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 .
Claim Objections
Claim 1 objected to because of the following informalities: the word “at lest” appears to be a spelling error. Appropriate correction is required.
Claims 13 and 14 are identical. Appropriate correction is required.
Claim 15 objected to because of the following informalities: the word “instrucitons” appears to be a spelling error. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1, 9 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,254,007.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the present application are anticipated by the claims of the parent patent, USPN 12,254,007. For example, claim 1 of the present application and corresponding claim 1 of the parent is compared below.
USPN 12,254,007
Application No. 19/081,319
A computer-implemented method, comprising:
before receiving, at a search system, a subsequent query that is associated with at least one or more features:
generating, using a predictive model and by the search system, data indicating a predicted relevance of a particular item of content to a query that is associated with the one or more features, wherein the data indicating the predicted relevance is generated by the predictive model based at least on the particular item of content; and
storing, by the search system and in a record entry of a database for a search index, data that associates at least the particular item of content with the data indicating the predicted relevance generated by the predictive model; receiving, at the search system, the subsequent query that is associated with at least the one or more features;
in response to receiving the subsequent query:
accessing, by the search system, the record entry of the database for the search index to identify the particular item and the data indicating the predicted relevance that was previously generated by the predictive model based at least on the one or more features associated with the subsequent query; selecting, using the data indicating the predicted relevance, the particular item of content; and
providing data identifying the particular item of content in response to the subsequent query
A computer-implemented method, comprising:
before receiving a subsequent model input that is associated with at lest one or more features:
generating, using a machine learning model, data indicating a model output for a model input that is associated with the one or more features, wherein the model output is generated by the machine learning model based at least on the one or more features, and
storing, in a record entry of a database, data that associates the model input with the data indicating the model output generated by the machine learning model;
receiving the subsequent model input that is associated with at least one or more features; and
in response to receiving the subsequent model input:
accessing the record entry of the database to identify the model output that was previously generated by the machine learning model as relevant to the subsequent model input, and
providing data corresponding to the model output in response to the subsequent model input.
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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Attenberg et al (“Attenberg” US 2011/0054999 A1), published on March 03, 2011, in view of Kim et al (“Kim” US 2011/0047028 A1), published on February 24, 2011.
As to claim 1, Attenberg teaches “before receiving a subsequent model input that is associated with at least one or more features: generating, using a machine learning model, data indicating a model output for a model input that is associated with the one or more features” in par. 0006 (“To predict user navigation within sponsored search advertisements, a click prediction classifier may be trained to predict a probability of a click on a sponsored advertisement using sets of features from sets of training data. Each set of features may include features of a user entity, features of a query, and features of a list of sponsored search results". Noting that “a click prediction classifier” corresponds to a machine learning model. “A probability of a click” corresponds to data indicating a model output for a model input that is associated with the one or more features).
Attenberg teaches “wherein the model output is generated by the machine learning model based at least on the one or more feature” in par. 0006 (“To predict user navigation within sponsored search advertisements, a click prediction classifier may be trained to predict a probability of a click on a sponsored advertisement using sets of features from sets of training data…". “A probability of a click” corresponds to the model output. “sets of features from sets of training data” corresponds to at least on the one or more feature).
It appears Attenberg does not explicitly teach “storing, in a record entry of a database, data that associates the model input with the data indicating the model output generated by the machine learning model”.
However, Kim teaches “storing, in a record entry of a database, data that associates the model input with the data indicating the model output generated by the machine learning model” in par. 0027, 0030 (“The expected keyword storage 112 A is stored with keyword and probability indexes of respective keywords”. Noting that storage 112 A corresponds to a database that stored a record including an expected keyword (corresponding to the model input) that associates the probability index (corresponding to the model output generated by the machine learning model)).
Attenberg and Kim are analogous art because they are in the same field of endeavor of target advertisement in a web browser. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to enhance predictive mode, disclosed by Attenberg, to include “storing, in a record entry of a database, data that associates the model input with the data indicating the model output generated by the machine learning model”, as suggested by Kim, in order to keep track with high frequently entered key word (see Kim par. 0012).
Kim teaches “receiving the subsequent model input that is associated with at least one or more features; and in response to receiving the subsequent model input: accessing the record entry of the database to identify the model output that was previously generated by the machine learning model as relevant to the subsequent model input, and providing data corresponding to the model output in response to the subsequent model input” in par. 0033 (“keywords having high probability index among the keywords stored in the expected keyword storage 112A are transmitted to the advertisement server 200 when the user starts the web browser 100 next time and are used in determining target advertisements, as described above". Noting that keywords with high probability index (corresponding to the model output that was previously generated by the machine learning model as relevant to the subsequent model input) are sent to the server so that target advertisements (corresponding to providing data corresponding to the model output in response to the subsequent model input) are identified as a result of received search query (user enters a search query in par. 0030)).
As to claim 15, it is rejected for similar reason as claim 1.
As to claim 2, Attenberg teaches “wherein the model input is associated with a particular item described by the one or more features” in par. 0007 (“features of a user entity, features of a search query and features of the list of sponsored advertisements may be input to a click prediction classifier applied to predict a probability of a click on each of the sponsored advertisements in the list and may also be input to a dwell time prediction classifier to predict a probability of a dwell time on web pages of a website of each of the sponsored advertisements in the list". Noting that “features of search query” describes what user searched for (a particular item described by the one or more features)).
As to claim 16, it is rejected for similar reason as claim 2.
As to claim 3, Attenberg teaches “wherein the particular item of content is a video, document, advertisement, image, or an audio file” in par. 0005 (“In various embodiments, a web browser executing on a client device may be operably coupled to a server for receiving a list of sponsored advertisements from the server for display by the web browser on a search results page”).
As to claim 4, Kim teaches “the subsequent model input represents a query item; and accessing the record entry of the database to identify the model output as relevant is based on the one or more features of the query item and the one or more features of the particular item” in par. 0030 (search input from user), par. 0033 (“keywords having high probability index among the keywords stored in the expected keyword storage 112A are transmitted to the advertisement server 200 when the user starts the web browser 100 next time and are used in determining target advertisements, as described above". Noting that Storage 112A corresponds to the database used for user query. The probability index corresponds to a model output based on features of user’s query).
As to claim 17, it is rejected for similar reason as claim 4.
As to claim 5, Kim teaches “the subsequent model input is associated with a user” in par. 0030 (user search box to query the database).
Kim teaches “the model output represents a likelihood of the particular item being selected by the user” in par. 0033 (“keywords having high probability index among the keywords stored in the expected keyword storage 112A are transmitted to the advertisement server 200 when the user starts the web browser 100 next time and are used in determining target advertisements, as described above". Target advertisements are determined based on high probability index key words indicated a likelihood of the particular item being selected by the user).
As to claim 18, it is rejected for similar reason as claim 5.
As to claim 6, Kim teaches “wherein accessing the record entry of the database to identify the model output as relevant includes determining a similarity of the one or more features of the model input and the one or more features of the subsequent model input” in par. 0033 (“keywords having high probability index among the keywords stored in the expected keyword storage 112A are transmitted to the advertisement server 200 when the user starts the web browser 100 next time and are used in determining target advertisements, as described above". User’s search keywords are compared with Keywords stored in the keyword storage 112A; user search keyword and keyword stored (in storage 112A) similarity is determined).
As to claim 19, it is rejected for similar reason as claim 6.
As to claim 7, Kim teaches “wherein the record entry reflects an association between the one or more features of the model input and a probability of a given outcome” in par. 0012 (“analyze keywords inputted into a search box by a user and site visit information such as addresses (URLs) of web sites visited by the user and duration times in respective sites, extract user preference information, expect keywords having high probability for the user to input based on the preference information, and realize a target advertisement on a first page of the web browse”).
As to claim 8, Attenberg teaches “wherein the data corresponding to the model output represents an affinity between the model input and the subsequent model input” in par. 0007 (“A list of sponsored advertisements may be ranked at least in part by the prediction of user navigation within sponsored search advertisements and served for display on a search results page. To do so, a list of sponsored advertisements for display on a web page of a plurality of search results may be received. A probability of user navigation may be predicted for each of the sponsored advertisements using a probability of a click on each of the sponsored advertisements and a probability of a dwell time on web pages of a website of each of the sponsored advertisements". “a probability of a click” corresponds to a model output where the probability represents an affinity between user search input and user click on each of the sponsored advertisements).
As to claim 20, it is rejected for similar reason as claim 8.
As to claim 9, Attenberg teaches “before receiving a subsequent request that is associated with one or more features: generating, using a machine learning model, data indicating a likelihood of a user associated with the one or more features selecting a particular item of content that is associated with the one or more features, wherein the data indicating the likelihood is generated by the machine learning model based at least on the one or more features” in par. 0028 ("The prediction engine 222 may also include a click prediction classifier 224 that predicts the probability of a click on a sponsored advertisement and a dwell time prediction classifier 226 that predicts the probability of a dwell time on web pages of a website of a sponsored advertisement". It is noted that “the probability of a click on a sponsored advertisement” corresponds to a data indicating a likelihood of a user associated with the one or more features selecting a particular item of content that is associated with the one or more features. In par. 0032, "a model with the click prediction classifier and the dwell time prediction classifier may be output to predict user navigation originating from a sponsored search result such as a sponsored search advertisement" as to disclose of a machine learning model).
It appears Attenberg does not explicitly teach “storing, in a record entry of a database for an index, data that associates the particular item of content and the one or more features with the data indicating the likelihood generated by the machine learning model”.
However, Kim teaches “storing, in a record entry of a database for an index, data that associates the particular item of content and the one or more features with the data indicating the likelihood generated by the machine learning model” in par. 0027, 0030 (“The expected keyword storage 112 A is stored with keyword and probability indexes of respective keywords”. Noting that storage 112 A corresponds to a search index that include expected keyword that associates the probability index (corresponding to a record entry of a database for an index, data that associates the particular item of content and the one or more features with the data indicating the likelihood generated by the machine learning model)).
Attenberg and Kim are analogous art because they are in the same field of endeavor of target advertisement in a web browser. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to enhance predictive mode, disclosed by Attenberg, to include “storing, in a record entry of a database for an index, data that associates the particular item of content and the one or more features with the data indicating the likelihood generated by the machine learning model” in order to keep track with high frequently entered key word (see Kim et al par. 0012).
Kim teaches “receiving the subsequent request; and in response to receiving the subsequent request: accessing the record entry of the database to identify the particular item and the data indicating the likelihood that was previously generated by the machine learning model, selecting, using the likelihood, the particular item of content, and providing data identifying the particular item of content in response to the subsequent request” in par. 0030 (keyword is entered by the user is used to search against data records of high probability index and expected keyword. The high probability index corresponds to the likelihood that was previously generated by the machine learning model), and in par. 0033 (“Keywords having high probability index among the keywords stored in the expected keyword storage 112A are transmitted to the advertisement server 200 when the user starts the web browser 100 next time and are used in determining target advertisements, as described above…”. The Keywords having high probability index correspond to selecting, using the likelihood (high probability index), the particular item of content (determined target advertisement), and providing data identifying the particular item of content in response to the subsequent request).
As to claim 10, Kim teaches “wherein at least the one or more features comprises one or more of: a location of a device that transmitted the subsequent request, a language of the subsequent request, or a time of day” in par. 0013 (“personal information, position information and a user’s web browser using time information stored in a mobile communication system" correspond to a location of a device that transmitted the subsequent request, a language of the subsequent request, or a time of day).
As to claim 11, Kim teaches “wherein the data indicating the likelihood is one or more predicted numerical values from which a probability that the particular item of content will be relevant to the one or more features can be derived” in par. 0033 (mathematical expression 1).
As to claim 12, Attenberg teaches “wherein the particular item is a video, a document, an advertisement, an image, or an audio file” in par. 0005 (“In various embodiments, a web browser executing on a client device may be operably coupled to a server for receiving a list of sponsored advertisements from the server for display by the web browser on a search results page”).
As to claim 13, Attenberg teaches “the subsequent request is generated based on a user selection of a given item of content, and the one or more features comprise characteristics of the user selection” in par. 0006 (“To predict user navigation within sponsored search advertisements, a click prediction classifier may be trained to predict a probability of a click on a sponsored advertisement using sets of features from sets of training data. Each set of features may include features of a user entity, features of a query, and features of a list of sponsored search results").
As to claim 14, Attenberg teaches “the subsequent request is generated based on a user selection of a given item of content, and the one or more features comprise characteristics of the user selection” in par. 0006 (“To predict user navigation within sponsored search advertisements, a click prediction classifier may be trained to predict a probability of a click on a sponsored advertisement using sets of features from sets of training data. Each set of features may include features of a user entity, features of a query, and features of a list of sponsored search results").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicants’ disclosure:
. Lin et al (US 8694540 B1)
. Botros (US 2013/0185306 A1)
. Chang (US-2015/0286710-A1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loc Tran whose telephone number is 571-272-8485. The examiner can normally be reached on Mon-Fri. 7:30am-5pm; First Fri Off.
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/LOC TRAN/
Primary Examiner, Art Unit 2164