DETAILED ACTION
Notice of Pre-AIA or AIA Status
Claims 1-20 are pending in this application. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/01/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-3, 5-8, 10-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2018/0075034) and in view of Kong (US 2017/0351709).
Re claim 1, Wang discloses a method, comprising:
establishing, by one or more computing devices, a connection with a client device via an application executing on the client device ([0025], Fig. 1, client device executes an application configured to connect and communicate with an online system);
transmitting, by the one or more computing devices, to the application of the client device over the established connection, each comprising one or more images of one or more products ([0028], request for content items from the client device. Content items include different types of media (images and videos) and provides the content to the user device);
extracting, by the one or more computing devices, a set of features from each of the one or more images, each set of features depicted in a different image of the one or more images ([0038]-[0042], machine learning module trains the content ranking models using features extracted from the training data store and the content which includes images);
receiving, by the one or more computing devices, a request from the client device over the connection ([0045], content delivery module receives a request from a client device for content items);
comparing, by the one or more computing devices, each extracted set of features to one or more sets of features of one or more images in a database ([0028], online system selects a content item from the ordered set based on the ranking. [0038]-[0045] features extracted from content items are used to determine a ranking score based on the user input information and trained content ranking models);
responsive to receiving the request for the content items, selecting, by the one or more computing devices, an image for inclusion based at least on the comparison of each extracted set of features to the one or more sets of features of the one or more images in the database ([0043]-[0045], content delivery module receives a request from the client device, the content deliver module selects a subset to provide to the client device. The subset is selected based on the ranked content items in the content item set using features of the content items to create a ranking score ); and
transmitting, by the one or more computing devices, to the client device over the connection for display with the selected image ([0043]-[0045], content delivery module provides the content items from the content item ordered set each time the content delivery module receives a request from the client device).
Wang discloses in [0073], the web server links the online system to the client devices, and the web server serves web pages as well as other web-related content. Wang does not disclose, however, in analogous art, Kong discloses requesting for a webpage (([0026], [0028], search query is received at a client device regarding content items associated with a particular web page of a particular website).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Wang in view of Kong to provide content items (images) that is requested on the web pages for the reasons of identifying matched type content based on a matched quality score associated with the search query (abstract).
Re claim 2, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses receiving, by the one or more computing devices, a request for the web page from the application executing on the client device, wherein transmitting the web page to the client device with the selected image is performed in response to receiving the request Kong ([0028]-[0031], search query is received at a client device regarding content items associated with a particular web page of a particular website. The identified images are retrieved and returned to the client device as part of a search result).
Re claim 3, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses storing, by the one or more computing devices, each image of the one or more images with a stored ranking value, the stored ranking value ranking the respective image in comparison to other images of the one or more images, wherein selecting the image from the one or more images comprises selecting, by the one or more computing devices, the image responsive to the image having a highest stored ranking value of the one or more images ([0018] [0028]-[0032], [0037], [0049], [0064]-[0066] a matching quality score is calculated based on some features of the image. Image selection can be based on query/image matching rules integrated with the matching quality score and ranking model trained using historic image data and user interactive data. Image recognition is performed to determine the content of the image obtained in the web page and utilizes prior user interactions with the image in the past. Image having highest overall ranking score for particular content item is selected).
Re claim 5, Wang discloses instantiating, by the one or more computing devices, a model responsive to establishing the connection with the client device via the application executing on the client device ([0047]-[0049], Content delivery module, is in connection with the client device and the online system which includes the machine learning module); and
updating, by the one or more computing devices, the model based on features extracted from the one or more images of the one or more web pages, wherein selecting the image comprises selecting, by the one or more computing devices, the image at least based on the model ([0049], the machine learning module can be used to re-train the content ranking models to update ranking scores).
Re claim 6, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses wherein comparing each extracted set of features to the one or more sets of features of the one or more images comprises: comparing, by the one or more computing devices, one or more features of the model extracted from the one or more images of the one or more web pages to the one or more sets of features of the one or more images ([0059]-[0060], determine the matching score between a content item and an image candidate based on features of the image).
Re claim 7, Wang discloses wherein selecting the image comprises: selecting, by the one or more computing devices, the image at least based on a uniqueness or similarity of image features of the image to the one or more features of the model ([0038]-[0039], [0042], content item generator generates content item using a machine learning model for presentation to users online a content item that is similar to the products and services the user previously liked. The machine learning module trains the content ranking models by extracting features from content items).
Re claim 8, Wang discloses wherein updating the model comprises: adding, by the one or more computing devices, the one or more features of each of the one or more images of the one or more web pages to the model ([0042], Machine learning module trains the content ranking models by extracting features from content items which the ranking has already been determined and creating a feature vector).
Re claim 10, Wang discloses transmitting, by the one or more computing devices, the image to a remote computing device, receipt of the image causing the remote computing device to ([0043]-[0045], content delivery module provides the content items from the content item ordered set each time the content delivery module receives a request from the client device):
execute a machine learning model associated to obtain a performance score for the image, and transmit the performance score for the image to the one or more computing devices ([0047]-[0049], Content delivery module, is in connection with the client device and the online system which includes the machine learning module);
assigning, by the one or more computing devices, a stored ranking value to the image based on comparison ([0044]-[0045], Machine learning module trains and ranks content over time with sets of features and stores the set of ranked content items in the content item ordered set. Content deliver selects the content items from the ordered set with the highest ranking score).
One of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses comparing, by the one or more computing devices, the performance score for the image to performance scores for the one or more images ([0059]-[0060], determine the matching score between a content item and an image candidate based on features of the image).
Re claim 11, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses wherein selecting the image comprises: selecting, by the one or more computing devices, the image based on the stored ranking value of the image . ([0064]-[0066], Further matching will be used to create an image ranking model to tank the image candidates to be matched with a content item in response to a search query that best matches the source that appeared on the selected content. Image having highest overall ranking score for particular content item is selected).
With respect to claims 12-14, and 16-20, they are similar to claims 1-3 and 5-6 and therefore are rejected for the same reasons above.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and in view of Kong and in view of Chandra Sekar Rao (US 2020/0034455).
Re claims 4 and 15, one of ordinary level of skill in the art would have been compelled to make the proposed modification to Wang for the same reasons identified in the rejection of claim 1. In addition, Kong discloses receiving, by the one or more computing devices, a request for a new web page from the application executing on the client device;
determining, by the one or more computing devices, the one or more computing devices has transmitted the image to the client device during the connection ([0049], Data collection module stores the metadata which includes the source from which the image is collected and a time of the collection obtained on a web page in which the image is attached);
in response to determining the one or more computing devices has transmitted the image to the client device during the connection, selecting, by the one or more computing devices, a second image from the one or more images based on the second image having a second highest stored ranking value of the one or more images ([0049]-[0051], [0064]-[0066], In addition, an analysis may be performed on the content of the source page to determine the content represented by the image. An image recognition is performed on the image to determine the content of the image and attributes of the images may also be collected. Further matching will be used to create an image ranking model to tank the image candidates to be matched with a content item in response to a search query that best matches the source that appeared on the selected content. Image having highest overall ranking score for particular content item is selected)
Kong discloses in [0049] that it utilizes prior user interactions to perform an analysis for rankings of the content of the image. Wang and Kong does not disclose, however, in analogous art, Chandra Sekar Rao discloses transmitting, by the one or more computing devices, the second image from the one or more images and a second web page to the client device via the connection in response to the request for the new web page ([0039], web-based search includes providing customized pages for subsequent visits).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Wang and Kong in view of Chandra Sekar Rao to provide customized pages for subsequent visits for the reasons of providing image-based search and recommendation techniques via AI (abstract).
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Wang and in view of Kong and in view of Fadeev (US 2018/0314880).
Re claim 9, Wang discloses adding, by the one or more computing devices, the one or more features of each of the one or more images of the one or more web pages to a model ([0042], Machine learning module trains the content ranking models by extracting features from content items which the ranking has already been determined and creating a feature vector).
Wang and Kong does not disclose, however, in analogous art, Fadeev discloses that the model is a Gaussian mixture model ([0035], trains the image analysis based on Gaussians models to measure dissimilarity between two images).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Wang and Kong in view of Fadeev to provide a Gaussian model for the reasons of compare images represented in a continuous probabilistic framework ([0035]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HO T SHIU whose telephone number is (571)270-3810. The examiner can normally be reached Mon-Fri (9:00am - 5:00pm).
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/HO T SHIU/Examiner, Art Unit 2443
HO T. SHIU
Examiner
Art Unit 2443
/CHRISTOPHER B ROBINSON/Primary Examiner, Art Unit 2443