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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/11/2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is directed to statutory category process. The claim recites “determining a first set of content items, wherein each content item of the first set of content items was previously selected at least twice by a given user of a plurality of users during a past time period; determining, using a first computer-implemented function, a respective category label for each content item of the first set of content items; generating a weighted category graph comprising a plurality of nodes, wherein each node is associated with a unique category label of the respective category labels such that content items of the first set of content items that share a common category label are associated with a single node, wherein a first edge of the weighted category graph that connects a first node and a second node represents a condition of a first category label of the first node being selected by a first user during the past time period and a second category label of the second node being selected by the first user during the past time period; determining, using a first computer-implemented clustering algorithm, a plurality of clusters of the weighted category graph based at least in part on the first edge, wherein the plurality of clusters comprises a first cluster and a second cluster; determining, for a second user, a second set of content items previously selected by the second user; determining, using the first computer-implemented function, a respective category label for each content item of the second set of content items; determining a first overlap between the respective category labels for the second set of content items and the first cluster; determining a second overlap between the respective category labels for the second set of content items and the second cluster; selecting the first cluster based on a comparison of the first overlap and the second overlap; determining a third category label of the first cluster that is not included in category labels associated with the second set of content items; determining a first content item associated with the third category label, wherein the first content item is determined as a recommendation for the second user”. The process of determining a set of content item based on number of user interactions for a period of time, determining category of the content items, generating weighted category graph based on user interaction information and grouping similar type of contents, determining clusters based on edges connecting nodes, determining overlap between category labels, determining a third type of category labels of a cluster which is not included in category label and associated with content items in an already categorized label, determining a content item associated with third category label involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “outputting a first graphical representation of the first content item on a display”, above mentioned additional element considered as insignificant extra solution activity of data output or presentation. This high-level recitations of a generic computer components and represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 1 is directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements outputting data is well-understood, routine or conventional activity based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim 1 is ineligible.
Claim 5 is directed to statutory category process. The claim recites “determining a first set of content items; determining a respective first category label for each content item of the first set of content items; generating a weighted category graph comprising a plurality of nodes, wherein each node is associated with a respective first category label for the first set of content items such that content items of the first set of content items that share a common category label are associated with a single node, and wherein a first edge connecting two nodes represents the respective category labels associated with the two nodes having been selected by a first user during a past time period; determining, for a second user, a plurality of second category labels associated with previous content selections; selecting, from among at least one cluster of the weighted category graph, at least a first cluster, wherein the first cluster is selected based at least in part on the plurality of second category labels; determining, based at least in part on the first cluster, a first content item for recommendation to the second user”. The process of determining a set of content, determining category of the content items, generating weighted category graph based on user interaction information and grouping of similar type of contents and further selecting cluster based on category labels, determining content item for recommendation from a cluster involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “generating output data representing a recommendation of the first content item”, above mentioned additional element considered as insignificant extra solution activity of data output or presentation. This high-level recitations of a generic computer components and represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 5 is directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements outputting data is well-understood, routine or conventional activity based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim 5 is ineligible.
Claim 15 differs from claim 5 in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 15 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 15 is not patent eligible.
Claim 2, 3, 4 are directed to statutory category process. The claims recite “determining an edge weight threshold and a node weight threshold stored in memory; prior to determining the plurality of clusters of the weighted category graph, removing those nodes with node weights less than the node weight threshold and those edges with edge weights less than the edge weight threshold from the weighted category graph; and removing disconnected nodes from the weighted category graph”. The processes of determining an edge weight and a node weight thresholds, removing nodes and edges based on their respective thresholds and removing disconnected nodes involve observation, judgement and evaluation. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “first edge is associated with a first edge weight, the first edge weight representing a total number of times that the first category label and the second category label were both selected during the past time period by respective users of the plurality of users” and ” a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period” above additional element recite insignificant extra-solution activity of mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). This generic high-level recitation of computer components is nothing more than mere instructions to apply on a computer. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 2, 3 and 4 directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering and such is well- understood, routine, and conventional (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Claim 17 and 18 differ from claim 2 and 3 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 17 and 18 are system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 17 and 18 are not patent eligible.
Claim 7 and 8 differ from claim 17 and 18 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors on . The invention of claim 7 and 8 are process. For reasons discussed above, the claimed steps are directed to mental steps. Accordingly, claim 7 and 8 are not patent eligible.
Claim 6 and 9 are directed to statutory category process. The claims recite “filtering the first set of content items from among a second set of content items, wherein the first set of content items comprises content items of the second set of content items that were previously selected at least twice by a given user of a plurality of users during a past time period” and “determining an edge weight threshold and a node weight threshold stored in memory; removing nodes from the weighted category graph that have node weights less than the node weight threshold and removing edges from the weighted category graph that have edge weights less than the edge weight threshold; and determining at least the first cluster and a second cluster from the weighted category graph after removing the nodes”. The processes of filtering content items based on a minimum interaction number determining an edge weight and a node weight thresholds, removing nodes and edges based on their respective thresholds and determining clusters after removing nodes and edges below their respective thresholds involve observation, judgement and evaluation. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, no additional elements is recited to be analyzed.
At step 2B, no additional element is recited to be analyzed.
Accordingly, claim 6 and 9 are not patent eligible.
Claim 16 and 19 differs from claim 6 and 9 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 16 and 19 are system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 16 and 19 are not patent eligible.
Claim 10, 11, 12, 13 and 14 are directed to statutory category process. The claims recite “determining the first cluster as a unique cluster different from any other cluster of the weighted category graph based at least in part by determining that each node of the first cluster is connected to each other node of the first cluster”, “determining, for the first cluster, a first cluster score by determining an overlap between the plurality of second category labels and category labels of nodes of the first cluster, wherein the first cluster is selected based at least in part on a comparison of the first cluster score and a second cluster score of the second cluster”, “determining, from among the plurality of second category labels, a category label with a highest count of past selections among the previous content selections by the second user; and selecting the first cluster from among the at least one cluster based at least in part on the category label with the highest count of past selections being associated with a node of the first cluster”, “determining, from among category labels of the first cluster, at least a first non-overlapping category label that does not overlap with any category label of the plurality of second category labels; and determining the first content item based at least in part on the first content item having the first non-overlapping category label” and “determining at least one content item selection associated with a current user session; determining a third category label associated with the at least one content item selection; and selecting the first cluster based at least in part on the third category label and the current user session.”. The processes of determining an unique cluster which is different than other clusters, determining that nodes within the unique clusters and connected, determining clusters by its score and which is determined by overlapping category labels, selecting cluster by comparing cluster scores, determining a category label having highest counted interaction, determining a cluster based on highest counted category label selection, determining from a cluster a non-overlapping category label, determining a content item from the non-overlapping category level, determining a content item based on current user session and selecting cluster based on a particular category label and current user session involve observation, judgement and evaluation. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, no additional elements is recited to be analyzed.
At step 2B, no additional element is recited to be analyzed.
Accordingly, claim 10, 11, 12, 13 and 14 are not patent eligible.
Claim 20 differs from claim 10 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 20 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 20 not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Linden, Gregory et al (PGPUB Document No. 20050071251), hereafter referred as to “Linden”, in view of Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter, referred to as “Xiao”, in further view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in further view of Zhang, John et al (US Patent No. 8954358), hereafter, referred to as “Zhang”.
Regarding Claim 1(Currently Amended), Linden teaches A computer-implemented method, comprising: determining a first set of content items, wherein each content item of the first set of content items was previously selected at least twice by a given user of a plurality of users during a past time period(Linden, Fig. 3B and para 0115 discloses determining items which are selected during a past time period (log session) and have been interacted by the same user “the process initially retrieves the query log records for all browsing sessions (step 300). In one embodiment, only those query log records that indicate sufficient viewing activity (such as more than 5 items viewed in a browsing session) are retrieved. In this embodiment, some of the query log records may correspond to different sessions by the same user”);
But Linden does not explicitly teach determining, using a first computer-implemented function, a respective category label for each content item of the first set of content items; generating a weighted category graph comprising a plurality of nodes, wherein each node is associated with a unique category label of the respective category labels such that content items of the first set of content items that share a common category label are associated with a single node, wherein a first edge of the weighted category graph that connects a first node and a second node represents a condition of a first category label of the first node being selected by a first user during the past time period and a second category label of the second node being selected by the first user during the past time period; determining, using a first computer-implemented clustering algorithm, a plurality of clusters of the weighted category graph based at least in part on the first edge, wherein the plurality of clusters comprises a first cluster and a second cluster; determining, for a second user, a second set of content items previously selected by the second user; determining, using the first computer-implemented function, a respective category label for each content item of the second set of content items; determining a first overlap between the respective category labels for the second set of content items and the first cluster; determining a second overlap between the respective category labels for the second set of content items and the second cluster; selecting the first cluster based on a comparison of the first overlap and the second overlap; determining a third category label of the first cluster that is not included in category labels associated with the second set of content items; determining a first content item associated with the third category label, wherein the first content item is determined as a recommendation for the second user; and outputting a first graphical representation of the first content item on a display.
However, in the same field of endeavor of content selection based on co-watched/selection activities Xiao teaches determining, using a first computer-implemented function, a respective category label for each content item of the first set of content items(Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”); generating a weighted category graph comprising a plurality of nodes, wherein each node is associated with a unique category label of the respective category labels (Xiao, Fig. 3 and para 0045 further discloses generation of weighted category/node graph “a node can further include a node weight, which can be a number to represent the relatively frequency the corresponding item represented by the node being viewed by the user account”) such that content items of the first set of content items that share a common category label are associated with a single node(Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item type in a node thus, similar items sharing common category of first set of contents taught by Linder would be in the same single node as well),
wherein a first edge of the weighted category graph that connects a first node and a second node represents a condition of a first category label of the first node being selected by a first user during the past time period and a second category label of the second node being selected by the first user during the past time period(Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the first node 311 and the second node 313 when the first item 321 and the second item 323 are viewed in sequence within a predetermined time interval”);
determining, for a second user, a second set of content items previously selected by the second user; determining, using the first computer-implemented function, a respective category label for each content item of the second set of content items(Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user (first or second) “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”; where para 0047 further teaches item categorization or node can be created for any other or second user “the view history 145 of the user account 141 can be a collection of a view history of each user of the multiple users, and the first item 321 being viewed by user account 141 can be viewed by any user of the multiple users of user account 141”); and outputting a first graphical representation of the first content item on a display(Xiao, para 0028 discloses recommending content to display device “where view history 145 can include item 113 being presented on display device 108. Content server 120 may further include a recommendation engine 143 that can recommend an item 115 to be watched after item 113 has been viewed”).
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 categorization of contents into nodes of Xiao into collecting user co-selected content information of Linden to produce an expected result of categorizing contents into nodes in a graph. The modification would be obvious because one of ordinary skill in the art would be motivated to reduce the bias of being viewed by adjusting the in weight of the edges which represents the probability of recommended items being viewed(Xiao, para 0004).
But Linden Xiao don’t explicitly teach determining, using a first computer-implemented clustering algorithm, a plurality of clusters of the weighted category graph based at least in part on the first edge, wherein the plurality of clusters comprises a first cluster and a second cluster; determining a first overlap between the respective category labels for the second set of content items and the first cluster; determining a second overlap between the respective category labels for the second set of content items and the second cluster; selecting the first cluster based on a comparison of the first overlap and the second overlap;
determining a third category label of the first cluster that is not included in category labels associated with the second set of content items; determining a first content item associated with the third category label, wherein the first content item is determined as a recommendation for the second user;
However, in the same field of endeavor of clustering contents Anerousis teaches determining, using a first computer-implemented clustering algorithm, a plurality of clusters of the weighted category graph based at least in part on the first edge, wherein the plurality of clusters comprises a first cluster and a second cluster(Anerousis, para 0060 discloses identifying plurality of cluster (first, second cluster etc.) based on analysis “The query module 118 performs one or more cluster analysis operations to identify a given number of clusters that correspond to the new node-ticket record 126”; where Xiao, Fig. 3 and para 0045 further discloses generation of weighted category/node graph);
determining a first overlap between the respective category labels for the second set of content items and the first cluster; determining a second overlap between the respective category labels for the second set of content items and the second cluster(Anerousis, para 0060 further disclose identifying match between node ticket/category label of the problem ticket with plurality of cluster (first, second cluster etc.) that matches or overlaps “In one embodiment, the query module 118 can identify the problem associated with the new node-ticket record 126 and selects clusters in the set of action clusters 120 with a similar problem to perform the cluster analysis operations on”);
selecting the first cluster based on a comparison of the first overlap and the second overlap(Anerousis, para 0060 further teaches selection of any cluster (first, second or any) based on category label (node-ticket) matching “the query module 118 can identify the problem associated with the new node-ticket record 126 and selects clusters in the set of action clusters 120 with a similar problem to perform the cluster analysis operations on. In this embodiment, the distance between the new node-ticket record 126 and the selected clusters is based on the system descriptors of the new node-ticket record 126 and the node-ticket records in the selected clusters”; para 0051 discloses labeling of categories “The taxonomic label o.sub.p represents the specificity of a node-ticket/action in the taxonomy. The taxonomic label o.sub.p can be viewed as a categorization”);
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 of clusters of Anerousis into categorization of contents form users’ content selection history of Linden Xiao to produce an expected result of improving content searching. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the content searching experience by clustering contents and searching only in the relevant cluster of contents(Anerousis, abstract).
But Linden, Xiao and Anerousis don’t explicitly teach determining a third category label of the first cluster that is not included in category labels associated with the second set of content items; determining a first content item associated with the third category label, wherein the first content item is determined as a recommendation for the second user;
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches determining a third category label of the first cluster that is not included in category labels associated with the second set of content items(Zhang, col 9:6-10 disclosing clustering content that are not labelled yet but associated with a set of content items (co-watched) “The clustering sequence 515 sorts the unlabeled videos by clustering them around the seed clusters based on co-watch relationships. In general, any two co-watched videos will be connected”); determining a first content item associated with the third category label, wherein the first content item is determined as a recommendation for the second user(Zhang, col 9:6-10 disclosing clustering content that are not labelled yet (third category) but associated with a set of content items (co-watched) “The clustering sequence 515 sorts the unlabeled videos by clustering them around the seed clusters based on co-watch relationships. In general, any two co-watched videos will be connected”; where Xiao in para 0028 discloses recommending content to display device);
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Linden, Xiao and Anerousis to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Regarding claim 2 (Original), Linden, Xiao and Anerousis and Zhang teach all the limitation of claim 1 and Zhang further teaches wherein the first edge is associated with a first edge weight, the first edge weight representing a total number of times that the first category label and the second category label were both selected during the past time period by respective users of the plurality of users(Zhang, col 9:16-20 discloses that weight of the edges is based on the count of items being viewed/selected “each edge in the cluster, the clustering sequence 515 assigns a weight to that edge based on the co-watch relationship of the two nodes connected to that edge. For example, the co-watch relationship may be the number (e.g. a count) of times the two videos were co-watched”).
Claim 5, 7, 14-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in further view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”.
Regarding Claim 5(Original), Xiao teaches A method comprising: determining a first set of content items; determining a respective first category label for each content item of the first set of content items(Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”);
generating a weighted category graph comprising a plurality of nodes(Xiao, Fig. 3 and para 0045 further discloses generation of weighted category/node graph “a node can further include a node weight, which can be a number to represent the relatively frequency the corresponding item represented by the node being viewed by the user account”),
wherein each node is associated with a respective first category label for the first set of content items such that content items of the first set of content items that share a common category label are associated with a single node (Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item type in a node thus, similar items sharing common category of first set of contents taught by Linder would be in the same single node as well), and wherein a first edge connecting two nodes represents the respective category labels associated with the two nodes having been selected by a first user during a past time period(Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the first node 311 and the second node 313 when the first item 321 and the second item 323 are viewed in sequence within a predetermined time interval”);
determining, for a second user, a plurality of second category labels associated with previous content selections(Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user (first or second) “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”; where para 0047 further teaches item categorization or node can be created for any other or second user “the view history 145 of the user account 141 can be a collection of a view history of each user of the multiple users, and the first item 321 being viewed by user account 141 can be viewed by any user of the multiple users of user account 141”); and generating output data representing a recommendation of the first content item(Xiao, para 0028 discloses recommending content to display device “where view history 145 can include item 113 being presented on display device 108. Content server 120 may further include a recommendation engine 143 that can recommend an item 115 to be watched after item 113 has been viewed”).
But Xiao does not explicitly teach selecting, from among at least one cluster of the weighted category graph, at least a first cluster, wherein the first cluster is selected based at least in part on the plurality of second category labels; determining, based at least in part on the first cluster, a first content item for recommendation to the second user;
However, in the same field of endeavor of clustering contents Anerousis teaches selecting, from among at least one cluster of the weighted category graph, at least a first cluster, wherein the first cluster is selected based at least in part on the plurality of second category labels(Anerousis, para 0060 further teaches selection of any/first cluster based on any category label (first or second) that matches or overlaps “the query module 118 can identify the problem associated with the new node-ticket record 126 and selects clusters in the set of action clusters 120 with a similar problem to perform the cluster analysis operations on. In this embodiment, the distance between the new node-ticket record 126 and the selected clusters is based on the system descriptors of the new node-ticket record 126 and the node-ticket records in the selected clusters”; para 0051 discloses labeling of categories which is to be matched (node-ticket) “The taxonomic label o.sub.p represents the specificity of a node-ticket/action in the taxonomy. The taxonomic label o.sub.p can be viewed as a categorization”);
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 of clusters of Anerousis into categorization of contents form users’ content selection history of Xiao to produce an expected result of improving content searching. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the content searching experience by clustering contents and searching only in the relevant cluster of contents(Anerousis, abstract).
But Xiao and Anerousis don’t explicitly teach determining, based at least in part on the first cluster, a first content item for recommendation to the second user;
However, in the same field of endeavor of content clustering green teaches determining, based at least in part on the first cluster, a first content item for recommendation to the second user(Green, para 0026 discloses recommending items to user based on content cluster info “clustering module 102 configured to facilitate the determination of additional content by recommender systems 104 to present to a user of a social networking system, according to an embodiment of the present disclosure. The recommender systems 104, including user-to-item recommender systems”);
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 recommending content to users of Green into categorization of contents form users’ content selection history of Xiao and Anerousis to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the recommendation process by reducing the noise during the interaction data collection and by optimizing memory usage with rebalancing edge weight(Green, para 0025).
Regarding claim 7(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 and Xiao further teaches wherein the weighted category graph comprises a first edge between a first node and a second node, wherein the first edge is associated with a first edge weight, the first edge weight representing a total number of times that a first category label of the first node and a second category label of the second node were both selected during a past time period by respective users of a plurality of users(Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the first node 311 and the second node 313 when the first item 321 and the second item 323 are viewed in sequence within a predetermined time interval”).
Regarding claim 14(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 and Green further teaches further comprising: determining at least one content item selection associated with a current user session(Green, para 0056 discloses determining contents based on user session information “the method 400 can generate session information based on information regarding items of a plurality of item types ……. At block 406, the method 400 can assign at least a first item of the items to a cluster based on similarity between the item and the cluster. At block 408, the method 400 can provide the cluster to a recommender system to facilitate selection of relevant information for potential presentation to a user”);
determining a third category label associated with the at least one content item selection; and selecting the first cluster based at least in part on the third category label and the current user session(Green, para 0056 further discloses determining content from a cluster where the content got added to a cluster based on user session information “the method 400 can generate session information based on information regarding items of a plurality of item types ……. At block 406, the method 400 can assign at least a first item of the items to a cluster based on similarity between the item and the cluster. At block 408, the method 400 can provide the cluster to a recommender system to facilitate selection of relevant information for potential presentation to a user”).
Regarding Claim 15(Currently Amended), Xiao teaches A system, comprising: at least one processor; and non-transitory computer-readable memory storing instructions that, when executed by the at least one processor, are effective to(Xiao, Fig. 5 discloses system comprising processor, memory and storages): determine a first set of content items; determine a respective first category label for each content item of the first set of content items (Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”);
generate a weighted category graph comprising a plurality of nodes (Xiao, Fig. 3 and para 0045 further discloses generation of weighted category/node graph “a node can further include a node weight, which can be a number to represent the relatively frequency the corresponding item represented by the node being viewed by the user account”),
wherein each node is associated with a respective first category label for the first set of content items such that content items of the first set of content items that share a common category label are associated with a single node (Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item type in a node thus, similar items sharing common category of first set of contents taught by Linder would be in the same single node as well), and wherein a first edge connecting two nodes represents the respective category labels associated with the two nodes having been selected by a first user during a past time period(Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the, and wherein a first edge connecting two nodes represents the respective category labels associated with the two nodes having been selected by a first user during a past time period (Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the first node 311 and the second node 313 when the first item 321 and the second item 323 are viewed in sequence within a predetermined time interval”);
determine, for a second user, a plurality of second category labels associated with previous content selections (Xiao, Fig. 3 and para 0044 further teaches categorizing or grouping each item viewed or selected by a user (first or second) “Co-watch graph 151 can include a plurality of nodes including a current node 317 corresponding to item 113, a first node 311 corresponding to a first item 321 being viewed by the user account 141, a second node 313 corresponding to a second item 323 being viewed by the user account 141…..”; where para 0047 further teaches item categorization or node can be created for any other or second user “the view history 145 of the user account 141 can be a collection of a view history of each user of the multiple users, and the first item 321 being viewed by user account 141 can be viewed by any user of the multiple users of user account 141”); and generate output data representing a recommendation of the first content item (Xiao, para 0028 discloses recommending content to display device “where view history 145 can include item 113 being presented on display device 108. Content server 120 may further include a recommendation engine 143 that can recommend an item 115 to be watched after item 113 has been viewed”).
But Xiao does not explicitly teach select, from among at least one cluster of the weighted category graph, at least a first cluster, wherein the first cluster is selected based at least in part on the plurality of second category labels; determine, based at least in part on the first cluster, a first content item for recommendation to the second user;
However, in the same field of endeavor of clustering contents Anerousis teaches select, from among at least one cluster of the weighted category graph, at least a first cluster, wherein the first cluster is selected based at least in part on the plurality of second category labels (Anerousis, para 0060 further teaches selection of any/first cluster based on any category label (first or second) that matches or overlaps “the query module 118 can identify the problem associated with the new node-ticket record 126 and selects clusters in the set of action clusters 120 with a similar problem to perform the cluster analysis operations on. In this embodiment, the distance between the new node-ticket record 126 and the selected clusters is based on the system descriptors of the new node-ticket record 126 and the node-ticket records in the selected clusters”; para 0051 discloses labeling of categories which is to be matched (node-ticket) “The taxonomic label o.sub.p represents the specificity of a node-ticket/action in the taxonomy. The taxonomic label o.sub.p can be viewed as a categorization”);
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 of clusters of Anerousis into categorization of contents form users’ content selection history of Xiao to produce an expected result of improving content searching. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the content searching experience by clustering contents and searching only in the relevant cluster of contents(Anerousis, abstract).
But Xiao and Anerousis don’t explicitly teach determine, based at least in part on the first cluster, a first content item for recommendation to the second user;
However, in the same field of endeavor of content clustering green teaches determine, based at least in part on the first cluster, a first content item for recommendation to the second user (Green, para 0026 discloses recommending items to user based on content cluster info “clustering module 102 configured to facilitate the determination of additional content by recommender systems 104 to present to a user of a social networking system, according to an embodiment of the present disclosure. The recommender systems 104, including user-to-item recommender systems”);
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 recommending content to users of Green into categorization of contents form users’ content selection history of Xiao and Anerousis to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the recommendation process by reducing the noise during the interaction data collection and by optimizing memory usage with rebalancing edge weight(Green, para 0025).
Regarding claim 17(Original), Xiao, Anerousis and Green teach all the limitation of claim 15 and Xiao further teaches wherein the weighted category graph comprises a first edge between a first node and a second node, wherein the first edge is associated with a first edge weight, the first edge weight representing a total number of times that a first category label of the first node and a second category label of the second node were both selected during a past time period by respective users of a plurality of users(Xiao, Fig. 3 and para 0046 disclose weighted node or category lates are connected via edges where nodes are being formed items viewed/selected in past time periods “co-watch graph 151 can include an edge 315 between the first node 311 and the second node 313 when the first item 321 and the second item 323 are viewed in sequence within a predetermined time interval”).
Claim 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Linden, Gregory et al (PGPUB Document No. 20050071251), hereafter referred as to “Linden”, in view of Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter, referred to as “Xiao”, in further view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Zhang, John et al (US Patent No. 8954358), hereafter, referred to as “Zhang”, in further view of Li, Li et al (PGPUB Document No. 20180285777), hereafter, referred to as “Li”.
Regarding claim 3(Original), Linden, Xiao, Anerousis and Zhang teach all the limitation of claim 1 and but don’t explicitly teach wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period.
However, in the same field of endeavor of assignment of node weight Li discloses wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period(Li, para 0050 discloses assigning node weight based on the number of user related to that node “a node of the machine-learning model can represent a value associated with an event and can have a corresponding weight.……the weight can correspond to a number of users who were previously associated with the event (e.g., how many users previously stayed at a particular hotel, how many users have flown to San Francisco, etc.)”; where prior art Xiao in para 0044 teaches first and second nodes of category and items which were selected during past time period).
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 assigning weight to nodes based on number of user related to users belonging to a node of Li into categorization of contents form users’ content selection history of Linden, Xiao, Anerousis and Zhang to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance user experience by advantageously getting correlated supporting information about a future event(LI, 0037).
Regarding claim 4(Original), Linden, Xiao, Anerousis, Zhang and Li teach all the limitation of claim 3 and Zhang further teaches further comprising: determining an edge weight threshold and a node weight threshold stored in memory; prior to determining the plurality of clusters of the weighted category graph, removing those nodes with node weights less than the node weight threshold and those edges with edge weights less than the edge weight threshold from the weighted category graph; and removing disconnected nodes from the weighted category graph (Zhang, col 9:40-55 discloses assigning weights to nodes and removing content based on threshold weight “each of the nodes in a cluster is given a cluster score in the form of a weight. This weight is the sum of weights of the incident edges to the node………… …The threshold pruner 535 analyzes each of the weighted videos. If the video has a weight lower than some threshold (e.g., 85%), the threshold pruner removes it from the cluster”).
Claim 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in further view of Linden, Gregory et al (PGPUB Document No. 20050071251), hereafter, referred to as “Linden”.
Regarding claim 6(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 but don’t explicitly tech further comprising: filtering the first set of content items from among a second set of content items, wherein the first set of content items comprises content items of the second set of content items that were previously selected at least twice by a given user of a plurality of users during a past time period.
However, in the same field of endeavor of analyzing co-watched contents Linden discloses further comprising: filtering the first set of content items from among a second set of content items, wherein the first set of content items comprises content items of the second set of content items that were previously selected at least twice by a given user of a plurality of users during a past time period(Linden, Fig. 3B and para 0115 discloses determining items which are selected during a past time period (log session) and have been interacted by the same user at least twice “the process initially retrieves the query log records for all browsing sessions (step 300). In one embodiment, only those query log records that indicate sufficient viewing activity (such as more than 5 items viewed in a browsing session) are retrieved. In this embodiment, some of the query log records may correspond to different sessions by the same user” ).
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 considering co-selected content interaction information with an acceptance limit of Linden into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving reliability in content history analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the reliability in contents relationship establishment by considering items which are sufficiently related(Linden, para 0109).
Regarding claim 16(Original), Xiao, Anerousis and Green teach all the limitation of claim 15 but don’t explicitly tech filter the first set of content items from among a second set of content items, wherein the first set of content items comprises content items of the second set of content that were previously selected at least twice by a given user of a plurality of users during a past time period.
However, in the same field of endeavor of analyzing co-watched contents Linden discloses filter the first set of content items from among a second set of content items, wherein the first set of content items comprises content items of the second set of content that were previously selected at least twice by a given user of a plurality of users during a past time period (Linden, Fig. 3B and para 0115 discloses determining items which are selected during a past time period (log session) and have been interacted by the same user at least twice “the process initially retrieves the query log records for all browsing sessions (step 300). In one embodiment, only those query log records that indicate sufficient viewing activity (such as more than 5 items viewed in a browsing session) are retrieved. In this embodiment, some of the query log records may correspond to different sessions by the same user” ).
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 considering co-selected content interaction information with an acceptance limit of Linden into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving reliability in content history analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the reliability in contents relationship establishment by considering items which are sufficiently related(Linden, para 0109).
Claim 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in further view of Li, Li et al (PGPUB Document No. 20180285777), hereafter, referred to as “Li”.
Regarding claim 8(Original), Xiao, Anerousis and Green teach all the limitation of claim 7 but don’t explicitly teach wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period.
However, in the same field of endeavor of assignment of node weight Li discloses wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period (Li, para 0050 discloses assigning node weight based on the number of user related to that node “a node of the machine-learning model can represent a value associated with an event and can have a corresponding weight.……the weight can correspond to a number of users who were previously associated with the event (e.g., how many users previously stayed at a particular hotel, how many users have flown to San Francisco, etc.)”; where prior art Xiao in para 0044 teaches first and second nodes of category and items which were selected during past time period).
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 assigning weight to nodes based on number of user related to users belonging to a node of Li into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance user experience by advantageously getting correlated supporting information about a future event(LI, 0037).
Regarding claim 18(Original), Xiao, Anerousis and Green teach all the limitation of claim 17 but don’t explicitly teach wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period.
However, in the same field of endeavor of assignment of node weight Li discloses wherein: a weight of the first node represents a first total number of users that selected a content item having the first category label during the past time period; and a weight of the second node represents a second total number of users that selected a content item having the second category label during the past time period (Li, para 0050 discloses assigning node weight based on the number of user related to that node “a node of the machine-learning model can represent a value associated with an event and can have a corresponding weight.……the weight can correspond to a number of users who were previously associated with the event (e.g., how many users previously stayed at a particular hotel, how many users have flown to San Francisco, etc.)”; where prior art v in para 0044 teaches first and second nodes of category and items which were selected during past time period).
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 assigning weight to nodes based on number of user related to users belonging to a node of Li into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance user experience by advantageously getting correlated supporting information about a future event(LI, 0037).
Claim 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in view of Li, Li et al (PGPUB Document No. 20180285777), hereafter, referred to as “Li”, in further view of Zhang, John et al (US Patent No. 8954358), hereafter, referred to as “Zhang”.
Regarding claim 9(Original), Xiao, Anerousis, Green and Li teach all the limitation of claim 8 but don’t explicitly teach further comprising: determining an edge weight threshold and a node weight threshold stored in memory; removing nodes from the weighted category graph that have node weights less than the node weight threshold and removing edges from the weighted category graph that have edge weights less than the edge weight threshold; and determining at least the first cluster and a second cluster from the weighted category graph after removing the nodes.
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches further comprising: determining an edge weight threshold and a node weight threshold stored in memory; removing nodes from the weighted category graph that have node weights less than the node weight threshold and removing edges from the weighted category graph that have edge weights less than the edge weight threshold; and determining at least the first cluster and a second cluster from the weighted category graph after removing the nodes (Zhang, col 9:40-55 discloses assigning weights to nodes and removing content based on threshold weight “each of the nodes in a cluster is given a cluster score in the form of a weight. This weight is the sum of weights of the incident edges to the node………… …The threshold pruner 535 analyzes each of the weighted videos. If the video has a weight lower than some threshold (e.g., 85%), the threshold pruner removes it from the cluster”).
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Xiao, Anerousis, Green and Li to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Regarding claim 19(Original), Xiao, Anerousis, Green and Li teach all the limitation of claim 18 and Zhang further teaches the non-transitory computer-readable memory storing further instructions that, when executed by the at least one processor, are further effective to: determine an edge weight threshold and a node weight threshold stored in memory; remove nodes from the weighted category graph that have node weights less than the node weight threshold and removing edges from the weighted category graph that have edge weights less than the edge weight threshold; and determine at least the first cluster and a second cluster from the weighted category graph after removing the nodes.
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches the non-transitory computer-readable memory storing further instructions that, when executed by the at least one processor, are further effective to: determine an edge weight threshold and a node weight threshold stored in memory; remove nodes from the weighted category graph that have node weights less than the node weight threshold and removing edges from the weighted category graph that have edge weights less than the edge weight threshold; and determine at least the first cluster and a second cluster from the weighted category graph after removing the nodes (Zhang, col 9:40-55 discloses assigning weights to nodes and removing content based on threshold weight “each of the nodes in a cluster is given a cluster score in the form of a weight. This weight is the sum of weights of the incident edges to the node………… …The threshold pruner 535 analyzes each of the weighted videos. If the video has a weight lower than some threshold (e.g., 85%), the threshold pruner removes it from the cluster”).
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Xiao, Anerousis, Green and Li to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Claim 10-11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in further view of Zhang, John et al (US Patent No. 8954358), hereafter, referred to as “Zhang”,
Regarding claim 10(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 but don’t explicitly teach further comprising determining the first cluster as a unique cluster different from any other cluster of the weighted category graph based at least in part by determining that each node of the first cluster is connected to each other node of the first cluster.
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches further comprising determining the first cluster as a unique cluster different from any other cluster of the weighted category graph based at least in part by determining that each node of the first cluster is connected to each other node of the first cluster(Zhang, col 9:5-10 discloses forming a cluster different than others for unlabeled contents but this cluster and its relationship is determined with respect to a co-watched/selected content “The seed set 510 and the unlabeled videos 246 are then passed into the clustering sequence 515. The clustering sequence 515 sorts the unlabeled videos by clustering them around the seed clusters based on co-watch relationships”).
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Regarding claim 11(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 but don’t explicitly teach further comprising: determining, for the first cluster, a first cluster score by determining an overlap between the plurality of second category labels and category labels of nodes of the first cluster, wherein the first cluster is selected based at least in part on a comparison of the first cluster score and a second cluster score of the second cluster.
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches further comprising: determining, for the first cluster, a first cluster score by determining an overlap between the plurality of second category labels and category labels of nodes of the first cluster(Zhang, col 12:13-19 discloses determining overlapping categories/node by its respective items/contents “determine which videos were co-watched, and compares these videos to the authoritatively labeled videos 224 to identify the set of co-watched videos 246. Similarly, the data supplementation module 231 performs queries of the video search module 106 using each category label of the category set 205 to identify the searched videos 247”; where claim 27 teaches overlapping category based on their score “wherein the cluster score of a video is influenced by a number of times the video was co-watched with other videos in the cluster, and the cluster scores of the other videos in the cluster that have been co-watched with the video”), wherein the first cluster is selected based at least in part on a comparison of the first cluster score and a second cluster score of the second cluster (Zhang, col 9:1-5 discloses clustering of nodes/category is based on their relationship/overlap “the clustering and pruning processes described below, the nodes and edges are given weights based on the co-watch relationship between the two videos represented by the two nodes attached to the edge. Nodes are added and removed from the seed set based on these weights”; claim 27 further discloses scoring clusters “wherein the cluster score of a video is influenced by a number of times the video was co-watched with other videos in the cluster, and the cluster scores of the other videos in the cluster that have been co-watched with the video” ).
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Regarding claim 20(Original), Xiao, Anerousis and Green teach all the limitation of claim 15 but don’t explicitly teach the non-transitory computer-readable memory storing further instructions that, when executed by the at least one processor, are further effective to: determine the first cluster as a unique cluster different from any other cluster of the weighted category graph based at least in part by determining that each node of the first cluster is connected to each other node of the first cluster(Zhang, col 9:5-10 discloses forming a cluster different than others for unlabeled contents but this cluster and its relationship is determined with respect to a co-watched/selected content “The seed set 510 and the unlabeled videos 246 are then passed into the clustering sequence 515. The clustering sequence 515 sorts the unlabeled videos by clustering them around the seed clusters based on co-watch relationships”).
However, in the same field of endeavor of content selection based on co-watched/selection activities Zhang teaches the non-transitory computer-readable memory storing further instructions that, when executed by the at least one processor, are further effective to: determine the first cluster as a unique cluster different from any other cluster of the weighted category graph based at least in part by determining that each node of the first cluster is connected to each other node of the first cluster(Zhang, col 9:5-10 discloses forming a cluster different than others for unlabeled contents but this cluster and its relationship is determined with respect to a co-watched/selected content “The seed set 510 and the unlabeled videos 246 are then passed into the clustering sequence 515. The clustering sequence 515 sorts the unlabeled videos by clustering them around the seed clusters based on co-watch relationships”).
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 clustering category nodes of Zhang into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content classification. The modification would be obvious because one of ordinary skill in the art would be motivated to enhance the accuracy of classification by analyzing relationship that exist between various categories of the category graph(Zhang, abstract).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in further view of Demiralp, Emre et al (PGPUB Document No. 20170309046), hereafter, referred to as “Demiralp”.
Regarding claim 12(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 but don’t explicitly teach further comprising: determining, from among the plurality of second category labels, a category label with a highest count of past selections among the previous content selections by the second user; and selecting the first cluster from among the at least one cluster based at least in part on the category label with the highest count of past selections being associated with a node of the first cluster..
However, in the same field of endeavor of representing user interaction in a graph with nodes and edges Demiralp discloses further comprising: determining, from among the plurality of second category labels, a category label with a highest count of past selections among the previous content selections by the second user; and selecting the first cluster from among the at least one cluster based at least in part on the category label with the highest count of past selections being associated with a node of the first cluster(Demiralp, para 0036 discloses determination of highest interacted content “Using the logged interaction data for the top-n content elements with the highest number of interactions, the computing device can calculate various interaction statistics”; where Green in para 0026 discloses recommending items to user based on content cluster info “clustering module 102 configured to facilitate the determination of additional content by recommender systems 104 to present to a user of a social networking system, according to an embodiment of the present disclosure. The recommender systems 104, including user-to-item recommender systems”).
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 user interaction count of Demiralp into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content statistics. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the recommendation process by selecting contents based on calculated interaction statistics(Demiralp, abstract).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Xiao, Fei et al (PGPUB Document No. 20240064354), hereafter referred as to “Xiao”, in view of Anerousis, Nikolaos et al (PGPUB Document No. 20140244816), hereafter, referred to as “Anerousis”, in view of Green, Ray (PGPUB Document No. 20160188713), hereafter, referred to as “Green”, in further view of John, Mathew et al (PGPUB Document No. 20240403348), hereafter, referred to as “John”.
Regarding claim 13(Original), Xiao, Anerousis and Green teach all the limitation of claim 5 but don’t explicitly teach further comprising: determining, from among category labels of the first cluster, at least a first non-overlapping category label that does not overlap with any category label of the plurality of second category labels; and determining the first content item based at least in part on the first content item having the first non-overlapping category label..
However, in the same field of endeavor of representing user interaction in a graph with nodes and edges John discloses further comprising: determining, from among category labels of the first cluster, at least a first non-overlapping category label that does not overlap with any category label of the plurality of second category labels; and determining the first content item based at least in part on the first content item having the first non-overlapping category label (John, para 0023 discloses selection of content from a non-overlapping node “Selecting non-adjacent or non-connected nodes and/or edges can be used to show additional information about the documents selected”).
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 user interaction count of John into categorization of contents form users’ content selection history of Xiao, Anerousis and Green to produce an expected result of improving content recommendation by content statistics. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the recommended content selection process using graph structures having nodes and edges by allowing selection of contents which are related to other nodes but not connected(John, para 0023).
Response to Arguments
I. 35 U.S.C §101
Regarding §101 abstract idea rejection, the applicant argued following on page 10 paragraph 2-3 “"generating a weighted category graph comprising a plurality of nodes, wherein each node is associated with a unique category label of the respective category labels such that content items of the first set of content items that share a common category label are associated with a single node, wherein a first edge of the weighted category graph that connects a first node and a second node represents a condition of a first category label of the first node being selected by a first user during the past time period and a second category label of the second node being selected by the first user during the past time period," as recited in amended claim 1, has no analog to any apparent human mental process and cannot practically be performed in the human mind. The claim requires the construction of a computer data structure (i.e., a weighted category graph) whose nodes and edges reflect historical content selections made by a plurality of users over a past time period, where each node is uniquely associated with a category label and items sharing a category label are grouped into a single node. At paragraph [0037], the specification describes that the data underlying the graph may be drawn from very large user populations (e.g., edge weight calculation involving 192,282 users selecting content from node A and 14,132 users electing content from node B). As a practical matter, no human mind can aggregate historical content selections across hundreds of thousands of users to construct such a graph.
Similarly, "determining, using a first computer-implemented clustering algorithm, a plurality of clusters of the weighted category graph based at least in part on the first edge," as recited in amended claim 1, cannot practically be performed in the human mind. The specification confirms that the clustering involves computer-implemented graph algorithms such as "the clique-finding algorithm," "Connected Components," a "Community Structure approach," "Breadth-first Search, Depth-first Search, the Bron-Kerbosch algorithm, the Louvain algorithm, the Min-cut algorithm, etc. See specification at paragraphs [0041]-[0042]. These are not operations that could be executed in the human mind at the scale contemplated by the specification.
Accordingly, at a minimum, Applicant respectfully submits that these recitations of claim 1 should not be interpreted as "mental processes,"”.
Applicant’s presented arguments above have been fully considered but the examiner respectfully disagrees for following reason;
Firstly, generating a weighted graph comprising plurality of nodes and edges representing categories and interaction activities respectively can easily be performed in human mind using observation, evaluation and judgement. Similarly, using algorithm, clustering of contents of category nodes and their relationship edges can practically be in human mind.
Secondly, applicant’s argument regarding analysis in Step 2A prong 2 of additional claim limitations contributing to improvement to existing technology not found persuasive and accordingly a detailed updated analysis is presented in this office action.
II. 35 U.S.C §103
Applicant’s arguments filed on 4/29/2025 have been fully considered but are
moot because the independent claim 1, 5 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 with addressing the argued deficits of prior arts.
Conclusion
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/ABDULLAH A DAUD/Examiner, Art Unit 2164 /AMY NG/Supervisory Patent Examiner, Art Unit 2164
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