Source: http://www.google.com/patents/US7941436?dq=6526440
Timestamp: 2014-03-07 14:41:35
Document Index: 336541883

Matched Legal Cases: ['art 700', 'art 700', 'art 800', 'art 900', 'art 1000', 'arts 700']

Patent US7941436 - Keyword bidding strategy for novel concepts - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA system and method are disclosed for identifying a keyword that is a novel concept or anomaly based on prior search results for the keyword. Advertisements may be sold for the keyword, or the keyword may be purchased or recommended for purchase based on anticipation of increased future searches on the...http://www.google.com/patents/US7941436?utm_source=gb-gplus-sharePatent US7941436 - Keyword bidding strategy for novel conceptsAdvanced Patent SearchPublication numberUS7941436 B2Publication typeGrantApplication numberUS 11/607,299Publication dateMay 10, 2011Filing dateNov 30, 2006Priority dateNov 30, 2006Also published asUS20080133503Publication number11607299, 607299, US 7941436 B2, US 7941436B2, US-B2-7941436, US7941436 B2, US7941436B2InventorsAlexandrin Popescul, Clifford A. Brunk, Byron DomOriginal AssigneeYahoo, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (35), Non-Patent Citations (9), Referenced by (10), Classifications (16), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetKeyword bidding strategy for novel conceptsUS 7941436 B2Abstract A system and method are disclosed for identifying a keyword that is a novel concept or anomaly based on prior search results for the keyword. Advertisements may be sold for the keyword, or the keyword may be purchased or recommended for purchase based on anticipation of increased future searches on the keyword.
monitoring a second frequency at which the identified query is searched for during a second time interval;
comparing the first frequency with a second frequency, wherein the comparison comprises determining whether the second frequency increased over the first frequency above a certain amount that comprises a spike recall constant multiplied by a standard deviation, wherein the standard deviation is based on at least one earlier time interval and the spike recall constant represents a relative amount of frequency change; and
performing at least one of selling the identified query at an adjusted rate, purchasing the identified query for an advertisement, or recommending the identified query for an advertisement purchase when either the first frequency or the second frequency is above a threshold frequency and when the comparison indicates an increase above the certain amount.
3. The method according to claim 1 wherein the at least one previous frequency comprises the first frequency and the first frequency comprises an average of a plurality of frequencies.
6. The method according to claim 1 wherein the first frequency is a mean value of frequencies over the first time interval.
7. The method according to claim 1 wherein the comparison indicates an increase above the certain amount when the following equation is satisfied: f/V−μ>k*G, wherein f is the frequency of the identified keyword, V is the total number of keywords over a time period, μ is mean of the normalized keyword frequency over the time period, k is the spike recall constant, and σ is the standard deviation of the frequency normalized over the time period.
8. The method according to claim 1 wherein the second frequency is divided by a volume of queries when comparing with the first frequency.
recognizing the keyword as having an unusually high frequency when the frequency exceeds the threshold frequency and when the frequency change exceeds a predetermined frequency change that is based on a spike recall constant multiplied by a standard deviation of at least one previous frequency, wherein the spike recall constant represents a relative amount of frequency change.
15. The storage medium according to claim 10 wherein the frequency change exceeds a predetermined frequency change if the following equation is satisfied: f/V−μ>k*σ, wherein f is the frequency of the identified keyword, V is the total number of keywords over a time period, μ is mean of the normalized keyword frequency over the time period, k is the spike recall constant, and σ is the standard deviation of the frequency normalized over the time period.
16. The storage medium according to claim 15 wherein the spike recall constant represents a required increase in frequency for recognizing the keyword as having an unusually high frequency.
a search engine with a processor configured to receive queries and provide search results based on the queries;
an event detector coupled with the search log database and configured to monitor the frequency of the queries that are input into the search engine, wherein the event detector is configured to recognize an anomalous query based on an increase in frequency of the anomalous query when the frequency exceeds a frequency threshold, and when a subsequent frequency divided by a volume of queries minus a prior frequency value is greater than a spike recall constant multiplied by a standard deviation.
20. The system according to claim 17 wherein if an anomalous query is recognized, the event detector is further configured to at least one of sell the anomalous query at an adjusted rate, purchase the anomalous query for an advertisement, or recommend the anomalous query for an advertisement purchase. Description
TECHNICAL FIELD The present embodiments relate to a system for keyword recognition and bidding.
BACKGROUND Online advertising may be an important source of revenue for enterprises engaged in electronic commerce. A number of different kinds of page-based online advertisements are currently in use, along with various associated distribution requirements, advertising metrics, and pricing mechanisms. Processes associated with technologies such as Hypertext Markup Language (HTML) and Hypertext Transfer Protocol (HTTP) enable a page to be configured to contain advertisements.
BRIEF SUMMARY By way of introduction, the embodiments described below include a system and method for keyword recognition and bidding. The embodiments relate to the analysis of keyword or search term activity to determine its popularity and to recognize those keywords that may represent a novel or anomalous event whose popularity may be increasing.
DETAILED DESCRIPTION The principles described herein may be embodied in many different forms. The embodiments relate to a system and method for keyword recognition and bidding. Keywords may also be referred to as searches, search terms, terms, or queries. The recognition of novel events or anomalies in keyword searches from search logs may be used to improve on pricing and cost inefficiencies in the buying and selling of the keyword for advertisements. Novel events may include previously low traffic searches, which suddenly start growing in popularity.
In FIG. 1, environment 100 includes an advertisement services server 110, which may provide a platform for selection, optimization, and/or distribution of advertisements for inclusion in pages, such as web pages. Further, the advertisement services server may be configured to monitor search logs and recognize novel events as discussed below. Pages may be provided to users by a portal server 104 and/or a third-party server 102. In FIG. 1, users are represented by a user device 106, depicted as a conventional personal computer, and/or other device such as a mobile user device 112, including a network-enabled mobile phone, personal digital assistant (PDA), pager, network-enabled television, digital video recorder, such as TIVO� (sold by TiVo, Inc., Alviso, Calif.), and/or automobile.
The client device 106 may be a computing device for a user to connect to a network 209, such as the Internet. Examples of a client device include but are not limited to a personal computer, personal digital assistant (�PDA�), cellular phone, or other electronic device. The client device 106 may be configured to access web pages or other data/information over the network 209. The client device 106 may be a computer enabling a user to view web pages over the network 109, such as the Internet. In one embodiment, a user views a browser containing data from the Internet. The data displayed on the browser may include advertisements. In an alternate embodiment, other software programs may also display advertisements received over the network 109 or from a different source.
The processor may include a memory 206, or the memory 206 may be a separate component. The interface 210 and/or software 208 may be stored in memory 206. The memory 206 may include, but is not limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one embodiment, the memory 206 includes a random access memory for the processor. In alternative embodiments, the memory 206 is separate from the processor, such as a cache memory of a processor, the system memory, or other memory. The memory 206 may be an external storage device or database for storing recorded image data. Examples include a hard drive, compact disc (�CD�), digital video disc (�DVD�), memory card, memory stick, floppy disc, universal serial bus (�USB�) memory device, or any other device operative to store image data. The memory 206 is operable to store instructions executable by the processor. The functions, acts or tasks illustrated in the figures or described herein may be performed by the programmed processor executing the instructions stored in the memory 206. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like.
In one embodiment, the software 208, possibly including a browser may be stored in memory 206. The processor is configured to execute the software 208. The browser may be a web browser configured to display information and data retrieved from the Internet or other network. For example, the browser may be INTERNET EXPLORER� (sold by Microsoft Corp., Redmond, Wash.), or some other web browser. The browser may be configured to receive and display advertisements to the user while providing various content, such as the results from a keyword search or query.
FIGS. 7-10 are exemplary illustrations of examples of frequency charts for particular keywords or queries. The frequency charts may identify keywords that have a surge or spike in popularity. Each of FIGS. 7-10 display the keyword quantity or frequency as a function of the time in ten minute bins for the month of July 2006. In other words, bin 1 represents the first 10 minutes of July 2006, bin 2 represents the second 10 minutes of July 2006 (12:10-12:20 a.m., July 1), bin 3 represents the third 10 minutes of July 2006 (12:20-12:30 a.m., July 1), etc. Accordingly, the x-axis of each of FIGS. 7-10 represent particular times during July 2006. In FIG. 7, chart 700 depicts the keyword �rolling blackout.� At approximately the 3,570th bin (35,700 minutes into the month of July 2006), there is a spike in frequency of the keyword that is indicative of anomalous behavior. Chart 700 shows that for most of the previous 370 bins, the frequency of �rolling blackout� was zero until the 3,570th bin when a spike is identified.
In FIG. 8, chart 800 depicts the frequency of the keyword �miss universe 2006 contestant pictures.� The spike is detected at approximately the 2,990th bin. The previous 1,900 minutes (bins 2800-2990) all had a frequency or quantity of approximately zero until a spike is observed. In FIG. 9, chart 900 depicts the frequency of the keyword �miss universe faints.� There is a spike for this keyword at approximately bin 3455. In FIG. 10, chart 1000 depicts the keyword �steve nash haircut.� This keyword began spiking in frequency at bin 3800. All of FIGS. 7-10, and charts 700-1000 illustrate examples of spikes in a particular keyword or query. As described herein, identification of those keywords and appropriate action based on that identification may be beneficial. FIGS. 7-10 are merely one embodiment of the monitoring of keywords. In alternate embodiments, the bins may be shorter or longer and measured less frequency or more frequently. The bins may be overlapping or may include gaps of time between bins. Alternatively, the monitoring of keywords may not be using bins based on a period of time, but rather the time may be measured to reach a certain quantity. Any monitoring of the frequency of a keyword may be used.
In block 406, the volume Vt is measured between time t-Δt and t. The volume Vt is a measure of overall traffic. The volume Vt is a normalization factor that may be used to account for seasonal, weekly, daily, or other variability in query traffic. For example, the traffic may be lower between midnight and 8 am. On December 25th, the traffic may also be lower. The volume Vt may be used to help establish what the frequency threshold should be set at. During times when the volume Vt is higher, the frequency threshold should be higher and during times when the volume Vt is lower, the frequency threshold should be lower. In an alternate embodiment, the volume Vt may include a subset of traffic, rather than the overall volume of queries. For example, the volume Vt may include only a certain-type of query.
In block 504, the identified query may be purchased. As discussed above, the price of a query is often dependent on the popularity of that query. The identification of a query in block 502 is indicative of a query that is likely to increase in popularity based on the current spike in its popularity. Accordingly, the price at which search engines will sell the query for is likely to soon rise. Therefore, shortly after identifying a novel event query or anomalous query is a good time to purchase that query from other search engines or websites. As one example, Yahoo!� may monitor its own search logs and identify anomalous queries and then proceed to purchase the same queries from other search engines such as Google�, in order to purchase at the lower price prior to an increase in popularity and price. Yahoo!� may advertise its Yahoo!� News site based on breaking news, so that when a consumer searches on another search site for the breaking news query Yahoo!� News is one of the ads that are displayed.
The computer system 600 may include a memory 604 that can communicate via a bus 608. The memory 604 may be a main memory, a static memory, or a dynamic memory. The memory 604 may include, but is not limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one embodiment, the memory 604 includes a cache or random access memory for the processor 602. In alternative embodiments, the memory 604 is separate from the processor 602, such as a cache memory of a processor, the system memory, or other memory. The memory 604 may be an external storage device or database for storing data. Examples include a hard drive, compact disc (�CD�), digital video disc (�DVD�), memory card, memory stick, floppy disc, universal serial bus (�USB�) memory device, or any other device operative to store data. The memory 604 is operable to store instructions executable by the processor 602. The functions, acts or tasks illustrated in the figures or described herein may be performed by the programmed processor 602 executing the instructions stored in the memory 604. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like.
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ClassificationG06F17/30W1, G06Q30/08, G06Q30/02, G06Q30/0609, G06Q30/0275Legal EventsDateCodeEventDescriptionNov 30, 2006ASAssignmentOwner name: YAHOO! INC., CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:POPESCUL, ALEXANDRIN;BRUNK, CLIFFORD A.;DOM, BYRON;REEL/FRAME:018663/0001Effective date: 20061129RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google