# US 8,572,115 B2 · Identifying negative keywords associated with advertisements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,572,115 B2 - Document ID: US8572115B2 - Status: Granted - Kind: B2 - Application number: 13/493,507 - Filed: 2012-06-11 - Published: 2012-09-27 - Granted: 2013-10-29 - Earliest claimed: 2007-04-17 - Assignees: Google Inc. - Applicants: Carl F. Hubinette - Inventors: Carl F. Hubinette - Web page: https://patentgrep.com/patent/US8572115B2 ## CPC - G06Q 30/02: Marketing; Price estimation or determination; Fundraising - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract REVISE this to be consistent with the new claim 1 . . . The present disclosure includes a system and method for identifying negative keywords associated with advertisements. In some implementations, a method for advertising includes identifying one or more keywords associated with an advertisement. The keywords used to embed the advertisement in web pages such as those including search results. By identifying, from the plurality of search criterion, a subset of search criterion as being off-topic to the advertisement item, one or more negative keywords may be derived from the subset of off-topic search criterion in order to limit the embedding of non-relevant advertisements in web page content. ## Claims ### Claim 1 A computer-implemented method comprising: receiving, from a first data storage device, data encoding an advertisement item and a log comprising a plurality of previously submitted search criteria, wherein each search criterion comprises one or more terms, and wherein when the search criterion was evaluated, the advertisement item was presented in a corresponding search result webpage; analyzing the plurality of previously submitted search criteria to identify one or more negative keywords including: identifying, from the plurality of previously submitted search criteria, a subset of search criteria as being off-topic to the advertisement item, and deriving the one or more negative keywords from the subset of off-topic search criteria; generating one or more files including the one or more negative keywords, the one or more negative keywords being used to prevent the advertisement item from being presented in a search result corresponding to a later submitted search criterion containing at least one of the identified negative keywords; and storing the one or more files on the first data storage device or a second data storage device. ### Claim 2 (depends on 1) The method of claim 1, further comprising: selecting the negative keywords to increase a number of search criteria identified as being off-topic to the advertisement item. ### Claim 3 (depends on 2) The method of claim 2, wherein selecting the negative keywords to increase the number of search criteria identified as being off-topic to the advertisement item maximizes the number of search criteria identified as being off-topic to the advertisement item. ### Claim 4 (depends on 1) The method of claim 1, further comprising: selecting the negative keywords to decrease the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 5 (depends on 1) The method of claim 1, wherein selecting the negative keywords to decrease the number of search criteria not identified as off-topic to the advertisement item minimizes the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 6 (depends on 1) The method of claim 1, wherein identifying the subset of search criteria as being off-topic to the advertisement item comprises: associating, with each of the plurality of search criteria, a relevance score, and if the relevance score is under a threshold relevance value, assigning the associated search criterion as being off-topic. ### Claim 7 (depends on 6) The method of claim 6, wherein the relevance score includes an average logarithmic odds that a term of the associated search criterion occurs in a set of keyword clusters associated with a topic of the advertisement item. ### Claim 8 (depends on 1) The method of claim 1, wherein deriving the one or more negative keywords from the subset of off-topic search criteria comprises: identifying subsets of words in each of the off-topic search criteria; associating each subset of words with an importance metric; and summing the associated importance metric for each occurrence of the subset of words in the off-topic search criteria. ### Claim 9 (depends on 8) The method of claim 8, wherein the importance metric is determined by multiplying a confidence number by an amount of impressions associated with a set of keyword clusters that in turn are associated with the advertisement item, the confidence number representing a confidence that the identified off-topic search criterion is not relevant to a set of keyword clusters associated with a topic of the advertisement item. ### Claim 10 (depends on 8) The method of claim 8, wherein deriving the one or more negative keywords from the subset of off-topic search criteria further comprises: when the summed importance metric is above a threshold importance value, assigning the subset of words as negative keywords. ### Claim 11 (depends on 8) The method of claim 8, deriving the one or more negative keywords from the subset of off-topic search criteria further comprises: assigning the subset of words with the highest summed importance metric as negative keywords. ### Claim 12 A non-transitory computer-readable medium having instructions stored thereon, which, when executed by at least one processor, cause the processor to perform operations comprising: receiving, from a first data storage device, data encoding an advertisement item and a log comprising a plurality of previously submitted search criteria, wherein each search criterion comprises one or more terms, and wherein when the search criterion was evaluated, the advertisement item was presented in a corresponding search result webpage; analyzing the plurality of previously submitted search criteria to identify one or more negative keywords including: identifying, from the plurality of previously submitted search criteria, a subset of search criteria as being off-topic to the advertisement item, and deriving the one or more negative keywords from the subset of off-topic search criteria; generating one or more files including the one or more negative keywords, the one or more negative keywords being used to prevent the advertisement item from being presented in a search result corresponding to a later submitted search criterion containing at least one of the identified negative keywords; and storing the one or more files on the first data storage device or a second data storage device. ### Claim 13 (depends on 12) The computer-readable medium of claim 12, wherein the operations further comprise: selecting the negative keywords to increase a number of search criteria identified as being off-topic to the advertisement item. ### Claim 14 (depends on 13) The computer-readable medium of claim 13, wherein selecting the negative keywords to increase the number of search criteria identified as being off-topic to the advertisement item maximizes the number of search criteria identified as being off-topic to the advertisement item. ### Claim 15 (depends on 12) The computer-readable medium of claim 12, wherein the operations further comprise: selecting the negative keywords to decrease the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 16 (depends on 15) The computer-readable medium of claim 15, wherein selecting the negative keywords to decrease the number of search criteria not identified as being off-topic to the advertisement item minimizes the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 17 (depends on 16) The computer-readable medium of claim 16, wherein identifying the subset of search criteria as being off-topic to the advertisement item comprises: associating, with each of the plurality of search criteria, a relevance score, and if the relevance score is under a threshold relevance value, assigning the associated search criterion as being off-topic. ### Claim 18 (depends on 17) The computer-readable medium of claim 17, the relevance score includes an average logarithmic odds that a term of the associated search criterion occurs in a set of keyword clusters associated with a topic of the advertisement item. ### Claim 19 (depends on 12) The computer-readable medium of claim 12, wherein deriving the one or more negative keywords from the subset of off-topic search criteria comprises: identifying subsets of words in each of the off-topic search criteria; associating each subset of words with an importance metric; and summing the associated importance metric for each occurrence of the subset of words in the off-topic search criteria. ### Claim 20 (depends on 19) The computer-readable medium of claim 19, wherein the importance metric is determined by multiplying a confidence number by an amount of impressions associated with a set of keyword clusters that in turn are associated with the advertisement item, the confidence number representing a confidence that the off-topic search criterion is not relevant to a set of keyword clusters associated with a topic of the advertisement item. ### Claim 21 (depends on 19) The computer-readable medium of claim 19, wherein deriving the one or more negative keywords from the subset of off-topic search criteria further comprises: when the summed importance metric is above a threshold importance value, assigning the subset of words as negative keywords. ### Claim 22 (depends on 19) The computer-readable medium of claim 19, wherein deriving the one or more negative keywords from the subset of off-topic search criteria further comprises: assigning the subset of words with the highest summed importance metric as negative keywords. ### Claim 23 A server for identifying negative words, comprising one or more processors, a memory coupled to the one or more processors, and a network interface, the one or more processors configured to: receive data encoding an advertisement item and a log comprising a plurality of previously submitted search criteria, wherein each search criterion comprises one or more terms, and wherein when the search criterion was evaluated, the advertisement item was presented in a corresponding search result webpage; evaluate the plurality of previously submitted search criteria to identify one or more negative keywords by: identifying, from the plurality of previously submitted search criteria, a subset of search criteria as being off-topic to the advertisement item, and deriving the one or more negative keywords from the subset of off-topic search criteria; generate one or more files including the one or more negative keywords, the one or more negative keywords prevent the advertisement item from being presented in a search result corresponding to a later submitted search criterion containing at least one of the identified negative keywords; and store the one or more files on computer-readable media. ### Claim 24 (depends on 23) The server of claim 23, wherein the one or more processors are further configured to select the negative keywords to increase the number of search criteria identified as being off-topic to the advertisement item. ### Claim 25 (depends on 24) The server of claim 24, wherein the one or more processors are further configured to select the negative keywords to further maximize the number of search criteria identified as being off-topic to the advertisement item. ### Claim 26 (depends on 23) The server of claim 23, wherein the one or more processors are further configured to select the negative keywords to decrease the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 27 (depends on 26) The server of claim 26, wherein the one or more processors are further configured to select the negative keywords to further minimize the number of search criteria not identified as being off-topic to the advertisement item. ### Claim 28 (depends on 23) The server of claim 23, wherein the one or more processors are further configured to identify, from the plurality of search criteria, the subset of search criteria as being off-topic to the advertisement item by: associating, with each of the plurality of search criteria, a relevance score, and if the relevance score is under a threshold relevance value, assigning the associated search criterion as being off-topic. ### Claim 29 (depends on 28) The server of claim 28, wherein the relevance score include an average logarithmic odds that a term of the associated search criterion occurs in a set of keyword clusters associated with a topic of the advertisement item. ### Claim 30 (depends on 23) The server of claim 23, wherein the one or more processors are further configured to derive the one or more negative keywords from the subset of off-topic search criteria by: identifying subsets of words in each of the off-topic search criteria; associating each subset of words with an importance metric; and summing the associated importance metric for each occurrence of the subset of words in the off-topic search criteria. ### Claim 31 (depends on 30) The server of claim 30, wherein the importance metric is determined by multiplying a confidence number by an amount of impressions associated with a set of keyword clusters that in turn are associated with the advertisement item, the confidence number representing a confidence that the off-topic search criterion is not relevant to a set of keyword clusters associated with a topic of the advertisement item. ### Claim 32 (depends on 30) The server of claim 30, wherein the one or more processors are further configured to derive the one or more negative keywords from the subset of off-topic search criteria by: if the summed importance metric is above a threshold importance value, assigning the subset of words as negative keywords. ### Claim 33 (depends on 30) The server of claim 30, wherein the one or more processors are further configured to derive the one or more negative keywords from the subset of off-topic search criteria by: assigning the subset of words with the highest summed importance metric as negative keywords.