DETAILED ACTION
This action is responsive to Request for Continued Examination filed on March 09, 2026.
Claim amendments filed on March 09, 2026 have been acknowledged and considered.
Claims 1-3, 5-6, 8, 10, 12-15, 19-20, 26 and 28-30 have been amended. Claims 4, 9, 11, 21 and 25 were previously canceled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 09, 2026 has been entered.
Response to Amendment
Applicant's Remarks, filed March 09, 2026, has been fully considered and entered.
Accordingly, Claims 1-3, 5-8, 10, 12-20, 22-24 and 26-30 are pending in this application. Claims 1-3, 5-6, 8, 10, 12-15, 19-20, 26 and 28-30 have been amended. Claims 4, 9, 11, 21 and 25 were previously been canceled. Claims 1 and 15 are independent claim.
Response to Arguments
Applicant’s arguments, see pages 8-9, filed March 09, 2026, with respect to the amendments of independent claims 1 and 15 have been fully considered, but they are moot in view of new grounds of rejection necessitated by amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites “in response to determining that the webpage includes an image that meets the inclusion criteria, generating the user interface for the cluster.”
Parent claim 1 already requires “generating a user interface for the cluster… causing display of the user interface in a browser.”
It is unclear whether: Claim 5 recites an additional, generating step that is conditional on the image inclusion criteria being met (the determining step seems to refer to the already displayed webpage title/image in the user interface), in which case it recites two generating steps resulting in two user interfaces; or claim 5 restates the generating step of claim 1 as conditional to the image inclusion criteria being met, in which case the claim should be expressed in a language that makes it clear whether the generating step of claim 1 is further constrained.
Examiner request clarification from Applicant. The Specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of “generating the user interface for the cluster” is unclear and thus the scope of the claim is indefinite.
Claim 28 recites “wherein the first webpage is selected in response to determining that the machine model determines that the image meets the inclusion criteria for the image.”
Parent claim 5 already requires “determining that the webpage includes an image meeting inclusion criteria for the user interface;”
It is unclear whether: the inclusion criteria for the image in claim 28 refers to the inclusion criteria for the user interface in claim 5, or claim 28 recites a separate inclusion criteria for the image, which in this case the claim lacks antecedent basis.
Examiner request clarification from Applicant. The Specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The metes and bounds of “inclusion criteria for the image” is unclear and thus the scope of the claim is indefinite.
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 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.
Claims 1-3, 6-7, 10, 12, 15-18, 20 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Sadahiro (US Patent Application Publication No. US 20200380051 A1 - hereinafter Reference1), in view of Eichstaedt (US Patent Publication No. US 6385619 B1- hereinafter Reference2).
Regarding claim 1, Reference1 teaches a method, the method comprising: identifying a cluster that includes a plurality of webpages from a browser history associated with a user; (See Reference1 [0005, 0056] “correlation scores are calculated that indicate correlations between web pages indicated in a browsing history of a user [Thus, associated with a user]… The web pages are clustered into a plurality of clusters based on the correlation scores… Cluster selector 310 is configured to identify a cluster from the plurality of clusters and provide an indication of a cluster of web pages… to provide to a user as one or more suggested web pages to revisit” [Thus, identifying a cluster that includes a plurality of webpages from a browser history associated with a user])
determining a relevance score for the cluster based at least on an attribute reflecting whether the cluster belongs to a frequent category for the user determined using a predetermined timeframe that is based on a current time; (See Reference1 [0046-0053] “cluster relevancy evaluator 308 may rank the clusters for relevancy to a user based on a relevancy algorithm… Cluster relevancy evaluator 308 is also configured to calculate a relevancy score for each of the clusters based on the determined likelihoods. For example, the likelihoods associated with each web page in a cluster may be aggregated to indicate an overall relevancy score for a cluster [Thus, determining a relevance score for the cluster] … The relevancy algorithm may be configured to take into account any combination of the following factors [Thus, based at least on an attribute reflecting whether the cluster belongs to a frequent category for the user determined using a predetermined timeframe that is based on a current time]: … Currency—the currency factor corresponds to how often the user has visited a particular web page at the current time of day (a timespan when the relevancy algorithm is being executed). If the user has often visited the web page at the current time of day, the greater the value of the currency factor… For instance, if all visits to the web page occurred within a current time window (e.g., last 3 hours), the currency factor value may be a “1.00” (highest value)… Category—the category factor corresponds to whether a particular web page is in a same category (e.g., sports, particular sports such as football, news, entertainment, social media, etc.) as the web page the user was just looking at. If so, the category factor value may be a “1.00” (highest value)… Total visits—the total visits factor corresponds to a number of visits made to a particular web page in the last time period (e.g., 14 days). To determine this factor for a particular page, the number of visits to that page may be divided by a total number of page visits by the user during that time period… each included factor may be multiplied by a coefficient so a score in a desired range may be generated (e.g., 0.00 to 1.00) for each page of the clusters… The scores for the pages in a cluster may be summed or otherwise combined to determine a score for each cluster.”
Thus, Reference1 relevancy algorithm uses a combination of factors in the cluster’s relevance score: a category factor (pages being in a same category as what the user is looking at), a currency factor based on the current time of the day, and a total visit factor measuring how frequently the user visited the page within the last time period. Therefore, it is determining the cluster’s relevance score based on at least on an attribute (e.g. the combination of a category factor, a currency factor, and a total visit factor) reflecting whether the cluster belongs to a frequent category (e.g. by having the highest value).
Reference1 lacks details regarding a category-level frequency attribute.
However, Reference2 teaches the use of a user interest profile that assigns an interest score per category based on frequency and currency of visits to documents having that classification and updated using a time-dependent decay factor so the score reflects current interest. (See Reference2 abstract “A system generates user interest profiles by monitoring and analyzing a user's access to… documents available at a web site… the documents are classified into categories using a known taxonomy… the profiles also are generated based on other factors including the frequency and currency of visits to documents [Thus, determined using a predetermined timeframe that is based on a current time] having a given classification [e.g. frequency category]… User profiles include an interest category code and an interest score to indicate a level of interest in a particular category. The profiles are updated automatically to accurately reflect the current interests of an individual, as well as past interests. A time-dependent decay factor is applied to the past interests.” See also Reference2 Col. 3, lines 26-67, Col. 4, lines 1-8 “Ranking Categories… The present system represents the user interest as a disjunction of interest categories. The basic interest units are the categories that are defined by the taxonomy tree… When a user has not clicked on documents from a certain category for a predetermined length of time (e.g., a week, or a month), the weight of the corresponding category will drop [Thus, an attribute reflecting a frequent category for the user determined using a predetermined timeframe that is based on a current time]… A scoring function s(w, r) then assigns a new weight to a category based on the previous weight w of that category and the result r=i(vs,cc,tc) of the interest function. We use a decay in the scoring function to express the fact that older votes become less important over time.” See also Reference2 claim 8 “analyzing the received requests to determine database access information, wherein the access information comprises categories of accessed documents and interest scores… and wherein the prior interest scores are reduced in value in accordance with a time-dependent decay factor; generating from the database access information a user interest profile for the user, wherein the user interest profile comprises a disjunction of a set of interest categories selected from a taxonomy tree and a set of weights corresponding to the interest categories”
Thus, Reference2 teaches a category-level frequency attribute determined using a predetermined timeframe that is based on a current time. Whether a cluster’s category is a “frequent category for the user”, Reference2 use the user interest profile to rank categories by weight based on frequency of visits to documents of the category and the currency (time-decay) of those visits, both measured relative to the current time.
Both Reference1 and Reference2 rank content based on user browsing history and predict which resources the user will want to revisit. Reference1 teaches the use of a category factor in its relevancy algorithm and contemplates aggregating per-page scores into per-cluster scores. Reference2 provides a well-known mechanism for scoring user interest by category over time. Incorporating Reference2’s per-category interest score into Reference1’s category-aware relevancy algorithm would predictably improve the cluster ranking by ensuring that categories the user has shown interest are elevated over other categories.
Therefore, a person having ordinary skills in the art would have been motivated to combine Reference2’s per-category time-decayed user interest profile with Reference1’s per-cluster relevancy algorithm, yielding predictable results of better cluster relevance rankings.
Reference1 further in view of Reference2, [hereinafter Reference1-Reference2] additionally disclose in response to identifying the cluster and determining that the relevance score is above a threshold, (See Reference [0056, 0061] “cluster selector 310 may receive a cluster ranking 324 from cluster relevancy evaluator 308 and select clusters to provide to a user above a certain threshold… The selected one or more clusters and/or web pages are output by cluster selector 310 as selected cluster information 326… cluster selector 310 may receive in cluster ranking 324 information related to the clusters, their corresponding relevancy scores, and may identify clusters above a predetermined threshold. [Thus, identifying the cluster and determining that the relevance score is above a threshold]”)
generating a user interface for the cluster, (See Reference1 [0069] “Selected cluster information 326 may be presented to the user in a user interface, such by being displayed in a graphical user interface (GUI)”
the user interface including a region displaying a webpage title and an image from a webpage of the plurality of webpages, the region being selectable to navigate to an address for the webpage; and causing display of the user interface in a browser. (See Reference1 [0070-0072] “selected cluster information 326 may include a title and a thumbnail of a web page from the one or more web pages that if a user clicks may enable to user to traverse to the web page [Thus, selectable to navigate to an address for the webpage]… As shown in FIG. 8, web browser 800 presents a page that indicates a plurality of clusters 802A-802E [Thus, display of the user interface in a browser]. FIG. 8 shows only clusters 802A-802E but any number of indications of a cluster may be displayed to a user… In FIG. 8, clusters 802A-802E are identified topically. For example, cluster 802A is titled “Bahamas” and cluster 802B is titled “Cooking”. Cluster 802A provides several titles of web pages and thumb nails 806 and 808 of web pages related to the Bahamas [Thus, including a region displaying a webpage title and an image from a webpage of the plurality of webpages]. The user may have previously visited these web pages while researching a trip to the Bahamas.”)
Regarding claim 2, Reference1-Reference2 teaches all limitations and motivations of claim 1, wherein the cluster is further identified by determining that at least one webpage of the plurality of webpages is associated with a page access time that is within a time threshold. (See Reference1 [0048] “Recency—the recency factor corresponds to how recent (e.g., how many minutes ago, hours ago, days ago, etc.) the user visited a particular web page. The more recently the user visited the web page, the greater the value of the recency factor. The longer ago the user visited the web page, the lower the value of the recency factor. For instance, if the user visited the web page today, the recency factor value may be a “1.00” (highest value on a 1.00 to 0.00 scale), while if the web page was visited 14 days ago or greater, the recency factor value may be a “0.00” (lowest value)” See also Reference1 [0053, 0055] also teaches that web pages may be culled out that have a 0 score, meaning that pages outside of the 14-day window (e.g. beyond a time threshold) are excluded from the cluster, and further teaches filtering clusters based on context of usage including time of day, the day of the week or month.
Thus, the cluster is further identified by determining that at least one of its webpage has a page access time within a time threshold.)
Regarding claim 3, Reference1-Reference2 teaches all limitations and motivations of claim 1, wherein the region is a first region and in response to identifying the cluster the method further includes obtaining a related search suggestion for a cluster topic for the cluster; and the user interface for the cluster further includes a second region displaying the related search suggestion, the second region being selectable to initiate a search using the related search suggestion. (See Reference1 [0071-0073], Fig. 9 “a user may traverse the provided clusters by clicking on an arrow button next to cluster 802E and search clusters using a search bar 804… a user may traverse the provided clusters [Thus, in response to identifying the cluster] by clicking on an arrow button next to cluster 802E and search clusters using a search bar 804… FIG. 9 provides an example of displaying indications of clusters in a browser based on a user's internet search. As depicted in FIG. 9, a user inputted “recipes for” into a search bar [Thus, a second selectable region selectable to initiate a search using the related search suggestion] of a web browser 900. In addition to providing recommendations to complete the statement of “recipes for” [Thus, obtaining/displaying a related search suggestion for a cluster topic for the cluster], web browser 900 recommends clusters 902A-902D related to the user's search to the user.”)
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Regarding claim 6, Reference1-Reference2 teaches all limitations and motivations of claim 1, wherein the region is a first region, the webpage is a first webpage, and the webpage title is a first webpage title and the user interface for the cluster further including a second region displaying a second webpage title from a second webpage of the plurality of webpages, the second region being selectable to navigate to an address for the second webpage. (See Reference1 Fig. 8 and [0070-0072] “FIGS. 8 and 9 provide example embodiments of how web pages [e.g. first and second webpages] of an identified cluster may be presented to a user in a web browser [e.g. user interface for the cluster]… In FIG. 8, clusters 802A-802E are identified topically. For example, cluster 802A is titled “Bahamas” and cluster 802B is titled “Cooking” [(e.g. first and second webpage titles) Thus, including a second region displaying a second webpage title from a second webpage of the plurality of webpages]. Cluster 802A provides several titles of web pages and thumb nails 806 and 808 of web pages related to the Bahamas. The user may have previously visited these web pages while researching a trip to the Bahamas. The user may click the titles or thumb nails 806 and 808 provided in cluster 802A to access these web pages that she previously has visited. [Thus, the second region being selectable to navigate to an address for the second webpage]”)
Regarding claim 7, Reference1-Reference2 teaches all limitations and motivations of claim 6, wherein the first webpage is associated with a higher ranking than a ranking of the second webpage, and wherein the first webpage is assigned to the first region based on the higher ranking. (See Reference1 [0046, 0053-0056] “Cluster relevancy evaluator 308 may use the likelihood values associated with web pages in a cluster to filter out web pages in the cluster (e.g., web pages below a threshold) and rank the web pages within the cluster… All scores may be maintained to provide to the user any combination of the clusters (e.g., the highest three ranked clusters), and/or the any combination of the web pages (e.g., the highest two ranked pages) [e.g. first webpage is associated with a higher ranking than a ranking of the second webpage]… cluster selector 310 may select only the highest ranked clusters (e.g., the top three) and provide indications to the user of one or more web pages [e.g. first webpage is assigned to the first region based on the higher ranking] from each of the highest ranked clusters”)
Regarding claim 10, Reference1-Reference2 teaches all limitations and motivations of claim 1, wherein the relevance score for the cluster is further based on a number of times the cluster has been shown to the user during a recent timeframe. (See Reference1 [0052-0053] teaches a total visits factor corresponding to a count of visits in the last time period (e.g., 14 days) [i.e. recent timeframe] used as part of the relevancy algorithm, which contemplates combining multiple count-based factors across configurable time windows to determine relevance score for each cluster.
Regarding claim 12, Refrence1-Reference2 teaches all limitations and motivations of claim 1, wherein the higher ranking of the first webpage is based at least on determining that the first webpage is associated with a list of saved items. (See Reference1 [0047-00] “The relevancy algorithm may be configured to take into account any combination of the following factors… the bookmarked factor corresponds to whether the user bookmarked a particular web page. If the user bookmarked [Thus, associated with a list of saved items] the web page, the greater the bookmarked factor value (e.g., “1.00”) [e.g. higher ranking of the first webpage is based at least on determining that the first webpage is associated with a list of saved items], while if the user did not bookmark the web page, the lower the bookmarked factor value (e.g., “0.00”).” Examiner notes that bookmarks are a list of saved items.
Regarding claim 15, Birch-Mancuso-Mehta teaches all of the elements of claims 1 and 6 in method form rather than system form. Therefore, the supporting rationale of the rejection of claims 1 and 6 applies equally as well to those elements of claim 15.
Regarding claim 16, Birch-Mancuso-Mehta teaches all of the elements of claim 2 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 2 applies equally as well to those elements of claim 16.
Regarding claim 17, Birch-Mancuso-Mehta teaches all of the elements of claim 3 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 3 applies equally as well to those elements of claim 17.
Regarding claim 18, Birch-Mancuso-Mehta teaches all of the elements of claim 7 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 7 applies equally as well to those elements of claim 18.
Regarding claim 20, Refrerence1-Reference2 teaches all limitations and motivations of claim 15, wherein the memory is further configured with instructions to modify the relevance score based on a category boost list, the category boost list including categories representing tasks that span multiple browsing sessions. (See Reference1 [0046-0055] “cluster relevancy evaluator 308 may rank the clusters for relevancy to a user based on a relevancy algorithm. For example, the relevancy algorithm may determine a likelihood that a user will access particular web pages of a cluster. Cluster relevancy evaluator 308 may use a machine learning model trained on other users' browsing history [i.e. tasks that span multiple browsing sessions] to assign the likelihoods to web pages… The relevancy algorithm may be configured to take into account any combination of the following factors… Category—the category factor corresponds to whether a particular web page is in a same category (e.g., sports, particular sports such as football, news, entertainment, social media, etc.) [e.g. category boost list including categories representing tasks that span multiple browsing sessions] as the web page the user was just looking at. If so, the category factor value may be a “1.00” (highest value) [Thus, modify the relevance score based on a category boost list]. If not, the currency factor value may be a “0.00” (lowest value) … Web pages may be culled out that have a 0 score. The scores for the pages in a cluster may be summed or otherwise combined to determine a score for each cluster… cluster relevancy evaluator 308 may exclude clusters having low relevancy at certain times of a day.”)
Reference2 also teaches modify the relevance score based on a category boost list, the category boost list including categories representing tasks that span multiple browsing sessions. (See Reference2 abstract “the profiles also are generated based on other factors including the frequency [e.g. tasks that span multiple browsing sessions] and currency of visits to documents having a given classification [i.e. category], and/or the hierarchical depth of the levels or parts of the documents viewed. User profiles include an interest category code and an interest score to indicate a level of interest in a particular category. The profiles are updated automatically to accurately reflect the current interests of an individual, as well as past interests. A time-dependent decay factor is applied to the past interests.” See also Reference2 Col. 3, lines 26-67, Col. 4, lines 1-8 “Ranking Categories… The present system represents the user interest as a disjunction of interest categories. The basic interest units are the categories that are defined by the taxonomy tree [i.e. category boost list]… When a user has not clicked on documents from a certain category for a predetermined length of time (e.g., a week, or a month), the weight of the corresponding category will drop… A scoring function s(w, r) then assigns a new weight to a category based on the previous weight w of that category and the result r=i(vs,cc,tc) of the interest function. We use a decay in the scoring function to express the fact that older votes become less important over time [e.g. modify the relevance score based on a category boost list].”)
Regarding claim 29, Reference1-Reference2 teaches all of the elements of claim 10 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 10 applies equally as well to those elements of claim 29.
Regarding claim 30, Reference1-Reference2 teaches all limitations and motivations of claim 1, wherein the relevance score for the cluster is further based on a number of times the category has been shown to the user during a recent timeframe. (See Reference1 [0046-0055] “cluster relevancy evaluator 308 may rank the clusters for relevancy to a user based on a relevancy algorithm. For example, the relevancy algorithm may determine a likelihood that a user will access particular web pages of a cluster. Cluster relevancy evaluator 308 may use a machine learning model trained on other users' browsing history [i.e. tasks that span multiple browsing sessions] to assign the likelihoods to web pages… The relevancy algorithm may be configured to take into account any combination of the following factors… Category—the category factor corresponds to whether a particular web page is in a same category (e.g., sports, particular sports such as football, news, entertainment, social media, etc.) as the web page the user was just looking at. If so, the category factor value may be a “1.00” (highest value). If not, the currency factor value may be a “0.00” (lowest value)… Total visits—the total visits factor corresponds to a number of visits made to a particular web page in the last time period (e.g., 14 days) [e.g. recent timeframe]. To determine this factor for a particular page [e.g. a particular web page is in a same category], the number of visits to that page may be divided by a total number of page visits by the user during that time period… Web pages may be culled out that have a 0 score. The scores for the pages in a cluster may be summed or otherwise combined to determine a score for each cluster [Thus, relevance score for the cluster is further based on a number of times the category has been shown to the user during a recent timeframe]”)
Claims 5 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Reference1-Reference2 in view of Birch (US Patent Application Publication No. US 20200042567 A1).
Regarding claim 5, Reference1-Reference2 teaches all limitations and motivations of claim 1, further comprising, in response to identifying the cluster: determining that the webpage includes an image meeting inclusion criteria for the user interface; and in response to determining that the webpage includes an image that meets the inclusion criteria, generating the user interface for the cluster.
Reference1 teaches presenting a thumbnail image from a webpage in the cluster UI (Fig. 8, [0062, 0072]), but it does not explicitly disclose determining an image meeting an inclusion criteria.
However, Birch teaches determining that the webpage includes an image meeting inclusion criteria for the user interface; and in response to determining that the webpage includes an image that meets the inclusion criteria, generating the user interface for the cluster in more details. (See Birch [0003] “The method includes selecting a group of web pages [e.g. cluster] from the one or more groups of web pages that are determined to be topically-related to content displayed in the web browser, where the selected group represents a task being carried out by a user of the web browser, and providing a navigation suggestion for display on a user interface [Thus, generating a user interface for the cluster] of the web browser based on the selected group” See also Birch [0096] “the navigation suggestion 128 may be a user interface (or element) that provides [Thus, display] at least a portion a list of the first group 116 [Thus, in response to identifying the cluster] on the user interface 124 of the web browser 122… the navigation suggestion 128 identifies the title (or short phrase describing the page) of each of the web pages 121 of the first group 116 [e.g. first webpage included in the at least one webpage] … the navigation suggestion 128 displays a representative image [Thus, an image meeting inclusion criteria for the user interface] associated with one or more of the web pages 121. [Thus, determining that the webpage includes an image meeting inclusion criteria for the user interface]”
Both Reference1 and Birch cluster web pages based on user browsing history and predict which resources the user will want to revisit. Reference1-Reference2 teaches displaying thumbnails of webpages in the cluster (Reference1 0072). Birch teaches displaying a representative image associated with the webpages in the cluster.
Therefore, a person having ordinary skills in the art would have been motivated to incorporate Birch’s inclusion criteria of determining a representative image associated with the webpages in the cluster into Reference1-Reference2 cluster UI, as it would improve the user’s ability to recognize the intended cluster. Thus, it would predictably improve the cluster-suggestion results.
Regarding claim 26, Refrence1-Refrence2 further in view of Birch, [hereinafter Reference1-Refrence2-Birch] teaches all of the elements of claim 5 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 5 applies equally as well to those elements of claim 26.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Reference1-Reference2, in view of Kumar (US Patent Application Publication No. US 20180367848 A1).
Regarding claim 8, Reference1-Reference2 teaches all limitations and motivations of claim 7, wherein the image is a first image and the second region includes a second image, and the higher ranking of the first webpage is based at least on a size and a resolution of the first image. (See Reference1 Fig. 8, [0056, 0072] teaches ranking web pages and display of images (thumbnails) in multiple regions (thumb nails 806 and 808).
Reference1-Reference2 does not explicitly disclose ranking the webpages based on a size and resolution of the first image.
However, Kumar teaches the higher ranking of the first webpage is based at least on a size and a resolution of the first image in more details. (See Kumar [0039-0045] “detecting (202) a plurality of webpages based on one of an interest corresponding to the at least one user and the user-input; retrieving (203) content from the detected plurality of webpages… the step of detecting (202) further comprises the steps of: identifying (207) a first set of web pages based on one of the interest corresponding to the at least user and the user-input; and selecting (208) the plurality of webpages from the first set of webpages based on at least one of a metadata [e.g. ranking of the first webpage] associated with the first set of webpages and content [e.g. image] of the first set of webpage matching the user-input… the metadata associated with a webpage includes: page rank of the web page… the interest corresponding to the at least one user includes one or more of: browsing history… most-visited webpages, recently visited webpages” See also Kumar [0089] "the content selecting unit (407) selects the content from the detected webpages, including the extended webpages, based on size of the display unit, font size of the content, resolution of the content (for example, image resolution, and viewing parameters with respect to the display unit. [Thus, based at least on a size and a resolution of the first image]”
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Refrence1-Reference2 which ranks web pages and display of images (thumbnails) in multiple regions of a user interface, to incorporate the teachings of Kumar of selecting the content from the detected webpages based on size and resolution (e.g. image resolution, and viewing parameters with respect to the display unit.)
One would be motivated to do so to enhance user experience by optimizing the rendering of content based on viewing parameters, and better inform the user of what the cluster’s pages are about.
Regarding claim 19, Reference1-Reference2 further in view of Kumar teaches all of the elements of claim 8 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 8 applies equally as well to those elements of claim 19.
Claims 13-14 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Reference1-Reference2 in view of Karam (US Patent Publication No. US 8433995 B1).
Regarding claim 13, Refrence1-Reference2 teaches all limitations and motivations of claim 12, wherein the list of saved items is a collection of bookmarks, the user interface including an indication that the first webpage is bookmarked in the browser. (See Reference1 [0047-00] “The relevancy algorithm may be configured to take into account any combination of the following factors… the bookmarked factor corresponds to whether the user bookmarked a particular web page. If the user bookmarked [Thus, associated with a list of saved items] the web page, the greater the bookmarked factor value (e.g., “1.00”) [e.g. higher ranking of the first webpage is based at least on determining that the first webpage is associated with a list of saved items], while if the user did not bookmark the web page, the lower the bookmarked factor value (e.g., “0.00”).” Examiner notes that bookmarks are a list of saved items.
Reference1-Refrence2 does not explicitly disclose the user interface including an indication that the first webpage is bookmarked in the browser.
However, Karam teaches the user interface including an indication that the first webpage is bookmarked in the browser in more details. (See Karam Col. 3, lines 56-67, Col. 4, lines 1-15 “The web browser (100) shown in FIG. 1 also contains a toolbar (102), which provides increased functionality to the web browser (100)… “bookmarks” button (106) shows a list of all previously stored bookmarks [Thus, a list of saved items (e.g. fourth region)], for a particular user.” See also Karam Col. 2, lines 27-35 “The graphical user interface can be a toolbar in a browser… Displaying a button can include displaying the button in one of… a third state that indicates that the displayed web page [e.g. first webpage] has been bookmarked [Thus, an indication that the first webpage is bookmarked in the browser]”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Reference1-Reference2 to incorporate the teachings of Karam of displaying a visual bookmarked indicator in Reference1’s cluster UI to communicate to the user why a given webpage ranked highly, a predictable use of a known UI technique.
Regarding claim 14, Refrence1-Reference2 teaches all limitations and motivations of claim 12, wherein the list of saved items is a collection of annotations, the user interface including an indication that the first webpage has an associated annotation. (See Karam Col. 4, lines 15-19 & Fig. 5 “this list of bookmarks can be organized in various categories or labels [i.e. collection of annotations], such that a bookmark [e.g. first webpage] for an online sports magazine, for example, can be associated with a “Sports” label and a “News” label [Thus, has an associated annotation], and thus be more easily found by the user. [Thus, the list of saved items is a collection of annotations, the indication indicating the first webpage has an associated annotation]”)
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Thus, the user interface including an indication that the first webpage has an associated annotation.)
Regarding claim 22, Reference1-Reference2 in view of Karam teaches all of the elements of claim 13 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 13 applies equally as well to those elements of claim 22.
Regarding claim 23, Reference1-Reference2 in view of Karam teaches all of the elements of claim 13 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 13 applies equally as well to those elements of claim 23.
Regarding claim 24, Reference1-Reference2 in view of Karam teaches all of the elements of claim 14 in method form rather than system form. Therefore, the supporting rationale of the rejection of claim 14 applies equally as well to those elements of claim 24.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Reference1-Reference2-Birch in view of Kumar (US Patent Application Publication No. US 20180367848 A1).
Regarding claim 27, Reference1-Reference2-Birch teaches all limitations and motivations of claim 5.
Reference1-Reference2 does not explicitly disclose the inclusion criteria relates to angle or size of an object within the image.
However, Kumar teaches the inclusion criteria relates to size of an object within the image in more details. (See Kumar [0089] "the content selecting unit (407) selects the content from the detected webpages, including the extended webpages, based on [e.g. inclusion criteria] size of the display unit, font size of the content [e.g. size of an object within the image], resolution of the content (for example, image resolution), and viewing parameters with respect to the display unit… This enables selection and subsequent presentation of the multimedia content”
Reference1-Reference2-Birch teaches displaying representative image associated with the webpages in the cluster. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Refrence1-Reference2-Birch to incorporate the teachings of Kumar of selecting the content from the detected webpages based on size including font size of the content [e.g. size of an object within the image].
One would be motivated to do so to enhance user experience by optimizing the rendering of content based on viewing parameters, and better inform the user of what the cluster’s pages are about.
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Reference1-Reference2-Birch in view of Mancuso (US Patent Application Publication No. US 20240202250 A1).
Regarding claim 28, Reference1-Reference2-Birch teaches all limitations and motivations of claim 5.
Reference1-Reference2-Birchdoes not explicitly disclose wherein the inclusion criteria are defined by a machine model trained on features of training images.
However, Mancuso teaches wherein the inclusion criteria are defined by a machine model trained on features of training images, and wherein the first webpage is selected in response to determining that the machine model determines that the image meets the inclusion criteria for the image. (See Mancuso [0005] “the disclosed systems can generate and provide a user interface element for restoring an application session (e.g., a cluster of content items [e.g. webpage is selected] centered around a common topic [e.g. inclusion criteria (Thus, selected in response to determining the inclusion criteria]).” See also Mancuso [0063] “A digital content item can include a file such as a digital text file, a digital image file, a digital audio file, a webpage [e.g. first webpage], a website, a digital video file, a web file, a link, a digital document file, or some other type of file or digital object.” See also Mancuso [0083] “the content scene system 102 analyzes topic data such as… images… as well as a determination that the image is located with other content items relating to a particular topic [e.g. inclusion criteria for the image]” See also Mancuso [0099-0100] "the content scene system 102 utilizes an object detection machine learning model to analyze pixels of the digital image to predict classifications of depicted objects. From a combination of object classifications, titles, keywords, and/or headers, the content scene system 102 can determine a topic or theme for a content item [e.g. image (Thus, determines the inclusion criteria for the image)].” See also Mancuso [0117-0119] “the content scene system 102 utilizes a content cluster machine learning model to generate content clusters from topic data [Thus, determines that the content item (e.g. image) meets the inclusion criteria for the image] and focus data [e.g. features of training images]… the content cluster machine learning model refers to a neural network that learns to cluster content items based on training with training data [e.g. features of training images]… As part of training the content cluster machine learning model 608, the content scene system 102 also performs a comparison 612. Specifically, the content scene system 102 compares the content cluster 610 with a stored result 614 (e.g., a ground truth content cluster that is designated as corresponding to a content item from which the topic data 602 and the focus data 604 were extracted).” See also Mancuso [0065] “the term “focus data” refers to information or data that indicates, corresponds to, or signifies an activity pattern or a focus session associated with a content item [e.g. image].”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Reference1-Referenc2 to incorporate the teachings of Mancuso of applying a machine-learned classifier for determining that content such as the image relates to a particular topic when determining clusters, to Reference1’s thumbnail selection for determining a representative image associated with the webpages in the cluster into Reference1-Reference2 cluster UI, as it would improve the user’s ability to recognize the intended cluster.
Thus, it would predictably improve the cluster-suggestion results.
Conclusion
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/OSCAR WEHOVZ/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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