Patent Document

CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims benefit of U.S. provisional application Ser. No. 61/232,378, filed Aug. 7, 2009, entitled “Search Method to Identify Local Entities of Interest Utilizing Social Graphs as Filters”, which is incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention relates generally to a search engine. The invention relates more particularly to providing localized search results based on social graph inputs. 
         [0004]    2. Background Art 
         [0005]    Since the Internet was launched in 1995, online services have emerged for many consumer and business needs and applications. The power of the Internet has been attributed to its unique properties as a global, two-way medium that allows any user the ability to reach global users or businesses and interact with them, literally at the speed of light. The term “globalization” has become the new order of the economic day, powerfully enabled by the Internet, which has caused massive shifts and transformations in how consumers and businesses conduct their day-to-day personal and commercial business. 
         [0006]    In today&#39;s “information age” people frequently perform various types of searches using computing devices connected to the Internet. The Internet has made searching for information far more simple than using manual techniques, which typically involve going to a library and searching through various indexes to find articles or books, for example. To perform a computerized search, a searcher simply enters a word or words (termed “keywords”) into a website query box in order to find information related to the entered words. The website providing the query box uses a search engine to scrutinize billions of documents on the Internet and return documents including the keywords entered by the searcher. 
         [0007]    Search engines are widely utilized over networks for locating the information sought by the user. Conventional search engines employ keyword matching in order to select candidate web page links to return to the user seeking data related to the keywords in the entered query. The page links are then ranked and displayed according to results from complex algorithms that determine relevance and in the case of advertising balance relevance with revenue to determine which links to present and in which order. The algorithms use a combination of link citations, click through rate, price per click, number of keywords matched, order of keywords matched, and many other factors to determine the ranking of search results returned. 
         [0008]    Unfortunately, for a consumer who is in the market to purchase a product or service quickly, one that matches his/her personal preferences, values or budget, the typical search or local search engine can return far too many documents in response to a buyer&#39;s needs resulting in information overload. Often this is because results are returned using keywords that could have many types of meaning. The keyword and location matching alone can produce documents for a search with local intent that are completely out of context from the context of local shopping and purchasing, or the keywords in the document from the results may be related to non-relevant, research-oriented subjects and types of web content. Another source of misdirection is that the most relevant web pages containing the keywords may be popular for reasons unrelated to the local shopping and purchasing. Of the hundreds or thousands of links to supposedly related web pages returned to the user, in the context of searching for local products and services it is hard for consumers to choose which of the local products and services being presented are right for them or will meet their immediate need. For example, when a consumer needs a local dentist fast, they typically only need 2-3 highly recommended dentists. 
         [0009]    Even the web page matches that are relevant to the user&#39;s search may not prove to be useful. The lack of utility is often due to a local of personal relevance. Although approximately half of United States commerce takes place within ten miles of a the principal&#39;s home or work location, an Internet user still has a very hard time finding a high quality local service that matches his specific needs, values, or budget. It is very difficult for a user of conventional search engines, to find a personally relevant product or service on the Internet, in the roughly one minute that consumers take to look up services in the Yellow Pages or to find an ad in the weekly advertising circulars of newspapers or direct mailings. 
         [0010]    In an effort to increase the relevancy of search engine results, local web search service providers provide topically relevant and geographically relevant search results using “explicitly-entered” keywords, categories, and geographies. In these solutions, consumers explicitly enter the categories/keywords and geography of their intended purchase, and small and medium business listings from merchant directories are matched against these categories and geographies to provide search results with local and topical relevance. The listings that are returned in response to this type of search query match the categories entered by the user, and are ranked in order of proximity to the user&#39;s explicitly declared location or to an implicitly derived (via the search software) location. However, the user may still receive far more business listings than prove useful (most users are looking for 2-3 possibilities). Again prior art search engine techniques require the searcher to research and read through a lot of information such as detailed information, reviews and ratings to make a quality decision among 1-3 businesses that are right for him. 
         [0011]    Another method implemented in some prior art search engines it to provide a ranking algorithm based on pay-for-position. That is, local businesses that pay the most advertising dollars secure the most visibility in the search results. A drawback to this methodology is that advertisers, by their expenditures of advertising dollars, control the user&#39;s search results. Worse, the local businesses listed returned may not have any personal relevancy and may simply not be qualified to the satisfaction of the user. Any or all of the listed businesses may or may not provide the kind of services that meet the needs, budgets, or expectations for quality of service of any given search engine user. 
         [0012]    At least in part due to the limited local and personal relevance and local value offered through today&#39;s local online solutions, only a small fraction of advertising expenditures have migrated from local offline promotion and direct mail advertising to the Internet. Therefore, there is a need for a local recommendation service that returns search local businesses similar to the way it happens in the offline world, via word of mouth from trusted friends, family and colleagues or neighbors. 
       SUMMARY OF THE INVENTION 
       [0013]    The present invention includes methods and systems for performing computer based searches utilizing social graphs as filters. The system allows users to search a data base that is maintained in real time for businesses that meet the search criteria for subject matter and geographic area. The resulting candidate businesses may be further filtered using a social graph designated by the searching party. The social graph may include varying levels of confidence as established by the searching party. 
         [0014]    The method of searching may include as a foundational element the establishment of a preference data base to store preferences of users of the method. Each user of a system on which the method is resident may input identifying data for one or more categorized businesses. The inputs of each user are associated with an identification of the user. The inputting of the business into the data base constitutes a “vote” for the subject business. The method does not require scaling a user&#39;s degree of satisfaction with the businesses input by the user. 
         [0015]    A searching party submits a general query to initiate a search. The query may include a category of business, keywords describing the searcher&#39;s needs or business names and a geographic location. The system selects candidate businesses based on this query. 
         [0016]    The searching party may also designate a social filter to rank the results of the search. The social filter may include one or more designated users of the system, a group of users predefined by the searching party, or all users of the system. The number of “votes” found for a business within the selected social filter may establish its position in the ranked results. The system may also weight the “votes” of the users based on a degree of relationship between the user and the searching party, or on one or more characteristics of the users. 
         [0017]    The results returned are then socially relevant to each user with choices that have a high probability of being personally relevant to the user. Users with personal preferences can therefore receive local search results that may be ranked differently from the results of another user with different personal preferences. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0018]      FIG. 1  is a schematic of the system of computer based searching using social graphs as filters. 
           [0019]      FIG. 2  illustrates an exemplary method of searching utilizing social graphs as filters. 
           [0020]      FIG. 3  illustrates a set of preference data associated with a user. 
           [0021]      FIG. 4  illustrates a social graph for a user. 
           [0022]      FIG. 5  illustrates the preference lists associated with a particular social graph. 
           [0023]      FIG. 6  illustrates exemplary search information related to searching utilizing social graphs as filters. 
           [0024]      FIG. 7  illustrates another example search. 
           [0025]      FIG. 8  is a screenshot of an exemplary interface for providing a preference list used in the system. 
           [0026]      FIG. 9  is a screenshot of an exemplary interface for providing a search result generated by the system. 
           [0027]      FIG. 10  is a screenshot of an exemplary interface for providing a search result. 
           [0028]      FIG. 11  is a screenshot of an exemplary interface for providing a search result. 
           [0029]      FIG. 12  illustrates screenshots of an exemplary interface for providing a specific inquiry and a response to the inquiry. 
           [0030]      FIG. 13  illustrates an exemplary computing system that may be used to implement an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0031]    Various embodiments of the present invention use a method and system of computer based searching that returns results that are both geographically, socially and personally relevant. A searching party using the method may select a geographic area and a topic to be searched as in a typical search function, but searching only business entities that have been recommended by other users of the method. The search is performed on a data base created by users of the method who input preference data to a master relational index data base. The search party may then further filter the returned search results by considering the recommendations of one or more users identified by the searching party. The user may specify a level of confidence (trust) that he wants to apply to the search filter by designating a desired social graph or grouping within a social graph. 
         [0032]      FIG. 1  shows an exemplary system  100  for implementing the method of the present invention. The system  100  of  FIG. 1  includes a plurality of users  110  which may interact with client devices  115  in communication with a network  120 . The network  120  includes a network server  130 , an application server  140 , and data storage  150 . The search application  160  may reside and be executed by a processor on application server  140  to perform searching utilizing social graphs as filters. 
         [0033]    Users  110  access network server  130 , application server  140  and data storage  150  via client devices  115 , such as for example workstations, servers, lap top computers, mobile devices, or other computing devices that can communicate over the network  120  using a browser application. 
         [0034]    Network server  130  may be implemented as one or more machines that communicate over network  120  and communicate with application server  140 . Application server  140  may communicate with network server  130  and host one or more applications or software modules which may be executed to implement the present technology. Data storage  150  may accessed by application server  140  (for example, through a query initiated by search application  160 ) and be implemented as one or more remote servers or within application server  140 . 
         [0035]    Network  120  may be implemented as any type of communication network including but not limited to the Internet, Wide Area Network (WAN), Local Area Network (LAN), intranet, extranet, private network, or other network. Application server  140  may be accessed via network server  130 . 
         [0036]      FIG. 2  is a flowchart of an exemplary method  200  for searching utilizing a social graph as a filter. In some embodiments, the method  200  may be performed by search application  160  residing on application server  140 . An element of the method is the establishment of a preference data base at step  210 . The preference data base includes preference entries created by the users of the system  100 . 
         [0037]      FIG. 3  illustrates a set of preference data associated with a user. As illustrated in  FIG. 3 , a user  300  may establish his preference data by inputting a preference list  310  with the names of one or more small to medium size businesses (SMB) through an interface provided by client device  115 . Client device  115  may then transmit the preference data to application server  140  via network server  130 . Application server  140  may store the preference data locally or store the data at data storage  150 .  FIG. 8  is a screenshot of an example interface provided by client device  115  for managing a preference list  310 . 
         [0038]    A search criterion or criteria may be received from a searching party at step  220 . The searching criteria may be received by search application  160  on application server  140 . Candidate businesses may then be selected at step  230 . Search application  160  may select the candidate businesses by sending a query to data storage  150  with the search criteria received at step  220 . Data storage  150  may then provide to search application  160  a list of candidate businesses that match the search criteria. 
         [0039]    The resulting candidate businesses may be filtered using a designated social graph at step  240 . By filtering the results, a subset of candidate businesses may be identified and provided to the searching party from which the search criteria were received at step  220 . 
         [0040]    The steps of method  200  are discussed in more detail below. Generally speaking, search application  160  may provide interfaces to one or more client devices  115 . A user  110  may provide input into an interface through client device  115 . Client device  115  may communicate with search application  160  on application server  140  to exchange the input and other data, such as for example search criteria, candidate businesses, filtered results. Application server  140  may receive data from client device  150  (via network  120  and network server  130 ) and process the data locally, as well as store the data at data storage  150  or locally to application server  140 . 
         [0041]    To populate an interface with data, such as candidate businesses, search application  160  may query data storage  150  for data that meets specific criteria. Data storage  150  may provide the requested data to search application  160  which may then provide the data to client device  115  through one or more interfaces. 
         [0042]    Returning to  FIG. 3 , each user  300  (e.g., user  110  in  FIG. 1 ) of the system  100  may input various business entities that the user  300  wants to have associated with their name. An identifying profile  320  (ID) is associated with each business entered by the user  300 . The identifying profile  320  may include the business name, a category, and a location. Each identifying profile  320  may also be associated with the user  300  who input the preference data. Therefore each identifying profile  320  may include a business name, category, and location, and is associated with the user  300  who input the preference “vote” for the subject business. 
         [0043]    In the method  200  of the present invention, there is no requirement that a rating scale be associated with the entry of a business into the preference data base. The user may simply submit an entry, such as for example a vote, if he likes the business; he does not submit an entry if he does not like the business. The preference data base listings may therefore be properly characterized as including only positive references to each business included in the listings. 
         [0044]    Each preference list  310  may include one or more user votes or “human citations” for a business entity that the user wants to endorse. The user votes may be collected and maintained via a master relational index which may be stored on data storage  150  and continuously updated. The master index may be used in real-time to rank, by preference as established by the selected social graph, identified businesses. A listing of businesses is generated, using the ranking, in response to a specific user&#39;s search query. The listing returned for the query will thus be tailored to the user&#39;s specific social graph selected at the time of their search. The listing may include multiple levels of recommendation. 
         [0045]      FIG. 4  illustrates a social graph for a user  300 . A first user, a searching party  300 , identifies one or more other users that are labeled friends  400 . In the social graph illustrated in  FIG. 3 , the searching party has identified a social graph including four friends  400 . Each of the friends  400  has only a single degree of separation from the searching party  300 . The user in this exemplary embodiment might specify that his search used preferences from friends  400  only. 
         [0046]    Each of the friends  400  may also have a social graph with one or more other users designated as friends. Therefore the searching party  300  may choose to initiate a search with varying levels of confidence or trust. That is, the searching party  300  may perform a search based on the preference only of the friends  400  of the searching party. The searching party  300  may expand his trust level to include friends of his friends, friends of those friends, and so on until the searching party simply searches based on all users of the system  100 . 
         [0047]    Each search performed by a searching party  300  may therefore be subject to varying levels of trust as defined by degree of separation. For each searching party  300 , the levels of trust, the degrees of separation, that are available for a given search may vary with the individual searching party  300 . The number of degrees of separation available to the searching party  300  is defined by the number of relationships formed between the searching party  300  and other users of the system  100 . 
         [0048]    At the outset of each search, the searching party  300  may define a search filter with search criteria specifying a desired level of trust, i.e. the range of parties to be consulted. If the searching party  300  sets as his filter “friends” then the preference lists of only those users at a first degree of separation are considered.  FIGS. 9 and 10  are screenshots of an exemplary interface for providing a search result generated by the system. In particular, the interface of  FIG. 10  shows the results of a search performed at the “friends” level. If the searching party  300  sets the filter as “friends and friends of friends” then preferences of users up to a second degree of separation would be considered. The searching party  300  may also expand the social graph to include all users of the system  100 . The interface of  FIG. 9  shows the search results of an “all users” search. Note that even though the search is performed at the “all users” level, the results may be grouped to show the preferences of friends of the searching party being listed first. 
         [0049]    In some instances, the searching party  300  might not find recommendations from his friends in the system  100 . In this case, or at any time he so desires, the searching party  300  may choose to access a third party system to identify more friends. The new friend list created for the searching party  300  may then be used as a social graph defining a search filter in the system  100 . 
         [0050]    The searching party  300  may define the search filter to be any single friend  400 . The searching party  300  may also define the social graph to be used as the search filter to be multiple or all friends  400 . The social graph may also be a pre-specified grouping of friends, such as friends with an MBA, Book Club members, PTA members, etc. 
         [0051]    For search decisions relating to a choice of business in which trust is paramount, such as finding a children&#39;s orthopedic specialist for a child&#39;s soccer injury or an in-home nanny, the user may select, through an interface provided on client device  115 , very close friends and personal network members to form the social graph filter. For decisions in which trust and personal relevancy is not as important, or if the user is seeking to “discover” new businesses such as restaurants, the searching party (i.e., user) may choose to view and/or select recommendations from friends of friends or all members. 
         [0052]    Each user of the system may expand his social graph either by associating with other established users of the system, or by recruiting his friends to also use the system. As the number of users at each level of trust relative to a searching party increases, so too does the likelihood of a successful search. A greater number of input preferences may be more likely to lead to results that meet the needs of the searching party  300 . 
         [0053]      FIG. 5  illustrates the preference lists associated with a particular social graph. In particular,  FIG. 5  shows the information that would be considered in a search using the social graph of  FIG. 4 . The searching party  300  in this instance has specified his search field as friends. The information from the master index that may be considered by the method includes the identification and preference information of the searching party  300  as well as the identification and preferences of the four other users identified as friends  400 . 
         [0054]    When a searching party  300  conducts a search, the social graph that is to be used as a filter may be either a predetermined default graph that is established by the system  100 . Alternatively, the searching party  300  may manually enter his selected social graph. The social graph may be as simple as one selected friend  400 , or it may be a group of friends  400 . The social graph applied may be any specified group such as “all friends”, “friends-of-friends”, or the more broad “all members”. 
         [0055]    In the master index, various business entities are associated with each user of the system  100 . When a search is initiated by a searching party  300 , the entities associated with those users selected to be in the social graph filter are returned as an initial list of candidates, and remain associated with a user. 
         [0056]    The social graph used for the search is then applied to count the votes of the users specified in the social graph. This plurality recommendation may be coupled with topical matching and proximity matching algorithms to rank the candidate entities. The resultant ranked listing is returned to the searching party as the end result of the search. The end result listing is therefore a listing of those business entities with the highest relevancy to the specified social graph of the searching party  300 . The entities with the highest topical and geographical relevancy may be listed first. Candidate entities with a second level of personal-social, topical, and geographic relevancy may be listed in a second results band. Additional bands of results based on expanded degrees of the applied social graph may also be listed. 
         [0057]      FIG. 6  illustrates exemplary search information related to searching utilizing social graphs as filters. In particular, example 1 of  FIG. 6  illustrates search criteria operated on by search application  160  and representative search result information (candidate businesses) received as a result of a query based on the search criteria. Example 1 assumes a searching party  300  associated with a User ID  1  is searching for a dentist in Los Altos, Calif. Hence, the search criteria may include “dentist” or “dental health” and “Los Altos” and “California.” In this example, the initial search specified by the searching party  300 , User ID  1 , is for a specific friend  400 , User ID  2 . The system selects as candidates those business entities associated with the friend  400 , User ID  2  in the dental health category in the master index. In Example 1, those businesses are identified as SMB ID 1 , SMB ID 4 , and SMB ID 5 . 
         [0058]    The candidate businesses may then be ranked according to the number of occurrences within a social graph specified by the searching party  300 , which in this example includes the friends of the searching party  300 . The first candidate business, SMB ID 1 , appears on the preference list of three friends in the social graph. The second candidate business, SMB ID 4 , is listed by two friends. The third candidate business, SMB ID 5 , appears on only one preference list in the social graph. The results list returned to the searching party  300  therefore shows SMB ID 1  with a vote count of three in the first position. SMB ID 4  with a vote count of two is second, and SMB ID 5  is listed third. 
         [0059]      FIG. 7  illustrates exemplary search information related to searching utilizing social graphs as filters. Example 2 of  FIG. 7  illustrates a search in which the system  100  may access the preference lists of three friends  400  to provide a candidates list of six businesses. As in Example 1, the candidate businesses are ranked according to the number of appearances in the preference lists of the users included in a specified social graph. However in Example 2, two businesses, SMB ID 1  and SMB ID 2 , are tied for the first position since both have a vote count of three. A user may choose to have the system implement any of a plurality of ranking priorities, or tie breakers. 
         [0060]    One way in which the system may establish ranking priorities is to use geographical proximity to the searching party. Another potential source of ranking weighting is the degree of matching between the search input and the products and services of the candidate business, that is, the availability of specific goods or services may be a factor in the ranking of the candidate businesses. Still another potential method of ranking weighting is the affinity of the searching party to the friends on whose lists the candidate business appears. Affinity may be determined by degree of separation, number of times the preference list has been accessed, or other factors that may be chosen by the user or established by the system. 
         [0061]    In example 2, if the friends  400  with User ID 3  and User ID 4  are more closely related to the searching party than are friends  400  with User ID 2  and User ID 5 , then SMB ID 2  would appear first on the results list. In example 2, a ranking priority may also be used to establish third place between SMB ID 3  and SMB ID 4  with each having two social graph votes, and fifth place would similarly be determined by a selected method of ranking priority between SMB ID 5  and SMB ID 6 . 
         [0062]    Various attributes of users within the social graph may be used to drive the priority ranking of the preference lists of users within the social graph filter. For example, the preference list of a user whose profile lists their home location as Sonoma, Calif., might be accessed to recommend businesses in Sonoma in response to a search. The preferences of the Sonoma user would receive additional weighting, as compared to a non-Sonoma resident member of the social graph, due to the fact that the Sonoma user is a Sonoma resident. Similarly, if a specific friend in a specified social graph lists more golf courses than any of the other selected friends in the social graph filter for a given search, the golf course preferences of the specific friend may be given greater weight in the ranking process. 
         [0063]    Preference list weighting may also be established on the basis of the experiences evidenced by the profile of a user. If a member of a specified social graph is employed in the industry of the desired search topic, the “reputation” of that member as an industry professional may be factored into the ranking. The professions of users of the system may be meta-tagged in the system database. So in the case of a searching party looking for an orthopedic doctor, the recommendation of the social graph member (via his preference list) whose profile indicates that he is an “orthopedic research engineer” may weigh more heavily than other members of the social graph as defined by the searching party. 
         [0064]    The historical activity between a searching party and the various users included in a specified social graph may also be used to establish weighted priorities. More activity between a searching party and a first friend might indicate a higher affinity between the searching party and the first friend as compared to the affinity of the searching party to a second friend who is accessed less. Preferences of the first friend would be weighed more heavily than those of the second friend. “Activity” between two users of the system may include clicking on a profile, asking for a recommendation, viewing a preference list, recommending the other user, and other activities that may be utilized within the system. 
         [0065]    A user of the system who receives the most “asks” or who has the most friends adding businesses recommended from their preference list may be labeled as an expert for the purposes of a social graph. The preferences of the expert may be given added weighting, thereby affecting the document rankings of searching parties that include the user designated as an expert in their social graph. 
         [0066]    Another factor that may be used to prioritize preference lists is identifying users with specific characteristics that may add value to their preferences. One such characteristic is residence. A searching party may choose to give additional weight to the preferences of users local to an area of search. A “local” user may be defined as a user having the same residence as the locality that is the target of the search enquiry, or a user residing within a specified distance from that locality. Users falling within the residence category may or may not have any connection to the searching party. The effect of this prioritization would be that a searching party looking, for instance, for a restaurant in a given city might give additional weight to the preferences listed by users resident in that city. 
         [0067]    Group affiliations may also be used as a ranking prioritization factor. Memberships and other designations listed in the user profiles, regardless of whether the user is in a specified social graph. Social graphs and group affiliations may be used in combination to provide prioritized ranking results. For example, a searching party looking for an accountant might specify as their social graph those users who hold an MBA degree. A first level of search results might therefore include accountants listed by MBA holders whose user profile indicates they went to the same school as the searching party. A second level of results under these search criteria might include accountants recommended by MBA holders who are friends of the MBAs who are friends of the searching party. A third level of search results might include accountant recommendations from all users who are MBA holders. 
         [0068]    Self-learning matching algorithms may also be included in the system  100  and method  200  to further refine ranking priorities. Using matching algorithms, the method may use characteristics of users across social graphs to recommend preferences from users not included in the specified social graph. The recommendations may be made if the characteristics of the user outside the social graph are such that they create a high degree of confidence in the relevance of the recommended preferences to the searching party. Using the matching algorithms, the system may implement collaborative filtering. For example, if the profile of a second user has a strong correlation to the profile of a searching party, the preferences of the second user may be utilized in a search initiated by the searching party. This matching may occur whether or not the second user is included in the social graph specified by the searching party. 
         [0069]    The present technology may further use attributes and classifications of the preference lists of the users to cross reference users with similar attributes. The system may use the cross referencing information to quickly search, rank, and sort for a first user the information input by users with similar attributes. 
         [0070]    Category links allow a first user to access the preferences of a second user in a given category. The category links may typically be used to access the preferences list of a user who may have a substantial number of entries in the given category. 
         [0071]    The system  100  may also be used to track the activity of the users.  FIG. 11  shows an exemplary interface for providing a search result. In particular, the interface of  FIG. 11  shows the results that might be returned for a search for the most accessed businesses (first column) in a given time period. The system  100  may also be queried as to newly entered businesses (second column), those businesses first appearing in the system  100  in the specified time period. 
         [0072]    A searching party may also query the system for a direct recommendation, an “Ask”.  FIG. 12  illustrates exemplary interfaces for providing a specific inquiry and a response to the inquiry. In particular, the interfaces of  FIG. 12  depict the generation of a specific request (the “Ask”, left screen), and the list that might be returned (the “Ask Response”, right screen). The “Ask” function may be particularly useful when a search has found no information available in the preference data base for the requested search target. The “Ask” may be targeted to one or more specific users selected by the searching party, or to all users. Any group predefined by the searching party may be the target of the “Ask”. 
         [0073]      FIG. 13  illustrates an exemplary computing system  1300  that may be used to implement an embodiment of the present invention. System  1300  of  FIG. 13  may be implemented in the contexts of the likes of client devices  115 , network server  130 , application server  140 , and data storage  150 . The computing system  1300  of  FIG. 13  includes one or more processors  1310  and memory  1320 . Main memory  1320  stores, in part, instructions and data for execution by processor  1310 . Main memory  1320  can store the executable code when in operation. The system  1300  of  FIG. 13  further includes a mass storage device  1330 , portable storage medium drive(s)  1340 , output devices  1350 , user input devices  1360 , a graphics display  1370 , and peripheral devices  1380 . 
         [0074]    The components shown in  FIG. 13  are depicted as being connected via a single bus  1390 . The components may be connected through one or more data transport means. Processor unit  1310  and main memory  1320  may be connected via a local microprocessor bus, and the mass storage device  1330 , peripheral device(s)  1380 , portable storage device  1340 , and display system  1370  may be connected via one or more input/output (I/O) buses. 
         [0075]    Mass storage device  1330 , which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit  1310 . Mass storage device  1330  can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory  1320 . 
         [0076]    Portable storage device  1340  operates in conjunction with a portable non-volatile storage medium, such as a floppy disk, compact disk or Digital video disc, to input and output data and code to and from the computer system  1300  of  FIG. 13 . The system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system  1300  via the portable storage device  1340 . 
         [0077]    Input devices  1360  provide a portion of a user interface. Input devices  1360  may include an alpha-numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. Additionally, the system  1300  as shown in  FIG. 13  includes output devices  1350 . Suitable output devices include speakers, printers, network interfaces, and monitors. 
         [0078]    Display system  1370  may include a liquid crystal display (LCD) or other suitable display device. Display system  1370  receives textual and graphical information, and processes the information for output to the display device. 
         [0079]    Peripherals  1380  may include any type of computer support device to add additional functionality to the computer system. Peripheral device(s)  1380  may include a modem or a router. 
         [0080]    The components contained in the computer system  1300  of  FIG. 13  are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system  1300  of  FIG. 13  can be a personal computer, hand held computing device, telephone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, and other suitable operating systems. 
         [0081]    The embodiments described herein are illustrative of the present invention. As these embodiments of the present invention are described with reference to illustrations, various modifications or adaptations of the methods and or specific structures described may become apparent to those skilled in the art in light of the descriptions and illustrations herein. All such modifications, adaptations, or variations that rely upon the teachings of the present invention, and through which these teachings have advanced the art, are considered to be within the spirit and scope of the present invention. Hence, these descriptions and drawings should not be considered in a limiting sense, as it is understood that the present invention is in no way limited to only the embodiments illustrated.

Technology Category: 5