DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claims 1-6, 15-20 have been fully considered and are
persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of
rejection is made in view of newly cited reference Liu.
Applicant's arguments with respect to claims 7-14 have been fully considered but they are not persuasive. Applicant argues that Gusev does not teach “a multiclassification model for a plurality of classes”. Examiner disagrees with this assessment because Gusev teaches “The specific implementation of the respective machine learning algorithm is not particularly limited and can include, broadly speaking, a supervised learning algorithm or a supervised machine learning algorithm” (Para 0113).
Claim Status
Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following is Examiner's analysis of the claimed invention under the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG)
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. Claim 1 recites a process (method), claim 15 recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. Claims 1 and 15 recites “determining a difference between the predicted benefit value and the predicted detriment value” which falls within the mathematical concepts grouping of abstract ideas. The step of “determining a difference between the predicted benefit value and the predicted detriment value” is a mathematical calculation, and therefore, the claims recite an abstract idea.
Claim 1 and 15 recites “generating the SERP with the given web content element being ranked at the given ranking position based on the difference between the predicted benefit value and the predicted detriment metric value” which falls within the mathematical concepts grouping of abstract ideas. Ranking web content based on a difference between values is organizing information and manipulating information through mathematical correlations; therefore, the claims recite an abstract idea (mathematical relationship).
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. Claims 1 and 15 recite “acquiring query data from the electronic device, the query data being indicative of a query submitted by the user; determining a set of web content elements based on the query data including a given web content element, the set of web content elements including relevant search results for the query; generating context data from the set of web content elements and the query data; feeding the context data to a model, the model being a multiclassification model for a plurality of classes, the plurality of classes including two classes dedicated to a given ranking position on the SERP, the two classes being: a win-dedicated class indicative of a predicted benefit of the given web content element being ranked at the given ranking position on the SERP, and a loss-dedicated class indicative of a predicted detriment of the given web content element being ranked at the given ranking position on the SERP; outputting, by the model, for the given ranking position on the SERP, a predicted benefit value of the win-dedicated class and a predicted detriment value of the loss- dedicated class by the model for the given web content element” which is mere necessary data gathering because all uses of the recited judicial exception require such data gathering or data output.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. Claim 1 and 15 recite mere necessary data gathering. The courts have determined mere data gathering to not be enough to qualify as “significantly more” when recited in a claim with a judicial exception (See CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
There is no indication that the elements of the claim, individually nor in combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
For the reasons above, claims 1 and 15 are rejected as being directed to nonpatentable subject matter under §101. This rejection applies equally to the dependent claims. The additional limitations of the dependent claims are addressed briefly below:
Regarding claims 2
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the model is configured to output predicted benefit values and predicted detriment values for each ranking position of a set of ranking positions; to determine a difference, for each ranking position of the set of ranking positions, between the corresponding predicted benefit value and predicted detriment value; and select the ranking position having maximum difference as the given ranking position for the given web content element in the SERP” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 3
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the given web content element is a widget to be inserted in web documents included in the relevant search results for the query” which is insignificant-extra solution activity tangentially related to the invention.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 4
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the context data includes ranked web documents from a first search engine” which is mere necessary data gathering.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 5
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the win-dedicated class corresponds to a probability of a user engagement with the given web content element at the given ranking position” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 6
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the loss-dedicated class corresponds to a probability of a user engagement with another web content element below the given ranking position” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claims 16
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the model is configured to output predicted benefit values and predicted detriment values for each ranking position of a set of ranking positions; to determine a difference, for each ranking position of the set of ranking positions, between the corresponding predicted benefit value and predicted detriment value; and select the ranking position having maximum difference as the given ranking position for the given web content element in the SERP” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 17
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the given web content element is a widget to be inserted in web documents included in the relevant search results for the query” which is insignificant-extra solution activity tangentially related to the invention.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 18
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the context data includes ranked web documents from a first search engine” which is mere necessary data gathering.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 19
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the win-dedicated class corresponds to a probability of a user engagement with the given web content element at the given ranking position” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 20
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the loss-dedicated class corresponds to a probability of a user engagement with another web content element below the given ranking position” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Taken alone, the additional elements of the dependent claims do not amount to significantly
more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an
ordered combination adds nothing that is not already present when looking at the elements taken
individually. There is no indication that the combination of elements improves the functioning of a
computer or improves any other technology. Their collective functions merely provide conventional
computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gusev et al (US 20170185602 A1) hereafter Gusev in view of Gao et al (US 20110258148 A1) hereafter Gao further in view of Liu et al (US 20230169130 A1) hereafter Liu
Regarding claim 1, Gusev teaches a method of generating a Search Engine Results Page (SERP), the SERP to be provided to an electronic device associated with a user of a search engine hosted by a server, the server being communicatively coupled with the electronic device, the method executable by the server and comprising: acquiring query data from the electronic device, the query data being indicative of a query submitted by the user (Para 0029, receiving, from the at least one electronic device, a search query); determining a set of web content elements based on the query data including a given web content element, the set of web content elements including relevant search results for the query (Para 0057, the second search result and the third search result being web search results, all of the first, second and third search results being responsive to the search query); feeding the data to a model, the model being a multiclassification model for a plurality of classes, the plurality of classes including two classes dedicated to a given ranking position on the SERP, the two classes being: a win-dedicated class indicative of a predicted benefit of the given web content element being ranked at the given ranking position on the SERP (Para 0115, the predicted first parameter 210 is a win parameter, i.e. a parameter indicative of a likelihood of a click for a given search result, the given search result placed at a particular position of the SERP), and a loss-dedicated class indicative of a predicted detriment of the given web content element being ranked at the given ranking position on the SERP (Para 0119, the predicted second parameter 310 is a loss parameter, i.e. a parameter indicative of a likelihood of a click for a following search result following immediately after the given search result mentioned above); generating a metric value for a given web content element-position pair, the given web content element-position pair including the given web content element and the given ranking position, the metric value being a difference between a predicted benefit value of the win- dedicated class by the model for the given web content element and a predicted detriment value of the loss-dedicated class by the model for the given web content element, the metric value being indicative of an overall usefulness of ranking the given web content element at the given ranking position (Para 0196, selecting a given one of the first ranked position and the second ranked position for placing the first search result, the given one of the first ranked position and the second ranked position being associated with a highest value of the usefulness parameter); generating the SERP with the given web content element being ranked at the given ranking position based on the metric value (Para 0199, the server 116 generates the SERP 510 including the first search result being placed at the given one of the first ranked position and the second ranked position).
Gusev does not appear to explicitly teach generating context data from the set of web content elements and the query data. In analogous art, Gao teaches generating context data from the set of web content elements and the query data (Para 0004, The browse search pattern may comprise a URL of the web page associated with the browse event and a query associated with the search event). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Gusev to include the teaching of Gao. One of ordinary skill in the art would be motivated to implement this modification in order to predict search intent, as taught by Gao (Abs, Predicting search intent allows search engines to tailor search results to particular information needs of the user).
Gusev in view of Gao does not appear to explicitly teach outputting, by the model, for the given ranking position on the SERP, a predicted benefit value of the win-dedicated class and a predicted detriment value of the loss- dedicated class by the model for the given web content element determining a difference between the predicted benefit value and the predicted detriment value.
In analogous art, Liu teaches outputting, by the model, for the given ranking position on the SERP, a predicted benefit value of the win-dedicated class and a predicted detriment value of the loss- dedicated class by the model for the given web content element determining a difference between the predicted benefit value and the predicted detriment value (Para 0226, determine a difference between a subject of each search result and a subject of each of the landing page; in a case where the feedback information is dissatisfaction, rank the search results with a larger difference higher than the search results with a smaller difference in the search engine result page, or in a case where the feedback information is satisfaction, rank the search results with a larger difference lower than the search results with a smaller difference in the search engine result page). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Gusev in view of Gao to include the teaching of Liu. One of ordinary skill in the art would be motivated to implement this modification in order to improve web search functionality, as taught by Liu (Para 0204, improving the search experience through query recommendations, re-ranking of search results and the like).
Regarding claim 2, Gusev in view of Gao further in view of Liu teaches the method of Claim 1, wherein the model is configured to output predicted benefit values and predicted detriment values for each ranking position of a set of ranking positions; determine a difference, for each ranking position of the set of ranking positions, between the corresponding predicted benefit value and predicted detriment value; and select the ranking position having maximum difference as the given ranking position for the given web content element in the SERP (Gusev, Para 0125, a given “page” of the SERP can have 10 positions displayable therein—as such the given search result will have a different predicted usefulness parameter 402 for each of the 10 positions of the given page of the SERP with one of SERP positions having a maximum predicted usefulness parameter 402 amongst the 10 positions of the given page of the SERP).
Regarding claim 3, Gusev in view of Gao further in view of Liu teaches the method of Claim 1, wherein the given web content element is a widget to be inserted in web documents included in the relevant search results for the query (Gusev, Para 0010, The number and exact types of the vertical domains can differ, but vertical domains allow the user to switch to a particular type of search results).
Regarding claim 4, Gusev in view of Gao further in view of Liu teaches the method of Claim 1, wherein the context data includes ranked web documents from a first search engine (Gusev, Para 0018, The first set of search results and the second set of search results may be ranked based on the inferred user intent).
Regarding claim 5, Gusev in view of Gao further in view of Liu teaches the method of Claim 1, wherein the win-dedicated class corresponds to a probability of a user engagement with the given web content element at the given ranking position (Gusev, Para 0028, the win component (which is representative of a clickability of a given search result at a given SERP position) allows predicting how useful (i.e. interesting) the given search result associated with the given SERP position is likely to be).
Regarding claim 6, Gusev in view of Gao further in view of Liu teaches the method of Claim 1, wherein the loss-dedicated class corresponds to a probability of a user engagement with another web content element below the given ranking position (Gusev, Para 0119, the predicted second parameter 310 is a loss parameter, i.e. a parameter indicative of a likelihood of a click for a following search result following immediately after the given search result mentioned above).
Claim 15 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 16 is the system claim corresponding to the method claim 2, and is analyzed and rejected accordingly.
Claim 17 is the system claim corresponding to the method claim 3, and is analyzed and rejected accordingly.
Claim 18 is the system claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 19 is the system claim corresponding to the method claim 5, and is analyzed and rejected accordingly.
Claim 20 is the system claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claims 7-14 are rejected under 35 U.S.C. 103 as being unpatentable over Gusev in view of Stankiewicz et al (US 20120117092 A1) hereafter Stankiewicz
Regarding claim 7, Gusev teaches a method of training a model for ranking objects on a Search Engine Results Page (SERP),the SERP to be provided to an electronic device associated with a user of a search engine hosted by a server, the server being communicatively coupled with the electronic device, the method executable by the server and comprising: acquiring query data associated with a training query, the training query being a previously submitted query to the search engine for which a training web content element has been provided as a search result at a given ranking position (Para 0029, receiving, from the at least one electronic device, a search query); acquiring context data associated with the training web content element, the context data including the query data (Para 0073, the items in the data collection are ordered within the data collection in an order of decreasing query-independent relevance); acquiring user-interaction data associated with the training web content element, the user-interaction data being indicative of (i) a benefit of the training web content element having been ranked at the given ranking position in response to the training query (Para 0115, the predicted first parameter 210 is a win parameter, i.e. a parameter indicative of a likelihood of a click for a given search result, the given search result placed at a particular position of the SERP) and (ii) a detriment of the training web content element having been ranked at the given ranking position in response to the training query (Para 0119, the predicted second parameter 310 is a loss parameter, i.e. a parameter indicative of a likelihood of a click for a following search result following immediately after the given search result mentioned above); training the model using the positive training set and the negative training set, the model being a multiclassification model for a plurality of classes, the plurality of classes including two classes dedicated to the given ranking position (Para 0025, the first machine learning algorithm using a first set of training factors, the first set of training factors including at least one factor ƒ that is used for training the usefulness function), the two classes being: a win-dedicated class indicative of a predicted benefit of the training web content element being ranked at the given ranking position, and a loss-dedicated class indicative of a predicted detriment of the training web content element being ranked at the given ranking position (Para 0026, As a result of the respective training of the first machine learning algorithm and the second machine learning algorithm, a set of values for the win or the loss components are generated).
Gusev does not appear to explicitly teach generating two training sets for a training query-web content element pair, the training query-web content element pair including the training query and the training web content element, the two training sets including: a positive training set having an input and a first label, the input having the context data, the first label being indicative of a ground-truth benefit of the training web content element having been ranked at the given ranking position in response to the query; a negative training set having the input and a second label, the second label being indicative of a ground-truth detriment of the training web content element having been ranked at the given ranking position in response to the query.
In analogous art, Stankiewicz teaches generating two training sets for a training query-web content element pair, the training query-web content element pair including the training query and the training web content element, the two training sets including: a positive training set having an input and a first label, the input having the context data, the first label being indicative of a ground-truth benefit of the training web content element having been ranked at the given ranking position in response to the query; a negative training set having the input and a second label, the second label being indicative of a ground-truth detriment of the training web content element having been ranked at the given ranking position in response to the query (Para 0027, Due to imbalance of positive and negative training data i.e., the majority of candidate phrases in the training data are typically not keywords, one may choose not to use the labels assigned by the classifier, but instead rank the candidates based directly on the probability scores, choosing, for example the 10 candidates with the highest probabilities in a given webpage). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Gusev to include the teaching of Stankiewicz. One of ordinary skill in the art would be motivated to implement this modification in order to process web content, as taught by Stankiewicz (Para 0003, The system first processes webpage text and uses its structure to extract phrases which serve as a keyword candidate pool).
Regarding claim 8, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the model is configured to output probabilities for each ranking position of a set of ranking positions for each of the win dedicated class and the loss dedicated class (Gusev, Para 0151, the third machine learning algorithm 406, in addition to the so-generated win and loss parameters can consider additional features associated with the given vertical search result and/or the given SERP position).
Regarding claim 9, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the training web content element is a widget to be inserted in web documents included in relevant search results for the query (Gusev, Para 0161, the preliminary ranking algorithm of the ranking routine 502 ranks the web search results).
Regarding claim 10, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the win-dedicated class corresponds to a probability of a user engagement with the training web content element at the given ranking position (Gusev, Para 0061, the third machine algorithm is based at least in part on a modified “win-loss” algorithm).
Regarding claim 11, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the loss-dedicated class corresponds to a probability of a user engagement with another web content element below the given ranking position (Gusev, Para 0061, the third machine algorithm is based at least in part on a modified “win-loss” algorithm).
Regarding claim 12, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the model is configured to output probabilities for each ranking position of a set of ranking positions for each of the win dedicated class and the loss dedicated class, and wherein the training includes adjusting the model to minimize a difference between the output probabilities and probabilities indicated by the positive training set and the negative training set (Gusev, Para 0171, adjusting the position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list).
Regarding claim 13, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the training includes random data collection by which the given ranking position is randomly selected (Gusev, Para 0073, They are thus likely to be located more quickly than if the data in the data collection had been randomly entered).
Regarding claim 14, Gusev in view of Stankiewicz teaches the method of Claim 7, wherein the positive training set represents an example when a user interacted with the training web content element at the given position in the given context; and the negative training set represents an example when a user interacted with the training web content element at a ranking position beneath the given position, and wherein the training adjusts the model M to predict probabilities of both the win dedicated class and the loss dedicated class (Gusev, Para 0151, in addition to the so-generated win and loss parameters can consider additional features associated with the given vertical search result and/or the given SERP position).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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