Source: http://www.google.de/patents/US8135833?hl=de
Timestamp: 2013-12-09 23:42:37
Document Index: 363968613

Matched Legal Cases: ['art 1008', 'art 1008', 'art 1008', 'art 1008', 'art 1008', 'art 1202', 'art 1202', 'art 1202']

Patent US8135833 - Computer program product and method for estimating internet traffic - Google PatenteSuche Bilder Maps Play YouTube News Gmail Drive Mehr » Erweiterte Patentsuche | Anmelden Erweiterte Patentsuche PatenteSite metrics are presented in association with search results. The site metrics are derived from site analytics that uses clickstream data collected from a panel of internet users to generate and present internet activity metrics. Data collected from a community of internet users may be augmented by...http://www.google.de/patents/US8135833?utm_source=gb-gplus-sharePatent US8135833 - Computer program product and method for estimating internet traffic Ver�ffentlichungsnummerUS8135833 B2PublikationstypErteilung AnmeldenummerUS 13/150,370 Ver�ffentlichungsdatum13. M�rz 2012Eingetragen1. Juni 2011 Priorit�tsdatum7. M�rz 2002Auch ver�ffentlicht unterUS7979544, US8356097, US20080189408, US20100030894, US20110296014, US20120131187 Ver�ffentlichungsnummer13150370, 150370, US 8135833 B2, US 8135833B2, US-B2-8135833, US8135833 B2, US8135833B2 ErfinderDavid Cancel, Laura Currea, Lauren S MooresUrspr�nglich Bevollm�chtigterCompete, Inc.Zitat exportierenBiBTeX, EndNote, RefManPatentzitate (119), Nichtpatentzitate (50), Referenziert von (1), Klassifizierungen (7), Juristische Ereignisse (3) Externe Links: USPTO, USPTO-Zuordnung, EspacenetComputer program product and method for estimating internet trafficUS 8135833 B2 Zusammenfassung Site metrics are presented in association with search results. The site metrics are derived from site analytics that uses clickstream data collected from a panel of internet users to generate and present internet activity metrics. Data collected from a community of internet users may be augmented by clickstream data store content, third party content, search results, and other sources to form estimates of internet activity, such as traffic, that is structured and analyzed to produce metrics of nearly any internet web site or domain. The data may be further augmented with ratings, such as web site trust ratings, retail deals, and analysis of web site content to form a comprehensive set of data that is mined to formulate various metrics of internet activity about web sites. Metrics of internet activity, a.k.a. site analytics, provides analysis that represents aspects of internet user access to a web site. Such aspects may include activity related to visitors, engagement, growth, trust, deals, and the like.
Bilder(22) Anspr�che(20)
refining the estimate of the population's Internet activity by applying a domain-specific bias adjustment to the estimate, wherein applying the domain-specific bias adjustment to the estimate comprises applying a weight to a subset of the data from the group of participants, the subset of the data that connotes a specific domain. Beschreibung
CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 12/495,771 filed Jun. 30, 2009, which is a continuation of U.S. patent application Ser. No. 11/938,716 filed Nov. 12, 2007, each of which is incorporated herein by reference in its entirety.
With an abundance of web sites on the Internet, it is becoming increasingly difficult to safely and efficiently navigate the Internet. In a practice known as �spoofing� or �phishing�, malicious web sites will often lure users into visiting their web site under the pretense of offering genuine information or legitimate business. These web sites may appear, for example, in search results or as links in an e-mail. Typically, the user does not know that they have accessed a malicious web site until sometime after visiting the web site. Often, personal information may have already been shared on the malicious web site before the user becomes aware that the web site is malicious. Knowing whether or not a web site can be trusted prior to visiting the web site is a valuable tool in combating these malicious web sites.
SUMMARY Site analytics may use clickstream data collected from a community of internet users to generate and present internet activity metrics. Data collected from a community of internet users may be augmented by clickstream data store content, third party content, search results, and other sources to form estimates of internet activity, such as traffic, that may be structured for analyzing to produce metrics of nearly any internet website or domain. The data may be further augmented with ratings, such as website trust ratings, retail deals, and analysis of web site content to form a comprehensive set of data that may be mined to formulate various metrics of internet activity about web sites. Metrics of internet activity, which may be called site analytics, may provide analysis that represents aspects of internet user access to a website. Such aspects may include, without limitation, activity related to visitors, engagement, growth, trust, deals, and the like. Data representing a number of visitors, unique visitors, and repeat visitors over a predetermined period of time may be analyzed to generate visitor metrics such as people counts, rank, and visits. Engagement metrics may use visitor data combined with duration data, such as duration per visit, to generate metrics such as attention (e.g. daily attention, monthly attention), average stay, and pages/visit. In addition to determining metrics associated with a period of time, growth may provide important metrics associated with daily changes and may represent velocity of attention, such as changes in daily attention.
BRIEF DESCRIPTION OF FIGURES The systems and methods described herein may be understood by reference to the following figures:
DETAILED DESCRIPTION OF FIGURES Referring first to FIG. 1, an aspect of the invention involves a toolbar 100 which comprises one or more of a search box 101, a trust indicator 102, a site profile 103, and an applicable deals indicator 104. When a user downloads the toolbar 100 through a download facility 105, they are given the opportunity to participate in real-time clickstream sharing. The users may opt-in or opt-out of this participation at any time. Clickstream activity by users is analyzed and stored in a clickstream data store. The analyzed clickstream data can be mined for a variety of statistical information including, but not limited to, user volume, user dwell time, user activity, click-throughs, click-aways, pageview ranking, user ranking, top search terms, other sites visited, site popularity, indicator of trust 102, site profile 103 and other similar information.
Referring now to FIG. 7, an aspect of the invention involves a search function. When a user initiates a search in the search box 101 of a toolbar 100 or through a search website 700, the request is processed by a search facility and search results are generated. The search facility can be a publicly available search engine, a subscription-based search engine, a proprietary search engine, a specialized search engine, and other similar search facilities. The search results are then used to query a clickstream data store to determine the relevancy of the results. A website that receives the most post-search activity, as determined by page views and other similar statistical information, in relation to a particular search term are promoted over domains that receive less activity. The search results are displayed in order of relevance with the most relevant results 701 being displayed first. A display of search results may be affected by relevance in other ways. Relevance may be used to identify social picks and the social picks may be prioritized to be displayed above other results. The other results may be displayed in an order based on relevance or based on search engine prioritization not taking relevance into consideration. Relevance may be used to display only a subset of results that are identified as relevant by the panel of users (e.g. social picks only). Social picks may alternatively be displayed and identified as social picks in a non-relevance based search result. Sponsored search results 702 may also be displayed. For example, a user searches for the term �books�. The top three results from the search may be the New York Public Library, eBay, and an independent bookseller. After querying the clickstream data store, however, different results from the same set of search results are deemed more relevant. Now, the top three results may be Amazon.com, Barnes & Noble, and Borders.
Referring now to FIG. 8, the process by which relevant results or social picks are determined is depicted. In the example, a user initiates a search query 800 for �digital camera�. The search results 801 generated in response to the query 800 include five sites, Sites A through E. The clickstream data store is then queried with each of the five results and an Interaction Index Post-Search Query 802 for the query 800 �digital camera� is associated with each of the results 801. The Interaction Index Post-Search Query 802 gives an indication of the relevancy of the result 801. The results 801 with the greatest Interaction Index are relevant results 803 and are promoted over the other results.
Traffic estimated may be based on a definition of �people� that is different compared to traffic reported through common local analytic solutions and traffic log analyzers. In an example, �people� may include U.S. consumers, which means a consumer is counted only once no matter how often he or she visits a site throughout the course of an estimation period. In a comparative example, local analytic solutions may include domestic and international traffic and often include spiders and bots that appear as traffic, but do not represent actual human activity. Common sources of local analytic solutions may rely on log files or cookies which do not support distinguishing consumers to generate accurate estimates. Data sources such as spiders, bots, log files, agents, pingbacks, RSS update traffic, IP addresses, and the like may not be included in internet traffic estimates herein disclosed and used.
Trust metrics may help users experience a safer web by warning of potentially malicious Web sites, such as those associated with spyware, phishing, and online scams. Trust metrics may be determined by site history, domain name evaluation, third-party security services, community feedback and research associated with the community of participants providing internet traffic data. In an example of site history, if a site does not achieve a minimum amount of visits from the community, it may be flagged as suspicious. Most spoof/phishing sites may be launched for short periods of time and may not have an established site history. Using the community as one measure of site history, it is difficult for malicious operators to create a fake site history. In an example of domain name evaluation, if a site is not a �named domain� and uses an IP address as its visual identifier it may be flagged as suspicious. In an example of third-party services, trust scores from third parties such as GeoTrust��a division of VeriSign��and CastleCops may be included in an analysis of a trust metric for a web site. In an example of community research, data may be collected from partners and through searching the web to identify web sites that offer free downloads that bundle unwanted adware and spyware. Calculating a trust metric may use research data supporting such unwanted downloads. Trust metrics may be based on data such as community based feedback, algorithms, traffic estimates as herein described, and the like. Each data source may be analyzed, weighted, normalized, adjusted, or otherwise manipulated to provide a measure of trust associated with a web site.
FIG. 10 depicts a site analytics screen 1000 as presented through a web browser. The screen 1000 may be a default presentation resulting from a user selecting action icon 908 as shown in FIG. 9. In this example screen 1000, a site metric people count 1002 is presented in chart form. In this screen 1000, a user may select additional web sites to be included in the presentation of the people counts metric by entering the web sites in the snapshot input bar 1004 and selecting the �GO� action button in the snapshot input bar 1004. People count metric 1002 is shown as discrete counts per month over a thirteen month time period. This information is presented as a line graph 1008 showing each monthly count as a point on the graphed line. The graph 1008 includes a horizontal axis of time (e.g. month-year) and a vertical axis of counts (e.g. people count). Each point in the chart 1008 represents the people count metric (vertical axis) for each month presented (horizontal axis). At the bottom of the chart 1008, a user is presented various information about the metric including, the date of the most recent data in the chart, the metric value (e.g. People) associated with the most recent date, a percentage of change in the metric from the most recent date from the next most recent date (monthly % change), a percentage of change in the metric from the most recent date to the oldest date shown on the chart 1008 (yearly % change), and an overview description of the metric being presented in the chart 1008 with a selectable link to �See Full Description� of the metric. Selecting this link may present a pop-up window such as is shown in FIG. 11.
In addition to the metric, each similar site analytics screen may include features that provide useful information about the subject web site. An analytics overview 1010 provides information about the site that may relate to sources of information or other aspects of the site that can be derived from site analytics data sources. Company profile 1012 may include information collected from public or private sources, such as company information data stores. A user may select to view additional company profile information by selecting �Show More� within the company profile 1012 portion of the web site. In addition to the presented site analytic (e.g. people count 1002), search analytics top keywords 1014 as herein described may be presented for the subject web site. Promotional deals available for the subject web site may be presented in a current promotions 1018 section of the site analytics screen. Also, a user may be invited to take advantage of advanced features such as comparing more than three sites, saving snapshots to a portfolio, submitting site ratings, exporting data, and the like. The invitation may be extended through registration offer 1020. Site analytic screen 1000 also includes drop down metric selection menu 1022 through which a user can select any of several other web site metrics for presentation in chart form. Selecting an entry in the drop down site analytics menu 1022 may result in a new window being presented for the selected metric from the menu, such as rank metric shown in FIG. 12.
FIG. 11 depicts a full description window 1100 that may be presented when a user selects a �See Full Description� link that is presented in the chart 1008 shown in FIG. 10. The window 1100 provides a detailed description 1102 of the metric and includes links 1104 to full descriptions of other metrics.
FIG. 12, a rank metric web browser window 1200 of site analytics for three sites, includes a rank graph 1202 of three sites. The graph shows a rank (vertical axis of the chart 1202) as herein described for each of the three sites in each month over a thirteen month period (horizontal axis of the chart 1202). At the bottom of the rank chart 1202 summary information about the rank metrics is presented. This summary includes the rank value of each web site in the most recent time period (e.g. August 07), a one month and a one year change in rank, and an overview description of the rank metric. Information that is not available or may not have sufficient support in the site analytics data sources may be represented as �N/A�.
It will be appreciated that an embodiment of gathering clickstreams from a plurality of sources may be described in steps 302, 304, 308, 310, 312, and/or 314 of FIG. 3 of U.S. patent application Ser. No. 10/267,978 entitled CLICKSTREAM ANALYSIS METHODS AND SYSTEMS (�the related application�). Moreover, it will be appreciated that FIG. 4 of the related application may disclose an embodiment of a process for gathering clickstreams from a plurality of sources. It will also be appreciated that, in embodiments, gathering clickstreams from a plurality of sources may involve converting files from a plurality of data providers into a common file format, as is disclosed at a high level in step 502 of FIG. 5 of the related application and as is disclosed in detail in flow diagram 600 of FIG. 6 of the related application. It will further be appreciated that a file cleansing process�such as that disclosed by element 800 of FIG. 8 of the related application�may be applied to files from the plurality of data providers and/or files in the common file format.
In embodiments, the inference may contain default or random information�especially in cases where a more enlightened inference is unavailable or when a plurality of inferences conflict to such a degree that it cannot be determined with an acceptable degree of certainty which one of the conflicting inferences is most likely to be accurate.
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Jan. 2013Compete, Inc.Computer program product and method for estimating internet trafficKlassifizierungen US-Klassifikation709/224, 709/223Internationale KlassifikationG06F15/173 UnternehmensklassifikationG06Q10/04, G06Q30/02 Europ�ische KlassifikationG06Q30/02, G06Q10/04Juristische Ereignisse DatumCodeEreignisBeschreibung10. Sept. 2012ASAssignmentEffective date: 20120820Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE ADDRESS PREVIOUSLY RECORDED ON REEL 028896 FRAME 0934. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT;ASSIGNOR:COMPETE, INC.;REEL/FRAME:028925/0516Owner name: COMPETE, INC., MASSACHUSETTS4. Sept. 2012ASAssignmentEffective date: 20120820Free format text: ADDRESS CHANGE;ASSIGNOR:COMPETE, INC.;REEL/FRAME:028896/0934Owner name: COMPETE, INC., MASSACHUSETTS1. Sept. 2011ASAssignmentOwner name: COMPETE, INC., MASSACHUSETTSFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:CANCEL, DAVID;MOORES, LAUREN S.;CURREA, LAURA;SIGNING DATES FROM 20090907 TO 20091014;REEL/FRAME:026842/0982DrehenOriginalbildGoogle-Startseite - Sitemap - USPTO-Bulk-Downloads - Datenschutzerkl�rung - Nutzungsbedingungen - �ber Google Patente - Feedback gebenDaten bereitgestellt von IFI CLAIMS Patent Services.© 2012 Google