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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/6/2026 has been entered.
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.
Claim 1-3, 5, 7, 8-10, 12, 14-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt, Jonathan (PGPUB Document No. 20220374483), hereafter referred as to “Hunt”, in view of Kanter, Andrew et al (PGPUB Document No. 20170017352), hereafter, referred to as “Kanter”, in view of Nakajima, Takuro (PGPUB Document No. 20190306486), hereafter, referred to as “Nakajima”.
Regarding Claim 1 (Currently Amended), Hunt teaches A computer-implemented method of pairwise comparison rating to reduce presentation bias in content recommendation, comprising(Hunt, Fig. 5 and para 0083-0088 teach a method for ranking contents using pairwise comparison): in response to a request for content by a first user device of the plurality of user devices, determining a respective pairwise distance between the respective ranking value for each content item of the plurality of content items and a respective weighted value for each historical content item of a plurality of historical content items that have been previously interacted with by the first user device(Hunt, Fig. 5 and para 0083-0085 teach determining pairwise distance between content items based on weights for ranking “The weighting of a pairwise rankings of a pair can be based on a probability of a user opening a push notification associated with a training content item of the of pairs of training content items ……..….In step S520 selecting a first and a second candidate content item are selected from the set of candidate content items. For example, the trained ranking model can use a pairwise expected negative response technique. Therefore, two candidate content items can be selected from the set of candidate content items and tested (e.g., compared) using the pairwise expected negative response technique”);
wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value for each historical content item of a plurality of historical content items is output by a trained model, and wherein training the model comprises(Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”; Fig. 6 and para 0089 teach a model can be trained with aforementioned features disclosed in para 0088 “In step S605 training content is selected. the training content can be historical content items that can be used to train at least one ranking model. For example, training content items can be historical social media posts/replies and candidate content items can be, for example, current or live social media posts/replies”): and causing the first user device to display a first content item of the plurality of content items based on the respective pairwise distance between the respective ranking value for the first content item and the respective weighted value for a first historical content item of the plurality of historical content items being a shortest determined pairwise distance(Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”).
But Hunt does not explicitly teach generating, by at least one computer processor, a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items;
adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items;
sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session;
However, in the same field of endeavor of ranking contents for recommendations Kanter teaches generating, by at least one computer processor of a content delivery system, a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items(Kanter, para 0049 discloses ranking/scoring content items based plurality of users’ interactions with contents “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140 (e.g., all users of the online system 140, users of the online system 140 having one or more specific characteristics) and orders the content items in the ordered set based on the engagement scores”);
Kanter further teaches adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items(Kanter, para 0043 discloses further ordering of the content items based on their predicted likelihood of user interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of content ranking using user interaction of Kanter into content ranking by pairwise content distance measurement of Hunt to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to exclude previously presented contents and including additional contents to improve the likelihood of the interacting with presented contents (Kanter, para 0069).
But Hunt and Katner don’t explicitly teach sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session;
However in the same field of endeavor of content delivery system Nakajima teaches sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration(Nakajima, para 0125 teaches determining if playback time of contents is more or less than a threshold “the control unit 101 determines whether the continuous playback time (time elapsed after the start of playback of a video) is less than a threshold M. If the continuous playback time is less than the threshold M, the process proceeds to S1003. If the continuous playback time is not less than the threshold M, the process proceeds to S1004”), wherein the first content item and the second content item are presented together to a user in the same session (Nakajima, para 0128 teaches displaying two video contents together “The method for displaying the first and second videos together is not limited to the superposition display, and these videos may be displayed at a time in separate display regions as videos for the same playback position”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining playback duration of video contents of Nakajima into content ranking by pairwise content distance measurement of Hunt and Kanter to produce an expected result of recommending contents based on content playing duration. The modification would be obvious because one of ordinary skill in the art would be motivated facilitated content scene searches which has been viewed in the past (Nakajima, para 0006-0008).
Regarding claim 2 (Original), Hunt, Kanter and Nakajima teach all the limitations claim 1 and Kanter further teaches wherein the respective ranking value for each content item of the plurality of content items represents at least one of popularity of the respective content item, engagement with the respective content item, or relevance of the respective content item(Kanter, para 0049 discloses ranking/scoring values of the contents represent engagement of the content item “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140”).
Regarding claim 3(Original), Hunt, Kanter and Nakajima teach all the limitations claim 1 and Kanter further teaches wherein the respective weighted value for each historical content item of the plurality of historical content items is determined based on historical interactions between the first user device and each content item of the plurality of historical content items (Kanter, para 0077 discloses scoring of content items are based on historical interactions “the online system 140 determines engagement scores for the content items 715A-715D based on the historical number of interactions per thousand impressions of each content item 715A-715D”; para 0076 further discloses weight value for the contents are getting determined differently for different contents based interactions “different interactions with the content items 715A-715D may be differently weighted when determining the engagement scores, or interactions with different content items 715A-715D may be differently weighted when determining the engagement scores”)
compared to predicted interactions between the first user device and each content item of the plurality of historical content items (Kanter, para 0043 discloses predicted likelihoods of user interactions are being incorporated to items ranked by score which is determined based on historical interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
Regarding claim 5(Original), Hunt, Kanter and Nakajima teach all the limitations claim 1 and Kanter further teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value that satisfies a threshold that indicates relevance to the first user device(Kanter, para 0037 discloses displaying contents that satisfies threshold “the content selection module 235 selects content items having the highest measures of relevance or having at least a threshold measure of relevance for presentation to the user”),
and the method further comprising: adjusting, based on an indication from the first user device of a selection of the first content item over the second content item, the ranking value for the second content item to below the threshold; and sending the first content item to the first user device (Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” ).
Regarding claim 7(Original), Hunt, Kanter and Nakajima teach all the limitations claim 1 and Kanter further teaches wherein the plurality of user devices comprises at least one of smart devices, content playback devices, or set-top boxes (Kanter, Fig. 1 and para 0021 disclose plurality of user devices 100 as smart device (smart phone) “a client device 110 may be a device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, a smartwatch or another suitable device”).
Regarding Claim 8 (Currently Amended), Hunt teaches A system for pairwise comparison rating to reduce presentation bias in content recommendation, comprising: one or more memories; at least one processor each coupled to at least one of the memories and configured to perform operations comprising(Hunt, Fig. 7 disclose a system comprising processor and memory and Fig. 5 and para 0083-0088 further teach a system for ranking contents using pairwise comparison):
in response to a request for content by a first user device of the plurality of user devices, determining a respective pairwise distance between the respective ranking value for each content item of the plurality of content items and a respective weighted value for each historical content item of a plurality of historical content items that have been previously interacted with by the first user device (Hunt, Fig. 5 and para 0083-0085 teach determining pairwise distance between content items based on weights for ranking “The weighting of a pairwise rankings of a pair can be based on a probability of a user opening a push notification associated with a training content item of the of pairs of training content items ……..….In step S520 selecting a first and a second candidate content item are selected from the set of candidate content items. For example, the trained ranking model can use a pairwise expected negative response technique. Therefore, two candidate content items can be selected from the set of candidate content items and tested (e.g., compared) using the pairwise expected negative response technique”); wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value for each historical content item of a plurality of historical content items is output by a trained model, and wherein training the model comprises (Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”; Fig. 6 and para 0089 teach a model can be trained with aforementioned features disclosed in para 0088 “In step S605 training content is selected. the training content can be historical content items that can be used to train at least one ranking model. For example, training content items can be historical social media posts/replies and candidate content items can be, for example, current or live social media posts/replies”): and causing the first user device to display a first content item of the plurality of content items based on the respective pairwise distance between the respective ranking value for the first content item and the respective weighted value for a first historical content item of the plurality of historical content items being a shortest determined pairwise distance (Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”).
But Hunt does not explicitly teach generating a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items; adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items; sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session;
However, in the same field of endeavor of ranking contents for recommendations Kanter teaches Kanter teaches generating a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items (Kanter, para 0049 discloses ranking/scoring content items based plurality of users’ interactions with contents “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140 (e.g., all users of the online system 140, users of the online system 140 having one or more specific characteristics) and orders the content items in the ordered set based on the engagement scores”); adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items (Kanter, para 0043 discloses further ordering of the content items based on their predicted likelihood of user interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of content ranking using user interaction of Kanter into content ranking by pairwise content distance measurement of Hunt to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to exclude previously presented contents and including additional contents to improve the likelihood of the interacting with presented contents (Kanter, para 0069).
But Hunt and Katner don’t explicitly teach sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session;
However in the same field of endeavor of content delivery system Nakajima teaches sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration(Nakajima, para 0125 teaches determining if playback time of contents is more or less than a threshold “the control unit 101 determines whether the continuous playback time (time elapsed after the start of playback of a video) is less than a threshold M. If the continuous playback time is less than the threshold M, the process proceeds to S1003. If the continuous playback time is not less than the threshold M, the process proceeds to S1004”), wherein the first content item and the second content item are presented together to a user in the same session(Nakajima, para 0128 teaches displaying two video contents together “The method for displaying the first and second videos together is not limited to the superposition display, and these videos may be displayed at a time in separate display regions as videos for the same playback position”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining playback duration of video contents of Nakajima into content ranking by pairwise content distance measurement of Hunt and Kanter to produce an expected result of recommending contents based on content playing duration. The modification would be obvious because one of ordinary skill in the art would be motivated facilitated content scene searches which has been viewed in the past (Nakajima, para 0006-0008).
Regarding claim 9 (Original), Hunt, Kanter and Nakajima teach all the limitations claim 8 and Kanter further teaches wherein the respective ranking value for each content item of the plurality of content items represents at least one of popularity of the respective content item, engagement with the respective content item, or relevance of the respective content item (Kanter, para 0049 discloses ranking/scoring values of the contents represent engagement of the content item “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140”).
Regarding claim 10(Original), Hunt, Kanter and Nakajima teach all the limitations claim 8 and Kanter further teaches wherein the respective weighted value for each historical content item of the plurality of historical content items is determined based on historical interactions between the first user device and each content item of the plurality of historical content items(Kanter, para 0077 discloses scoring of content items are based on historical interactions “the online system 140 determines engagement scores for the content items 715A-715D based on the historical number of interactions per thousand impressions of each content item 715A-715D”; para 0076 further discloses weight value for the contents are getting determined differently for different contents based interactions “different interactions with the content items 715A-715D may be differently weighted when determining the engagement scores, or interactions with different content items 715A-715D may be differently weighted when determining the engagement scores”) compared to predicted interactions between the first user device and each content item of the plurality of historical content items (Kanter, para 0043 discloses predicted likelihoods of user interactions are being incorporated to items ranked by score which is determined based on historical interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
Regarding claim 12(Original), Hunt, Kanter and Nakajima teach all the limitations claim 8 and Kanter further teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value that satisfies a threshold that indicates relevance to the first user device, and the operations further comprising (Kanter, para 0037 discloses displaying contents that satisfies threshold “the content selection module 235 selects content items having the highest measures of relevance or having at least a threshold measure of relevance for presentation to the user”),
adjusting, based on an indication from the first user device of a selection of the first content item over the second content item, the ranking value for the second content item to below the threshold; and sending the first content item to the first user device (Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” ).
Regarding claim 14(Original), Hunt, Kanter and Nakajima teach all the limitations claim 8 and Kanter further teaches wherein the plurality of user devices comprises at least one of smart devices, content playback devices, or set-top boxes(Kanter, Fig. 1 and para 0021 disclose plurality of user devices 100 as smart device (smart phone) “a client device 110 may be a device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, a smartwatch or another suitable device”).
Regarding Claim 15 (Currently Amended), Hunt teaches A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations for pairwise comparison rating to reduce presentation bias in content recommendation, the operations comprising(Hunt, Fig. 7 disclose a system comprising processor and memory and Fig. 5 and para 0083-0088 further teach a system for ranking contents using pairwise comparison): in response to a request for content by a first user device of the plurality of user devices, determining a respective pairwise distance between the respective ranking value for each content item of the plurality of content items and a respective weighted value for each historical content item of a plurality of historical content items that have been previously interacted with by the first user device(Hunt, Fig. 5 and para 0083-0085 teach determining pairwise distance between content items based on weights for ranking “The weighting of a pairwise rankings of a pair can be based on a probability of a user opening a push notification associated with a training content item of the of pairs of training content items ……..….In step S520 selecting a first and a second candidate content item are selected from the set of candidate content items. For example, the trained ranking model can use a pairwise expected negative response technique. Therefore, two candidate content items can be selected from the set of candidate content items and tested (e.g., compared) using the pairwise expected negative response technique”); wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value for each historical content item of a plurality of historical content items is output by a trained model, and wherein training the model comprises (Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”; Fig. 6 and para 0089 teach a model can be trained with aforementioned features disclosed in para 0088 “In step S605 training content is selected. the training content can be historical content items that can be used to train at least one ranking model. For example, training content items can be historical social media posts/replies and candidate content items can be, for example, current or live social media posts/replies”);
and causing the first user device to display a first content item of the plurality of content items based on the respective pairwise distance between the respective ranking value for the first content item and the respective weighted value for a first historical content item of the plurality of historical content items being a shortest determined pairwise distance(Hunt, element S540 of Fig. 5 and para 0088 teach determining pairwise distance, scoring contents based on pairwise distance and recommending contents accordingly (where lower the distance is more relevant and higher in scoring) “If all of the candidate content items have been ranked using the pairwise ranking technique the last remaining candidate content item (e.g., the higher ranking or highest scoring candidate content item of the last iteration) can be communicated using a push notification”).
But Hunt does not explicitly teach generating a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items; adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items; sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session;
However in the same field of endeavor of ranking contents for recommendations Kanter teaches generating a respective ranking value for each content item of a plurality of content items based on interaction data indicative of interactions between a plurality of user devices and each content item of the plurality of content items (Kanter, para 0049 discloses ranking/scoring content items based plurality of users’ interactions with contents “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140 (e.g., all users of the online system 140, users of the online system 140 having one or more specific characteristics) and orders the content items in the ordered set based on the engagement scores”); adjusting the respective ranking value for each content item of the plurality of content items based on additional interactions between the plurality of user devices and each content item of the plurality of content items compared to predicted interactions between the plurality of user devices and each content item of the plurality of content items, wherein the predicted interactions are derived from the respective ranking values for the plurality of content items (Kanter, para 0043 discloses further ordering of the content items based on their predicted likelihood of user interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of content ranking using user interaction of Kanter into content ranking by pairwise content distance measurement of Hunt to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to exclude previously presented contents and including additional contents to improve the likelihood of the interacting with presented contents (Kanter, para 0069).
But Hunt and Katner don’t explicitly teach sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration; sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration, wherein the first content item and the second content item are presented together to a user in the same session
However, in the same field of endeavor of content delivery system Nakajima teaches sampling pairs of content items that have impressed for a same user device in a same session, wherein a first content item of a pair of content items has playback of more than a threshold duration and a second content item of the pair of content items does not have playback of more than the threshold duration(Nakajima, para 0125 teaches determining if playback time of contents is more or less than a threshold “the control unit 101 determines whether the continuous playback time (time elapsed after the start of playback of a video) is less than a threshold M. If the continuous playback time is less than the threshold M, the process proceeds to S1003. If the continuous playback time is not less than the threshold M, the process proceeds to S1004”), wherein the first content item and the second content item are presented together to a user in the same session(Nakajima, para 0128 teaches displaying two video contents together “The method for displaying the first and second videos together is not limited to the superposition display, and these videos may be displayed at a time in separate display regions as videos for the same playback position”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining playback duration of video contents of Nakajima into content ranking by pairwise content distance measurement of Hunt and Kanter to produce an expected result of recommending contents based on content playing duration. The modification would be obvious because one of ordinary skill in the art would be motivated facilitated content scene searches which has been viewed in the past (Nakajima, para 0006-0008).
Regarding claim 16 (Original), Hunt, Kanter and Nakajima teach all the limitations claim 15 and Kanter further teaches wherein the respective ranking value for each content item of the plurality of content items represents at least one of popularity of the respective content item, engagement with the respective content item, or relevance of the respective content item (Kanter, para 0049 discloses ranking/scoring values of the contents represent engagement of the content item “the online system 140 computes an engagement score for each content item in the ordered set based on a historical number of interactions with each content item by various users of the online system 140”).
Regarding claim 17(Original), Hunt, Kanter and Nakajima teach all the limitations claim 15 and Kanter further teaches wherein the respective weighted value for each historical content item of the plurality of historical content items is determined based on historical interactions between the first user device and each content item of the plurality of historical content items(Kanter, para 0077 discloses scoring of content items are based on historical interactions “the online system 140 determines engagement scores for the content items 715A-715D based on the historical number of interactions per thousand impressions of each content item 715A-715D”; para 0076 further discloses weight value for the contents are getting determined differently for different contents based interactions “different interactions with the content items 715A-715D may be differently weighted when determining the engagement scores, or interactions with different content items 715A-715D may be differently weighted when determining the engagement scores”) compared to predicted interactions between the first user device and each content item of the plurality of historical content items(Kanter, para 0043 discloses predicted likelihoods of user interactions are being incorporated to items ranked by score which is determined based on historical interactions “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
Regarding claim 20(Original), Hunt, Kanter and Nakajima teach all the limitations claim 15 and Kanter further teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value that satisfies a threshold that indicates relevance to the first user device, and the operations further comprising (Kanter, para 0037 discloses displaying contents that satisfies threshold “the content selection module 235 selects content items having the highest measures of relevance or having at least a threshold measure of relevance for presentation to the user”),
adjusting, based on an indication from the first user device of a selection of the first content item over the second content item, the ranking value for the second content item to below the threshold; and sending the first content item to the first user device(Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction” ).
Claim 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt, Jonathan (PGPUB Document No. 20220374483), hereafter referred as to “Hunt”, in view of Kanter, Andrew et al (PGPUB Document No. 20170017352), hereafter, referred to as “Kanter”, in view of Nakajima, Takuro (PGPUB Document No. 20190306486), hereafter, referred to as “Nakajima”, in further view of Amershi, Saleema et al (PGPUB Document No. 20150242761), hereafter, referred to as “Amershi”.
Regarding claim 4 (Original), Hunt, Kanter and Nakajima teach all the limitations claim 1 and Kanter further teaches and the method further comprising: adjusting, based on an indication from the first user device of a selection of the second content item over the first content item, the ranking value for the second content item to satisfy the threshold; and sending the second content item to the first user device (Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
But Hunt, Kanter and Nakajima don’t explicitly teach wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device,
However, in the same field of endeavor of ranking based on trained model Amershi teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device (Amershi, para 0080 discloses changing contents ranking from below a threshold to above a threshold “a change that results in a changed prediction about the item (i.e., the previous score was below the prediction threshold 430 and the updated score is above the prediction threshold 430”; where Kanter in para 0043 discloses displaying contents having ranking score above a threshold value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of updating ranking scores of content items of Amershi into content ranking by pairwise content distance measurement of Hunt, Kanter and Nakajima to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the performance of machine learning model for content ranking by providing additional information about the contents to the learning model(Amershi, para 0019).
Regarding claim 11(Original), Hunt, Kanter and Nakajima teach all the limitations claim 8 and Kanter further teaches and the operations further comprising: adjusting, based on an indication from the first user device of a selection of the second content item over the first content item, the ranking value for the second content item to satisfy the threshold; and sending the second content item to the first user device(Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
But Hunt, Kanter and Nakajima don’t explicitly teach wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device.
However, in the same field of endeavor of ranking based on trained model Amershi teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device(Amershi, para 0080 discloses changing contents ranking from below a threshold to above a threshold “a change that results in a changed prediction about the item (i.e., the previous score was below the prediction threshold 430 and the updated score is above the prediction threshold 430”; where Kanter in para 0043 discloses displaying contents having ranking score above a threshold value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of updating ranking scores of content items of Amershi into content ranking by pairwise content distance measurement of Hunt, Kanter and Nakajima to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the performance of machine learning model for content ranking by providing additional information about the contents to the learning model(Amershi, para 0019).
Regarding claim 18 (Original), Hunt, Kanter and Nakajima teach all the limitations claim 15 and Kanter further teaches and the operations further comprising: adjusting, based on an indication from the first user device of a selection of the second content item over the first content item, the ranking value for the second content item to satisfy the threshold; and sending the second content item to the first user device (Kanter, para 0043 discloses further ordering/ranking of the content items based on their predicted likelihood of user interactions and displaying them “content items having higher predicted likelihoods of user interaction have higher positions in the ordered set than content items having lower predicted likelihoods of user interaction, so the content items having higher predicted likelihoods of user interaction also have higher positions for display by the scrollable content unit than the content items having lower predicted likelihoods of user interaction”).
But Hunt, Kanter and Nakajima don’t explicitly teach wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device,
However, in the same field of endeavor of ranking based on trained model Amershi teaches wherein the causing the first user device to display the first content item further comprises causing the first user device to display a second content item with a ranking value below a threshold that indicates relevance to the first user device(Amershi, para 0080 discloses changing contents ranking from below a threshold to above a threshold “a change that results in a changed prediction about the item (i.e., the previous score was below the prediction threshold 430 and the updated score is above the prediction threshold 430”; where Kanter in para 0043 discloses displaying contents having ranking score above a threshold value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of updating ranking scores of content items of Amershi into content ranking by pairwise content distance measurement of Hunt, Kanter and Nakajima to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the performance of machine learning model for content ranking by providing additional information about the contents to the learning model(Amershi, para 0019).
Claim 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt, Jonathan (PGPUB Document No. 20220374483), hereafter referred as to “Hunt”, in view of Kanter, Andrew et al (PGPUB Document No. 20170017352), hereafter, referred to as “Kanter”, in view of Nakajima, Takuro (PGPUB Document No. 20190306486), hereafter, referred to as “Nakajima”, in view of Zhao, Chong (US Patent No. 11478716), hereafter, referred to as “Zhao”.
Regarding claim 6 (Original), Hunt, Kanter and Nakajima teach all the limitations of claim 1 and Hunt further teaches wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value (Hunt, Fig. 5 and para 0083-0085 teach determining pairwise distance between content items based on weights for ranking “The weighting of a pairwise rankings of a pair can be based on a probability of a user opening a push notification associated with a training content item of the of pairs of training content items ……..….In step S520 selecting a first and a second candidate content item are selected from the set of candidate content items. For example, the trained ranking model can use a pairwise expected negative response technique. Therefore, two candidate content items can be selected from the set of candidate content items and tested (e.g., compared) using the pairwise expected negative response technique”)
But Hunt, Kanter and Nakajima don’t explicitly teach for each historical content item of a plurality of historical content items is output by a Siamese neural network pre-trained for Elo rating-based analysis of content items.
However, in the same field of endeavor of ranking based on trained model Zhao teaches for each historical content item of a plurality of historical content items is output by a Siamese neural network pre-trained for Elo rating-based analysis of content items(Zhao, col 6:12-18 teaches model training using Siamese neural network based on MMR (match making rating) based on historical data “Once the model is trained, the system 100 may utilize the trained model (e.g., neural networks 114, 124, match outcomes 130) to calculate accurate MMRs for players in a pool of players to facilitate matchmaking (e.g., via a Siamese network). For example, the model may receive historical player data (e.g., player statistics 112, 122) for a player, and may output an MMR for the player”; where col 3:25-28 further discloses that MMR is based on Elo rating “A popular method of calculating MMRs originates from the Elo rating system, where players all begin with the same MMR, which is then increased or decreased as players win or lose against each other”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of model trained using Siamese neural network of Zhao into content ranking by pairwise content distance measurement of Hunt, Kanter and Nakajima to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the accuracy of prediction model by adjusting weights of the neural network(Zhao, col 8:25-28).
Regarding claim 13 (Original), Hunt, Kanter and Nakajima teach all the limitations of claim 8 and Hunt further teaches wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value (Hunt, Fig. 5 and para 0083-0085 teach determining pairwise distance between content items based on weights for ranking “The weighting of a pairwise rankings of a pair can be based on a probability of a user opening a push notification associated with a training content item of the of pairs of training content items ……..….In step S520 selecting a first and a second candidate content item are selected from the set of candidate content items. For example, the trained ranking model can use a pairwise expected negative response technique. Therefore, two candidate content items can be selected from the set of candidate content items and tested (e.g., compared) using the pairwise expected negative response technique”)
But Hunt, Kanter and Nakajima don’t explicitly teach wherein the respective pairwise distance between the respective ranking value for each content item of the plurality of content items and the respective weighted value.
However in the same field of endeavor of ranking based on trained model Zhao teaches for each historical content item of a plurality of historical content items is output by a Siamese neural network pre-trained for Elo rating-based analysis of content items(Zhao, col 6:12-18 teaches model training using Siamese neural network based on MMR (match making rating) based on historical data “Once the model is trained, the system 100 may utilize the trained model (e.g., neural networks 114, 124, match outcomes 130) to calculate accurate MMRs for players in a pool of players to facilitate matchmaking (e.g., via a Siamese network). For example, the model may receive historical player data (e.g., player statistics 112, 122) for a player, and may output an MMR for the player”; where col 3:25-28 further discloses that MMR is based on Elo rating “A popular method of calculating MMRs originates from the Elo rating system, where players all begin with the same MMR, which is then increased or decreased as players win or lose against each other”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of model trained using Siamese neural network of Zhao into content ranking by pairwise content distance measurement of Hunt, Kanter and Nakajima to produce an expected result of recommending most relevant item to users. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the accuracy of prediction model by adjusting weights of the neural network(Zhao, col 8:25-28).
Response to Arguments
I. 35 U.S.C §101 Abstract idea Rejection
§101 Abstract idea Rejection to claim 1-20 has been withdrawn in light of applicant’s argument consideration and claim amendments.
II. 35 U.S.C §103
Applicant’s arguments filed on 1/6/2026 have been fully considered but are
moot because the independent claim 1, 8 and 15 have been amended with newly added
features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
Conclusion
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/ABDULLAH A DAUD/Examiner, Art Unit 2164 /AMY NG/Supervisory Patent Examiner, Art Unit 2164
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