria to quantitatively and qualitatively identify Selected Marketable Reviews for the purpose of helping a user, such as a advertisement representative of a media company or a business owner, generate Marketing Materials. 
       SUMMARY OF THE DISCLOSURE 
       [0008]    The present disclosure provides for a software system and method for automatically generating Marketing Materials for a business by receiving identifying information about a business from a user. The software system automatically searches a plurality of Content Sources for Reviews related to a business, and filters the Reviews based on pre-defined criteria to generate Marketing Materials. 
         [0009]    The present disclosure further provides for a method that uses identifying information from the user to search databases to generate a list of Content Sources that may contain Reviews related to the business. 
         [0010]    The present disclosure also provides for a method that uses the identifying information and searches Content Sources for Reviews. The Reviews are then filtered using pre-defined criteria to identify Selected Marketable Reviews for use in Marketing Materials. 
         [0011]    These and other and further features and advantages are provided by a method that a) receiving a set of reviews of a topic; b) filtering reviews in the set of reviews based upon filtering criteria to generate a set of filtered reviews; and c) generating a report based upon filtered reviews in the set of filtered reviews. 
         [0012]    These and other objects and advantages of the present invention are provided through its ability to identify Selected Marketable Reviews. This is achieved by receiving a set of Reviews of a business and filtering the Reviews into a set of Reviews containing a quantitative rating and a set of Reviews without such a rating. The method further filters the Reviews containing a quantitative rating to include only those with ratings that exceed a quantitative threshold. The method further semantically filters these Reviews along with the set of Reviews without a quantitative rating to generate a set of Reviews that exceed a semantic threshold of satisfaction for the business. The method then ranks these Reviews based on quantitative rating, and further ranks the Reviews based on semantic analysis. 
         [0013]    A system including a processor that performs a method that includes: receiving a set of Reviews of a business and filtering the Reviews into a set of Reviews containing a quantitative rating and a set of Reviews without such a rating. The method further filters the Reviews containing a quantitative rating to include only those with ratings that exceed a quantitative threshold. The method further semantically filters these Reviews along with the set of Reviews without a quantitative rating to generate a set of Reviews that exceed a semantic threshold of satisfaction for the business. The method then ranks these Reviews based on quantitative rating, and further ranks the Reviews based on semantic analysis. 
         [0014]    A computer readable storage medium having stored therein instructions that are executable by a processor for performing a method includes: receiving a set of Reviews of a business and filtering the Reviews into a set of Reviews containing a quantitative rating and a set of Reviews without such a rating. The method further filters the Reviews containing a quantitative rating to include only those with ratings that exceed a quantitative threshold. The method further semantically filters these Reviews along with the set of Reviews without a quantitative rating to generate a set of Reviews that exceed a semantic threshold of satisfaction for the business. The method then ranks these Reviews based on quantitative rating, and further ranks the Reviews based on semantic analysis. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]    The foregoing will be more apparent from the following detailed explanation of the preferred embodiments of the invention in connection with the accompanying drawings. 
           [0016]      FIG. 1  illustrates a hardware and software system for carrying out the method of the present disclosure; 
           [0017]      FIG. 2  illustrates exemplary methods of obtaining a set of Reviews; 
           [0018]      FIG. 3  illustrates a flowchart illustrating the method of filtering using predefined filtering criteria to quantitatively and qualitatively identify Selected Marketable Reviews for the purpose of helping a user generate Marketing Materials; 
           [0019]      FIG. 4  illustrates a Review and an excerpt of a Review, according the method of the present invention; 
           [0020]      FIG. 5  illustrates Marketing Materials in the form of an advertisement that incorporates an excerpt of a Review and a quantitative rating, according to the method of the present invention; and 
           [0021]      FIG. 6  illustrates Marketing Materials in the form of a website that is generated using the method of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0022]    Referring to the drawings and in particular to  FIG. 1 , a block diagram of the system of the present disclosure is shown and generally referenced by reference numeral  100 . System  100  includes a computer system  300 . An operator  305  is able to program computer  300 . Computer system  300  includes a user interface  310 , a processor  315 , memory  320 , and a bus  327 . Computer  300  may be implemented on a general-purpose microcomputer. Processor  315  is configured of logic circuitry that responds to and executes instructions. Memory  320  stores data and instructions for controlling the operation of processor  315 . Memory  320  may be implemented in a random access memory (RAM), a hard drive, a read only memory (ROM), or a combination thereof. One of the components of memory  320  is a program module  325 . 
         [0023]    Program module  325  contains instructions for controlling processor  315  to execute the methods described herein. For example, as a result of execution of program module  325 , processor  315  is able to receive instructions/input from a user  220 , search computer network  200  (e.g. Internet) using input and retrieve a list of Content Sources from computer network  200  that is used to generate a report such as an advertisement. The term “module” is used herein to denote a functional operation that may be embodied either as a stand-alone component or as an integrated configuration of a plurality of sub-ordinate components. Thus, program module  325  may be implemented as a single module or as a plurality of modules that operate in cooperation with one another. Moreover, although program module  325  is described herein as being installed in memory  320 , and therefore being implemented in software, it could be implemented in any hardware (e.g., electronic circuitry), firmware, software, or a combination thereof. 
         [0024]    User  220  has access to system  100  via a computer network  200 , as shown, or from a server. User  220  may be a sales person at a media company, an employee at a business  201 , a business owner, or other person who may be otherwise charged with preparing Marketing Materials for business  201 , such as a graphic designer. User  220  accesses system  100  using a computer  105  having a user interface  110 . Computer  105  is coupled to and has access to system  100  via a network  200 . Computer  105  also has associated therewith local storage mediums  218 . 
         [0025]    Network  200  provides access to websites  205 , internet servers  210  and various Content Sources  215 , for example. Computer  105  includes an input device such as a keyboard or speech recognition subsystem for enabling a user to communicate information and command selections through network  200  to processor  315 . User interface  110  also includes an output device such as a display or a printer. A cursor control such as a mouse, track-ball, or joy stick, allows the user to manipulate a cursor on the display for communicating additional information and command selections through network  200  to processor  315 . User interface may also be a personal digital assistant (PDA), or the like. 
         [0026]    User interface  110  and computer  105  are able to access program module  325  of computer system  300  from network  200 . Operator  305  makes program module  325  available to user  220  via network  200  from, for example, a website. 
         [0027]    Referring to  FIG. 2 , there are numerous ways that user  201  can obtain a set of Reviews to be reviewed by method of present invention.  FIG. 2  provides exemplary methods by which user  201  may obtain set of Reviews to be reviewed in present application, although other methods may be used to obtain set of Reviews. Referring again to  FIG. 2 , a method is shown and referenced by reference numeral  400 . 
         [0028]    In step  405 , after the start, system  100  prompts user  220  to enter information related to business  201  into a field on a screen, such as a business name and/or location. The location of business can be a segment of a business location, such as a street address, a postal code, a state/region, or any combination thereof (“Location Input”). Such information is preferably entered by user  220  via user interface  110 . 
         [0029]    In step  406 , processor  315  searches various sources that contain standardized business names and locations. Step  406 , process searches using information provided by user  220  in step  405 . Step  406  results in a standardized business name and location, or if none is found, a descriptor of such business that was provided by user in step  405 . 
         [0030]    In step  407 , system  100  searches network  200  and compiles a list of Content Sources using standardized name and location. 
         [0031]    Content Sources are preferably one or a plurality of websites identified by one or a plurality Uniform Resource Locators (URLs) that may include Reviews. 
         [0032]    In step  408 , system  100  searches Content Sources compiled in step  407  for Reviews using descriptor and forwards Reviews to system  100  in step  500 . 
         [0033]    Alternatively, in step  410 , user  201  can provide a URL to a webpage containing Reviews of a business, and a set of Reviews can be extracted from such webpage for use in step  500 . 
         [0034]    Alternatively, in step  411 , user  201  can provide an offline article containing one or more Reviews of a business, which can be transcribed for use in step  500 . 
         [0035]    Alternatively, in step  412 , third parties or partners can provide structured data feeds from which Reviews can be extracted for use in step  500 . 
         [0036]    Alternatively, in step  413 , user  220  directly provides a list of Reviews for use in step  500 . 
         [0037]    Method  400  provides examples of how Reviews for a business may be obtained and is not in intended in any way to limit the scope of the method of  FIG. 2  of the present disclosure. 
         [0038]    Referring to  FIG. 3 , in step  550 , system  100  obtains a set of Reviews that are to be filtered based on predefined criteria created by operator  305 . 
         [0039]    In step  550 , processor  315  filters Reviews that contain a quantitative rating. Quantitative ratings may be based on a symbol, such as a star, or a numerical rating. If a review is found to contain a quantitative rating, such rating is standardized in step  563  to comply with a predetermined scale. For example, all quantitative ratings may be converted to a standardized 5-star scale for ease of comparison. 
         [0040]    For example, a rating on a 4 star rating scale can be converted to a standardized 5-star scale by multiplying the rating by 5/4. As another example, a numerical rating based on a rating scale of 100 can be converted to a standardized 5-star scale by dividing the numerical rating by 20. 
         [0041]    In step  570 , only Reviews containing a standardized rating exceeding a minimum quantitative threshold are accepted. For example, the minimum quantitative threshold could be 4 stars on a 5-star scale. If a review contains a rating not exceeding the threshold, it is discarded in step  571 . 
         [0042]    Reviews from step  570  that exceed the quantitative threshold, as well as Reviews from step  550  that do not have quantitative ratings, are both filtered semantically in step  560 . 
         [0043]    In step  560 , processor  315  filters Reviews semantically by searching for particular keywords, phrases, or sentiments—both positive and negative—within the content of the Review. For example, step  560  may search for positive words or phrases such as “best,” “excellent,” or “best of my life” and/or negative words or phrases such as “rodent,” “worst,” or “bland.” Step  560  may also use other semantic techniques to analyze the content of the Reviews and identify Selected Marketable Reviews for inclusion in Marketing Materials. 
         [0044]    Processor  315  analyzes these keywords, phrases, and sentiments and in step  575  determines whether the review exceeds a pre-defined semantic threshold to qualify as Selected Marketable Reviews. An example where a Review may not exceed the semantic threshold is if it contains any negative words or phrases or is otherwise deemed unmarketable. Reviews that exceed the semantic threshold are saved and those that do not are discarded in step  576 . 
         [0045]    In step  580 , excerpts are created from the Reviews that exceed the pre-defined semantic threshold from step  575 . For example, processor  315  can create an excerpt for a Review based on certain predefined keywords and punctuation marks surrounding the keyword. The technique of extracting these excerpts aims to find a portion of the Review that portrays the business most favorably. 
         [0046]    In step  590 , processor  315  ranks Reviews. For example, Reviews with “5” ratings are ranked together ahead of Reviews with “4” ratings. Reviews without a quantitative rating are ranked together after Reviews with the lowest quantitative ranking. 
         [0047]    In step  600 , within a grouping of similarly ranked Reviews, Reviews are further ranked using semantic analysis and/or based on the presence or absence of certain keywords. In similar quantitative groupings, Reviews with keywords more conducive to being Selected Marketable Reviews are ranked higher than those without. For example, among Reviews containing a 5-star rating, those containing the phrase “best meal” would be ranked ahead of those containing the phrase “good meal.” 
         [0048]    In step  610 , a ranked set of Selected Marketable Reviews from the preceding steps is stored or catalogued according to their ranking for use in Marketing Materials. 
         [0049]    Referring to  FIG. 4 , an illustration of a screen shot  700  containing Reviews  705  and  720  is shown. Excerpts  710  and  715  are generated by processor  315  by identifying keywords and punctuation. Links  725  and  730  provide links to the sources of the Reviews. 
         [0050]    Referring to  FIG. 5 , a sample advertisement for a restaurant is illustrated by reference numeral  800 . Advertisement  800  includes a Review excerpt  805 , the name of the individual providing the Review in excerpt  805 , a rating  810 , and business information  815 . Advertisement  800  also provides additional information using link  825 . 
         [0051]      FIG. 6  illustrates an example of a website  900  generated by method  490  of the present invention. Website  900  contains Review excerpts  905 ,  910 , and  915 . Website  900  also provides business details related to the restaurant and awards won by the restaurant. While method  490  is used to generate advertisement  800  and website  900 , other Marketing Materials in general could also be created using the method of the present disclosure. 
         [0052]    The present invention has been described with particular reference to the preferred embodiments. It should be understood that the foregoing descriptions and examples are only illustrative of the present invention. Various alternatives and modifications thereof can be devised by those skilled in the art without departing from the spirit and scope of the present invention. Accordingly, the present invention is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the appended claims.